#Required skills in Python programming

[deepak-garhwal]

Embarking on a Python Course in Delhi After 12th

Your complete roadmap to Python course after 12th. Build a solid foundation for your tech career. Explore the best Python courses in Delhi.

[education43]

What Are the Qualifications for a Data Scientist?

In today's data-driven world, the role of a data scientist has become one of the most coveted career paths. With businesses relying on data for decision-making, understanding customer behavior, and improving products, the demand for skilled professionals who can analyze, interpret, and extract value from data is at an all-time high. If you're wondering what qualifications are needed to become a successful data scientist, how DataCouncil can help you get there, and why a data science course in Pune is a great option, this blog has the answers.

The Key Qualifications for a Data Scientist

To succeed as a data scientist, a mix of technical skills, education, and hands-on experience is essential. Here are the core qualifications required:

1. Educational Background

A strong foundation in mathematics, statistics, or computer science is typically expected. Most data scientists hold at least a bachelor’s degree in one of these fields, with many pursuing higher education such as a master's or a Ph.D. A data science course in Pune with DataCouncil can bridge this gap, offering the academic and practical knowledge required for a strong start in the industry.

2. Proficiency in Programming Languages

Programming is at the heart of data science. You need to be comfortable with languages like Python, R, and SQL, which are widely used for data analysis, machine learning, and database management. A comprehensive data science course in Pune will teach these programming skills from scratch, ensuring you become proficient in coding for data science tasks.

3. Understanding of Machine Learning

Data scientists must have a solid grasp of machine learning techniques and algorithms such as regression, clustering, and decision trees. By enrolling in a DataCouncil course, you'll learn how to implement machine learning models to analyze data and make predictions, an essential qualification for landing a data science job.

4. Data Wrangling Skills

Raw data is often messy and unstructured, and a good data scientist needs to be adept at cleaning and processing data before it can be analyzed. DataCouncil's data science course in Pune includes practical training in tools like Pandas and Numpy for effective data wrangling, helping you develop a strong skill set in this critical area.

5. Statistical Knowledge

Statistical analysis forms the backbone of data science. Knowledge of probability, hypothesis testing, and statistical modeling allows data scientists to draw meaningful insights from data. A structured data science course in Pune offers the theoretical and practical aspects of statistics required to excel.

6. Communication and Data Visualization Skills

Being able to explain your findings in a clear and concise manner is crucial. Data scientists often need to communicate with non-technical stakeholders, making tools like Tableau, Power BI, and Matplotlib essential for creating insightful visualizations. DataCouncil’s data science course in Pune includes modules on data visualization, which can help you present data in a way that’s easy to understand.

7. Domain Knowledge

Apart from technical skills, understanding the industry you work in is a major asset. Whether it’s healthcare, finance, or e-commerce, knowing how data applies within your industry will set you apart from the competition. DataCouncil's data science course in Pune is designed to offer case studies from multiple industries, helping students gain domain-specific insights.

Why Choose DataCouncil for a Data Science Course in Pune?

If you're looking to build a successful career as a data scientist, enrolling in a data science course in Pune with DataCouncil can be your first step toward reaching your goals. Here’s why DataCouncil is the ideal choice:

Comprehensive Curriculum: The course covers everything from the basics of data science to advanced machine learning techniques.

Hands-On Projects: You'll work on real-world projects that mimic the challenges faced by data scientists in various industries.

Experienced Faculty: Learn from industry professionals who have years of experience in data science and analytics.

100% Placement Support: DataCouncil provides job assistance to help you land a data science job in Pune or anywhere else, making it a great investment in your future.

Flexible Learning Options: With both weekday and weekend batches, DataCouncil ensures that you can learn at your own pace without compromising your current commitments.

Conclusion

Becoming a data scientist requires a combination of technical expertise, analytical skills, and industry knowledge. By enrolling in a data science course in Pune with DataCouncil, you can gain all the qualifications you need to thrive in this exciting field. Whether you're a fresher looking to start your career or a professional wanting to upskill, this course will equip you with the knowledge, skills, and practical experience to succeed as a data scientist.

Explore DataCouncil’s offerings today and take the first step toward unlocking a rewarding career in data science! Looking for the best data science course in Pune? DataCouncil offers comprehensive data science classes in Pune, designed to equip you with the skills to excel in this booming field. Our data science course in Pune covers everything from data analysis to machine learning, with competitive data science course fees in Pune. We provide job-oriented programs, making us the best institute for data science in Pune with placement support. Explore online data science training in Pune and take your career to new heights!

[mostlysignssomeportents]

What kind of bubble is AI?

My latest column for Locus Magazine is "What Kind of Bubble is AI?" All economic bubbles are hugely destructive, but some of them leave behind wreckage that can be salvaged for useful purposes, while others leave nothing behind but ashes:

https://locusmag.com/2023/12/commentary-cory-doctorow-what-kind-of-bubble-is-ai/

Think about some 21st century bubbles. The dotcom bubble was a terrible tragedy, one that drained the coffers of pension funds and other institutional investors and wiped out retail investors who were gulled by Superbowl Ads. But there was a lot left behind after the dotcoms were wiped out: cheap servers, office furniture and space, but far more importantly, a generation of young people who'd been trained as web makers, leaving nontechnical degree programs to learn HTML, perl and python. This created a whole cohort of technologists from non-technical backgrounds, a first in technological history. Many of these people became the vanguard of a more inclusive and humane tech development movement, and they were able to make interesting and useful services and products in an environment where raw materials – compute, bandwidth, space and talent – were available at firesale prices.

Contrast this with the crypto bubble. It, too, destroyed the fortunes of institutional and individual investors through fraud and Superbowl Ads. It, too, lured in nontechnical people to learn esoteric disciplines at investor expense. But apart from a smattering of Rust programmers, the main residue of crypto is bad digital art and worse Austrian economics.

