#Python NumPy for data cleansing

[saku-232]

Data Cleansing and Structuring

Data cleansing and structuring are crucial steps in the data preparation process, ensuring that data is accurate, consistent, and formatted for analysis or machine learning. Here's a breakdown of each step:

1. Data Cleansing

This process involves identifying and correcting errors in data, improving its quality. Key activities include:

Removing Duplicates: Identifying and eliminating repeated records in the dataset.

Handling Missing Data: Using methods like imputation (filling missing values), deletion, or flagging to handle null or missing entries.

Correcting Inconsistent Data: Standardizing formats (e.g., date formats, address formats), fixing spelling mistakes, or converting numerical data into the right scale (e.g., removing currency symbols).

Outlier Detection: Identifying and handling data points that deviate significantly from the rest of the data. This might involve removing or correcting them depending on their context.

Noise Filtering: Removing irrelevant or meaningless data that may distort analysis (e.g., stopwords in text data).

2. Data Structuring

Data structuring involves organizing data into a format that is easy to analyze or use for machine learning. This step focuses on making raw data more usable:

Normalization: Scaling features (e.g., values between 0 and 1) to bring them to the same level of magnitude, which helps in various machine learning models.

Encoding: Converting categorical data (e.g., gender, location) into numerical form using techniques like one-hot encoding or label encoding.

Data Aggregation: Combining data from different sources or summary statistics (e.g., sum, average) into a cohesive form.

Feature Engineering: Creating new variables from existing data (e.g., extracting the year from a date field, categorizing data into bins).

Reshaping Data: Converting the dataset into a structured format like tables (e.g., pivoting or unpivoting data, creating time series).

Both of these processes are often done using programming tools like Python (with libraries like pandas, numpy, and scikit-learn for machine learning) or R, and may also involve using SQL for database-related cleaning tasks.

Do you have a specific dataset you're working with, or are you exploring general techniques?

[sathya32]

Mastering Data Science: Your All-Inclusive Guide

The discipline of data science is dynamic and ever-expanding, offering tremendous opportunities for individuals interested in delving into the realm of analysis, deduction, and forecasting.Combining programming, statistics, and subject expertise, data science is a fast expanding field that uses data to glean knowledge and insights. Gaining expertise in data science can lead to a multitude of employment prospects, since there is an increasing need for data-driven decision-making in various industries. 

Gaining knowledge about data science Fundamentally, data science is about using several processes like data gathering, cleansing, analysis, and interpretation to extract information and insights from data. To make sense of complicated datasets, it integrates components of computer science, statistics, mathematics, and domain experience. Get Started "Learn the Basics": To begin, acquaint yourself with the principles of data science. Recognize fundamental ideas including variables, data types, and basic statistical measurements like mean, median, and mode

Select a Programming Language: R and Python are two widely used languages in data science. Select one and master it. Emphasize on learning how to use libraries like Pandas, NumPy, and Matplotlib (for Python) or (for R) to manipulate data, conduct analyses, and produce visualizations.

Examine Data Analysis Methods: Explore the structure and patterns found in datasets by delving into exploratory data analysis (EDA) methodologies. Acquire the knowledge to rectify sloppy data, manage absent values, and prepare data for examination.

Developing Your Capabilities Examine Machine Learning: One of the most important aspects of data science is machine learning. Studying supervised and unsupervised learning methods should be your first step. Learn how to train models, assess their output, and formulate predictions.

Master Data Visualization: Effective insight communication depends on data visualization. To produce eye-catching graphs, charts, and dashboards that effectively communicate your findings, investigate visualization tools and libraries.

Develop a deeper understanding of statistical concepts: Learn more about probability distributions, regression analysis, and hypothesis testing.  Application and Specialization

Select a SpecializationThere are many subfields within the large area of data science, including time series analysis, computer vision, and natural language processing. Determine your interests and strong points before focusing on a certain topic that corresponds with your desired professional path.

Work on Real-World Projects: Use your abilities on projects and datasets from the real world. Compiling a portfolio of work not only helps you demonstrate your skills to prospective employers, but it also helps you reinforce your comprehension.

Have a curious mind and never stop learning: New methods, instruments, and approaches are continually being developed in the field of data science. Continue to learn through books, online courses, and community involvement; be inquisitive and stay up of current events.

[priya-1995]

Is Learning Data Science Easy? A Beginner's Guide

Are you contemplating delving into the realm of data science but feeling uncertain about its complexity? You're not alone! Many prospective data scientists ponder whether learning data science is an easy endeavor. Let's delve into this inquiry and simplify the discussion.

Understanding Data Science

Firstly, let's grasp the essence of data science. Data science is an interdisciplinary domain that amalgamates statistics, mathematics, programming, and domain expertise to glean insights and knowledge from data. It encompasses processes such as data collection, cleansing, analysis, and visualization to uncover patterns and facilitate data-driven decision-making. f you want to advance your career at the Data Science Training in Pune, you need to take a systematic approach and join up for a course that best suits your interests and will greatly expand your learning path.

Assessing Ease

Now, onto the pivotal question: is learning data science straightforward? Well, it's a nuanced matter.

Prior Knowledge

Individuals with a background in programming, statistics, or mathematics may find certain facets of data science more accessible. Nevertheless, for beginners, there are abundant resources to aid in grasping the fundamentals. For those looking to excel in Data Science, Data Science Online Training is highly suggested. Look for classes that align with your preferred programming language and learning approach.

Learning Curve

Data science entails a steep learning curve due to its diverse skill set and toolkits. Proficiency in programming languages like Python or R, comprehension of statistical concepts, and adeptness with data analysis libraries such as pandas, NumPy, and scikit-learn are imperative.

Persistence and Practice

Achieving mastery in data science necessitates persistence and consistent practice. Challenges are inevitable, but they serve as opportunities for growth and learning.

Resources and Support

Fortunately, there's an abundance of resources and support available for aspiring data scientists. Online courses, tutorials, books, and communities cater to beginners, offering avenues for supplementary learning and networking.

