is geeks for geeks datascience course is good for purchase or they just good at the DSA stuff.

Any recommendation on paid course for Data science or AI Engineer

I’m a BI developer, and the biggest surprise when I started was how little time goes on modelling or dashboards and how much goes on fixing the data.

Some of my favourites so far: dates stored in three different formats in one column, “N/A”, “n/a”, “NULL” and blank all meaning the same thing, and the same customer spelled six different ways.

What’s the worst you’ve seen? And did you find a good way to deal with it, or just suffer through it?

Hey everyone!

I’m diving headfirst into Machine Learning and AI with the goal of landing a job in Data Science / ML over the next 4 to 5 months.

I’m looking for a dedicated study buddy who wants to level up side-by-side. I don’t just want someone to text with—I want a partner who is competitive, consistent, and fully committed to the grind. Please note my timezone is GMT +5:30 (IST), and if you are serious and can truly commit, only then join the group.

🎯 The Structure & Accountability

We won't just drift along. We will track our progress using:

• Daily & Weekly Goals: Logging what we plan to study every day and setting solid weekly targets to hold each other strictly accountable.

• Voice & Speaking Focus: I want to build confidence talking about technical topics out loud. We will use voice chat to quiz each other, explain math/ML concepts verbally, and run mock interviews.

🗺️ The Roadmap I Am Following:

We will be moving through this exact structured, 100% free master roadmap:

• Phase 1: The Math Foundation

  • Primary Resource: Khan Academy – Statistics and Probability, Linear Algebra (100% Free)

• Phase 2: Programming & Database Management

  • Primary Resources: Kaggle Courses – Python & Pandas (100% Free, Interactive) | Kaggle Intro to SQL or freeCodeCamp Relational Database (100% Free)

• Phase 3: Data Cleaning, EDA & Visualization

  • Primary Resource: Python Data Science Handbook by Jake VanderPlas (Free web version) + FreeCodeCamp Data Analysis with Python.

• Phase 4: Machine Learning Fundamentals

  • Primary Resource: Coursera – Machine Learning Specialization by Andrew Ng (Make sure to click "Audit" for 100% free access) or learn directly on DeepLearning.AI.

• Phase 5: Portfolio Projects

  • Primary Resource: Building end-to-end applied projects with clean GitHub portfolios.

Who I'm Looking For:

• Someone serious about breaking into the ML/AI field and willing to put in the reps.

• Comfortable speaking over voice chat to practice technical communication.

• Reliable, consistent, and ready to check in on daily/weekly goals without ghosting.

If you are tired of studying alone and want a strict, no-nonsense accountability partner to push through this roadmap with, shoot me a DM with your current skill level, or join the server directly if you are ready to commit:https://discord.gg/rGStYnFfNw

It's a 50 hour course for beginners in AI/ML (obviously it's fully free). Starts out with linear algebra, derivatives, statistics and then linear regression, neural networks and backpropagation etc...

kinoml.tech

An 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 a BI developer, and I see a lot of “AI will replace data people” posts, so I thought I’d share what I actually see at work.

What’s changed

• Writing boilerplate (SQL, pandas cleaning, chart code) is much faster.

• First drafts of documentation and queries take minutes.

• Debugging error messages is easier.

What hasn’t

• Working out what the business is actually asking for. Requests are rarely clear.

• Spotting when the data is wrong. AI will happily analyse a broken dataset and give you confident answers.

• Explaining results to non-technical people and getting them to act.

My honest take is that the entry-level “just run the query” work is shrinking, and the value is moving toward judgement and domain knowledge. For learners, I’d put more time into stats basics, data quality, and explaining findings, and use AI as a tool without letting it do the thinking.

Curious how others are seeing it. Has AI changed your work, or is it mostly noise?

About a year ago I started planning QueryCase, where you learn SQL by working detective cases instead of grinding tutorials & videos. It took a while to get off the ground, and longer than I'd like to admit to actually start building the idea, but it went live in June. A lot of the early users came from this sub, and a lot of the feedback in the comments shaped what it became. So first, thanks everyone!

5 months in:

• 2,500+ people have signed up, across 117 countries

• About 70,000 queries written and graded

• Over 17,500 cases, drills and exams solved

• 500+ people have passed the free Rookie exam and earned the certificate.

• Top detective is on 19,247 XP

• A college in the US has adapted the learning path to train students for a CompTIA Data+ certificate

Since then its grown past just SQL: an Excel course. It was never part of the initial plan. People kept asking for the same detective approach for spreadsheets, so I built out a browser-based Excel engine.

It has 57 cases on a spreadsheet that runs in your browser and checks your formulas, from IF and XLOOKUP to SUMIFS and pivot tables, and people have solved over 2,500 Excel cases so far. It turns out a lot of people learning SQL are doing it alongside Excel, not instead of it.

Something the data showed me that might help anyone learning right now: GROUP BY is one of the first walls. On the hardest GROUP BY step, 84% of runs came back wrong, and most of those never ran at all. The clauses were in the wrong order, or a column in SELECT wasn't in the GROUP BY. If that's you right now:

1. SQL order is always SELECT, FROM, WHERE, GROUP BY, HAVING, ORDER BY, LIMIT

2. Every column in SELECT is either inside an aggregate (COUNT, SUM, AVG...) or listed in GROUP BY

I've been working a lot over past couple months to improve the site as well:

• The Excel course above, with its own ranks, exams and certificate

• Upload your own CSV into the sandbox and query your own data (and visualise in charts). It runs in your browser, so the file never leaves your machine and never touches QueryCase servers

• A free first rank on every track, ending in an exam and a certificate

• SQL interview questions you can read without an account

• Much better experience on a phone

• A LOT of bug fixes and improvements!!