Or think of Worldcom vs Enron. Both bubbles were built on pure fraud, but Enron's fraud left nothing behind but a string of suspicious deaths. By contrast, Worldcom's fraud was a Big Store con that required laying a ton of fiber that is still in the ground to this day, and is being bought and used at pennies on the dollar.

AI is definitely a bubble. As I write in the column, if you fly into SFO and rent a car and drive north to San Francisco or south to Silicon Valley, every single billboard is advertising an "AI" startup, many of which are not even using anything that can be remotely characterized as AI. That's amazing, considering what a meaningless buzzword AI already is.

So which kind of bubble is AI? When it pops, will something useful be left behind, or will it go away altogether? To be sure, there's a legion of technologists who are learning Tensorflow and Pytorch. These nominally open source tools are bound, respectively, to Google and Facebook's AI environments:

https://pluralistic.net/2023/08/18/openwashing/#you-keep-using-that-word-i-do-not-think-it-means-what-you-think-it-means

But if those environments go away, those programming skills become a lot less useful. Live, large-scale Big Tech AI projects are shockingly expensive to run. Some of their costs are fixed – collecting, labeling and processing training data – but the running costs for each query are prodigious. There's a massive primary energy bill for the servers, a nearly as large energy bill for the chillers, and a titanic wage bill for the specialized technical staff involved.

Once investor subsidies dry up, will the real-world, non-hyperbolic applications for AI be enough to cover these running costs? AI applications can be plotted on a 2X2 grid whose axes are "value" (how much customers will pay for them) and "risk tolerance" (how perfect the product needs to be).

Charging teenaged D&D players $10 month for an image generator that creates epic illustrations of their characters fighting monsters is low value and very risk tolerant (teenagers aren't overly worried about six-fingered swordspeople with three pupils in each eye). Charging scammy spamfarms $500/month for a text generator that spits out dull, search-algorithm-pleasing narratives to appear over recipes is likewise low-value and highly risk tolerant (your customer doesn't care if the text is nonsense). Charging visually impaired people $100 month for an app that plays a text-to-speech description of anything they point their cameras at is low-value and moderately risk tolerant ("that's your blue shirt" when it's green is not a big deal, while "the street is safe to cross" when it's not is a much bigger one).

Morganstanley doesn't talk about the trillions the AI industry will be worth some day because of these applications. These are just spinoffs from the main event, a collection of extremely high-value applications. Think of self-driving cars or radiology bots that analyze chest x-rays and characterize masses as cancerous or noncancerous.

These are high value – but only if they are also risk-tolerant. The pitch for self-driving cars is "fire most drivers and replace them with 'humans in the loop' who intervene at critical junctures." That's the risk-tolerant version of self-driving cars, and it's a failure. More than $100b has been incinerated chasing self-driving cars, and cars are nowhere near driving themselves:

https://pluralistic.net/2022/10/09/herbies-revenge/#100-billion-here-100-billion-there-pretty-soon-youre-talking-real-money

Quite the reverse, in fact. Cruise was just forced to quit the field after one of their cars maimed a woman – a pedestrian who had not opted into being part of a high-risk AI experiment – and dragged her body 20 feet through the streets of San Francisco. Afterwards, it emerged that Cruise had replaced the single low-waged driver who would normally be paid to operate a taxi with 1.5 high-waged skilled technicians who remotely oversaw each of its vehicles:

https://www.nytimes.com/2023/11/03/technology/cruise-general-motors-self-driving-cars.html

The self-driving pitch isn't that your car will correct your own human errors (like an alarm that sounds when you activate your turn signal while someone is in your blind-spot). Self-driving isn't about using automation to augment human skill – it's about replacing humans. There's no business case for spending hundreds of billions on better safety systems for cars (there's a human case for it, though!). The only way the price-tag justifies itself is if paid drivers can be fired and replaced with software that costs less than their wages.

What about radiologists? Radiologists certainly make mistakes from time to time, and if there's a computer vision system that makes different mistakes than the sort that humans make, they could be a cheap way of generating second opinions that trigger re-examination by a human radiologist. But no AI investor thinks their return will come from selling hospitals that reduce the number of X-rays each radiologist processes every day, as a second-opinion-generating system would. Rather, the value of AI radiologists comes from firing most of your human radiologists and replacing them with software whose judgments are cursorily double-checked by a human whose "automation blindness" will turn them into an OK-button-mashing automaton:

https://pluralistic.net/2023/08/23/automation-blindness/#humans-in-the-loop

The profit-generating pitch for high-value AI applications lies in creating "reverse centaurs": humans who serve as appendages for automation that operates at a speed and scale that is unrelated to the capacity or needs of the worker:

https://pluralistic.net/2022/04/17/revenge-of-the-chickenized-reverse-centaurs/

But unless these high-value applications are intrinsically risk-tolerant, they are poor candidates for automation. Cruise was able to nonconsensually enlist the population of San Francisco in an experimental murderbot development program thanks to the vast sums of money sloshing around the industry. Some of this money funds the inevitabilist narrative that self-driving cars are coming, it's only a matter of when, not if, and so SF had better get in the autonomous vehicle or get run over by the forces of history.

Once the bubble pops (all bubbles pop), AI applications will have to rise or fall on their actual merits, not their promise. The odds are stacked against the long-term survival of high-value, risk-intolerant AI applications.

The problem for AI is that while there are a lot of risk-tolerant applications, they're almost all low-value; while nearly all the high-value applications are risk-intolerant. Once AI has to be profitable – once investors withdraw their subsidies from money-losing ventures – the risk-tolerant applications need to be sufficient to run those tremendously expensive servers in those brutally expensive data-centers tended by exceptionally expensive technical workers.

