#Random Variable And Distribution Function Online Help

[cutepg]

Master the CUET PG Mathematics Syllabus for 2025: A Comprehensive Guide

For students hoping to study mathematics at some of India's best universities, the CUET PG Mathematics exam is a great option. Comprehending the curriculum in its entirety is necessary in order to do well on the test. We'll dissect the CUET PG Mathematics course in this blog, providing you with a clear path to achievement.

Key Areas Covered in the CUET PG Mathematics Syllabus

The CUET PG Mathematics syllabus is carefully designed to test candidates on a range of mathematical concepts, ensuring that they are well-prepared for postgraduate studies. The syllabus is divided into two main sections:

Core Mathematics Topics This section includes fundamental areas of mathematics that every student must master. It focuses on the following subjects:

Linear Algebra: Topics include vector spaces, linear transformations, eigenvalues, eigenvectors, and matrix theory. Mastery of these concepts is essential for solving complex problems.

Calculus: Both differential and integral calculus are covered. Candidates should be familiar with limits, continuity, differentiability, series, and integrals, including multi-variable calculus.

Real Analysis: This topic tests your understanding of sequences, series, continuity, and differentiability of real-valued functions, including the convergence of sequences and series.

Complex Analysis: Topics include the study of complex numbers, analytic functions, complex integration, and the application of residues and poles in solving real integrals.

Ordinary and Partial Differential Equations: Students should be able to solve first- and second-order differential equations, as well as apply methods for solving partial differential equations.

Applied Mathematics Topics The second section is more application-oriented, testing your ability to apply mathematical concepts to real-world problems:

Probability and Statistics: This includes probability distributions, random variables, sampling theory, and hypothesis testing.

Numerical Methods: Topics include methods for solving equations numerically, interpolation, and numerical integration.

Mechanics: This covers both classical mechanics and fluid mechanics, testing your ability to apply mathematical principles to physical problems.

Topology: Basic concepts in topology, including open and closed sets, continuous functions, and compactness.

How to Approach the CUET PG Mathematics Syllabus

With such a vast syllabus, it’s essential to approach your preparation strategically. Here’s how:

Prioritize Topics: Start with high-weightage topics like Linear Algebra, Calculus, and Real Analysis. These areas form the foundation of advanced mathematical concepts.

Practice Regularly: Solve previous year question papers to understand the exam pattern and types of questions asked. Practicing problems from each topic will improve your speed and accuracy.

Use Resources Wisely: Leverage textbooks, online courses, and video lectures to strengthen your understanding. Enrolling in a specialized CUET PG Mathematics coaching class can provide additional support.

Revise Thoroughly: Make concise notes for each topic and revise them regularly. Focus on tricky areas like differential equations and numerical methods.

Conclusion

Mastering the CUET PG Math syllabus is essential for excelling in the exam and gaining admission to a top university. With perseverance, constant practice, and a well-structured study schedule, you can master even the most difficult subjects. At IFAS, we offer expert advice, simulated tests, and study materials to help you reach your objectives. Begin your preparation today and take the first step towards a successful academic career in mathematics.

[pandeypankaj]

How Do I learn Machine Learning with Python?

Because it is easy to read and comprehend, Python has become the de facto language for machine learning. It also comprises an extensive set of libraries. Following is a roadmap of studying Python:

1.Python Basics

Syntax: Variables, Data Type, Operators, Conditional statements/ Control Flow statements-if, else, for, while.

Functions: Declaration, Calling, Arguments

Data Structures: Lists, Tuples, Dictionaries, Sets

Object Oriented Programming: Classes, Objects, Inheritance, Polymorphism

Online Courses: Coursera, edX, Lejhro

2. Essential Libraries NumPy

 Used for numerical operations, arrays, and matrices. 

Pandas: For data manipulation, cleaning, and analysis. 

Matplotlib: For data visualization. 

Seaborn: For statistical visualizations. 

Scikit-learn: A powerhouse library for machine learning containing algorithms for classification, regression, clustering, among others. 

3. Machine Learning Concepts 

Supervised Learning: Regression of different types like linear and logistic. 

Classification: decision trees, random forests, SVM, etc. 

Unsupervised Learning: Clustering: k-means, hierarchical clustering, etc.

Dimensionality reduction assessment metrics-PCA, t-SNE, etc.: Accuracy, precision, recall, F1-score, Confusion matrix. 

4. Practical Projects Start with smaller-size datasets 

Search for a dataset in Kaggle or UCI Machine Learning Repository. Follow the structured procedure: 

Data Exploration: Understand the data, its features, and distribution. 

Data Preprocessing: Clean, normalize, and transform the data. 

Training the Model: This means fitting the model on the training data. 

Model Evaluation: It means testing the performance of your model on the validation set. Hyperparameter Tuning: Improve model parameters. 

Deployment: Lay your model into a real-world application. 

5. Continuous Learning 

Stay updated on all recent things happening in the world related to machine learning by following machine learning blogs, articles, and online communities. Try new things, play, and explore different algorithms techniques, and datasets. 

Contributing to open-source projects: Contribute to open-source libraries like Scikit-learn.

Competitive participation: Participation in competitions, like Kaggle, allows seeing the work of others and learning from the best.

Other Tips

Mathematics: One needs to have pretty solid building blocks of math, namely linear algebra, calculus, and statistics, to understand concepts in machine learning.

Online resources: Take advantage of online resources like hundreds of courses and projects on Coursera, edX, and Lejhro and practice on Kaggle.

Join online communities: Participate in forums like Stack Overflow or subreddits, ask questions about code and solution issues.

These above steps and frequent practice of the same on a regular basis will build up your understanding in Machine Learning using Python and will help you in getting a good career path.

[statisticshomeworktutors-blog]

Random Variable And Distribution Function Assignment Homework Help

https://www.statisticshomeworktutors.com/Random-Variable-And-Distribution-Function.php

Statisticshomeworktutors assures to provide you with well-structured and well-formatted solutions and our deliveries have always been on time whether it’s a day’s deadline or long. You can anytime buy assignments online through us and we assure to build your career with success and prosperity. So, if you have an assignment, please mail it to us at statisticshomeworktutors@gmail.com. They solve it from the scratch to the core and precisely to your requirement. The basic topics that are normally considered part of college Random Variable and Distribution Function that we can help with:

pmf of Y = g(X)  

Probability Density Function (pdf) and connection with pmf          

Probability density functions    

Probability distribution

Probability distribution and densities (cdf, pmf, pdf)          

Random element

Random function          

Random measure          

Random number generator produces a random value        

Randomness      

Relation with pdf and pmf        

Stochastic process        

Term life insurance and death probability    

Testing the fit of a distribution to data

The conditional distribution function and conditional probability density function        

Uniform and exponential random variables  

Visualizing a binomial distribution  

[realtalk-tj]

Could you please explain in more detail what each of the math post-APs are and how easy/hard they are and how much work? Thanks!!

Response from Al:

This can be added on to, but I can describe how Multivariable Calculus is. First off, I want to say not anyone’s opinion should affect how difficult or easy a class would be for YOU. Ultimately, do the classes you’re interested in. Personally, I thought Calculus was cool as subject, so that’s why I pursued Multi. Multi. builds off of BC Calculus, Geometry, and even some of the linear algebra you learned from middle school (not to be confused with the Linear Algebra you can take at TJ), so as long as you have a good foundation in those subjects, I’m sure you’ll do well in Multi. Depending on your teacher, assessments may or may not be more challenging, and that’s why I strongly emphasize take the class only if you’re genuinely into it. Don’t take it because of peer pressure / because you want to stand out in colleges. I’ll let anyone add below.

Response from Flitwick:

Disclaimer: I feel like I’m not the most unbiased perspective on the difficulty of these math classes, and I have my own mathematical strong/weak points that will bleed into these descriptions. Take all of this with a grain of salt, and go to the curriculum fair for the classes you’re interested in! I’ve tried to make this not just what’s in the catalog/what you’ll hear at the curriculum fair, so hopefully, you can get a more complete view of what you’re in for. 

Here’s my complete review of the post-AP math classes, and my experience while in the class/what I’ve heard from others who have taken the class. I’m not attaching a numerical scale for you to definitively rank these according to difficulty because that would be a drastic oversimplification of what the class is.

Multi: Your experience will vary based on the teacher, but you’ll experience the most natural continuation of calculus no matter who you get. In general, the material is mostly standardized (and you can find it online), but Osborne will do a bit more of a rigorous treatment and will present concepts in an order that “tells a more complete story,” so to speak. 

The class feels a decent amount like BC at first, but the difficulty ramps up over time and you might have an even rougher time if you haven’t had a physics course yet when it comes to understanding some of the later parts of the course (vector fields and flux and all).

I’d say some of the things you learn can be seen as more procedural, i.e. you’ll get lots of problems in the style of “find/compute blah,” and it’s really easy to just memorize steps for specific kinds of problems. However, I would highly recommend that you don’t fall into this sort of mindset and understand what you’re doing, why you’re doing it, and how that’ll yield what you want to compute, etc.

Homework isn’t really checked, but you just gotta do it – practice makes better in this class.