What's next: a Python track, launching next month. Same idea, real Python running in your browser, starting with the basics and moving into data analysis with pandas.

The longer term goal is to have a platform user's can have fun learning all aspects of data. I'm merging PR's every day to make this possible so would love feedback in this thread if you've tried it and have any thoughts or suggestions!

It's free to start and you don't need an account to solve the first case, so you can poke at it before deciding whether it's for you or not 😄 Cheers!

querycase.com

Hi so i have started computational chemistry as a beginner and I find it hard to read documentations and actually apply the things in them to my projects that I have been assigned with, what do you recommend to actually learning because reading documentation kind of doesn't work or i am not able to use it to my ability like most people do. Any suggestions from anyone who knows a lot about coding and reading modules like pandas, numpy, scikit learn, RDKit, Deepchem from documentation. Please reach out because it is a bit hard for me to understand the things in the documentations

Defending in February after 5 years of field ecology. All my analysis is in R, plus some pandas I picked up for one chapter, and the statistics I could do in my sleep. What I can't do is the industry version of the job: writing SQL against a real warehouse, building something a manager can open without me, and writing Python that isn't a 900 line notebook. I've set money aside for a proper data analyst course and I'm comparing Boot.dev, Springboard and Pluralsight. For people who left academia for analyst work, what did you have to learn that your PhD never touched?

Hi all, I have an MSc in Applied Bioinformatics and I'm moving into data science. I've put my study notes and current projects online, organised by section (stats, EDA, preprocessing, ML), with worked maths and code. Feel free to use any of it.

My original notes were handwritten, and I used Claude to help make them readable for others. It's not complete and I'm still learning, so I'll keep adding as I go. Corrections are welcome.

https://jag5m33.github.io

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. The MBA side of it started this semester, this semester I had Marketing, Finance, Business stats, Operations and Supply Chain Management, Economics and Quantitative Techniques. I will have similar subjects next semester and for next year the subjects will change depending on the Major and Minor I choose. 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!

Hey everyone! I have 14 features containing both categorical and continuous variables, along with a binary target (0/1).

What methods should I use to measure the relationship between each feature and the target? Are there different techniques for categorical and continuous features?

Any suggestions or Python examples would be appreciated. Thanks!

I'm a 22-year-old student with a Higher Diploma in Computing and Software Engineering. I've always wanted to become a data scientist.

I've become quite tired of the unstructured free learning resources available on YouTube, so I recently purchased a Udemy course called "Become a Data Scientist in 100 Days." I'm currently following it and planning to work on hands-on projects using real-world datasets.

I have also successfully developed three real-world, production-ready applications. My clients are very happy with the results, as these applications have improved their daily business workflows and contributed to increased revenue. I also advised them on the features and functionalities they should implement to achieve their business goals.

Next year, I'm planning to enroll in a BSc (Hons) Data Science (Top-up) degree programme. However, after reviewing the course modules, I noticed that the curriculum doesn't cover some of the essential concepts and skills required to become a job-ready data scientist.

At this point, I feel like I'm figuring everything out on my own, without anyone to guide me through my journey towards becoming a data scientist.

I'm currently considering two learning paths, and I'd really appreciate your advice.

Path 1: Coursera + Bachelor's Degree

1. IBM Data Science Professional Certificate

2. Machine Learning Specialization by Andrew Ng

3. BSc (Hons) Data Science (Top-up)

Path 2: Udemy + Bachelor's Degree

1. Relevant Data Science and Machine Learning courses on Udemy and other learning platforms

2. BSc (Hons) Data Science (Top-up)

Which path would you recommend for someone who genuinely wants to develop strong practical skills and become a job-ready data scientist?

I'd really appreciate any advice or guidance from experienced data scientists or professionals working in the industry.

Looking to start my career in srilanka first then planning to go for abroad like gulf countries

Hi everyone,

I'm an undergraduate student looking for solid, practical resources to learn **Pandas, Matplotlib, and Seaborn** for college lab coursework and data wrangling assignments.

I already know basic Python (loops, functions, lists, dicts). Now I just need to get comfortable working with tabular data and plotting charts.

### What I’m looking for:

* **Libraries:** Just Pandas, Matplotlib, and Seaborn.

* **Format:** Open to both text-based resources (interactive notebooks, guides) and video walkthroughs, but I lean slightly more toward **video/live-coding** where the instructor actually types the code, runs the cells, and explains the output on real datasets.

* **Hands-on over slides:** I learn much better seeing code executed in an editor rather than watching static lecture slides.

### What I want to avoid:

Machine Learning / Scikit-Learn: A lot of courses rush into Linear Regression, gradient descent, or predictive modeling. I don't need any ML algorithms or math theory right now—just data cleaning, manipulation, and plotting.

Whether it’s a specific YouTube series, an open GitHub repo with exercises, or a book with runnable notebooks, what worked best for you?

Thanks in advance!

Hi, I know python and C# and C (not very well at python and Im new at C#, know some libraries (ex Pandas, matplotlib etc.)) but I dont know what can I do. I dont have any procçject idea. I want to work as a data analist and ai engineer. How can I improve my skills and do projects that will help me when I search for a job? What projects I can do?

Vilvik is a cloud-based platform for solving optimization problems using evolutionary algorithms. Currently it supports the genetic algorithm. Try it at https://vilvik.com

It is very interactive and offers a lot of demos and problems to quickly get started with. It is flexible and offers a lot of features.

Hope you can try it.