If they aren't, then the business case for running those servers goes away, and so do the servers – and so do all those risk-tolerant, low-value applications. It doesn't matter if helping blind people make sense of their surroundings is socially beneficial. It doesn't matter if teenaged gamers love their epic character art. It doesn't even matter how horny scammers are for generating AI nonsense SEO websites:

https://twitter.com/jakezward/status/1728032634037567509

These applications are all riding on the coattails of the big AI models that are being built and operated at a loss in order to be profitable. If they remain unprofitable long enough, the private sector will no longer pay to operate them.

Now, there are smaller models, models that stand alone and run on commodity hardware. These would persist even after the AI bubble bursts, because most of their costs are setup costs that have already been borne by the well-funded companies who created them. These models are limited, of course, though the communities that have formed around them have pushed those limits in surprising ways, far beyond their original manufacturers' beliefs about their capacity. These communities will continue to push those limits for as long as they find the models useful.

These standalone, "toy" models are derived from the big models, though. When the AI bubble bursts and the private sector no longer subsidizes mass-scale model creation, it will cease to spin out more sophisticated models that run on commodity hardware (it's possible that Federated learning and other techniques for spreading out the work of making large-scale models will fill the gap).

So what kind of bubble is the AI bubble? What will we salvage from its wreckage? Perhaps the communities who've invested in becoming experts in Pytorch and Tensorflow will wrestle them away from their corporate masters and make them generally useful. Certainly, a lot of people will have gained skills in applying statistical techniques.

But there will also be a lot of unsalvageable wreckage. As big AI models get integrated into the processes of the productive economy, AI becomes a source of systemic risk. The only thing worse than having an automated process that is rendered dangerous or erratic based on AI integration is to have that process fail entirely because the AI suddenly disappeared, a collapse that is too precipitous for former AI customers to engineer a soft landing for their systems.

This is a blind spot in our policymakers debates about AI. The smart policymakers are asking questions about fairness, algorithmic bias, and fraud. The foolish policymakers are ensnared in fantasies about "AI safety," AKA "Will the chatbot become a superintelligence that turns the whole human race into paperclips?"

https://pluralistic.net/2023/11/27/10-types-of-people/#taking-up-a-lot-of-space

But no one is asking, "What will we do if" – when – "the AI bubble pops and most of this stuff disappears overnight?"

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2023/12/19/bubblenomics/#pop

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

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tom_bullock (modified) https://www.flickr.com/photos/tombullock/25173469495/

CC BY 2.0 https://creativecommons.org/licenses/by/2.0/

[rigelmejo]

learning to code!

When I was 9 years old, I learned enough html to code neopets pages, my own geocities websites, and I even made forums on my own sites so my friends could all roleplay together or rant together lol. And then? I forgot so much. I no longer no how to make a forum, or even a 'next page' button - so even the dream of just making a simple blog or webnovel site feels like a huge hurdle now. (9 year old me could probably figure it out in 2 hours).

So I'm relearning! I figured this would be a fun post to place resources I find for coding, since there's coding languages, and I figure maybe if you like running you're blog then you also might be interested in tools for making blogs!

First, for those of you who miss the old geocities and angelfire type of sites to make your own free site on: neocities.org

You can make free sites you can code yourself, the way 9 year old me did. A lot of people have made SUCH amazing sites, it's baffling my mind trying to figure out how they did, I definitely wish I could make an art portfolio site even a fourth as cool as some of the sites people have made on here.

And for those pressed for time, who aren't about to learn coding right now: wix.com is the place I recommend for building a site, it requires no coding skill and is fairly straightforward about adding pages or features by clicking buttons. I used it to make my art portfolio site, I am testing out using it for my webnovel - the alternative is Wordpress, but wix.com is letting me basically make a wordpress blog Inside my own site. It's very beginner friendly in terms of "how the fuck do I set up a 'sign up for updates' message and have my site actually email these people my novel updates?" and "I need a 4x20 grid of my art down the page, that lets people click the art to see it's information and make it bigger."

I did neocities.org's little html tutorial today, it's the part of html I DID remember (links, paragraphs, headers).

My next step is to go through htmldog.com's tutorials. They go from beginner, to intermediate, to CSS. Unlike many a coding tutorial I've seen, they explain what program on your computer you need to WRITE the code in and then how to save it and how to open it. (You'd think this isn't a big deal but I've been looking into how to learn Python for months and I can't find a tutorial explaining what fucking program to write my python in... notepad? do I need something else? I don't fucking know!! My dad finally gave me a printed textbook which supposedly tells you what to download to start... I learned C++ in college and for that you needed Visual Basic to code C++, so I figured I needed Something to Write the fucking python IN.)

[womaneng]

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A post shared by Zeynep Küçük Woman Engineer (@woman.engineer)

Learning to code and becoming a data scientist without a background in computer science or mathematics is absolutely possible, but it will require dedication, time, and a structured approach. ✨👌🏻 🖐🏻Here’s a step-by-step guide to help you get started:

1. Start with the Basics:

- Begin by learning the fundamentals of programming. Choose a beginner-friendly programming language like Python, which is widely used in data science.

- Online platforms like Codecademy, Coursera, and Khan Academy offer interactive courses for beginners.

2. Learn Mathematics and Statistics:

- While you don’t need to be a mathematician, a solid understanding of key concepts like algebra, calculus, and statistics is crucial for data science.

- Platforms like Khan Academy and MIT OpenCourseWare provide free resources for learning math.

3. Online Courses and Tutorials:

- Enroll in online data science courses on platforms like Coursera, edX, Udacity, and DataCamp. Look for beginner-level courses that cover data analysis, visualization, and machine learning.

4. Structured Learning Paths:

- Follow structured learning paths offered by online platforms. These paths guide you through various topics in a logical sequence.

5. Practice with Real Data:

- Work on hands-on projects using real-world data. Websites like Kaggle offer datasets and competitions for practicing data analysis and machine learning.