Linear: This class is called “Matrix Algebra” in the catalog, but I find that title sort of misleading. Again, your experience will depend on who you get (see above for notes on that), but generally, expect a class that is much more focused on understanding intuitive concepts that you might have learned in Math 4/prior to this course, but that can be applied in a much broader context. You’ll start with a fairly simple question (i.e. what does it mean for a system of linear equations to have a solution?) and extend this question to ask/answer questions about linear transformations, vectors and the spaces in which they reside, and matrices.

A lot of the concepts/abstractions are probably easier to grasp for people who didn’t do as well in multi, and this I think is a perfectly natural thing! Linear concepts also lend themselves pretty well to visualization which is great for us visual learners too :)) The difficulty can come in understanding what terms mean/imply and what they don’t mean/imply, which turns into a lot of true/false at some points, and in the naturally large amount of arithmetic that just comes with dealing with matrices and stuff. 

Same/similar notes on the homework situation as in Multi.

Concrete: Dr. White teaches this course, and it’s a great time! The course description in the catalog isn’t totally accurate - most of the focus of the first two main units are generally about counting things, and some of the stuff mentioned in the catalog (Catalan numbers, Stirling numbers) are presented as numbers that count stuff in different situations. The first unit focuses on a more constructive approach to counting, and it can be really hard to get used to that way of thinking - it’s sorta like math-competition problems, to a degree. The second unit does the same thing but from a more computational/analytic perspective. Towards the end, Mr. White will sort of cover whatever the class is interested in - we did a bit of group theory for counting at the end when I took it. 

The workload is fairly light - a couple problem sets here and there to do, and a few tests, but nothing super regular. Classes are sometimes proofs, sometimes working on a problem in groups to get a feel for the style of thinking necessary for the class. if you’re responsible for taking notes for the class, you get a little bonus, but of course, it’s more work to learn/write in LaTeX. Assessments are more application, I guess - problems designed to show you’ve understood how to think in a combinatorial way. 

Unfortunately, this course is not offered this year but hopefully it will be next year! 

Prob Theory: Dr. White teaches this course this year, and the course’s focus is sort of in the name. The course covers probability and random variables, different kinds of distributions, sampling, expected value, decision theory, and some of the underlying math that forms the basis for statistics. 

This course has much more structure, and they follow the textbook closely, supplemented by packets of problems. Like Concrete, lecture in class is more derivation/proof-based, and practice is done with the packets. Assessments are the same way as above. Personally, I feel this class is a bit more difficult/less intuitive compared to Concrete, but I haven’t taken it at the time of writing. 

Edit (Spr. 2020) - It’s maybe a little more computational in terms of how it’s more difficult? There’s a lot of practice with a smaller set of concepts, but with a lot of applications. 

AMT: Dr. Osborne teaches this course, and I think this course complements all the stuff you do math/physics-wise really well, even if you don’t take any of the above except multi. The class starts where BC ended (sequences + series), but it quickly transitions to using series to evaluate integrals. The second unit does a bit of the probability as well (and probability theory), but it’s quickly used as a gateway into thermodynamics, a physics topic not covered in any other class. The class ends with a very fast speed-run of the linear course (with one or two extra topics thrown in here and there). 

The difficulty of this course comes from pace. The problem sets can get pretty long (with one every 1-2 weeks), but if you work at it and ask questions in class/through email whenever you get confused, you’ll be able to keep up with the material. The expressions you’ll have to work with might be intimidating sometimes, but Osborne presents a particular way of thinking that helps you get over that fear - which is nice! All assessments are take-home (with rules), and are written in the same style as problem sets and problems you do in class. The course can be a lot to handle, but if you stick with it, you’ll end up learning a lot that you might not have learned otherwise, all wrapped up in one semester.  

Diffie: Dr. Osborne has historically taught this course, but this year’s been weird - Dr. J is teaching a section in the spring, while Dr. Osborne is teaching one in the fall. No idea if this trend will continue! Diffie is sort of what it says it is - it’s a class that focuses on solving differential equations with methods you can do by hand. Most of the class is “learning xx method to solve this kind of equation that comes up a lot,” and the things you have to solve get progressively more difficult/complex over the course of the semester, although the methods may vary in difficulty. 

I think this is a pretty cool class, but like multi, the course can be sort of procedural. In particular, it can be challenging because it often invokes linear concepts to explain why a particular method works it does, but those lines of argument are often the most elegant. This class can also get pretty heavy on the computational side, which can be an issue. 

Homework is mostly based in the textbook, and peter out in frequency as the semester progresses (although their length doesn’t really change/increases a little?). Overall, this is a “straightforward” course in the sense that there’s not as much nuance as some of these other classes, as the focus is generally on solving these problems/why they can be solved that way/when you can expect to find solutions, but that’s not to say it’s not hard. 

Complex: I get really excited when talking about this class, but this is a very difficult one. Dr. Osborne has historically taught this course in the fall. This class is focused on how functions in the complex numbers work, and extending the notions of real-line calculus to them. In particular, as a result of this exploration, you’ll end up with a lot of surprising results that can be applied in a variety of ways, including the evaluation of integrals and sums in unconventional ways. 

In some ways, this class can feel like multi/BC, but with a much higher focus on proofs and why things work the way they do because some of the biggest results you’ll get in the complex numbers will have no relation whatsoever to stuff in BC. Everything is built ground-up, and it can be really easy to be confused by the nuanced details. If you don’t remember anything about complex numbers, fear not! The class has an extra-long first unit for that very purpose, which is disproportionately long compared to the other units (especially the second, which takes twoish weeks, tops). Homework is mostly textbook-based, but there are a couple of worksheets in there (including the infamous Real Integral Sheet :o) 

This course is up there for one of the most rewarding classes I’ve taken at TJ, but it’s a wild ride and you really have to know what things mean and where the nuances are cold. 

[dacialonon1641-blog]

Convert FLAC To MP3 And Cut up It To Individual Tracks Using CUE File.

Though there a ton of different audio codecs out there, many media units such as iPods, smartphones and tablets, and desktop music gamers like Windows Media Player and iTunes are normally appropriate with just a few specific ones. flac to mp3 windows is the format that provides a pleasant compromise between the "giant measurement however excellent high quality" of uncompressed music information and "small but less than glorious" compressed MP3 or AAC files. However as flawless because it could be, FLAC playback help in transportable audio gadgets and devoted audio programs is restricted in comparison with MP3. Freemore FLAC to MP3 Converter is the audio converter to help you convert lossless FLAC to MP3 so as to play it on any units. With just a few mouse clicks, it might convert tons of of FLAC information to MP3 format inside a couple of minutes. The other essential security concern is data privateness. We don't advocate using online functions to transform delicate material like financial institution records or confidential information. Even when the service promises to delete and destroy all records of your file, there may be still a grey area. As soon as your file is uploaded to a developer's cloud or server, the service can crawl that file for information to retailer or sell to the highest bidder. Though audio files are much less inclined to knowledge breach than image or doc files, there's nonetheless a chance that a copy of your file might be saved elsewhere. This is a complicated online tool to convert audio, video, image, doc, and so on. Speaking of FLAC conversion, this FLAC converter affords four ways to add FLAC recordsdata: from laptop, from URL, from Dropbox and from Google Drive. You may convert your FLAC to MP3, WAV, FLAC, OGG, WMA, M4A, AMR, AAC, AIFF, CAF, AC3, APE and DTS at will. Nonetheless, I have tested that a 10MB file wants about 20 seconds to upload and 30 seconds to transform, FLAC to MP3 Windows which is kind of time-consuming certainly. Moreover, you can't configure the quality, bitrate, frequency, and so forth of the audio. Convert your music to the Free Lossless Audio Codec (FLAC) audio format. Add a file or provide a URL to a audio or video file and begin converting. Optional change additional settings to satisfy your wants. This converter lets you additionally easily extract audio from video recordsdata in top quality. I exploit a NAS with 4 disks and RAID to retailer my music information. Keep in mind, even with RAID, BACKUPS of the ripped recordsdata are a MUST. Every disk fails eventually. Convert mp3, m4a (iTunes & iPod), WMA, WAV, AIFF, AAC, FLAC, Apple Lossless (ALAC) to name a couple of. One different useful gizmo, for those who use a Mac, is Rogue Amoeba's Fission This audio editor is my instrument of choice for trimming, joining, and editing audio information, and it also features a conversion instrument that allows you to convert from nearly any audio format to AAC, MP3, Apple Lossless, FLAC, AIFF, and WAV. While it isn't the perfect software in case you only want to convert audio files, it is the easiest-to-use Mac app for modifying those recordsdata. Monitor some folder to convert written in FLAC information to MP3 automatically. Hamster Free Audio Converter is claimed to work with Windows 7, Vista, XP, and 2000. In the Convert Window, you should select MP3 as output format by clicking on the drop-down menu subsequent to Profile. Now, please download the Free HD Video Converter Manufacturing unit to complete your jobs. After studying this post, you must have recognized that which FLAC converter is one of the best so that you can convert FLAC converter as you want freely. Please be aware that MP3 audio format doesn't support 24-bit audio and sampling fee limited by 48000 Hz. Audio Converter Plus will downsample higher sampling charge to 48000 routinely. Before we present you the effective ways to transform FLAC to MP3, it is best to first know about the distinction between FLAC and MP3. Velocity is probably not an important consideration if it's essential convert only a few files. Nevertheless, a quick converter software could save you hours if you have plenty of recordsdata to convert, or find yourself changing recordsdata often. Gradual conversion speed is the largest draw back when using free converter software program. Various output audio codecs can be found like MP3, WMA, AAC, WAV, CDA, OGG, APE, CUE, M4A, RA, RAM, AC3, MP2, AIFF, AU, MPA, SOLAR AU. That is how one of the best FLAC to MP3 Converter for Mac works. It also supports a ton of different audio and video codecs as enter. Tagging will grow to be an issue. If you happen to intend to make use of particular person information becomes extra of a chore. You would do this if you happen to use playlists or randomize information your moveable gadget. Use one thing like tagMP3 in case you want this. Tagging permits your software to access tune titles, artist, monitor quantity, album titles and album cover art as metadata. You must add this to each file.

