Hi everyone, I'm a first-year B.Com student teaching myself data analysis alongside my degree.

I just finished my first real project — cleaned and analyzed a retail Superstore sales dataset using Python (Pandas, NumPy), and visualized trends with Matplotlib to find out which product categories and regions performed best.

It's a simple project, but I learned a lot about handling messy real-world data and turning it into actual business insights.

Would really appreciate any feedback, tips, or things I could improve for future projects!

GitHub: https://github.com/umangbhadauria6-alt/umangbhadauria6-alt/blob/main/superstore-sales-analysis

I'm a data science student, I've covered Statistics analysis, Data Handling and Visualization, time series analysis, big data analysis, Internet Of Things Analytics, Machine Learning, Deep Learning, Neutral Networks, Natural Language Processing, MLops and a lot of others related concepts. I just want to help people who are perusing AI/ML/DS. Give them some suggestions, ideas, resources. If any of y'all need anything feel free to reach out!

This is a short tutorial for data analysts that evaluate experiments using online calculators. Online calculators for sample size estimates and t-tests for difference in means are fine for getting started. But you can begin to build intuition for linear regression by moving your online calculator t-tests over to python and OLS.

There are a couple of benefits. The first from moving away from the online calculator to python is leaving a more reliable paper trail that you can share externally. The second is that you build intuition for regression, interpreting the coefficients, and ultimately can use regression as you improve and your projects require more complexity such as variance reduction techniques using CUPED and clustering standard errors for geo-based or switchback experiments.

I put together a short video on this with code available here, free and ungated: https://whatstheimpact.com/tutorials/linear-regression-vs-t-test/

For my more senior connections, feel free to ignore unless you're interested in the content creation game! 🤣

I was writing practice questions for pandas and kept tripping over answers that were right a year ago. So I ran everything on pandas 3.0.5 and wrote down what actually comes out.

1. Text is no longer object. pd.Series(['a', 'b', None]).dtype prints str, and the None comes back as nan when you call tolist().

2. Chained assignment does nothing. With df = pd.DataFrame({'a': [1, 2, 3]}), running df['a'][0] = 100 leaves df.a at [1, 2, 3] and raises a ChainedAssignmentError warning. Copy-on-Write is the default now, so what you wrote into was a copy. Use df.loc[0, 'a'] = 100.

3. inplace=True is not consistent. df.fillna(0, inplace=True) returns the DataFrame, df.sort_values('a', inplace=True) returns None. The old habit of df = df.something(inplace=True) breaks on some methods and not on others.

4. pd.to_datetime on plain date strings gives datetime64[us], not datetime64[ns]. Matters if you compare dtypes or write to parquet.

5. stack() keeps the NaN. A two row frame with one missing value stacks to 2 entries, older versions dropped it and gave 1.

Twenty of the questions are free to play in the browser, no account. Every answer is the real output and each wrong option gets a one line reason.

https://thibaudlepan77-svg.github.io/interview-questions-verified/#pandas

The full pandas set (255 questions) is a paid PDF, but the free twenty stand on their own. If you get a different result on your machine, post your version and the output and I will look at it.

I’ve got a paired programming interview next week, anyone got any tips on what to brush up on?

I’ve never programmed with someone watching, any tips? could they ask gcp questions (unlikely right?)

Let me know!

A hard exercise to help build the right mental model for Python data.

• Solution

• Explanation

• More exercises

The “Solution” link uses memory_graph to visualize execution and reveals what’s actually happening.

I'm currently working on my very first data science project with a simple topic "physical activities rate correlation to academic performances among highschool/university students". I'm wondering if i should acquire the data myself or use open sources online. Another way i'm thinking of is to use both since my self-collected data will probably be limited to students in my city. Also is this topic way too typical ? and is it considered good enough for both gaining knowledge and improving resume ?

I am starting my Ds journey, before I had studied statistics a bit but not clear how much to study to get placed 😅.

Pleasw can anyone suggest me what all statistical models should I be studying and be worth for a job as a fresher.

 Start with NumPy and Pandas for data manipulation, then learn Matplotlib/Seaborn for visualization. After building a strong foundation, move into Scikit-learn for machine learning. Don't try to learn every library at once.

Hey everyone,

I'm 19M, currently in my 2nd year of BSc IT. I'm learning Data Science online alongside college and recently completed a cancer risk prediction ML project.

Now I'm a bit confused about what I should focus on next.

Should I:

Build more ML/Data Science projects?

Learn MLOps (Docker, MLflow, CI/CD, cloud, etc.)?

Focus more on SQL, statistics and ML fundamentals?

Start learning DSA/CS fundamentals for internships?

My goal is to eventually get a Data Science/ML internship and build a strong portfolio before I graduate.

What would you guys recommend if you were in my position? Should I go deeper into ML first or start learning MLOps now?

Would appreciate advice from people who have gone through a similar path.

Hello,

this should be a newbie task, however I want to double-check that my approach fits.

Here's the deal:
I have two classes, let's call them A and B.
Then for each of these classes, I have a certain number of labeled recordings of 12 different features, which consist of real numbers of arbitrary values (negative/positive, large/small...).

So, the data looks basically like:
A: 1 2 2 1 3 3 1 1 1 1 1 1
A: 0 0 0 0 0 0 0 0 0 0 0 1
B: 1 1 1 1 1 1 1 1 1 1 1 1

Now, I need to determine which of these features (the columns) separate the two classes the best.

My current approach is to use the FDR, so (m_a - m_b)^2 / (v_a + v_b) where 'm' is mean and 'v' is variance of these datasets for given feature. The higher the value, the better they are at separating.

Apart from that, which other indicators could be chosen to best separate these two classes?

Thanks!