6. Coding Exercises:

- Practice coding regularly to build your skills. Sites like LeetCode and HackerRank offer coding challenges that can help improve your programming proficiency.

7. Learn Data Manipulation and Analysis Libraries:

- Familiarize yourself with Python libraries like NumPy, pandas, and Matplotlib for data manipulation, analysis, and visualization.

For more follow me on instagram.

[kitscodingblog]

Coding Update 6

I think its been a while since I've updated. I fell behind a little on my learning cause life has been really difficult lately.

Hope y'all had a good Thanksgiving and having a good start into the holiday season!! Yadda yadda more under the cut.

So I just finished Part 1 of my book. This mostly contained the introduction to Python, obviously, while learning a lot of the major functions of the program. I think it took me a bit to get into the swing of coding, especially cause it felt like I've had to rewire my own brain doing this haha.

The good news is I feel a LOT more comfortable with Python now. Not like "i can do anything!" yet but enough that it's actually super fun and I'm excited to work on projects!

The last part of the chapter taught me to use the "pytest" ability. I.E: writing test code so that I can make sure my programs are working properly and as intended. That part was really interesting, mostly because it was super duper busted at first for me.

That ended up being because where my "default folder" is set is like my main python hub, so i have to use the uh. What's it called? True access link? Where I write the entire string to the code's location.

Which also taught me that in the Terminal I have to use quotes for the location cause before I learned proper coding practices, I used spaces in some of the initial folders.

We're good now though.

The next part, Part II, is all about learning to build fully functional projects!! I'm so HYPED. There's four projects, of which it was like choose whatever you want! But I'm gonna start with an Alien Invasion remake. You know, the game where you're the little ship at the bottom shooting at aliens as they slowly decend on the screen. I should learn a lot from this one.

The other project I'm looking forward to is a simple online blog database. It'll have users create accounts, be accessible online, and you can make little journal posts! That should actually teach me a lot of stuff that I want to do.

There's another for data visualization, which I think I'll send to my cousin. He works in a lab at MIT and I know they use python for their programs. Maybe I can work my way into his work by doing that lmao.

Anyway, I'm really excited for all of this. It should teach me a ton of usable skills, and then i can add these projects to my portfolio to show off. Also I can spin off and make my own stuff.

Also also, if anyone wants to help me test my projects, feel free to let me know! I already know a few who are more than willing, but I'd appreciate any and all feedback as I go.

Oh! It also recommended learning version control, which I know almost nothing about. So I'll learn how to use GitHUB to store projects and recall old ones as I go if things break horribly. Which will be fun! Cause I know that for sure is going to be an important skill to have.

For a last fun fact, did you know places are like "requirements: typing 30 words per second." Do you know how fast I can type? At my peak I'm like over 110. I baseline at like 95. I don't know if that's actually fast but it makes me feel like the specialist little guy.

I hope you all have a good holiday season. Sorry no code in this post, I'm writing it so I can give you all an update, and I'm dog tired today. But but I promise to snip actual code for you as I go forward. And It'll be fun, especially cause this alien project will teach me about making VISUAL things!

Seasons Greasons Tumblr! -Kit

[oneictskills]

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[riseandinspire]

Can AI make coding skills obsolete? With the rise of natural language computing, the future may not require us to speak in Python anymore—just English. Discover how AI is transforming the way we interact with machines.

[educationmore]

Python for Beginners: Launch Your Tech Career with Coding Skills

Are you ready to launch your tech career but don’t know where to start? Learning Python is one of the best ways to break into the world of technology—even if you have zero coding experience.

In this guide, we’ll explore how Python for beginners can be your gateway to a rewarding career in software development, data science, automation, and more.

Why Python Is the Perfect Language for Beginners

Python has become the go-to programming language for beginners and professionals alike—and for good reason:

Simple syntax: Python reads like plain English, making it easy to learn.

High demand: Industries spanning the spectrum are actively seeking Python developers to fuel their technological advancements.

Versatile applications: Python's versatility shines as it powers everything from crafting websites to driving artificial intelligence and dissecting data.

Whether you want to become a software developer, data analyst, or AI engineer, Python lays the foundation.

What Can You Do With Python?

Python is not just a beginner language—it’s a career-building tool. Here are just a few career paths where Python is essential:

Web Development: Frameworks like Django and Flask make it easy to build powerful web applications. You can even enroll in a Python Course in Kochi to gain hands-on experience with real-world web projects.

Data Science & Analytics: For professionals tackling data analysis and visualization, the Python ecosystem, featuring powerhouses like Pandas, NumPy, and Matplotlib, sets the benchmark.

Machine Learning & AI: Spearheading advancements in artificial intelligence development, Python boasts powerful tools such as TensorFlow and scikit-learn.

Automation & Scripting: Simple yet effective Python scripts offer a pathway to amplified efficiency by automating routine workflows.

Cybersecurity & Networking: The application of Python is expanding into crucial domains such as ethical hacking, penetration testing, and the automation of network processes.

How to Get Started with Python

Starting your Python journey doesn't require a computer science degree. Success hinges on a focused commitment combined with a thoughtfully structured educational approach.

Step 1: Install Python

Download and install Python from python.org. It's free and available for all platforms.

Step 2: Choose an IDE

Use beginner-friendly tools like Thonny, PyCharm, or VS Code to write your code.

Step 3: Learn the Basics

Focus on:

Variables and data types

Conditional statements

Loops

Functions

Lists and dictionaries

If you prefer guided learning, a reputable Python Institute in Kochi can offer structured programs and mentorship to help you grasp core concepts efficiently.

Step 4: Build Projects

Learning by doing is key. Start small:

Build a calculator

Automate file organization

Create a to-do list app

As your skills grow, you can tackle more complex projects like data dashboards or web apps.

How Python Skills Can Boost Your Career

Adding Python to your resume instantly opens up new opportunities. Here's how it helps:

Higher employability: Python is one of the top 3 most in-demand programming languages.