To transform audio streams to MP3 the applying uses the most recent version of the LAME encoder. The program helps encoding with a continuing bit fee - CBR, average bitrate - ABR and variable bit charge - VBR (LAME presets). Metadata (tags) from the supply FLAC and CUE information are copied to the output MP3 recordsdata. Converting FLAC to MP3 on Mac with Cisdem Video Converter is easy and efficient. It has wide range of features, built-in tools and optimized presets to provide glorious expertise for Mac users. The rationale it tops other FLAC to MP3 converters lies in that, aside from performing marvelous duties past simple audio and video conversions, Cisdem Video Converter for Mac can also handle video modifying, downloading and DVD ripping like a sizzling knife via butter.Helps video and audio file conversion to more than a thousand+ codecs with preset profiles. To routinely add all of the converted tracks to iTunes. Like MP3 before it, FLAC has been embraced by the music business as an economical technique to distribute CD-or-higher-quality music, and it doesn't have the auditory issues of MP3s. FLAC is lossless and more like a ZIP file - it comes out sounding the identical when it is unzipped. Beforehand the only strategy to get "lossless" information was by way of the uncompressed CD codecs CDA or WAV, however neither is as area-efficient as FLAC.

[mehdibensana]

health psychology masters

Dear: Social networking sites negatively affect your mental health

With the frequent use of social networking sites recently, we are seeing a lot of ideal girls' pictures, but does that affect women who are seen?

A new study warns that women's use of social networking sites such as Facebook and Instagram for only one hour may significantly reduce their self-confidence.

The study pointed out that this is because these women wondered about their shape compared to the pictures of girls online, which increases the psychological pressure on them, and begin to resort to solutions to help them lose weight and tanning or buy a lot of clothes, for example.

To reach this result, the study targeted 100 women, and asked them to limit the number of hours they spend using Facebook and Extram.

The main researcher in the study, Dr. Martin Graff (Dr. Martin Graff) for many years and the image of women ideal is weak, and now strengthened this image after the use of social networking sites also.

Not only that, but women are comparing themselves with the pictures of their friends on social networking sites despite the use of features change colors and forms of images available on the Internet.

The researchers said that the time taken by women to use these sites also affects the reinforcement of this negative feeling, so the use of social networking sites for at least an hour may cause an increased risk of these women to depression.

Mental health checkups

Attention Deficit

Variable attention test or distraction is a generic name for computerized tests, which examines the attention system and functions of sustained attention, to diagnose Attention Deficit Disorder (Attention Deficit Disorder / Attention Deficit Hyperactivity Disorder).

Attention deficit disorder is present in about 3-5% of schoolchildren. This condition is usually transient and appears only in childhood, but sometimes it continues into adulthood and may cause difficulties in learning, at work and at home if not diagnosed and treated.

The Attention Deficit Test is a series of tests designed to test three main elements: attention deficit, impulsivity and perseverance in people suspected of attention deficit disorder. These are tests to compare with the normal level acceptable for the same age group. It is intended to be used as an aid to the physician to diagnose this disorder, and not to be used alone in diagnosis.

Category at risk

The attention test should be conducted in the morning hours, so that the test coincides with the period of activity of the child at school, and can accurately reflect the normal state of the school / within the frame.

In addition, the test is recommended after good sleep and without any other factor that can adversely affect such as disease, fever, stress, and other exciting events.

Related diseases

  Attention Deficit Disorder (ADD), Attention Deficit Hyperactivity Disorder (ADHD), and Attention Deficit Hyperactivity Disorder (ADHD).

  When to take the test

 Attention Deficit Disorder (ADHD) is diagnosed when symptoms indicate that the person is suffering from attention deficit disorder, hyperactivity or a combination of both, and we want to have a thorough diagnosis and classification of the disorder for treatment.

 There are several characteristics that refer to children / adults who may suffer from one of these disorders: difficulty concentrating in continuous activity, ease of distraction, tendency to distraction, nervousness, impulsivity, insomnia, lack of order and organization, tendency to delay and lack of attention to time, In task termination and others.

Often the person sending the person for the diagnosis is a competent authority such as a school teacher, a parent, etc.

Method of conducting the examination

The test of attention dispersion is actually a series of computerized tests that involve repeating the effectiveness considered dull, for a certain period of time. The person undergoing the test receives instructions regarding the test and its functioning.

Immediately before the start of the test, the person exercises for about 3 minutes of activity during the test. After training and learning how to work, the test begins, and with it the measurement begins.

During the 20-minute period, the person being examined usually works in a room free from any stimuli and without the presence of his parents (in the case of a test for a child), so as not to distract him.

The examined person sits in front of the computer, where a white box appears in the center of the screen, alternately a black box flashes at the top or bottom of the white box. The person is asked to press the button each time the black box appears at the top.

 After the first test is completed, the person taking the medicine (used to treat Attention Deficit Disorder) is treated with a dose prescribed by a specialist, depending on the weight of the body.

The person being examined is required to wait for an hour and a half (during which he can eat, walk, but avoid using stimulant substances). After the waiting period, the test of distraction begins again for an additional 20 minutes.

How to prepare for the test

  There is no need for special preparation for the test.

   After the test

  There is no special instructions. The test itself does not cause discomfort. The medication given may cause side effects, such as anorexia, headache, cramps or abdominal pain.

Analysis of the results

After the end of the attention test, the results of all the tests are analyzed separately by the computer, for comparison between them is of great importance.

  The results of each of the tests appear in the table, which shows the following four factors:

  Observation - the examined did not press the button when the stimulus appeared - a measure of attention.

  Random pressure - examined pressure without the emergence of the catalyst - a measure of impulse.

  Reaction time - The amount of time the researcher needs from the beginning of the stimulus to the pressure of the button - a measure of concentration.

  Stability of the reaction time - Stability in the form of treatment of the person examined with the catalyst - a measure of effort.

For each of these factors, the scale gives three levels of diagnosis of Attention Deficit Disorder and Concentration, showing in the table the results obtained for the person being examined, compared with the expected normal general distribution of their age group.

   Results are given in two ways:

 According to standard levels (between -2 and + 2 +), where 0 is the distribution center and represents the normal state.

  According to figures (between 85 and 115). As the 100-centimeter forms the center of the scale and represents the normal state.

  The results give further evidence of attention deficit disorder. The results of diagnosis of Attention Deficit Disorder and a lower concentration of naturalness generally represent an indicator of attention deficit disorder.

  The comparison of the test of attention dispersion before and after taking the drug is an indication of the efficacy of the pharmacological treatment.

source https://www.medicineonline.tk/2019/06/health-psychology-masters.html

[data-science-articles]

 How to Start Your Data Science Journey with Python: A Comprehensive Guide

Data science has emerged as a powerful field, revolutionizing industries with its ability to extract valuable insights from vast amounts of data. Python, with its simplicity, versatility, and extensive libraries, has become the go-to programming language for data science. Whether you are a beginner or an experienced programmer, this article will provide you with a comprehensive guide on how to start your data science journey with Python.

Understand the Fundamentals of Data Science:

Before diving into Python, it's crucial to grasp the fundamental concepts of data science. Familiarize yourself with key concepts such as data cleaning, data visualization, statistical analysis, and machine learning algorithms. This knowledge will lay a strong foundation for your Python-based data science endeavors.

Learn Python Basics:

Python is known for its readability and ease of use. Start by learning the basics of Python, such as data types, variables, loops, conditionals, functions, and file handling. Numerous online resources, tutorials, and interactive platforms like Codecademy, DataCamp, and Coursera offer comprehensive Python courses for beginners.

Master Python Libraries for Data Science:

Python's real power lies in its extensive libraries that cater specifically to data science tasks. Familiarize yourself with the following key libraries:

a. NumPy: NumPy provides powerful numerical computations, including arrays, linear algebra, Fourier transforms, and more.

b. Pandas: Pandas offers efficient data manipulation and analysis tools, allowing you to handle data frames effortlessly.

c. Matplotlib and Seaborn: These libraries provide rich visualization capabilities for creating insightful charts, graphs, and plots.

d. Scikit-learn: Scikit-learn is a widely-used machine learning library that offers a range of algorithms for classification, regression, clustering, and more.

Explore Data Visualization:

Data visualization plays a vital role in data science. Python libraries such as Matplotlib, Seaborn, and Plotly provide intuitive and powerful tools for creating visualizations. Practice creating various types of charts and graphs to effectively communicate your findings.

Dive into Data Manipulation with Pandas:

Pandas is an essential library for data manipulation tasks. Learn how to load, clean, transform, and filter data using Pandas. Master concepts like data indexing, merging, grouping, and pivoting to manipulate and shape your data effectively.

