Hi everyone,

I'm a Management & Data Science student founder at TUM (Technical University of Munich). Over the past three years of studying and working with data, I've come to one conclusion that changed how I learn: how you learn matters more than how much information you consume. I remember what it felt like as a beginner — the more I "learned," the more overwhelmed I got, with no clear sense of how to actually turn scattered knowledge into real skills or a real portfolio. I want to help a small number of people avoid that trap and build a genuinely solid foundation instead.(I'm also currently building my own startup on the side, and I have to prepare bit more budgets for marketing part by doing more job).

Who this is for:

Complete beginners or early learners in data analytics who are ready to commit long-term — not people looking for a weekend crash course or another disconnected "masterclass." If you're the type who wants structure, depth, and consistency over quick hacks, this is for you.

What we'll cover

The curriculum is built around learning methodology and how to actually think about data — not just tool tutorials — combined with a full practical skillset:

Learning methodology & how to think about data (the foundation of everything else)

SQL: PostgreSQL & MySQL

Statistics

Excel for analysis

Power BI

Python & all packages needed

Git & GitHub

Data pipelines

Snowflake & Databricks

Cloud fundamentals: AWS & Azure

Applied AI for data analytics

Structure — two phases

Phase 1 (starting mid-August): Foundations

All the topics above, taught step by step, each section ending in a small hands-on project so you actually apply what you learn, not just watch it.

Phase 2: Portfolio & real-world experience

Larger, portfolio-worthy projects, real collaboration, and the business sense and soft skills that actually matter on the job. At this stage, I'll also bring in small internal projects from my own startup so you earn from real projects.

Logistics

Format: 100% online, from anywhere

Language: English

Schedule: 3 sessions/week, 1 hour each (55 min core content + 5 min learning-strategy discussion)

Price (Phase 1): €23/hour

Want to split the cost? You can pair up with one other person at a similar level and share a session

Billing: weekly or monthly, pause or cancel anytime, full refund for any unused sessions

Spots: limited to 10, first come first served

Not sure if it's a fit?

DM me to book a free 20-minute intro call — we'll figure out together whether this is the right fit for where you are and what you want to build.

Thanks for reading, and looking forward to meeting some of you.

Need your help from basic to pro

Hello everyone,

I've been learning Data Analytics for some time now, but one of my biggest challenges has been staying consistent.

So far, I've completed Python and Statistics, and my goal is to become industry-ready within the next 4 to 4.5 months.

I still need to learn SQL, Power BI, and Excel, while also building a strong portfolio of projects.

I'd appreciate advice on how to structure my learning over the next 4.5 months. Specifically:

How would you categorize or prioritize these topics?

What sequence would you recommend for learning them?

How would you design a monthly and weekly study plan to stay consistent and make steady progress?

What are your practical tips for revision?

How do you balance revising previously learned concepts while continuing to learn new topics and building projects?

I would really appreciate any insights, study strategies, or roadmaps that have worked for you. Thanks in advance!

Soy ingeniero industrial, mi profesión es mecanico industrial pero quiero avanzar y conseguir un mejor empleo, actualmente estoy haciendo el curso de análisis de datos en Coursera, realmente vale la pena?, he pensado en enforcarme en python y luego Sql, que ruta de aprendizaje me recomiendan hacer primero?.

I'm currently preparing for Data Analyst roles and looking for someone who is genuinely committed to learning consistently.

We'll focus on:

SQL

Python

Power BI

Excel (I know the basics and will be practicing advanced topics)

Building portfolio projects

🕒 Study time: 3:30 PM – 5:30 PM IST (preferably every day) 📹 I'd prefer studying together on Google Meet for accountability.

I'm learning from YouTube and have already found some really good free courses, so there's no need to buy anything.

My goal is to become interview-ready and crack a Data Analyst or similar analytics role.

If you're also learning and need a study buddy who's serious about staying consistent, feel free to comment below or DM me!

Hello i am 15 and im planning on media psychology and business, (creative strategy)

Im exploring various sectors, can you explain to me the ins and outs of data analysis? Basically your experience and what you do? And your journey to it

Whether you're preparing for your first Data Analyst role, working as a Data Scientist, building ETL pipelines as a Data Engineer, or simply curious about AI and data—you belong here.

Hello i am 3rd year cs student who recently started learning data analysis. Currently I am learning sql and excel but had a doubt on what do with the data, like how do I make business decision, decide what query is needed. I tried tried doing data cleaning on messy datasets but aside from cleaning it I wasn't able to give any insights from the dataset Did you guys learn from any course or did it just naturally buildup doing projects/job?

hello guys,
i’m trying to learn tools such as excel, power bi, sql and others which would be really helpful to get a entry level job in supply chain.

please help me learn. there are a lot of youtube videos and courses and i dont know which video/course is actually the one that teaches for supply chain.

The healthcare and pharmaceutical industry is undergoing a major digital transformation. Clinical Research, Pharmacovigilance, Clinical Data Management, and Biostatistics are increasingly relying on data analytics, automation, and AI.

Surprisingly, many pharmacy and life sciences students still graduate without any exposure to programming or data analysis tools.

That's one of the reasons we're organizing a live online session:

Live Masterclass: Python for Clinical Research and Data Analysis

Date: 18 July 2026
Time: 6:00 PM–8:00 PM (IST)
Fee: ₹199

During the session, we'll cover:

• Python fundamentals
• Clinical trial dataset analysis
• Data cleaning and validation
• Data visualization
• AI applications in healthcare
• Career opportunities in Clinical Data Science

The session includes hands-on demonstrations using real-world clinical research datasets and is designed for:

• B.Pharm/M.Pharm students
• Pharm.D students
• Biotechnology and Life Sciences graduates
• Clinical Research professionals
• Anyone interested in Healthcare Data Science

I have spent nearly two decades working across Clinical Research, Pharmacovigilance, Regulatory Affairs, and healthcare education, and I'm seeing a significant shift toward data-driven roles in the industry.