Better salaries: Python developers earn competitive salaries across the globe.

Remote job opportunities: Many Python-related jobs are available remotely, offering flexibility.

Even if you're not aiming to be a full-time developer, Python skills can enhance careers in marketing, finance, research, and product management.

If you're serious about starting a career in tech, learning Python is the smartest first step you can take. It’s beginner-friendly, powerful, and widely used across industries.

Whether you're a student, job switcher, or just curious about programming, Python for beginners can unlock countless career opportunities. Invest time in learning today—and start building the future you want in tech.

Globally recognized as a premier educational hub, DataMites Institute delivers in-depth training programs across the pivotal fields of data science, artificial intelligence, and machine learning. They provide expert-led courses designed for both beginners and professionals aiming to boost their careers.

Python Modules Explained - Different Types and Functions - Python Tutorial

[xaltius]

Unlocking the Power of Data: Essential Skills to Become a Data Scientist

In today's data-driven world, the demand for skilled data scientists is skyrocketing. These professionals are the key to transforming raw information into actionable insights, driving innovation and shaping business strategies. But what exactly does it take to become a data scientist? It's a multidisciplinary field, requiring a unique blend of technical prowess and analytical thinking. Let's break down the essential skills you'll need to embark on this exciting career path.

1. Strong Mathematical and Statistical Foundation:

At the heart of data science lies a deep understanding of mathematics and statistics. You'll need to grasp concepts like:

Linear Algebra and Calculus: Essential for understanding machine learning algorithms and optimizing models.

Probability and Statistics: Crucial for data analysis, hypothesis testing, and drawing meaningful conclusions from data.

2. Programming Proficiency (Python and/or R):

Data scientists are fluent in at least one, if not both, of the dominant programming languages in the field:

Python: Known for its readability and extensive libraries like Pandas, NumPy, Scikit-learn, and TensorFlow, making it ideal for data manipulation, analysis, and machine learning.

R: Specifically designed for statistical computing and graphics, R offers a rich ecosystem of packages for statistical modeling and visualization.

3. Data Wrangling and Preprocessing Skills:

Raw data is rarely clean and ready for analysis. A significant portion of a data scientist's time is spent on:

Data Cleaning: Handling missing values, outliers, and inconsistencies.

Data Transformation: Reshaping, merging, and aggregating data.

Feature Engineering: Creating new features from existing data to improve model performance.

4. Expertise in Databases and SQL:

Data often resides in databases. Proficiency in SQL (Structured Query Language) is essential for:

Extracting Data: Querying and retrieving data from various database systems.

Data Manipulation: Filtering, joining, and aggregating data within databases.

5. Machine Learning Mastery:

Machine learning is a core component of data science, enabling you to build models that learn from data and make predictions or classifications. Key areas include:

Supervised Learning: Regression, classification algorithms.

Unsupervised Learning: Clustering, dimensionality reduction.

Model Selection and Evaluation: Choosing the right algorithms and assessing their performance.

6. Data Visualization and Communication Skills:

Being able to effectively communicate your findings is just as important as the analysis itself. You'll need to:

Visualize Data: Create compelling charts and graphs to explore patterns and insights using libraries like Matplotlib, Seaborn (Python), or ggplot2 (R).

Tell Data Stories: Present your findings in a clear and concise manner that resonates with both technical and non-technical audiences.

7. Critical Thinking and Problem-Solving Abilities:

Data scientists are essentially problem solvers. You need to be able to:

Define Business Problems: Translate business challenges into data science questions.

Develop Analytical Frameworks: Structure your approach to solve complex problems.

Interpret Results: Draw meaningful conclusions and translate them into actionable recommendations.

8. Domain Knowledge (Optional but Highly Beneficial):

Having expertise in the specific industry or domain you're working in can give you a significant advantage. It helps you understand the context of the data and formulate more relevant questions.

9. Curiosity and a Growth Mindset:

The field of data science is constantly evolving. A genuine curiosity and a willingness to learn new technologies and techniques are crucial for long-term success.

10. Strong Communication and Collaboration Skills:

Data scientists often work in teams and need to collaborate effectively with engineers, business stakeholders, and other experts.

Kickstart Your Data Science Journey with Xaltius Academy's Data Science and AI Program:

Acquiring these skills can seem like a daunting task, but structured learning programs can provide a clear and effective path. Xaltius Academy's Data Science and AI Program is designed to equip you with the essential knowledge and practical experience to become a successful data scientist.

Key benefits of the program:

Comprehensive Curriculum: Covers all the core skills mentioned above, from foundational mathematics to advanced machine learning techniques.

Hands-on Projects: Provides practical experience working with real-world datasets and building a strong portfolio.

Expert Instructors: Learn from industry professionals with years of experience in data science and AI.

Career Support: Offers guidance and resources to help you launch your data science career.

Becoming a data scientist is a rewarding journey that blends technical expertise with analytical thinking. By focusing on developing these key skills and leveraging resources like Xaltius Academy's program, you can position yourself for a successful and impactful career in this in-demand field. The power of data is waiting to be unlocked – are you ready to take the challenge?

[izicodes]

Hello, I'm only wondering how you would go about building a track to get a job in these lines of works, if you have advice. Thank you :)

Hiya! 💗

I have some advice yeah! Do bear in mind, the way I got into Software Development, now focusing on Web Development, was:

A couple of months of self-studying HTML, CSS, JavaScript and Python

Applied for a Software Development Technician apprenticeship - working in a company whilst studying at a college (had to do it online because of COVID restrictions)

Completed the apprenticeship + 2 exam certificates in Programming and Software Development

The company I did my apprenticeship hired me straight after I passed.

Other people had similar routes e.g. via higher education at a university or college, or did the complete self-study route and got a job at a company or just freelancing. Everyone's journey is different!