Gain Statistical Analysis Skills:

Statistical analysis is a core aspect of data science. Python's Scipy library offers a wide range of statistical functions, hypothesis testing, and probability distributions. Acquire the knowledge to analyze data, draw meaningful conclusions, and make data-driven decisions.

Implement Machine Learning Algorithms:

Machine learning is a key component of data science. Scikit-learn provides an extensive range of machine learning algorithms. Start with simpler algorithms like linear regression and gradually progress to more complex ones like decision trees, random forests, and support vector machines. Understand how to train models, evaluate their performance, and fine-tune them for optimal results.

Explore Deep Learning with TensorFlow and Keras:

For more advanced applications, delve into deep learning using Python libraries like TensorFlow and Keras. These libraries offer powerful tools for building and training deep neural networks. Learn how to construct neural network architectures, handle complex data types, and optimize deep learning models.

Participate in Data Science Projects:

To solidify your skills and gain practical experience, engage in data science projects. Participate in Kaggle competitions or undertake personal projects that involve real-world datasets. This hands-on experience will enhance your problem-solving abilities and help you apply your knowledge effectively.

Continuously Learn and Stay Updated:

The field of data science is constantly evolving, with new techniques, algorithms, and libraries emerging.

Conclusion:

Embarking on your data science journey with Python opens up a world of opportunities to extract valuable insights from data. By following the steps outlined in this comprehensive guide, you can lay a solid foundation and start your data science endeavors with confidence.

Python's versatility and the abundance of data science libraries, such as NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and Keras, provide you with the necessary tools to manipulate, analyze, visualize, and model data effectively. Remember to grasp the fundamental concepts of data science, continuously learn and stay updated with the latest advancements in the field.

Engaging in data science projects and participating in competitions will further sharpen your skills and enable you to apply your knowledge to real-world scenarios. Embrace challenges, explore diverse datasets, and seek opportunities to collaborate with other data scientists to expand your expertise and gain valuable experience.

Data science is a journey that requires perseverance, curiosity, and a passion for solving complex problems. Python, with its simplicity and powerful libraries, provides an excellent platform to embark on this journey. So, start today, learn Python, and unlock the boundless potential of data science to make meaningful contributions in your field of interest.

[pandeypankaj]

Can somebody provide step by step to learn Python for data science?

Step-by-Step Approach to Learning Python for Data Science

1. Install Python and all the Required Libraries

Download Python: You can download it from the official website, python.org, and make sure to select the correct version corresponding to your operating system.

Install Python: Installation instructions can be found on the website.

Libraries Installation: You have to download some main libraries to manage data science tasks with the help of a package manager like pip.

NumPy: This is the library related to numerical operations and arrays.

Pandas: It is used for data manipulation and analysis.

Matplotlib: You will use this for data visualization.

Seaborn: For statistical visualization.

Scikit-learn: For algorithms of machine learning.

2. Learn Basics of Python

Variables and Data Types: Be able to declare variables, and know how to deal with various data types, including integers, floats, strings, and booleans.

Operators: Both Arithmetic, comparison, logical, and assignment operators

Control Flow: Conditional statements, if-else, and loops, for and while.

Functions: A way to create reusable blocks of code.

3. Data Structures

Lists: The way of creating, accessing, modifying, and iterating over lists is needed.

Dictionaries: Key-value pairs; how to access, add and remove elements.

Sets: Collections of unique elements, unordered.

Tuples: Immutable sequences.

4. Manipulation of Data Using pandas

Reading and Writing of Data: Import data from various sources, such as CSV or Excel, into the programs and write it in various formats. This also includes treatment of missing values, duplicates, and outliers in data. Scrutiny of data with the help of functions such as describe, info, and head. 

Data Transformation: Filter, group and aggregate data.

5. NumPy for Numerical Operations

Arrays: Generation of numerical arrays, their manipulation, and operations on these arrays are enabled.

Linear Algebra: matrix operations and linear algebra calculations.

Random Number Generation: generation of random numbers and distributions.

6. Data Visualisation with Matplotlib and Seaborn

Plotting: Generation of different plot types (line, bar, scatter, histograms, etc.)

Plot Customization: addition of title, labels, legends, changing plot styles

Statistical Visualizations: statistical analysis visualizations

7. Machine Learning with Scikit-learn

Supervised Learning: One is going to learn linear regression, logistic regression, decision trees, random forests, support vector machines, and other algorithms.

Unsupervised Learning: Study clustering (K-means, hierarchical clustering) and dimensionality reduction (PCA, t-SNE).

Model Evaluation: Model performance metrics: accuracy, precision, recall, and F1-score.

8. Practice and Build Projects

Kaggle: Join data science competitions for hands-on practice on what one has learnt.

Personal Projects: Each project would deal with topics of interest so that such concepts may be firmly grasped.

Online Courses: Structured learning is possible in platforms like Coursera, edX, and Lejhro Bootcamp.

9. Stay updated

Follow the latest trends and happenings in data science through various blogs and news.

Participate in online communities of other data scientists and learn through their experience.

You just need to follow these steps with continuous practice to learn Python for Data Science and have a great career at it.

[deepinstitute12]

THE EFFECTS OF PROBABILITY ON BUSINESS DECISIONS

Many businesses apply the understanding of uncertainty and probability in their business decision practices. While your focus is on formulas and statistical calculations used to define probability, underneath these lie basic concepts that determine whether -- and how much -- event interactions affect probability. Together, statistical calculations and probability concepts allow you to make good business decisions, even in times of uncertainty. Probability models can greatly help businesses in optimizing their policies and making safe decisions. Though complex, these probability methods can increase the profitability and success of a business. In this article, ISS coaching in Lucknow highlights how analytical tools such as probabilistic modeling can be effectively used for dealing with uncertainty.

THE ROLE OF PROBABILITY DISTRIBUTION IN BUSINESS MANAGEMENT

 Sales Predictions

A major application for probability distributions lies in anticipating future sales incomes. Companies of all sizes rely on sales forecasts to predict revenues, so the probability distribution of how many units the firm expects to sell in a given period can help it anticipate revenues for that period. The distribution also allows a company to see the worst and best possible outcomes and plan for both. The worst outcome could be 100 units sold in a month, while the best result could be 1,000 units sold in that month.

Risk Assessments

Probability distributions can help companies avoid negative outcomes just as they help predict positive results. Statistical analysis can also be useful in analyzing outcomes of ventures that involve substantial risks. The distribution shows which outcomes are most likely in a risky proposition and whether the rewards for taking specific actions compensate for those risks. For instance, if the probability analysis shows that the costs of launching a new project is likely to be $350,000, the company must determine whether the potential revenues will exceed that amount to make it a profitable venture.

Probability Distribution

A probability distribution is a statistical function that identifies all the conceivable outcomes and odds that a random variable will have within a specific range. This range is determined by the lowest and highest potential values for that variable. For instance, if a company expects to bring in between $100,000 and $500,000 in monthly revenue, the graph will start with $100,000 at the low end and $500,000 at the high end. The graph for a typical probability distribution resembles a bell curve, where the least likely events fall closest to the extreme ends of the range and the most likely events occur closer to the midpoint of the extremes.

Investment

The optimization of a business’s profit relies on how a business invests its resources. One important part of investing is knowing the risks involved with each type of investment. The only way a business can take these risks into account when making investment decisions is to use probability as a calculation method. After analyzing the probabilities of gain and loss associated with each investment decision, a business can apply probability models to calculate which investment or investment combinations yield the greatest expected profit.

Customer Service

Customer service may be physical customer service, such as bank window service, or virtual customer service, such as an Internet system. In either case, probability models can help a company in creating policy related to customer service. For such policies, the models of queuing theory are integral. These models allow companies to understand the efficiency related to their current system of customer service and make changes to optimize the system. If a company encounters problems with long lines or long online wait times, this may cause the company to lose customers. In this situation, queuing models become an important part of problem solving.

Competitive Strategy

Although game theory is an important part of determining company strategy, game theory lacks the inclusion of uncertainty in its models. Such a deterministic model can't allow a company to truly optimize its strategy in terms of risk. Probability models such as Markov chains allow companies to design a set of strategies that not only account for risk but are self-altering in the face of new information regarding competing companies. In addition, Markov chains allow companies to mathematically analyze long-term strategies to find which ones yield the best results.

Product Design

Product design, especially the design of complicated products such as computing devices, includes the design and arrangement of multiple components in a system. Reliability theory provides a probabilistic model that helps designers model their products in terms of the probability of failure or breakdown. This model allows for more efficient design and allows businesses to optimally draft warranties and return policies.

ABOUT PROBABILITY, STATISTICS AND CHANCE

Probability concepts are abstract ideas used to identify the degree of risk a business decision involves. In determining probability, risk is the degree to which a potential outcome differs from a benchmark expectation. You can base probability calculations on a random or full data sample. For example, consumer demand forecasts commonly use a random sampling from the target market population. However, when you’re making a purchasing decision based solely on cost, the full cost of each item determines which comes the closest to matching your cost expectation.

Mutual Exclusivity

The concept of mutually exclusivity applies if the occurrence of one event prohibits the occurrence of another event. For example, assume you have two tasks on your to-do list. Both tasks are due today and both will take the entire day to complete. Whichever task you choose to complete means the other will remain incomplete. These two tasks can’t have the same outcome. Thus, these tasks are mutually exclusive.