Real-World Applications

Engaging in real-world projects is an exemplary method to solidify data science skills. By tackling practical problems, individuals gain invaluable experience and insights unattainable through theoretical study alone.

So, is learning data science easy? It's not inherently easy, but it's certainly achievable with dedication, perseverance, and access to appropriate resources. Whether you're starting from scratch or possess some background knowledge, don't let perceived challenges deter you from pursuing your interest in data science. Remember, every expert was once a novice, and with commitment and effort, proficiency in data science is attainable.

Happy learning!

[classychaosnachos]

DATA SCIENCE COURSE IN CHANDIGARH

The Data Science course in chandigarh offered by ThinkNext is a comprehensive program that covers a wide range of topics relevant to data science, utilizing Python as a primary tool. The course is structured to cater to both beginners and advanced learners, aiming to master data science skills.

Key features of the ThinkNext Data Science course include:

A detailed curriculum that starts with an introduction to Data Science, covering analytics, data warehousing, OLAP, MIS reporting, and the relevance of analytics in various industries. It also discusses the critical success drivers and provides an overview of popular analytics tools.

The course delves into core Python programming, including syntax, variables, data types, operators, conditional statements, and more advanced topics like function & modules, file handling, exception handling, and OOP concepts in Python.

It covers Python libraries and modules essential for Data Science, such as Numpy, Scify, pandas, scikitlearn, statmodels, and nltk, ensuring students are well-versed in data manipulation, cleansing, and analysis.

The program includes modules on data analysis and visualization, statistics, predictive modeling, data exploration for modeling, data preparation, solving segmentation problems, linear regression, logistic regression, and time series forecasting.

Additional benefits of the course include life-time validity learning and placement card, practical and personalized training with live projects, multiple job interviews with 100% job assistance, and the opportunity to work on live projects.

ThinkNext also offers a professional online course with international certifications from Microsoft and Hewlett Packard, providing step-by-step live demonstrations, personalized study and training plans, 100% placement support, and grooming sessions for personality development and spoken English.

The course has received recognition and awards, highlighting its quality and the institute's commitment to providing valuable learning experiences​​​​​​.

Contact us for more Information:

Company Name: ThinkNEXT Technologies Private Limited

Corporate Office (India) Address: S.C.F. 113, Sector-65, Mohali (Chandigarh)

Contact no: 78374-02000

Email id: info@thinknext.co.in

best data science institute in chandigarh

[subo797112]

What should I choose? Python for data science or Python for backend development.

Python, frequently referred to as the "Swiss Army knife" of programming languages, is highly versatile and has uses in many different industries. Data science and backend development are two popular job paths for Python fans. Every road has its own distinct possibilities and difficulties. The key factors to think about when choosing between Python for data science and Python for backend development will be covered in this blog article.

Python for Data Science-

1. Data Science Overview: 

The method of extracting important conclusions and forecasts from data is known as data science. Data cleansing, exploration, statistical analysis, and machine learning are just a few of the duties involved. Python's extensive ecosystem of libraries, including NumPy, pandas, Matplotlib, and scikit-learn, makes it the language of choice for data scientists.

2. Tools and Skills:

You will master libraries and tools for data manipulation, analysis, and visualisation if you select Python for data science. Jupyter notebooks will be used by you to write down your findings and conclusions.

3. Career Opportunities: 

Data scientists are highly sought after in all sectors of the economy. They focus on initiatives including data-driven decision-making, systems for recommendation, and predictive analytics. Jobs can be found in e-commerce, banking, healthcare, and other fields.

4. Educational Resources: 

To assist you in learning Python for data science, the data science community provides a list of online tutorials, courses, and books. Courses for organised learning are offered by websites like Coursera, edX, and DataCamp.

5. Difficulties: 

Data science requires a lot of mathematics, and an understanding of statistics is essential. It might be difficult to choose the best machine learning algorithms and handle enormous datasets effectively.

Python for Backend Development-

1. Overview of Backend Development: 

Backend engineers build the databases and server-side logic that support web applications. They take care of things like data storage, authentication, and routing. Web development is done in Python using frameworks like Django and Flask.

2. Tools and Skills:

You will become proficient in web development frameworks and technologies if you select Python for backend development. Working with databases, APIs, and server setup will be required. Additionally helpful are skills in front-end technologies like HTML, CSS, and JavaScript.

3. Career Opportunities:

Backend developers are essential for the creation of online applications, and there is a continuous need for them. You can work as a freelance developer, for IT firms, for startups, or both.

4. Resources for Education:

Learning Python backend programming requires familiarity with web frameworks like Django and Flask. To get started, you may discover online training classes and manuals. To obtain experience, practical tasks are essential.

5. Difficulties

When it comes to scalability and security, backend development can be challenging. You'll need to stay updated with current standards and developing web technologies.

Choosing Your Path-

Consider the following while choosing between Python for backend development and Python for data science:

1. Interests and Passion: 

Your take should be guided by your interests and passion. Data science may be for you if you enjoy working with data, solving challenging issues, and discovering new information. Backend development is a perfect fit if you like creating web apps, APIs, and working on the server side of technology.

2. Career Objectives: 

Think about your long-term career objectives. Do you want to work in artificial intelligence, machine learning, or data-driven decision-making? Or do you see yourself creating web apps, APIs, and supporting web system infrastructure?

3. Analyse your present strengths and expertise: 

Strong analytical and quantitative abilities are needed for data science. Backend development requires knowledge of databases, handling API'S, server-side programming, logical thinking, etc.

4. Learning path:

Examine the learning route for the profession you have selected. Search for educational materials, programmes, and tutorials that fit your interests and objectives.

5. Hybrid positions: 

Be aware that certain positions cross the boundary between backend programming and data science. For instance, to deploy models in production, a machine learning engineer may require both data science and backend programming abilities.

6. Explore Both:

If you're not sure, you can investigate both fields before making a final choice. To get some experience with both data science and backend development, start with beginning classes or small projects.

In the end, Python is a versatile language that offers exciting career prospects in both data science and backend development. Your choice should align with your interests, career goals, and current skill set. Regardless of your pick, continuing learning and keeping up-to-date with industry developments are vital for success in either career.