Building a successful track to get a job in Software Development requires a combination of a lot of things and not just learning how to program. I will assume you want to get into Web Dev, but this can be applied to other areas e.g. Game Dev or Moblie Dev. Here are some steps you could take:

Education and Skill Development

The most obvious: you need the skills...

Could find schools, online schools, colleges or universities to learn the subject: This is if you can. Some people learn better with a teacher there to help them so maybe attending a school setting is better for you!

Online courses and tutorials: Enroll in online platforms like Coursera, Udemy, or Codecademy to learn specific programming languages (such as HTML, CSS, JavaScript), frameworks, and development tools commonly used in web development.

Build a portfolio: Create a collection of projects that showcase your skills. Develop websites, and web applications, or contribute to open-source projects to demonstrate your abilities to potential employers. Use places like GitHub or GitLab!

Practical Experience

If you don't have the opportunity to be already working in a company in their IT department for experience, try these two types of experience you could try for experience:

Internships and part-time jobs: Seek internships or part-time positions in software development companies. This provides hands-on experience, exposes you to real-world projects, and helps you understand industry practices.

Freelance work: Take up freelance web development projects to gain practical experience and expand your portfolio. Platforms like Upwork and Freelancer can help you find clients and build a reputation.

Networking and Professional Development

Join online communities: Engage with online forums, developer communities (such as Stack Overflow), and social media groups to connect with like-minded individuals, seek advice, and stay updated on industry news.

Create a presence and show off your coding journey: I am a huge advocate for this. I had friends that I've mentioned on my blog that got their first developer job solely because they were posting their projects and learning journey on their Twitter accounts. For example, my friend Hikari (her Twitter) got her job because the employer saw her tweets of her progress then he noticed her portfolio page and asked for an interview with her - then she got the job. Try your chances with this method!

Contribute to open-source projects: Collaborate on open-source projects on platforms like GitHub. This not only helps you enhance your coding skills but also showcases your ability to work in a team and contribute to larger projects! Working in a team is a key skill!

Hope this helps! Thanks for the ask! 🙌🏾💗

[deepak-garhwal]

Mastering Python After 12th: A Delhi-Based Course Guide

Explore Python courses tailored for 12th graders. Kickstart your coding adventure with confidence.

[sindhu14]

What is Python, How to Learn Python?

What is Python?

Python is a high-level, interpreted programming language known for its simplicity and readability. It is widely used in various fields like: ✅ Web Development (Django, Flask) ✅ Data Science & Machine Learning (Pandas, NumPy, TensorFlow) ✅ Automation & Scripting (Web scraping, File automation) ✅ Game Development (Pygame) ✅ Cybersecurity & Ethical Hacking ✅ Embedded Systems & IoT (MicroPython)

Python is beginner-friendly because of its easy-to-read syntax, large community, and vast library support.

How Long Does It Take to Learn Python?

The time required to learn Python depends on your goals and background. Here’s a general breakdown:

1. Basics of Python (1-2 months)

If you spend 1-2 hours daily, you can master:

Variables, Data Types, Operators

Loops & Conditionals

Functions & Modules

Lists, Tuples, Dictionaries

File Handling

Basic Object-Oriented Programming (OOP)

2. Intermediate Level (2-4 months)

Once comfortable with basics, focus on:

Advanced OOP concepts

Exception Handling

Working with APIs & Web Scraping

Database handling (SQL, SQLite)

Python Libraries (Requests, Pandas, NumPy)

Small real-world projects

3. Advanced Python & Specialization (6+ months)

If you want to go pro, specialize in:

Data Science & Machine Learning (Matplotlib, Scikit-Learn, TensorFlow)

Web Development (Django, Flask)

Automation & Scripting

Cybersecurity & Ethical Hacking

Learning Plan Based on Your Goal

📌 Casual Learning – 3-6 months (for automation, scripting, or general knowledge) 📌 Professional Development – 6-12 months (for jobs in software, data science, etc.) 📌 Deep Mastery – 1-2 years (for AI, ML, complex projects, research)

Scope @ NareshIT:

At NareshIT’s Python application Development program you will be able to get the extensive hands-on training in front-end, middleware, and back-end technology.

It skilled you along with phase-end and capstone projects based on real business scenarios.

Here you learn the concepts from leading industry experts with content structured to ensure industrial relevance.

An end-to-end application with exciting features

Earn an industry-recognized course completion certificate.

For more details:

[tech-insides]

What are the skills needed for a data scientist job?

It’s one of those careers that’s been getting a lot of buzz lately, and for good reason. But what exactly do you need to become a data scientist? Let’s break it down.

Technical Skills

First off, let's talk about the technical skills. These are the nuts and bolts of what you'll be doing every day.

Programming Skills: At the top of the list is programming. You’ll need to be proficient in languages like Python and R. These are the go-to tools for data manipulation, analysis, and visualization. If you’re comfortable writing scripts and solving problems with code, you’re on the right track.

Statistical Knowledge: Next up, you’ve got to have a solid grasp of statistics. This isn’t just about knowing the theory; it’s about applying statistical techniques to real-world data. You’ll need to understand concepts like regression, hypothesis testing, and probability.

Machine Learning: Machine learning is another biggie. You should know how to build and deploy machine learning models. This includes everything from simple linear regressions to complex neural networks. Familiarity with libraries like scikit-learn, TensorFlow, and PyTorch will be a huge plus.

Data Wrangling: Data isn’t always clean and tidy when you get it. Often, it’s messy and requires a lot of preprocessing. Skills in data wrangling, which means cleaning and organizing data, are essential. Tools like Pandas in Python can help a lot here.

Data Visualization: Being able to visualize data is key. It’s not enough to just analyze data; you need to present it in a way that makes sense to others. Tools like Matplotlib, Seaborn, and Tableau can help you create clear and compelling visuals.

Analytical Skills

Now, let’s talk about the analytical skills. These are just as important as the technical skills, if not more so.