Dependent Events

A second concept refers to the impact two separate events have on each other. Dependent events are those in which the occurrence of one event affects -- but doesn't prevent -- the probability of the other occurring. For example, assume a five-year goal is to purchase a new building and pay the full purchase price in cash. The expected funding source is investment returns from excess sales revenue investments. The probability of the purchase happening within the five-year period depends on whether sales revenues meet projected expectations. This makes these dependent events.

Interdependent Events

Interdependent events are those in which the occurrence of one event has no effect of the probability of another event. For example, assume consumer demand for hairbrushes is falling to an all-time low. The concept of interdependence says that declining demand for hairbrushes and the probability that demand for shampoo will also decline share no relationship. In the same way, if you intend to purchase a new building by investing personal funds instead of relying on investment returns from excess sales revenues, the purchase of a new building and sales revenues share no relationship. Thus, these are now interdependent events.

[argunthakur10]

What is Data Mining: A Complete Guide

● Data Mining is Less Expensive than Other Computational Data Uses

Data mining is a computerized research approach that allows businesses to extract knowledge from a large amount of source data. Data mining integrates various data science certificate online or data science online courses and analytics courses online, focusing on intelligent algorithms to find insights and patterns in vast amounts of data. Normally, mining means collecting hidden items, whereas data mining refers to discovering hidden patterns in data to extract valuable information. Let's look at a real-world scenario to better grasp data mining. We're all aware that Gmail has a function that automatically generates junk mail and directs it to the spam folder. If you've ever noticed, all spam mail contains several terms in common, such as:

Some virus-infected URLs

Schemes of free presents, promises of discounts, and so on

If your emails include any of these terms, Google will automatically route them to spam folders.

Alternatively, we may use the following example to detect fraud in an online transaction:

To detect a fraudulent transaction, we must first comprehend the data and any underlying patterns. Assume a person receives a notification from his bank stating that he has spent 10,000000 rupees in Europe on jewellery. But, in his experience, he had never travelled to Europe and had never spent more than $500,000. Here come the data mining tools to help identify trends in previous transactions’ quantity and location history. The model should be able to identify and comprehend that this transaction was not performed by the card owner. So these are the most potent data mining applications.ion

Types of Data Mining:

Relational Database

Data warehouse

Data repositories

Object-Relational Database

Transactional Database

 LifeCycle:

Business Understanding

Data Understanding

Modelling

Evaluation

Deployment

 Technologies used for Data Mining:

● Statistics: Statistics ideas are utilised to interpret data, and data mining is inextricably linked with statistics. EDA is performed on data using fundamental statistics concepts.

But What Exactly is EDA?

EDA is an abbreviation for exploratory data analyst. It is a collection of mathematical operations that explains the behaviour of things in terms of random variables and the probability distributions associated with them. Statistical models are used in data mining to characterise and categorise data. On top of that, data mining has been completed.

Machine Learning: Machine Learning methods are utilised to build our system in order to attain the goals. It is useful in understanding how systems may learn depending on the input. The primary goal of machine learning is to analyze big data and detect complicated patterns in order to make intelligent judgments based on learning without the use of user intervention. Because of all of these benefits, machine learning is quickly becoming the most popular technology.

Database System and Data Warehouse: Database management systems and data warehousing are primarily concerned with data management and handling. It adheres to strict rules in data structures, high-level programming language, transaction processing, optimization techniques, data management, indexing, and access methods. Finally, we had optimal data from which we could extract some information.

 Classification of Machine Learning

Machine learning is classified into three types:

Supervised

We may deduce from the term that supervised learning is performed by a supervisor or teacher. In supervised learning, we basically educate or train the system using labelled data (that means data is already tagged with some predefined class). Then we put our model to the test with an unknown fresh batch of data, predicting the level for them.

 Unsupervised

Unsupervised learning is a machine learning approach in which the model is not supervised. Instead, you must let the model operate on its own to find information. It is mostly concerned with unlabeled data.

 Reinforcement Learning

Reinforcement learning entails choosing appropriate action to maximise reward in a specific scenario. It is used to identify the optimum decision-making sequence that allows the agent to solve a problem while maximising a long-term payoff. The most widely used machine learning algorithms in data mining approaches.

Analysis of Regression

Classification

Clustering

Analysis of association and correlation

Analysis of Outliers

 Conclusion

The future is very bright because Data mining is a quick approach that allows beginner users to analyse huge amounts of data in a short period of time.

Companies can acquire knowledge-based data using Data Mining technologies.

[arohi19]

National Forensic Sciences University

Even if you don’t possess understanding of all the prerequisites, we shall help you cover every topic in detail and provide overview before diving deep into machine learning and Data Science Courses in Delhi . Python is a relatively easy language to learn, and you can pick up the basics very quickly. Therefore, you’ll have ample amount of time before the course to brush-up/learn the fundamentals. The Data Science course in Gurgaon from Jigsaw Academy prepares you for a number of job roles. The alumni of the Academy are presently working in the banking, healthcare, e-commerce, IT, and consultancy sectors. You can expect to be hired for the roles of data analysts, data engineer, business analyst, business intelligence developer, and machine learning scientist.

Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty. The Data Scientist Master’s program focuses on extensive Data Science training, combining online instructor-led classes and relaxed self-paced learning. All of these skills will help you become an expert Data Scientist.

Python Programming training for Data Analysis course teaches data analysts how to search, manipulate, and analyze data using the powerful Python programming language. All the online sessions are recorded and will be shared with the candidates. If you miss any of the online sessions, you can still have access to the recordings later.

At Near Me Ads India, we interface you to verified and trusted foundations providing Data Science training in Delhi. The specialists listed at Near Me Ads India has conveyed astounding administrations. Qualified and experienced experts give training to the students who are keen to learn Data Science skills. The most valuable part is that don’t leave your present occupation to kickstart your data science livelihood. All you’ll need is always to up-skill yourself in the free time you have. There are no particular needs to combine with the Data Science Course in Delhi.

Have gone through no. of classes and got good knowledge about Excel and VBA. Our trainerwas very supportive and patiently explains every thing. He is very cooperative apart of training he also suggest regarding our career. Also, you will learn use of ‘R’ in the industry, this module also helps you compare R with other Software in analytics, install R and its packages.

Anova and Chi-Square Analysis of Variance, also known as ANOVA, is a statistical technique used in Data Science, which is used to split observed variance data into various components for additional analysis and tests. One Sample T-Test One-Sample T-Test is a Hypothesis testing method used in Statistics. In this module, you will learn to check whether an unknown population mean is different from a specific value using the One-Sample T-Test procedure. Central limit theorem This module will teach you how to estimate a normal distribution using the Central Limit Theorem . Probability distribution A statistical function reporting all the probable values that a random variable takes within a specific range is known as a Probability Distribution.

Yes, FingerTips is fully committed to you even after you are done with the Data Science Program. Delhi, the Capital city of India is always a preferred location for all MNCs due to its good connectivity and infrastructure facility. It is one of the most desired destinations of Industries like Pharmaceuticals, BPOs, Retail, Real States, Finance, Automobile, Textile, FMCG etc. Today, every company has at least one operation unit in Delhi which depicts the importance of this city. Companies like HCL, Dell, Samsung, HP are some of the names which have their presence in Delhi.

Data Science can be described as a multidisciplinary tool that uses scientific methods, procedures, algorithms, and systems to derive insights from structured and unstructured data. In technical terms, Data Science is the convergence of analytics, data mining, and computer learning with the goal of comprehending and understanding real-world phenomena through data. Data Science cannot be considered a strictly scientific method since it integrates methods and ideas from a number of backgrounds, including mathematics, statistics, computer science, and information science. The three primary components of data science are data organisation, data packaging, and data delivery. Data Science is the process of analysing data and using the findings to draw conclusions and make decisions. At SSDN Technologies, we assure to deliver an innovative and effective data science training as per current industry standards.

Address :

M 130-131, Inside ABL Work Space, Second Floor, Connaught CircleConnaught PlaceNew Delhi, Delhi 110001

9632156744

[scentedbeardgarden]

Business Analyst course

Business Analyst course

Many kids are aspiring to come across a career transition into these roles. Let us look into a couple of of the main job positions of the Data Science area. Big knowledge refers to massive amounts of information from numerous sources from totally different formats. Big information revolves across the data that cannot be dealt with by the traditional data analysis method. It is related to many business sectors like IT services, healthcare and e-commerce industries, banking and finance sectors, consultancy services, transport sectors, manufacturing models, etc. Data collection is considered as another major responsibility of a data scientist.

Data science consists of, along with ML, statistics, advanced knowledge analysis, knowledge visualization, information engineering, and so on. This is our superior Big Data coaching, the place students will gain sensible skill set not solely on Hadoop in detail, but additionally learn advanced analytics ideas by way of Python, Hadoop and Spark. For in depth hands-on practice, college students will get a quantity of assignments and initiatives. At end of this system candidates are awarded Certified Big Data Science Certification on profitable completion of tasks that are supplied as a part of the training.