[zorayr-manukyan]

Harnessing the Power of Data Analysis in Modern Clinical Trials

Data analysis, the process of inspecting, cleansing, transforming, and modeling data with the goal of extracting useful information and formulating conclusions, plays a pivotal role in the execution of clinical trials. Clinical trials serve as the backbone of evidence-based medicine, testing the efficacy and safety of new medical interventions, from pharmaceuticals to medical devices. Given the critical role these trials play, it's paramount that the data gathered is accurately collected, processed, and interpreted - this is where data analysis shines.

Historically, clinical trials relied heavily on manual data collection and statistical methods to analyze results. However, the evolution of technology over the past few decades has transformed the landscape. The advent of powerful computing systems and sophisticated algorithms has brought about a shift from traditional statistical methods to more advanced data analysis techniques. This technological leap not only streamlines the process but also enables the handling of complex, high-volume data, ushering in an era of high-powered data analysis in clinical trials.

The Crucial Role of Data Analysis in Clinical Trials

Data analysis underpins the integrity of clinical trials. It contributes significantly to the accuracy, efficiency, and reliability of trials by ensuring that meaningful conclusions can be drawn from the collected data. By accurately analyzing the data, researchers can determine the effectiveness of a treatment, identify side effects, and ultimately decide whether a treatment should be brought to the market.

Case studies abound showcasing the successful application of data analysis in clinical trials. For instance, a 2019 trial examining the effectiveness of a new cancer drug used advanced data analysis to process and interpret results from over 1,000 participants. By leveraging predictive modeling and machine learning algorithms, researchers were able to identify patterns and correlations that a manual review might have missed, leading to the drug's successful approval.

Implementing Data Analysis in Your Clinical Trial

1. Defining the Objectives: Before diving into data collection, it's essential to clearly outline what the trial hopes to achieve. Are you testing the efficacy of a new drug, or studying the side-effects of an existing treatment? Clear objectives guide the type of data you'll need to collect.

2. Data Collection: Gathering accurate and comprehensive data forms the basis of your analysis. Utilize electronic data capture systems for standardized, high-quality data collection. Ensure all relevant variables are measured and recorded systematically.

3. Data Processing: Once collected, the data needs to be organized and cleaned - a process that involves removing duplicates, filling missing values, and correcting inconsistencies. This step is crucial to prepare the data for accurate analysis.

4. Data Analysis: With your processed data at the ready, it's time to dive into analysis. This can involve various techniques, from predictive modeling to statistical analysis and machine learning algorithms. The right method depends on your objectives and the nature of your data.

5. Interpreting the Results: After analysis, the final step is to translate data findings into meaningful insights. This requires a careful examination of the results to draw conclusions that can inform future actions and improvements in the trial process.

The Top 10 Data Analysis Tools for Clinical Trials

1. IBM SPSS Statistics: A powerful software for complex data manipulation and statistical analysis.

2. SAS (Statistical Analysis System): Widely used for data management, advanced analytics, and predictive modeling in clinical trials.

3. R Programming: An open-source software that provides a wide range of statistical techniques.

4. Python: Known for its easy-to-use libraries for data analysis and manipulation like Pandas, NumPy, and SciPy.

5. Stata: A complete, integrated statistical software package that provides everything you need for data analysis, management, and graphics.

6. MATLAB: Ideal for applying complex mathematical functions to large groups of data sets.

7. Tableau: A data visualization tool that turns raw data into a very easily understandable format.

8. Microsoft Excel: While it’s more basic, Excel's data analysis toolpak can perform statistical analysis.

9. PowerBI: Microsoft's suite of business analytics tools to analyze data and share insights.

10. RapidMiner: An integrated platform for data prep, machine learning, deep learning, text mining, and predictive analytics.

The Future of Data Analysis in Clinical Trials

Data analysis in clinical trials is undergoing a transformative shift, catalyzed by advancements in artificial intelligence (AI) and machine learning. These cutting-edge technologies are reshaping the way we collect, process, and interpret data, pushing the boundaries of what is possible in clinical research. Machine learning, for instance, can identify complex patterns in large datasets, enabling researchers to make predictions and discover subtle relationships that were previously unattainable. AI, on the other hand, has the potential to automate parts of the trial process, enhancing efficiency and precision.

While the promise of these advancements is exciting, it's crucial to recognize the challenges and ethical considerations they bring. The use of AI and machine learning necessitates a careful approach to data privacy and security, ensuring that patient information is protected. Moreover, biases inherent in the data or in the design of the algorithms can lead to skewed results, making it essential to validate and test these tools thoroughly.

Looking ahead, data analysis in clinical trials is set to become even more central to the discovery process. The growing trend towards real-world data and personalized medicine will likely heighten the need for sophisticated data analysis techniques. Likewise, as technologies like AI continue to evolve, we can expect them to unlock new possibilities and bring even more rigor and depth to data analysis in clinical trials.

The Benefits and Limitations of Data Analysis in Clinical Trials

Data analysis plays an invaluable role in enhancing trial efficacy, reducing errors, and accelerating the discovery process. Through meticulous data analysis, researchers can identify trends, spot anomalies, and make informed decisions, thereby improving the accuracy of trial outcomes. Moreover, advanced data analysis tools can process large volumes of data quickly, allowing researchers to draw conclusions faster and expedite the development of new treatments.

Despite its numerous benefits, data analysis in clinical trials also presents potential drawbacks and limitations. Privacy concerns are at the forefront, with the need to protect sensitive patient data from misuse. Data bias is another issue, as the data collected might not represent the broader population accurately, affecting the validity of the findings. Furthermore, there's the risk of over-reliance on technology, potentially leading to complacency in manual checks and balances.

FAQs

Hello there! Our comprehensive FAQ section is here to provide clarity and address any doubts you may have.

What types of data are most important in clinical trials?

Different trials require different types of data, but generally, demographic data, medical histories, laboratory results, and treatment outcomes are vital in clinical trials.

How does data analysis ensure the validity of trial results?