Problem-Solving: At its core, data science is about solving problems. You need to be curious and have a knack for figuring out why something isn’t working and how to fix it. This means thinking critically and logically.

Domain Knowledge: Understanding the industry you’re working in is crucial. Whether it’s healthcare, finance, marketing, or any other field, knowing the specifics of the industry will help you make better decisions and provide more valuable insights.

Communication Skills: You might be working with complex data, but if you can’t explain your findings to others, it’s all for nothing. Being able to communicate clearly and effectively with both technical and non-technical stakeholders is a must.

Soft Skills

Don’t underestimate the importance of soft skills. These might not be as obvious, but they’re just as critical.

Collaboration: Data scientists often work in teams, so being able to collaborate with others is essential. This means being open to feedback, sharing your ideas, and working well with colleagues from different backgrounds.

Time Management: You’ll likely be juggling multiple projects at once, so good time management skills are crucial. Knowing how to prioritize tasks and manage your time effectively can make a big difference.

Adaptability: The field of data science is always evolving. New tools, techniques, and technologies are constantly emerging. Being adaptable and willing to learn new things is key to staying current and relevant in the field.

Conclusion

So, there you have it. Becoming a data scientist requires a mix of technical prowess, analytical thinking, and soft skills. It’s a challenging but incredibly rewarding career path. If you’re passionate about data and love solving problems, it might just be the perfect fit for you.

Good luck to all of you aspiring data scientists out there!

[kon4ka]

Challenge: Drawing D&D classes - Topic 7 - Druid 2

🎆 Druid Hacker in the world of Cyberpunk - Aleph zero. 🎆

No, I didn’t eat strange colored mushrooms… 🍄

🖥 Backstory: Born with unique computing abilities, at the age of three she was kidnapped by a corporation that connected her to the network and made her a netrunner. Until my twentieth birthday, I never left the network. She was forced to first learn to live online, and then to comply with the requirements of the Corporation. But thanks to the innate runner's intuition, she was able to look behind the ice (firewall) of the corporation and travel through the open network and fragments of the Old Network that remained after the Fall of the First Network in backward countries.

Over time, she learned the rules and laws of each region and even learned to imitate programs so that the corporation could not distinguish her from one of the AIs. During my “work” for the corporation, I also saw enough of the human world and burned with a burning desire to get out of the world of ones and zeros and feel at least something, feel what it’s like to live, look at the world with your own eyes, eat food and experience other everyday joys. Presumably it was saved during the merger of two corporations; the container with it was simply stolen along with the weapons and the data center. The container ended up in the hands of one of the gangs, from which Aleph escaped, burning the brains of everyone who was connected to the gang’s local network. I found myself on the street not understanding what to do and barely able to control her body. She had one contact outside the corporation where she worked, Aleph was able to contact him and ask for help. They helped her; someone she knew only by his online nickname came for her and took her away from the bandit’s lair, and then took her to the ripper. Aleph Zero sat for a long time on the necks of the people who sheltered her, slowly relearning how to speak with my mouth and control my own body.

✒ Character: Still a big child, delighted with the most banal things. Still often wrong about trivial things. Good at everything that requires logic. She is laconic, or rather, she still forgets that in order to be heard it is not enough to just think. Cheerful and not serious in many ways. She takes orders extremely responsibly and is accustomed to repaying her debts.

🪢 Skills: From the Druids she adopted the ability to change forms on the network, can become a Worm, Virus, Trojan, Python, Interversion (inward-facing artificials), etc.

✨ Features: There are almost no implants in the body. Professional cyberdeck inherited from the company. She dresses very colorfully, making up for the time when everything around her consisted of zeros and ones. There are special implants in the eyes, in one there is the letter Aleph, in the other there is a zero.

P.S. Aleph zero or ℵ0 - Mathematical term. The number of elements in an infinite set. P. P. S. Inward facing Iskin (cube in the background), Iskins found in the Old Network, they cannot perceive anything outside themselves, that is, they seem to “look inside themselves” and nothing outside can influence them. This design is risky, but can be used as a shield in extreme cases.

RU

🎆 Друид Хакер в мире Киберпанка - Алеф ноль. 🎆

Нет, я не ела грибов странного цвета… 🍄

🖥 Предыстория: Родилась с уникальными вычислительными способностями, в три года была похищена корпорацией, которая подключила её к сети и сделала из нее нетраннера. До своего двадцатилетия ни разу не покидала сеть. Была вынуждена сначала учиться жить в сети, а потом и выполнять требования Корпорации. Но благодаря врожденной раннерской интуиции смогла заглянуть за лёд (брандмауэр) корпорации и путешествовать по открытой сети и осколкам Старой Сети, оставшимся после Падения Первой Сети в отсталых странах. Со временем выучила правила и законы каждого региона и даже научилась подрожать программам, чтобы корпорация не могла отличить ее от одного из Искинов (ИИ). За время своей "работы" на корпорацию так же насмотрелась на мир людей и горела жгучим желанием выбраться из мира единиц и нулей и почувствовать хоть что-то, почувствовать каково это - жить, смотреть на мир своими глазами, есть еду и ощущать прочие житейские радости. Предположительно была спасена при слиянии двух корпораций, контейнер с ней просто выкрали вместе с оружием и датацентром. Контейнер попал к одной из группировок, от которой Алеф сбежала, спалив мозги всем, кто был подключен к локальной сети банды. Оказалась на улице, не понимающая что делать и едва способная контролировать свое тело. У нее был один контакт вне корпорации где она работала, Алеф смогла с ним связаться и попросить помощи. Ей помогли, тот, кого она знала лишь по нику в сети приехал за ней и увёз из бандитского логова, а после отвез к риперу. Алеф Ноль долго сидела на шее приютивших её людей, медленно заново учась говорить ртом и владеть собственным телом.