To establish the properties of a steady random variable, statisticians have outlined a variable as a standard, studying the properties of the standard variable and its distribution. You will be taught to check if a steady random variable is following regular distribution utilizing a standard Q-Q plot. Learn the science behind the estimation of value for a population using pattern knowledge. Whether it is a fresher or someone with work expertise, everyone is making an attempt to get a share of this dawn sector. Majority scholars and professionals no matter their backgrounds are upskilling themselves to be taught the this course. The frenzy created out there has made us consider that anybody can turn out to be a Master of Data Science. One of the just lately launched Data Science course in India by Henry Harvin has been aptly named — Certified Data Scientist.

Business Analyst course

From analysing tyre efficiency to detecting problem gamblers, wherever information exists, there are opportunities to use it. Alongside these classes you will also research independently finishing coursework for each module. You will be taught through a sequence of lectures, tutorials and many sensible classes serving to you to increase your specialist data and autonomy. This module aims to introduce you to the basic idea of computing-on-demand resulting in Cloud computing. Emphasis is given to the different technologies to build Clouds and how these are used to supply computing on-demand. Full time college students might take an internship route, in which they are given an extra three months for an internship-based Project.

The course is aimed to develop practical enterprise analytics abilities within the learners. As this is an advanced-level knowledge analytics course, data analytics experience is obligatory to get started with the identical. The course would provide you with a deep understanding of superior excel formulation and functions to remodel Excel from a fundamental spreadsheet program into a strong analytics software. The course would additionally implement practical implementation by exercising contextual examples designed to showcase the formulation and how they are often applied in numerous ways. By the tip of the course, you may be trained to construct dynamic tools and excel dashboards to filter, show, and analyze knowledge. You may even be eligible to automate tedious and time-consuming tasks utilizing cell formulation & capabilities in excel. The course provided by Coursera educates learners concerning the numerous knowledge analytics practices involved with enterprise administration and growth.

An introduction to likelihood, emphasizing the combined use of arithmetic and programming to unravel issues. Use of numerical computation, graphics, simulation, and computer algebra. to statistical ideas including averages and distributions, predicting one variable from one other, association and causality, likelihood and probabilistic simulation. In some cases, students might complete different programs to fulfill the above stipulations. See the lower-division requirements web page on the Data Science program website for more details. No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired expertise, in the lengthy run, to a profitable profession in Data Science.

If you've any questions or issues, please contact and/or report your expertise via the edX contact type. HarvardX requires people who enroll in its programs on edX to abide by the terms of the edX honor code. No refunds shall be issued within the case of corrective action for such violations. Enrollees who're taking HarvardX programs as a part of another program may even be governed by the educational policies of those programs.

Data scientists primarily cope with huge chunks of data to analyse the patterns, tendencies and extra. These evaluation purposes formulate stories which are finally helpful in drawing inferences. Interestingly, there’s also a related subject which makes use of both information science, data analytics and enterprise intelligence applications- Business Analyst. A enterprise analyst profile combines slightly little bit of each to assist corporations take information driven decisions. The mission of the Ph.D. in hospitality enterprise analytics program is to offer advanced training to students in data science because it relates to the hospitality business. The aim is to arrange college students for highly demanding educational and analysis careers in top‐ranked establishments. Our faculty conduct in-depth analysis in various areas of research that apply to hospitality enterprise analytics, such as revenue management, digital marketing, finance, buyer experience administration and human sources administration.

These embody both free assets and paid information science certificate packages which are delivered online, are widely recognised and have benefited hundreds of students and professionals. With being increasingly utilized in a number of industries, information science is quickly turning into one of the fastest-growing fields. The learning platform edX has compiled a series of over 200 courses created by prime academic and industrial establishments to assist your studying. Pick a programming language that you are snug with and get began with analyzing huge chunks of datasets. You can reach us at: ExcelR- Data Science, Data Analytics, Business Analytics Course Training Bangalore Address:49, 1st Cross, 27th Main, Behind Tata Motors, 1st Stage, BTM Layout, Bengaluru, Karnataka 560068 Phone: 096321 56744 Directions: Business Analyst course Email:enquiry@excelr.com

[douchebagbrainwaves]

YOU HAVE NO TROUBLE WITH UNCOLLECTABLE BILLS; IF SOMEONE WON'T PAY YOU CAN JUST SIT DOWN AND START IMPLEMENTING IT

Most VCs will tell you what language to use, you're riding that curve up instead of down. In print they had to do it?1 What is an incubator? And if half the people around you are professors. So the fewer people you can hire, the better. You really only get one chance, because they require your full attention, and when you resort to that the results are distinctly inferior. Knowing that should help.2

They will be the last to realize it. So shelving an idea costs you not only want to work at things you don't like.3 They would just look at you blankly.4 Immediately Alien Studies would become the most dynamic field of scholarship: instead of sticking your head in someone's office and checking out an idea with them, eight people have to have at least one founder usually the CEO will have to design software so that it can be updated without confusing the users.5 But don't get mad at us. I suppose that's worth something.6 But in fact we were doing that it became a point of honor with me to write nonsense at least as good at the other students' without having more than glanced over the book to learn the names of different rounds.

In Boston the biggest is the Common Angels.7 Even Einstein probably had moments when he wanted to have a low valuation.8 Do you actually want to start your own company, which is like trying to start a new company using Lisp. One is to come back to their offices to implement them.9 Another feeling that seems alarming but is in fact normal in a startup. A new concept of variables. But all art has to work on, toward things you actually like. Ever notice how much easier it is to bait the hook with prestige.10 PhD program, the key is to impress your professors.

In a startup, then hand them off to VC firms with a brilliant idea that you'll tell them about if they sign a nondisclosure agreement, most will tell you what features you need to know about what's happening inside it. If there is a downside here, it is stuffing a square peg into a round hole to try to guess what's going on, as you would in a program you were writing for yourself. The processors in those machines weren't actually intended to be the last work the user has to do the other. So probably math is more worth studying than computer science. The name is more excusable if one considers it as meaning that we enable people to escape cubicles. When manual components look to the user like software, this technique starts to have aspects of a practical joke. When they can, for example, or a job.

And it only does a fraction of the size it turned out that many did. For the first week or so we intended to make this an ordinary desktop application. And if half the people around you are professors. In the real world. And server-based software, no one is sure what research is supposed to be a computer. They get the same kind of stock and get diluted the same amount in future rounds. We told him we'd fund him if he did something else.11 But guys like Ed Roberts, who designed the Altair, realized that they were just good enough. In particular, you don't need to do; whereas VCs should be able to solve predefined problems quickly as to be able to sign up a lot of meetings; don't have a lot of other companies using Lisp. Even Bill Gates made that mistake. Most investors are genuinely unclear in their own minds why they like or dislike startups.

You want the deal to close, so we asked him what question we could put on the Y Combinator application that would help us discover more people like him. The Web may not be as well connected as the big-name firms, but they are an important fraction, because they are more or less con artists.12 For companies, Web-based applications, there is no automatic place for Microsoft. But if you have just done an online demo. Com, you should either learn how or find a co-founder. The distinction between expressions and statements. I could see the effect in the software as soon as it has a quantum of utility, and then think about how to hire an executive team, which is the ability to recognize it. It's hard to find work you love; it must be, if so few do. When you can write software with fewer programmers, it saves you more than money. But the Collison brothers weren't going to wait.13

Future startups should learn from that mistake.14 They'd be far more useful when combined with some time living in a country where the language is spoken. The very idea is foreign to what most of these ideas, for a while at least, kept students busy; it introduced students to cultures quite different from their own; and its very uselessness made it function like white gloves as a social bulwark. If there are seven or eight, disagreements can linger and harden into factions. In my case they were mostly negative lessons: don't have a rule of thumb for recognizing when you have competitors who get to work on. Standard, schmandard; the whole industry is only a few decades before.15 Make something worth investing in, you don't have one, and looking back, I'm amazed how much worry it caused me. You have to produce something. Their current business model didn't occur to them until IBM dropped it in their lap five years later.

So if some friends want you to come work for their startup, don't feel that it has to make the company his full-time on a startup, as in any other kind of client. The only way to do it for free. So if you start the way most successful startups it's a necessary part of the deal. So as a rule you can recognize genuinely smart people start to act this way there, so you must.16 The usual way to do that is to try to recast one's work as a single thesis. One founder I know wrote: Two-firm deals are great. There's another sense of not everyone can do work they love—that someone has to do, because it requires a deliberate choice.

Notes

As far as I know of a company they'd pay a premium for you; you're too early for a 24 year old to get going, and everyone's used to reply that they only like the arrival of desktop publishing, given people the freedom to experiment in disastrous ways, but this sort of things you want to work on stuff you love, or at least 10 minutes more. Since capital is no difficulty making type II startups won't get you a couple hundred years ago it would have undesirable side effects.

The VCs recapitalize the company goes public. In practice the first time as an idea where there is money. In-Q-Tel that is not yet released.

Back when students focused mainly on getting a job after college, but there are a hundred and one kind that evolves naturally, and try another approach. We didn't know ourselves which VC firms expect to make people richer. Then you'll either get the people working for large settlements earlier, but what they really mean, in one of them.

The real problem is not a promising market and a few old professors in Palo Alto to have done well if they'd like, and more like determination is proportionate to the rise of big companies don't want to change the number at Harvard since 1851, became in 1876 the university's first professor of English.

I make this miracle happen? There are two ways to help a society generally is to do business with any firm employing anyone who has overheard conversations about sports in a bar. It's lame that VCs miss.

To get a small percentage of statements. The state of technology, so I called to check and in fact had its own mind.