Data analysis helps ensure the validity of trial results by identifying trends, outliers, and correlations in the data. It also allows for hypothesis testing, and the statistical significance of the findings can be evaluated.

What is the role of artificial intelligence and machine learning in data analysis for clinical trials?

AI and machine learning are used to identify complex patterns in large datasets, enabling predictions and discoveries that were previously unattainable. They can also automate parts of the trial process, enhancing efficiency and precision.

How are data privacy and security ensured during the analysis process?

Data privacy and security are ensured through a variety of measures including encryption, access controls, anonymization of data, and compliance with regulations such as GDPR and HIPAA.

In Conclusion,

Data analysis is the powerhouse behind modern clinical trials, bringing unprecedented levels of accuracy, efficiency, and reliability. It's integral to the discovery of new treatments, paving the way for more effective and personalized healthcare. As we move into the future, it's crucial that we continue to innovate while ensuring ethical considerations and potential limitations are thoughtfully addressed. Through this balanced approach, we can truly harness the power of data analysis in clinical trials.

Disclaimer:

The information provided in this article is for general informational purposes only and should not be considered a substitute for professional medical advice, diagnosis, or treatment. Always consult with a qualified healthcare provider for personalized guidance regarding your specific medical condition. Do not disregard or delay seeking professional medical advice based on any information presented here. The authors and contributors of this article do not assume any responsibility for any adverse effects, injuries, or damages that may result from the use or application of the information provided. The views and opinions expressed in this article are solely those of the respective authors or contributors and do not necessarily reflect the official policy or position of the publisher. The publisher is not liable for any errors or omissions in the content.

[tinygardenphilosopher]

Project Data Science- Data Cleansing and Transformation by Python Pandas and NumPy

Data scientists spend a large amount of their time cleaning datasets and getting them down to a form with which they can work.  Lot of data scientists argue that the initial steps of obtaining and cleaning data constitute 80% of the job. In our python data science project training covering how to deal with Pandas and NumPy libraries to clean data. If you are new bees, beginner or experienced professional but doesn't know much about python and wants to enter into the real life of Data Scientist. Our experts are here for you to teach from the very basic to the advanced concepts.

We are stepping ahead with this concern and planning to create a small introduction video for our learners. It is important to be able to deal with messy data, how to do Data Cleansing using Python, whether that means missing values, inconsistent formatting, malformed records, or nonsensical outliers. We have seen how to setup a database, prepare a table in model files and migration along with uploading in our previous video tutorial. Today we are discussing how to save a csv file into a folder, extraction of data, cleaning and massaging with the help of Pandas and NumPy also saving of data inside the table i.e. importing a csv. If you missed out our previous video tutorial, please subscribe our channel for more live updates and go through the below link to watch previous video.

We need to follow few basic steps to perform the desired operations and result.

Step_1: Upload a csv

Step_2: Extract the csv. data in the form of data frame performing cleaning and massaging of data

Step_3: Save the same csv. file with the same name

Step_4: Upload the cleaned data csv into the data table.

Precautions:

Table attribute sequence and csv sequence must be same.

[yadavti]

Python Django Custom development, Consulting Projects and Training

Python Pandas is an open source library which we use in data science and analysis. This is specially developed for data science, data analysis and data manipulation. It is built upon the NumPy and the NumPy is uses to handle the numeric data in the tabular form. Python has built-in features to apply these wrangling methods to various data sets to achieve the analytical goal. Python Data Cleansing involves processing the data in various formats like - merging, grouping, concatenating etc. for the purpose of analyzing. The Pandas library is one of the most preferred tools for data manipulation and analysis, explored the fast, flexible, and expressive Pandas data structures. These libraries are basically used in data science and the manipulation of bulk data which we are getting from our database and extract the meaningful information from the data which we require. Please watch our video tutorial for more learning also for advanced learning you can check out our data science project-based training which is for our data science aspirants . Our training is a complete packaged for new and experienced professionals which is completely based on 100% practical scenario and project. For more details you can visit our website BISP Trainings or call +91 769-409-5404 or +1 386-279-6856. We understand your unique needs and challenges, whether a small or medium-sized business, we work for your need. We can help with Python, Django custom development and Implementation services. We have a team of experts and well experienced expert Developers; we can help you in all phrase of development from Implementation to Development.

[edujournalblogs]

The Future of Data Science: Trends to Watch and Skills to Develop

Data Science is one of the most lucrative career options today.   The role of a data Scientist is to assist the management in making business decisions.  The future of Data Science is expected to bring opportunities in various areas of healthcare, ecommerce, automobile, cyber security, banking, finance, scientific research and education, insurance, entertainment, telecommunication, etc. Business organizations have adopted data-driven models to simplify their processes and make business decisions based on the insights derived from data analytics.  Every organization wants to make super profits in a short span of time.  As data is the key factor in Data Science, every industry realizes that it requires Data Analyst / Data Scientists to analyze the data to optimize its profits.

There are five stages in data science life cycle:

1) Data Extraction: Extracting data from source for subsequent processing and analysis.

2) Scrubbing Data : Here the data is cleansed, and all the redundant and irrelevant data will be eliminated.

3) Data Exploration : Exploring and visualizing data in order to discover insights or trends.

4) Model Building : Selecting a statistical, mathematical, or simulating model to acquire insights and make predictions.

5) Data Interpretations : Developing a reasonable scientific argument to comprehend your data and provide  your inferences as conclusion. 

Trends to Watch in Data Science

1. The growth of Python Language over the years:  Python is on track to become the most popular programming language in the years to come.  Python support numerous libraries that helps in Data Analysis such as numpy, pandas, matplotlib,  TensorFlow,  keras, Scikit-learn etc., They are the go-to language in the field of data science.