✒

Характер: Всё ещё большой ребёнок, в восторге от самых банальных вещей. Всё ещё часто ошибается в банальных вещах. Хороша во всем, что требует логики. Немногословна, вернее, всё ещё забывает, что чтобы ее услышали недостаточно просто думать. Веселая во многом несерьезная. Крайне ответственно относится к поручениям и привыкла отдавать долги.

🪢 Навыки: От друидов переняла способность менять формы в сети, может становиться Червём, Вирусом, Трояном, Питоном, Интерверсией (обращенный внутрь искин) и т. д.

Особенности: В теле почти нет имплантов. Профессиональная кибердека доставшаяся от корпы. Очень пёстро одевается, отыгрываясь за время, когда все вокруг нее состояло из нулей и единиц. В глазах особые импланты, в одном буква Алеф, в другом ноль.

P. S. Алеф ноль или ℵ0 - Математический термин. Количество элементов в бесконечном множестве. P. P. S. Обращенный внутрь Искин (куб на заднем плане), Искины, встречающиеся в Старой Сети, не могут воспринимать ничего снаружи себя, то есть они как бы "смотрят внутрь себя" и ничто снаружи не может повлиять на них. Подобная конструкция рискована, но применима как щит на крайний случай.

[biopractify]

How to Transition from Biotechnology to Bioinformatics: A Step-by-Step Guide

Biotechnology and bioinformatics are closely linked fields, but shifting from a wet lab environment to a computational approach requires strategic planning. Whether you are a student or a professional looking to make the transition, this guide will provide a step-by-step roadmap to help you navigate the shift from biotechnology to bioinformatics.

Why Transition from Biotechnology to Bioinformatics?

Bioinformatics is revolutionizing life sciences by integrating biological data with computational tools to uncover insights in genomics, proteomics, and drug discovery. The field offers diverse career opportunities in research, pharmaceuticals, healthcare, and AI-driven biological data analysis.

If you are skilled in laboratory techniques but wish to expand your expertise into data-driven biological research, bioinformatics is a rewarding career choice.

Step-by-Step Guide to Transition from Biotechnology to Bioinformatics

Step 1: Understand the Basics of Bioinformatics

Before making the switch, it’s crucial to gain a foundational understanding of bioinformatics. Here are key areas to explore:

Biological Databases – Learn about major databases like GenBank, UniProt, and Ensembl.

Genomics and Proteomics – Understand how computational methods analyze genes and proteins.

Sequence Analysis – Familiarize yourself with tools like BLAST, Clustal Omega, and FASTA.

🔹 Recommended Resources:

Online courses on Coursera, edX, or Khan Academy

Books like Bioinformatics for Dummies or Understanding Bioinformatics

Websites like NCBI, EMBL-EBI, and Expasy

Step 2: Develop Computational and Programming Skills

Bioinformatics heavily relies on coding and data analysis. You should start learning:

Python – Widely used in bioinformatics for data manipulation and analysis.

R – Great for statistical computing and visualization in genomics.

Linux/Unix – Basic command-line skills are essential for working with large datasets.

SQL – Useful for querying biological databases.

🔹 Recommended Online Courses:

Python for Bioinformatics (Udemy, DataCamp)

R for Genomics (HarvardX)

Linux Command Line Basics (Codecademy)

Step 3: Learn Bioinformatics Tools and Software

To become proficient in bioinformatics, you should practice using industry-standard tools:

Bioconductor – R-based tool for genomic data analysis.

Biopython – A powerful Python library for handling biological data.

GROMACS – Molecular dynamics simulation tool.

Rosetta – Protein modeling software.

🔹 How to Learn?

Join open-source projects on GitHub

Take part in hackathons or bioinformatics challenges on Kaggle

Explore free platforms like Galaxy Project for hands-on experience

Step 4: Work on Bioinformatics Projects

Practical experience is key. Start working on small projects such as:

✅ Analyzing gene sequences from NCBI databases ✅ Predicting protein structures using AlphaFold ✅ Visualizing genomic variations using R and Python

You can find datasets on:

NCBI GEO

1000 Genomes Project

TCGA (The Cancer Genome Atlas)

Create a GitHub portfolio to showcase your bioinformatics projects, as employers value practical work over theoretical knowledge.

Step 5: Gain Hands-on Experience with Internships

Many organizations and research institutes offer bioinformatics internships. Check opportunities at:

NCBI, EMBL-EBI, NIH (government research institutes)

Biotech and pharma companies (Roche, Pfizer, Illumina)

Academic research labs (Look for university-funded projects)

💡 Pro Tip: Join online bioinformatics communities like Biostars, Reddit r/bioinformatics, and SEQanswers to network and find opportunities.

Step 6: Earn a Certification or Higher Education

If you want to strengthen your credentials, consider:

🎓 Bioinformatics Certifications:

Coursera – Genomic Data Science (Johns Hopkins University)

edX – Bioinformatics MicroMasters (UMGC)

EMBO – Bioinformatics training courses

🎓 Master’s in Bioinformatics (optional but beneficial)

Top universities include Harvard, Stanford, ETH Zurich, University of Toronto

Step 7: Apply for Bioinformatics Jobs

Once you have gained enough skills and experience, start applying for bioinformatics roles such as:

Bioinformatics Analyst

Computational Biologist

Genomics Data Scientist

Machine Learning Scientist (Biotech)

💡 Where to Find Jobs?

LinkedIn, Indeed, Glassdoor

Biotech job boards (BioSpace, Science Careers)

Company career pages (Illumina, Thermo Fisher)

Final Thoughts

Transitioning from biotechnology to bioinformatics requires effort, but with the right skills and dedication, it is entirely achievable. Start with fundamental knowledge, build computational skills, and work on projects to gain practical experience.

Are you ready to make the switch? 🚀 Start today by exploring free online courses and practicing with real-world datasets!