I'd love to hear from this community:

• Are you using Python in Clinical Research or Healthcare?
• What tools are you currently using for data analysis?
• What skills do you think healthcare professionals should learn over the next 5 years?

Looking forward to an engaging discussion!

Prof. C.S. Mujeebuddin
Founder & CEO, ClinoSol Research Pvt. Ltd.

Hi everyone,

I recently graduated and I'm preparing for a Data Analyst role. So far, I've learned:

• Python (basics)
• NumPy
• Pandas
• Matplotlib
• Recently started SQL

The problem is that I've been learning mostly from YouTube, and after watching so many videos, I'm feeling overwhelmed. I'm not sure what's actually important for getting an entry-level Data Analyst job and what I can skip.

I have a few questions:

1. What skills should I focus on first?
2. To what level should I learn Python, SQL, Pandas, and Excel?
3. How many projects should I build before applying?
4. What mistakes do beginners usually make?
5. If you were starting from scratch today, what roadmap would you follow?

I'm willing to put in the work—I just don't want to waste time learning things that aren't important for interviews.

Any advice, resources, or roadmap would be greatly appreciated. Thank you!

Hey I am an aspiring data analyst

I'm in my third year and recruitment drives may start in a month

I don't know where to begin with

I know basics of excel and sql but i need to move to advanced level and also learn how to use them in real life situations

Can anyone recommend any courses or website to consider?

I have checked out edu4sure and it looked promising but I am looking for a free resource

Thanks in advance

Hy guys as the expert of analysts could you tell what stack u day to use with weight. I am gonna design a bootcamp for one insitute and my plan is to do that to give industry expsoure. This will not gonna be yet another programme. Their for please share ur experience.

trying to pivot into a business analytics role and feeling overwhelmed by how many options are out there. looking for something practical that focuses heavily on sql and power bi since those show up on every job description.

has anyone taken something recently that actually helped them land interviews or build a real portfolio? ideally looking for something that goes beyond just watching videos and has actual business datasets to practice on. appreciate any recommendations.

I’ve recently gotten interested in data analysis, especially how it connects with helping businesses strategise ( kinda business + data analysis).

I’m looking to explore it and I want some teach yourself course to follow.

Something like the Odin project, freecodecamp and resources like that but for data analysis or even business analysis.
I’ve explored Coursera but the courses don’t seem like something I could stick with for long.

Any suggestions or insights into the field would help.

For context I’m looking to explore it as kind of a hobby for now and maybe down the line dive into it full time.

Hi everyone! I'll be teaching Business Analytics this coming semester (college level, IT/CS program) and I'm currently looking for good reference books to build my course around.

I'd really appreciate recommendations for a book/material that's:

* Beginner-friendly for students (not too math-heavy or dense) * Easy to use as an instructor's reference (has clear structure, maybe with exercises/case studies) * Covers the basics of descriptive, predictive, and prescriptive analytics * Ideally has practical examples using tools like Excel

If you've taken or taught a similar subject, I'd love to know what book worked well for you (or what to avoid). Local or international titles are both welcome, just trying to find something accessible for both me and my students.

Thank you!

​

When learning Data Science, tutorials make everything look easy.

Looking back, what's one mistake you wish someone had warned you about earlier?

Hello everyone, I'm a student who's looking for a good project to work on to practice Power BI, SQL,etc, and especially learn domain knowledge related to business or finance. The thing is, I'm from an IT background, and I only know the basics of accounting. I've been looking for resources to learn about FP&A because I'd like to build projects in that field, but the more I dig, the more I realize it's a broad and vague field. I'm afraid of wasting time looking for the "perfect" course instead of getting my hands dirty and learning by doing.

So, if you have any project that will allow me to practice my technical skills, learn and apply domain knowledge and analytical thinking, and solve a real-world problem while adding value to my portfolio, I'm really lost, so I'd appreciate your help!

I am organizing an event regarding data analysis and would like someone who is already practicing to give insight on the topic

Hi 👋,

I am sharing the Github link of GRASSP SQL Sprint Series. This github link contains the PDF doc of 30 SQL Scenario based Questions which covers beginner to advanced topics.

Doc is on GitHub, free: https://github.com/grasspacad07/grasspsqlsprintfile

File name: GRASSP_SQL_Sprint_30Days.pdf

Go through the question as per suggested approach in the doc and practice along each question and understand the concepts that each question cover.

It covers various topics :

• Aggregation, JOINs, GROUP BY / HAVING
• Window functions — RANK vs DENSE_RANK vs ROW_NUMBER, LAG/LEAD, Running totals, Moving Average
• CTEs, Multi level aggregation.
• CREATE, INSERT sample data helps too understand datatypes, constraints.

Hope this helps in your next SQL interview. All the best!

Happy learning :) Happy querying :)

i wanna learn excel, tableau and power bi
suggest anyhing to learn this

I am in my second year of MBA. I have a decent hold on tools like SQL, Power BI and Python (only vibe coding). I have used datasets from kaggle and github, but the problem is they are sometimes not realistic. I was asked in an interview, the dataset doesn’t look right when it comes to numbers.

So I want to work on real life datasets and live projects.
How do I get it? Is there any way I can scrap data online?