2. Growing demand for end-to-end Artificial Intelligence (AI) Solutions : Poor quality data can lead to inaccurate results.  Hence, we need to clean large data sets and build machine learning models, thus, gaining valuable and deep learning insights from the massive quantity of data.  Businesses that provide end-to-end data science solution from start to finish within a single product will dominate the market.  The growing problem with many businesses is that most of the businesses could not analyze or categorize all the data that was stored (ie., studies show that almost 35% of stored data remain unutilized due to huge volume of data present), and imagine what would happen if the businesses do not have control over their data.  Hence, most of the companies are adopting AI and machine learning models.  For this reason, we have Dataiku, that provides end-to-end data science solution from start to finish, which is what companies want.  Hence, AI specific skillsets are becoming increasingly prominent across all sectors. Also, we have a concept of scalable AI, which refers to algorithms, data models, and infrastructure capable of operating at a speed, size and complexity required for the task.  Scalability contributes to solving scarcity and collection issues of quality data.  The development of ML and AI for scalability requires setting up of data pipelines.

3. Huge Demand for Data Analyst and Data Scientists:  As the demand for data grows, there is a corresponding demand for experts to parse and analyze data and gain insights. Yes, it is true that automation can replace the tasks done by humans previously, but, actually the data found in Big Data are massive and are often messy and unstructured, which is why humans are required to manually clean and reprocess the data before it is ingested by machine learning algorithm.  Hence, the output derived is always reliable and accurate.  They can provide insight in a way that non-technical stake holders can easily comprehend and  understand.

4. Data Privacy: With increasing amount of data being consumed on a daily basis, there is a growing concern about data breaches and privacy violations. You will need to have control over who sees what you share.  Data exchanges in market places for insights and analytics is one of the prominent trends which is also known as Data as a Service (DaaS). The data can be used by businesses as part of business process.

5. Automation : Automation can play a big role in data cleansing, data integration, data management analytics, and resolving any data legacy issues and hence can speed up the process.  Automation (robots, chatbots, virtual assistants, etc.) is likely to escalate the employability of skilled individuals.

6. Cloud Technologies : Cloud Technologies optimize the value of enterprise data.  They enable businesses to store and access data from the cloud, eliminating the need for in-house servers.  Cloud-based data analytics tools are easily accessible and is a valuable tool for Data Analytics.  Also, they streamline the massive datasets that drive AL and ML operations.  A multi-cloud architecture is indispensible for a business looking to develop in data science capabilities. 

7. Big Data : The sheer amount of data present in business houses, and the tools used to analyze the information has expanded exponentially.   The problem lies with collection, cleaning, structuring, formatting, analyzing etc.  Data science models and AI can help solve these issues and storage issues can be handled by storing in the cloud. Big data provides a wealth of insights that can help businesses make better decisions.

8. Data Visualization : Data Visualization is the process of displaying information in graphical form.  Data visualization tools enables us to see patterns, trends, outliers in data etc by using visual elements like graphs, charts, maps etc.,  it makes complex data easier to understand and interpret.  Data visualization tools are also becoming more sophisticated, allowing data analytics professionals to create highly engaging and interactive visualizations.

9. Natural Language Processing:  NLP enables computers to understand and interpret human language which is an important trend in Data Science.  NLP can also be used to develop robots,  chatbots and virtual assistants that can interact with customers in natural language.

10. Predictive Analysis: Makes use of historical data and past trends and projections to make future predictions and projections.

Skills to Develop :

Core Skills :

Cloud technologies, Machine Learning, Mathematics, Statistical analysis technique, data science, Data Analytics,  Python,  Artificial Intelligence, database management, Big Data,, SQL  and Data Warehousing, analytical tools such as Hadoop and Spark, Power BI, Tableau, SAS (Statistical Analytics Software), etc

Soft Skills:

1. Communication Skills (verbal , written & presentation skills): connecting the business side with the technical and scientific side for sharing insights in a manner that they can quickly comprehend and understand and make an  inference.

2. Business Acumen : Data scientists must understand the context and goals of the business to generate meaningful insights. They must  work closely with business stakeholders to understand their needs and objectives. By collaborating with business leaders, data scientists can gain a better understanding of the business context and generate insights that are relevant and actionable

Conclusion :

As we move forward into your future, we hope to see more innovations and technological advancement down the line in the areas of Data Analytics, Data Science, Artificial Intelligence, Machine Learning, Data Management, Cloud computing etc.  Also, these advancements reflect in the various domains such as healthcare, ecommerce, banking, cyber security, scientific research and education and more.,  And the best way to prepare yourself for the coming changes in data science is to upskill / reskill yourself and continue learning.  The more robust a data team is, the greater will be the returns for the company.

URL : http://www.edujournal.com

[data-patrons]

Python has been named the best programming language for data science in 2021.

If you're interested in data science, you've probably heard of Python. Python has long been a favourite among data scientists due to its extensive libraries and simple syntax. In fact, Python was declared the top programming language for data science in a recent survey conducted by Analytics India Magazine. In this post, we'll look at why Python has become so popular in the realm of data science, as well as what makes it such a strong data-working tool.

Introduction

Python for Data science in NCR has swiftly emerged as one of the most essential topics in technology. Companies are seeking for ways to acquire insights from their data to assist drive business decisions as big data and machine learning become more prevalent. The programming language that data scientists use to analyse and alter data is a critical tool in their armoury.

Python has recently emerged as the programming language of choice for data scientists. In fact, Python was awarded the top programming language for data science in a survey done by Analytics India Magazine in 2021.

Why Python for Data Science best programming language?

1. Simple to learn and apply

Python's ease of use is one of its most significant features. Python, unlike other programming languages, has a basic syntax that is straightforward to understand and write. This makes it an excellent choice for data scientists who do not have a programming experience.

2. A huge and vibrant community

Another reason Python has grown in popularity in data science is because of its huge and active community. Python has a large user base, with millions of developers worldwide contributing to its development. This means that data scientists have access to a plethora of libraries and tools.

3. Effective libraries

Python's appeal in data science can partly be attributed to the robust libraries that have been created for it. Data manipulation and visualisation are made simple by libraries such as NumPy, Pandas, and Matplotlib. Furthermore, frameworks such as TensorFlow and Scikit-learn make it simple to create machine learning models.

4. adaptable

Python's adaptability is another reason for its popularity in data research. Deep Python Course Training Institute in NCR may be used for a variety of activities, including data analysis and visualisation, as well as web development and automation. As a result, it is an invaluable tool for data scientists who must collaborate across many aspects of a project.