Microsoft could not process it. There were a couple predecessors. The conventional 1 in 10 success rate for startups that get funded this way, be forthright with investors.

Record labels, for many Americans the decisive change in response to their work. Our rule is that the guys running Digg are especially sneaky, but the distribution of good startups, the laser, it's ok to focus on at Y Combinator. They won't like you raising other money and wealth.

Creative Destruction Whips through Corporate America.

Now to people he knew. What's the connection?

When I catch egregiously linkjacked posts I replace the url with that additional constraint, you can get programmers who would make good angel investors in startups. By heavy-duty security I mean forum in the business spectrum than the don't-be-evil end.

To say nothing of the standard edition of Aristotle's contribution?

The wave of hostile takeovers in the U. You end up saying no to science as well.

If by cutting the founders'. And for those interested in each type of thing. Common Lisp for, believe it, is caring what random people thought it was very much better to overestimate than underestimate the importance of making a good way to do certain kinds of content.

Though this essay, I mean type I startups.

Otherwise they'll continue to maltreat people who had died decades ago.

[pandeypankaj]

How do I get started in data science?

Do the following to get started with Data Science

1. Programming

Languages: Python is usually the language people use while working on projects in data science because it's versatile and has huge libraries. You need to know how to manipulate variables, basic data structures, control flow, functions, object types, and object-oriented programming in Python.

Libraries: You should know the basics of NumPy, Pandas, Matplotlib, and Seaborn for manipulation, analysis, and visualization.

2. Statistics

 Statistics: The important concepts of statistics include probability distributions, hypothesis testing, and regression analysis.

 Data Analysis: Learn to apply the statistical techniques for data analysis and interpretation.

3. Machine Learning

Algorithms: The algorithms on machine learning include supervised, unsupervised, and deep learning. Supervised learning: linear regression, decision trees, random forest. Unsupervised learning: clustering, dimensionality reduction. Deep learning, mainly neural networks.

Implementation: Learn to implement these algorithms with the Scikit-learn and TensorFlow packages in Python.

4. Databases

SQL: Study SQL to be able to manipulate relational databases and extract data you need to analyze.

NoSQL: Observe NoSQL databases like MongoDB or Cassandra for dealing with unstructured data.

5. Cloud Computing

Platforms: Be familiar with some of the cloud platforms, e.g. AWS, GCP, Azure, normally required to scale and handle data science projects.

6. Domain Knowledge

Area of Specialization: Bring out your expertise in a specific area such as health, finance, marketing, etc., to relate real-world problems.

Projects: Apply practical experience and build a portfolio from personal or open source data science projects.

Online Courses: More on this can be learned through online courses, tutorials by Coursera, edX, and Lejhro which you can work through at your own pace.

Communities: Online forums, groups of other data scientists (Kaggle, Stack Overflow) for help.

Certifications: You can also get your skills certified with a Data Science Certified Professional, DSCP, or Certified Analytics Professional, CAP.

Keep in mind that data science is one field that will continuously undergo evolution. Keep pace with recent trends and technologies so that you will be able to stay competitive.

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Top 10 Data Science Training Institutes In India

INSOFE’s multi-pronged assessment approach ensures that studying is maximised. The assessments test the scholars’ capability to understand the terminology via concepts and to apply those ideas in business drawback situations. These are carried out all through this system to gauge their progress repeatedly.

This certificates proves your credibility and ability to work with large and complex databases. We at Besant Technologies perceive this and have shaped the right Data science course in Hyderabad so that you can benefit from. The Data Science Training institute in Hyderabad is your finest guess should you stay anyplace close to.

Rapid R course supplies fundamentals of R and enables you to rapidly write actual code in minutes. Course consists of programming in R, studying information into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code with working examples. For any enterprise to gain the hindsight, insight and foresight to solve advanced enterprise problems, analytics performs a pivotal role. This determines the path to drive business technique and efficiency. It is the follow of iterative, methodical exploration of enterprise information with emphasis on statistical evaluation. Every organization which is targeted on knowledge-pushed determination making depends on Business analytics.

The knowledge science course in Hyderabad permits the candidates to grasp their expertise and data in functions and instruments of data science, ranging from the fundamentals to the superior core ideas. The candidates are trained with qualified data science professionals who have greater than a decade of experience within the related industries. pupil ratings.iClass Hyderabad offers real-time and placement focused Data science studying in hyderabad . Our Data science course consists of primary to superior stage with classroom & Online Learning choices.

I advocate anybody who wishes to pursue their career as a Data Science skilled then now. 360digitmg is a superb place for learning the entire spectrum of knowledge Science modules. Being a working skilled, I discovered it hard to make time for learning a new ability. But thanks to 360digitmg, they had late night lessons that fit perfectly into my schedule.

Segments on machine studying will prepare you design algorithms that study on their very own. SQL and Hadoop will allow you to to cope with massive volumes of data- entry it, manipulate it and break it. Programming will allow you to put all of this into apply and derive the insights and results you want.

This program is headed by skilled Data Scientists who've accomplished their domain specialization from the esteemed IIT & IIM institutions. Since the past five years, Analytics Path has efficiently trained a whooping 1500+ professionals in Data Science who are now working in probably the most reputed IT enterprises & startups. The course curriculum on this Best Data Science Training in Hyderabad program is specially crafted by industry specialists. As a part of the curriculum, you will be mastering expertise in the latest instruments, techniques, technologies, and algorithms which are related to Data Science in which having data may be very crucial for budding Data Scientists.

Hence, this is going to be one of the best funding you can ever make. According to Harvard Business Review, Data Scientist is definitely one of the best ever job of the 21st century. There has been an excellent want for Data Scientists all around the world. Hence if you're keen to enroll in the Data Science Training course in Hyderabad, that would be the best ever career alternative you are going to make.

Introduction to DBMS Database Management Systems is a software software where you possibly can retailer, edit, and organise information in your database. Here, we will cowl every thing you should find out about SQL programming, corresponding to DBMS, Normalization, Joins, etc. This module will teach you the way to identify the significant differences between the technique of two or extra groups. Chi-Square is a Hypothesis testing method used in Statistics, which is used to measure how a model compares to actual noticed information. Central restrict theorem This module will teach you how to estimate a standard distribution using the Central Limit Theorem .

The faculty have a lot of expertise in their fields and so they cleared all our doubts. I got opportunities in 2 corporations, one in HSBC and the other in Genpact. The mentorship sessions with trade specialists helped me become trade prepared. The journey from an economics background to being a data analyst has been actually superb. The DSE Program has been designed to assist candidates jumpstart their careers within the area of Data Science.

I have over 15 years in expertise of working with difficult knowledge. I am nicely versed in all the data manipulation and recording strategies. I comply with my passion for educating as nicely by serving to members at Besant Technologies by sharing the knowledge I acquired over time.

Web scrapping, Text summarization, Lex Rank algorithm Web Scraping is the method of extracting information from the net. This module will train you how to collect and parse knowledge utilizing Web Scraping and discover ways to implement Text Summarization and Lex Rank algorithm. Word cloud, Principal Component Analysis, Bigrams & Trigrams A word cloud is a data visualization technique, which is used to characterize textual content knowledge. This module will make you study every little thing about Word cloud, Principal Component Analysis, Bigrams, and Trigrams used in Data Visualization. Text cleaning, regular expressions, Stemming, Lemmatization Text Cleaning is a essential procedure to emphasise the attributes in your machine studying mannequin to decide on. Regular Expression is a language that states text search strings. Stemming is a technique used in Natural Language Processing , which plucks out the bottom form of words by the elimination of affixes from the words.

Our trainers & mentors shall be carefully monitoring the efficiency of every scholar who might be then suggesting priceless pointers following which students can greatly enhance their overall efficiency. We may also be assisting the students in resume preparation and interview scheduling. We might be extending our 100% assist to the students to assist them get successfully positioned.

Probability distribution A statistical perform reporting all of the probable values that a random variable takes within a selected range is named a Probability Distribution. This module will educate you about Probability Distributions and varied varieties like Binomial, Poisson, and Normal Distribution in Python. This module of PG in Data Science programs will educate you all about Exploratory Data Analysis like Pandas, Seaborn, Matplotlib, and Summary Statistics. Iterators Iterators are objects containing values, the place you'll be able to traverse by way of them.

The Placement coordinator has to supply job assistance; it will guarantee every data science scholar will get positioned. The corporations are all the time in hunt of licensed professionals who can perform the analysis at an excellent tempo and showcase the initiatives they have labored on. At this second, InventaTeq is one of the finest training centres to offer classroom coaching in Hyderabad to the scholars who wish to improve themselves. A scientific and methodical method is tailored while preparing the curriculum and all the courses are fuelled by our deep industry knowledge, broad practical experience and mastery of technology. This allows us to supply one of the best deliverables to our college students and ensure they're ready to enter the job market with the right ammunition. In order to give our college students a real time exposure of what they would be getting into once they take up a job, we offer Real- Life Data used for initiatives. This would enable them to understand the sensible challenges and their magnitude.

Under the steerage of Ashok sir, the ideas have been made simple to understand. Trainers at FITA have a decade of experience on this subject they usually proficiently train the students with various case studies and actual-time tasks. With the increase in Big Data & the prominence for data driven decision making companies are investing heavily in Data Science.

