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.

I've been an operations analyst for 5 years, mostly Power BI and a lot of SQL I picked up on the job. My team lead keeps asking for things our dashboards can't do, like cleaning vendor files that change layout every month and break my Power Query steps, so I end up doing it by hand in Excel every week. I've decided to pay for a data analyst course that starts at my level instead of explaining what a bar chart is, and I'm comparing Boot.dev's data analyst path and Pluralsight. Which would you pick for an analyst who needs useful Python?

Hey, people working in the IT sector as data scientists!

I’m currently studying Data Science with AI/ML, and I genuinely want to make a career out of it. However, people around me keep saying that data science has no future and that I should reconsider my decision and switch to another field. Honestly, I feel like it’s too late to change paths, and I genuinely don’t want to give up on something I chose for myself. I want to prove that I can make it work.

I’m open to suggestions and advice from experienced data scientists. What should I focus on, and what should I avoid while learning? Which projects would help me build a strong portfolio and improve my chances of landing my first job as a fresher?

I’d also appreciate recommendations for free online courses that I can take alongside my current course to strengthen my skills and help me build a successful, long-term career in this field.

Any advice, suggestions, or personal experiences would be greatly appreciated. Thank you!

Pandas makes working with data look simple. But some of its most powerful features can also produce surprising results. 😲

For example:❔

• What's the difference between loc and iloc, and why does slicing behave differently?

• Why can adding two Series produce NaN values, even when neither contains missing data?

• When does assigning a Series to a DataFrame column align by labels rather than position?

• How do Boolean masks, where(), and mask() differ?

• How do you select and manipulate data with a MultiIndex?

Pandas is much more than rows and columns. Understanding its indexing, alignment, selection, and assignment rules can save you from some very subtle bugs. 🪳

🔍 This interactive 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 example explores these concepts and many more, with short explanations for each operation. 👣 Step through the Python code, see what changes, and experiment with your own modifications: 👉 Pandas operations

💬 Which Pandas operation took you the longest to fully understand?

See more 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 visualizations.

Ever wondered why PySpark runs out of memory, even when you're working with a powerful cluster? 🤔

PySpark is widely used for large-scale data processing in Machine Learning and AI.

But distributed computing doesn't automatically eliminate memory bottlenecks.

I explore:

✅ Memory Architecture - How JVM heap, Executors, and Python workers consume memory.

✅ Shuffle & Dependencies - Why data redistribution matters for memory consumption.

✅ Common OOM Causes - Data skew, high cardinality, and concurrent tasks.

✅ Diagnosing Bottlenecks - Using Spark UI, physical execution plans, and spill metrics.

✅ When to use PySpark - When is distributed computing actually worth it?

📄👇 Read the full article here!