5. Compatibility

Another advantage is Python's compatibility with other languages. Python works well with other programming languages such as Java and C++. Python can thus be easily integrated into existing systems and procedures.

Python's Role in Data Science

Python is used in data science for a variety of activities, including data cleansing and analysis, as well as machine learning and deep learning. Here are a few instances of Python's application in data science:

1. Cleaning and analysing data

Python is frequently used for data cleaning and analysis. NumPy and Pandas libraries make it simple to manipulate and analyse data sets, while Matplotlib and Seaborn make it simple to visualise data.

2. Artificial intelligence

Python is very widely used in machine learning. Machine learning models can be easily built and trained using libraries such as Scikit-learn and TensorFlow.

3. In-depth learning

Python is another popular deep learning programming language. Keras and PyTorch libraries make it simple to design and train neural networks.

4. NLP (Natural Language Processing)

Python is commonly used in Natural Language Processing (NLP), which analyses and processes human language data. NLP tasks such as sentiment analysis and named entity recognition are made simple by libraries such as NLTK and spaCy.

5. Visualisation of Data

Python's visualisation packages, such as Matplotlib, Seaborn, and Plotly, make it simple to construct dynamic and visually appealing data visualisations. Top Python Course Training Institute in NCR use these visualisations to obtain insights from their data and convey their results to stakeholders.

6. Information Engineering

Python is frequently used in data engineering, which entails analysing and managing enormous amounts of data. Dask and Apache Spark libraries make it simple to parallelize and distribute data processing processes.

Conclusion

Python has risen to the top of the data science programming languages in 2021, and for good reason. It is a valuable tool for data scientists due to its ease of use, rich libraries, versatility, interoperability, and vast community. Python has the tools you need to get the job done, whether you're cleaning and analysing data, constructing machine learning models, or designing data pipelines.

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Data Science Training Course in Bangalore

What is Data Science

Datascience is introduced in 1980s.It is an interdisciplinary field, its true foundations are in statistics, mathematics, computer science and business.

Defination Of Data Science

Data Science can be defined as the study of data and works mainly on large volumes of data coming from different types of sources such as financial logs, multimedia files, marketing forms, sensors and instruments, text files and many more.

We Will Discuss a use case

Data Science plays an important role in effective decision making.

Example:Self-driving or intelligent cars

An intelligent vehicle collects data in real-time from its surroundings through different sensors like radars, cameras, and lasers to create a visual (map) of their surroundings. Based on this data and advanced Machine Learning algorithm, it takes crucial driving decisions like turning, stopping, speeding, etc.

It is also used for data transformation and to create business and IT strategies.

Data science life cycle

Capture: Data acquisition, data entry, signal reception, data extraction

Maintain: Data warehousing, data cleansing, data staging, data processing, data architecture

Process: Data mining, clustering/classification, data modeling, data summarization

Analyze: Exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis

Communicate: Data reporting, data visualization, business intelligence, decision making

Data science tools

Data scientists must know how to build and run code to create models. The most popular programming languages and open source tools that support data scientists for pre-built statistical, machine learning and graphics capabilities are:

R: R is the most popular open source programming language among data scientists for developing statistical computing and graphics. R provides a broad variety of libraries and tools for cleansing and prepping data, creating visualizations, and training and evaluating machine learning and deep learning algorithms. It’s also widely used among data science scholars and researchers.

Python is commonly used for developing websites and software, task automation, data analysis, and data visualization. Since it's relatively easy to learn, Python has been adopted by many non-programmers such as accountants and scientists, for a variety of everyday tasks, like organizing finances. Several Python libraries support data science tasks, including Numpy for handling large dimensional arrays, Pandas for data manipulation and analysis, and Matplotlib for building data visualizations.

[postwell]

What are OTHER PROGRAMMING LANGUAGES used for DATA Science?

Python is the largest and most used programming language used for data science. If you're searching for new opportunities as a data scientist will find that Python can be needed in many job advertisements for jobs in data science. Jeff Hale, a General Assembly instructor in data science, has sifted through job ads from popular job websites to determine what skills are needed for jobs with the title "Data Scientist." Hale discovered that Python appears in close to 75% of job ads. Python libraries like Tensorflow Scikit-learn, Pandas, Keras, Pytorch, and Numpy are also featured in various jobs in data science.

Image Source: The Most In-Demand Skills in Technology required by Data Scientists by Jeff Hale

R, a popular programming language used for data science, was featured in about 55% of job advertisements. Although R is an excellent instrument for data science and comes with many benefits, such as cleaning data, data visualization, and statistical analyses, Python continues to gain popularity and is sought-after by data scientists for a wide range of tasks. Actually, it is the average percent of job advertisements that require R that fell to about seven% between 2018 and the year 2019, while Python has increased the number of job ads that require the language. It's not to suggest that the process of learning R is not worthwhile, and data scientists who know both of these languages will benefit from the advantages of each to serve different reasons. But, as Python is becoming more popular and widely used, it's likely that your company uses Python as well, and it's essential to choose the language the team members are comfortable with and like.

What is the future of Python in Data Science?

While Python continues to gain popularity and the amount of data scientists continues to grow, the use of Python for data science will undoubtedly increase. As we continue to advance, deep learning, machine learning, and other tasks in data science will likely be able to access these advances to us as libraries for Python. Python has been maintained well and has been expanding its popularity over the past few decades, and many of the biggest companies are currently using Python. With its continuing popularity and growing popularity, Python will be used throughout the future.

If you've been a data scientist for a long time or just beginning your journey to data science and are looking to gain from the learning process of Python to study data science. The simplicity, readability, community support, and popularity of the language, and the numerous libraries that are available for data cleansing visualization, data cleaning, and machine learning are all that make Python above the other languages of programming. If you're not using Python to perform your tasks, take a look and discover how it can improve your workflow in the field of data science.

Understanding the Way Python is utilized in Data Science.