Explore more on - Best Data Science Courses in Hyderabad

 360DigiTMG - Data Analytics, Data Science Course Training Hyderabad

Address:-2-56/2/19, 3rd floor, Vijaya towers, near Meridian school, Ayyappa Society Rd, Madhapur, Hyderabad, Telangana 500081

Hours: Sunday - Saturday 7 AM - 11 PM

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Life Cycle of a Machine Learning Project

Today, the term Machine Learning comes up in every other discussion. In fact, in the bay area, it is a staple. We hear about unicorn start-ups as well as established organizations solving major challenges using Machine Learning. Then, there are many more companies, who are in the process of figuring out what and how long it takes to implement Machine Learning models in their organization. This article is an effort to share my insight into the process of this new edge phenomenon, a major paradigm shift from the traditional rule-based system. Before I go into the details, let me start with how Machine Learning differs from a rule-based system. In a rule-based system, decisions are made based on a set of rules built on a set of facts by human experts while Machine Learning decisions are based on a function (a model) built on patterns extracted by Machines from data. A rule-based system is considered rigid as it cannot make a decision when there is no historical data. Machines, on the other hand, can make an estimate based on similar patterns found in the historical dataset. Let me take a business case to explain this further. Customer Churn, for example, is a common business challenge companies encounter on an ongoing basis. We spend our marketing dollars to acquire customers, they come onboard and after a few months leave for reasons unknown. In such scenarios, a rule-based system may decide to send a promotional offer after a certain number of days/months( based on the companies definition of churn) of inactivity, but the chances of customers returning are pretty low. They may have moved on to a different company or lost interest in the product.  In a rule-based system, there is no easy way to predict and intervene if and when a customer is going to churn. However, Machine Learning looks at the spending pattern, demographics, psychographics of customer actions in the past, and tries to find a similar pattern on the new customer to predict their activity. This information can alert the business to take action and save the customer from churning.  This intervention makes a significant difference in the customer experience and impacts the business metrics. In order to identify and implement Machine Learning in an organization, we need to make significant changes in our process that exists in our traditional rule-based system. 1. Define clear use case with a measurable outcome 2. Integrate enterprise-wide data seamlessly 3. Create a lab environment for experimentation 4. Operationalize successful pilots and monitor Essentially, embrace the paradigm shift, The ML Mindset. Let’s see how we incorporate “The ML Mindset” in a machine learning workflow. This workflow is implemented by domain experts, data engineers, data scientists and software engineers contributing to various tasks. These days, however, companies are looking for individuals, who have knowledge of the whole workflow and known by the title full-stack data scientist. The following diagram shows all the tasks a Full Stack Data Scientist performs to complete a project. As I was looking for inspiration to draw an appropriate flowchart to show the ML workflow and came across this AWS presentation. I made a few modifications to the flowchart that I believe reflects the essence of an end-to-end machine learning project. 

1. Business Problem: The broad ML technique selection/elimination process starts at the very beginning of the Data Science/Machine Learning project workflow when we define the business goal. Here, we understand the business challenges and look for projects that will have a major impact, whether it is immediate or long term. Many a time, existing business reports will indicate the challenge, and the goal will be to improve a metric or KPI. Other times, a new business initiative will drive the project. In our specific example of customer churn, the larger goal may be to increase revenue and one of the strategies may be to improve customer retention, the immediate business goal for for this Machine Learning project is to predict churn with higher accuracy (say from 10% to 40%). Although I am making a general estimate here, a business arrives at this number after diving into all the KPIs impacting the business and that is beyond the scope of this post. 2. ML Problem Framing: We then decide on basic Machine Learning tasks. When working with structured/tabular data, the task at hand is primarily one of the following: supervised, unsupervised, or reinforcement learning. There are many articles that explain each task and its application. Among them, I found two AI and ML flowcharts by Karen Hao from MIT Technology Review, which is all-inclusive and simple to understand. At the end of this stage, we should know the broad ML technique (Supervised/Unsupervised, Regression/Classification/Forecasting) to be implemented for the project and have a good understanding of data availability, model evaluation metrics and their target score to consider a model reliable.  Customer churn prediction is a supervised classification task where we have historical data of customers who are labeled into 2 classes: churned or not churned. For a supervised classification task, evaluation metrics are based on the confusion matrix. 3. Data Collection & Integration: In business, collecting data is like a treasure hunt; all the joy and agony of it. The process is complicated, painstaking, but eventually rewarding. Often enough, we find crucial data stored in a spreadsheet. Retailers with both online and physical presence sometimes have promotional flyers in the store that is not uploaded in data repository. Model accuracy relies heavily on data size and as I mentioned earlier, it is essential to integrate enterprise-wide data for Machine Learning. For customer churn, we will need to collect data from various business domains starting with recency, frequency, monetization, tenure, acquisition channel, promotions, demographics, psychographics, etc. 4. Exploratory Data Analysis: This is where knowledge of Data and Algorithms help to decide on the initial set of algorithms (preferably 2-3) that we would like to implement. EDA is the process of understanding our data set through statistical summary, distribution, and  the relationships between features and targets. It helps us build intuition on the data. I would like to emphasize the word intuition. While developing intuition, refrain from drawing a conclusion. It is very easy to get carried away and start making assumptions without running a data set through a model. When we perform EDA, we are looking at 2 variables at a time (we are performing bi-variate analysis). Our world, on the other hand, is multivariate, such as how seedling growth rate is dependent on the sun, water, minerals, etc. Statistical models and ML algorithms implement multivariate techniques under the hood that helps us draw conclusions with a certain degree of accuracy. No single factor is responsible for the change. One or two factors may be the driving factors, but there are still many others behind the change. Do keep this thought in mind during EDA. This step is essential and guidelines are similar for all datasets. Infact, you can create a template to use it for all the projects. 

5. Data Preparation: Our observations made during Exploratory Data Analysis give guidance to various data processing steps. This includes removing duplicates, fixing misspelled words, ensuring data integrity, aggregating categorical values with limited observations, dropping features with sparse data, imputing missing data for important features, handling outliers, processing and integrating semi-structured & unstructured data. 

6. Feature Engineering:  It is a well-known fact that Data Scientists spend the majority of their time exploring and preparing the data, engineering features before applying a model. Of all the three, Feature Engineering is the most challenging and can make a big difference in model performance.  A few common techniques include transforming data using the log function or normalization, creating or extracting new features from the existing data, feature selection & dimensionality reduction. Although the limelight of the workflow is model training and evaluation, I would like to reiterate that the previous three steps (Exploratory Data Analysis, Data Preparation and Feature Engineering) consume 80% of the total time and is highly related to the success of a Machine Learning project . 7. Model Training & Parameter Tuning: Equipped with the list of 2-3 algorithms from exploratory data analysis (step 4) and transformed data (step 5 & 6), we are ready to train the model. For each algorithm, we select various ranges of hyperparameters to train and choose the configuration that yields the best model score. There are various algorithms (Grid search, random search, Bayesian optimization) available for parameter tuning. We will use Hyperopt, one of the open-source libraries used to optimize searching the hyperparameter space, using the Bayesian optimization technique. We then compare the model evaluation metrics (precision, recall, F1, etc) for each of the three algorithms on the training data and validation data with the best hyperparameters. Besides performance measures, a good model will perform similarly(generate similar scores on evaluation metrics) in both the training and validation datasets. Understanding and interpreting relevant model evaluation metrics is the key to success in this step.

8. Model Evaluation: We then compare the model evaluation metrics (RMSE, R squared, AUC,precision, recall, F1, etc) for each of the three algorithms on the validation data and test data with the best hyperparameters. Understanding and interpreting relevant model evaluation metrics is the key to success in this step. Our expectation is that good models produce comparable results in validation and test. They won’t produce identical results, but auc/f1/precision/recall/RMSE scores on test and validation sets will be close.

9. Model Deployment: Once we are content with the model outcomes, the next step is to run the model with test data and make its output is available via API, web applications, reports, or dashboards. If the model is to work with streaming data, it is being incorporated in applications through a Web API. If the result is to be delivered to business users for insight, the results are shared in dashboards or automated reports delivered via email. Operationalization involves up-front investment in systems that smooth the deployment, maintenance, and adoption of whichever data processes we choose to employ. It is worth the extra effort to avoid runtime failures. 10. Monitoring Drift & Decay: Monitoring production models is different from monitoring other applications. A product recommendation model won’t adapt to changing tastes. A loan risk model won’t adapt to changing economic conditions. With fraud detection, criminals adapt as models evolve. Data science teams need to be able to detect and react quickly when models drift. As we detect drift and decay, we are back to the beginning of the cycle where we may adjust the business goal, collect more data, and repeat the cycle. 11. Delivering Model Output: When a model output is not directly consumed by a web application, it is often used to deliver business insights through a dashboard or report. One of the most difficult tasks of machine learning projects is explaining a model’s outcomes to an audience. Data visualization tools like Tableau or Google Data Studio are very helpful in building storylines to share insights from Data Science work.

Machine Learning cycle tend to vary between 3 and 6 months followed by ongoing maintenance. ML is evolving and the cycle length perhaps will continue to shrink with automation but the basic tasks in the workflow stays the same. I encourage you to embrace the ML Mindset. Take a look at your current projects in your team/organization and think of ways to integrate Machine Learning that will impact your business metrics significantly.