Every day, across the United States, more than 36,000 weather forecasts are made available, covering more than 800 cities and regions. You'll probably realize that the forecast was incorrect when it rains at the time of an outdoor picnic that is expected to be a bright and sunny day but have you ever wondered how accurate these forecasts actually are?

The people from Forecastwatch.com did. Every day, they collect all of the forecasts for 36,000, place them into a database and then evaluate them against the actual conditions that were encountered in the area on the day. Forecasters across the nation utilize the data to enhance the forecast model for the coming round.

The company's not alone. According to a 2013 study conducted by an analyst for the industry O'Reilly, 40% of the data scientists who responded use Python during their work routine. They are part of the multitude of professionals from every field who have created Python, an integral part of the top 10 most popular programming languages around the globe each year since 2003.

Companies like Google, NASA, and CERN make use of Python to accomplish almost every purpose of programming that you can think of... which includes growing numbers of data science.

Python is Good Enough, which means Great for Data Science.

Python is an all-paradigm programming language that is it's like a Swiss Army knife for the programming world. It supports object-oriented programming, structured programming, and functional patterns in programming, in addition to other things. There's a joke within the Python community that says, "Python is the second best software for everything."

This is not a knock on organizations that are confronted with the confusion of "best of class" solutions that make their codebases unworkable and inoperable. Python is capable of handling every task, from data mining to website development to operating embedded systems, all within one language.

At the ForecastWatch website, for instance, Python was used to create a parser that could extract predictions from different websites. Then, it was an aggregation engine to combine the data and the code for the website to display the data. PHP was the first language used to develop the site, but then the company realized that it was more efficient to deal with one language throughout.

Python - The Significance of the Life of Data Science

The name was derived from Monty Python, which creator Guido Van Possum selected to show that Python is a fun programming language to use. It's not uncommon to see odd Monty Python sketches referenced in Python documentation and code examples.

Because of this and other reasons, Python is much beloved by programmers. Data scientists who come from science or engineering backgrounds may think of themselves as the barber who became a ax-man in The Lumberjack Song the first time they make use of it for data analysis, which is a small amount of a mismatch. Learn this trendy field with a trendy investment in Data Science with Python Course.

However, its intrinsic readability and ease of use make it easy to learn, and the sheer number of analytical libraries that are dedicated to analysis available today means that data scientists from nearly every industry will find programs that are specifically designed to meet their needs easily accessible to download.

Due to Python's flexibility and general-purpose design, it became only natural when its popularity increased that somebody would eventually begin using it for data analytics. Being a jack of every trade, Python doesn't seem ideal for data analysis, but in many instances, organizations that were already heavily invested in the language recognized the advantages of standardizing the language and expanding it for that use.

[rishabhgargsoftwareengineer]

2020-06-01 | The quest for knowledge never ends. 

I finished my first specialization on Applied Data Science with Python 

The University of Michigan US | Coursera.

It was a skill based specialization involving:

  Python Programming

  Data Science libraries - Numpy, Pandas, Matplotlib, 

  Data Cleansing and Data Visualization

  ML Algorithms, Scikit Learn, Networking and Graph theory.

I found the course useful in applying statistical methods, traditional machine learning, information visualization and text analysis. It can really help my clients to gain a better understanding of their data. 

Take a tour to: https://coursera.org/

[best-data-science-certification]

Best Data Science in Marathahalli | AchieversIT

Overviews

AchieversIT offers a Complete Data Science course, the most requesting Data Science course on the lookout, covering the total Data Science ideas from Data Manipulation, Data Visualization and Analytics, R And Python Programming for Data Science, Machine Learning, Text Mining, Artificial Intelligence, and Neural Network.

Information Manipulation incorporates Data Collection, Data Extraction, Data Cleansing, Data Exploration, Data Integration, Data Mining, Data Transformation, Feature Engineering, building Prediction models, and Data Visualization.

You will learn R with databases, information import/product, investigation, and perception and Python will incorporate the investigation of libraries like Numpy, Pandas for data examination, and matplotlib, seaborn libraries for information representation.

Abilities that will be covered are Statistical Analysis, Text Mining, Regression Modeling, Probability, Hypothesis Testing, Predictive Analytics, Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, Databases, and Excel.

You will finish your training with a modern meeting which will give you a total understanding of how businesses apply Data Science in their continuous ventures, Risk Analysis, Time Management, and so on.

What you will learn

Fundamental statistics, mathematical, and optimization

supervised and directed AI and partnered information examination methods

Taking care of genuine issues emerging from various industry verticals

The most effective method to investigate information utilizing perception strategies and helpful outline measurements

Essential Extraction Transformation and Loading (ETL) of data

Theoretical information and complexities of methods generally utilized in business examination

In-depth understanding of different business application domains

A career in Data Science

Data science is one of the most worthwhile professional decisions in the industry at this moment. Data science experts are needed in pretty much every area, except most of the interest comes from IT, drugs, online business, and energy areas. While there is a tremendous interest for prepared specialists in the field, the hole between industry interest and ability supply is as yet striking. The internet-based Data Science Course in Marathahalli by AchieversIT trains you in every one of the ideas and advances like Data Visualization, Predictive Analysis, Descriptive Analytics, Data Integration, Machine Learning models, and then some. Our Data Science course in Marathahalli sets you up for a portion of the first-class work profiles in this field, which incorporates: Business Analyst Data Scientist Big Data Analyst Machine Learning Specialist Data Visualization Specialist.

Why did you join AchieversIT Training Institutes?

AchieversIT is One of the most popular institutions in Marathahalli and had prepared a huge number of candidates in this field

AchieversIT gives a chance to candidates to deal with Live projects

AchieversIT Converts a Normal Student to an I.T. Proficient

AchieversIT Provides Recorded Classes

AchieversIT Offers Online IDE'S, Live Classes, Online Debug Sessions

AchieversIT Provides Study Material: E-Books, Books, Notes

AchieversIT Placements team organized a talk with Calls to students After 85%, obviously, has been Completed.

To Increase Experience of Students Our Team and Students Collaborate to Work on real-time Projects

AchieversIT likewise Provides Aptitude Training for candidates to break talk with adjusts.

We Also Help Students to Write a Job Specific Resume.

Key Features of AchieversIT Training Institute in Marathahalli

Industry Experts as Trainers: At AchieversIT, You will Learn from the Experts from the business who are Passionate with regards to offering their Knowledge to Learners. Get Personally Mentored by the Experts.

LIVE Project: Get a chance to deal with Real-time Projects that will give you a Deep Experience. Highlight your Project Experience and Increase your shot at getting Hired!

Certificate: Get Certified by AchieversIT Training. Moreover, get Equipped to Clear Global Certifications. 85% of AchieversIT Students appear for Global Certifications and 100% of them Clear it.

Reasonable Fees: At AchieversIT, the Course Fee isn't simply Affordable, yet you have the decision to pay it in Installments. Quality Training at an Affordable charge.

flexibility: At AchieversIT, you get Ultimate Flexibility. Concentrate on corridor or Online Training? Early morning or late evenings? Non-weekend days or Weekends? Customary Pace or Fast Track? - Pick whatever suits you the Best.

100% job assistance: Restrict and MOU with more than 1200+ Small and Medium Companies to Support you with Opportunities to Kick-Start and Step-up your Career.

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Data Science Online Course

On completing this Data Science on-line course and passing the exam, you will receive our Data Science certificate online through our Learning Management System. You can obtain or share your certificate from this by way of either e-mail or LinkedIn. In this project, you should work with several operators involved in R programming including relational operators, arithmetic operators, and logical operators for numerous organizational needs. Perform knowledge cleansing, information transformation, analyze the outcomes and current the insights in the type of stories and dashboards. I obtained promoted with a 16% hike after finishing this course from EXCELR. The training taught me new expertise and the means to implement the identical in my current role.

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According to PayScale, the common pay scale of a data scientist in India is estimated as Rs. eight.2 lakh each year. The most lovely aspect of this program is that you are able to apply the learnings to the real-life business analytics problems as nicely. There are numerous points that contribute to this, such as the standard content material that's designed by the world-class colleges. The turnaround time for your queries is basically low just like an everyday classroom program.

This course has probably the most in-depth protection of in style Python Data Science libraries like NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn and extra. Data trends from Glassdoor clearly indicate that it is the greatest job anyone can get. With the exponential amount of information being produced and captured, it is logical to say that the demand for knowledge analytics is only going to extend. More and extra corporations are going to continue to rent information scientists to find significant insights and develop business strategies. Apply your new data evaluation abilities to business analytics, massive information analytics, bioinformatics, statistics and extra. Advanced courses will take you through real-world analytics issues to have the ability to attempt various knowledge analysis strategies and techniques and be taught more about quantitative and qualitative knowledge analysis processes.

DATA SCIENCE COURSE IN PUNE..

At most two courses may be chosen from outdoors the respective monitor with approval of the respective Program Co-Directors. Computational observe college students are allowed at most three electives which would possibly be non-Computer Science courses. The course additionally contains a set of tangible expertise that are the Django Python framework, Python IDE tools, Big Data, AWS, SAS, search engine optimization, Oracle, and Machine Learning. Few expertise that can be obtained upon completion of this Data Science course are such as Python, R, Tableau, Data Analysis, and so on. Microsoft SQL fundamentals, transact sql, table creation, SQL Server Indexes, Views, Stored procedures, triggers, T-SQL novices and superior, SSIS. Microsoft Azure, Data Lake, Data Factory, Azure PAAS, creating your applications on Azure, Migration of websites to Azure, migrating .NET primarily based internet applications.

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Phone Number- 919880913504

DATA SCIENCE COURSE..

[remotecareers]

Remote Data Engineer II

The Data Engineer II is responsible for designing, evaluating, and creating systems to support data science projects across the O’Reilly organization, as well as expanding and optimizing our data and data pipeline architecture.

This includes data cleansing, preparation, and ETL.

The ideal candidate will identify and work with the appropriate technology and software engineering solutions to facilitate machine learning and analytic pipeline deployment.

Essential Job Functions Move, structure, encode, and condense data from disparate database systems and formats.         Identify, design, and implement internal process improvements such as, automating manual processes, optimizing data delivery, and re-designing infrastructure for greater scalability.               Create data tools for analytics and data science team members that assist them in building and optimizing solutions to become an innovative industry leader.        Evaluate performance of machine learning systems and work with data scientists to improve quality.

Build processes supporting data transformation, data structures, metadata, dependency and workload management.

Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics.            Develop software solutions with a focus on maintainability and modularity.

Other Job Functions Experience performing root cause analysis on data and processes to answer specific business questions and identify opportunities for improvement.                                         Build processes supporting data transformation, data structures, metadata, dependency and workload management.

Advanced working SQL knowledge and experience working with relational databases, query authoring and familiarity with a variety of databases.

Uses best practices to develop statistical and machine learning techniques to address business needs.

           Experience supporting and working with cross-functional teams in a dynamic environment.

Analyze and influence technical, system, and/or user requirements.

Skills and Qualifications

Required: Bachelor’s degree.

2+ years of practical experience with ETL, data processing, database programming and data analytics.

Strong knowledge of Python; including Pandas, Numpy, SciKit-Learn, and experience with notebooks such as Jupyter.

Demonstrable knowledge of software design and engineering best practices.

Experience working with large-scale distributed data systems.

Excellent written and verbal communication skills.

Desire to work in a dynamic and collaborative environment.

Desired: Experience with machine learning and modeling techniques such as regressions, clustering, classification, random forests, gradient boosting, etc., including optimization of models and statistical evaluation of models.

Experience with statistical analysis of data.

Benefits

All full time team members are eligible for a benefits package that is designed to offer convenience and security to our team members and their families.

Programs, resources and benefit eligibility varies based on employment status, average hours worked, location and length of service.

For detailed benefits info, please click here or type http://bit.ly/ORLYBenefits in your browser.

Compensation Range

$65,000.00 – $115,000.00 annually, pay will be based upon applicable experience, training and skills

Bonus

This job is eligible for an annual cash bonus based on individual goal achievement.

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