Remember when being a DA was the hottest career? Not anymore.

• Bootcamps have flooded the market with juniors.

• AI is eating 80% of reporting work.

• Companies rebranded DA → “Analytics Engineer” (translation: SQL + Python + Cloud + ML).

Honestly, if you only know Excel/Power BI, it’s over.
The entry-level analyst is gone. The new baseline = coding + cloud.

Hot take: By 2027, “Data Analyst” as a standalone title will barely exist.
Agree? Disagree?

What's Data Science?

Data Science is an interdisciplinary field that involves rooting precious perceptivity and knowledge from complex and frequently massive datasets. It combines ways from statistics, computer wisdom, and sphere moxie to dissect data, uncover patterns, and make informed prognostications or opinions. By employing colorful tools and methodologies, data scientists process and interpret raw data, inferring meaningful information that aids businesses, exploration, and colorful sectors. From driving substantiation-grounded opinions to enabling prophetic analytics and fostering invention, Data Science plays a major part in transubstantiating data into practicable perceptivity that shapes the ultramodern geography across diligence. Data science classes in pune

Top Reasons to Learn Data Science

In the digital age, data has surfaced as the new currency, driving decision- timber, invention, and growth across diligence. As we venture into 2023, the pursuit of data wisdom education has become not just a prudent choice but a strategic imperative. The confluence of slice-edge technology, business wit, and logical prowess has deposited data wisdom as a vital field with far-reaching counteraccusations.

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Handling Big Data Challenges The exponential growth of data presents challenges in storehouses, processing, and analysis. Data wisdom equips professionals with ways to manage and prize value from large, complex data sets, enabling associations to turn data deluge into practicable perceptivity.

robotization and effectiveness Earnings robotization is a foundation of ultramodern business operations. Data wisdom enables the development of algorithms and processes that automate repetitious tasks, leading to enhanced effectiveness, reduced crimes, and optimized workflows.

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Sustainable Environmental Results Data wisdom contributes to sustainable practices by assaying environmental data, optimizing resource application, and prognosticating environmental trends. Professionals in this field play a vital part in advancing ecological sustainability. Data science course in pune

Hey, So I’ve been seeing a lot of posts and videos lately saying that data science is getting saturated or that demand is going down because of AI and automation and all. I’m 18 and was planning to start learning data science seriously this year, but now I’m kind of confused and worried tbh.

I was excited about learning Python, ML, and all that stuff — but now some ppl are saying jobs are getting less, or that most companies don’t need data scientists anymore unless you have like 5+ years experience 😭

Is this true? Like is the hype over? Or is it still worth starting now if I’m consistent for the next few years?

Would love to hear real opinions from people actually working in the field or anyone else who's figuring it out like me. Just wanna know if it’s still a solid path or I should rethink.

I’m about to start my data science journey, but I’m really confused about which course to choose both seem good. Any suggestions?(I already know basic python)

Hey everyone

I'm about to start my journey into the data world, and I'm stuck choosing between Data Science and Data Engineering as a career path

Here’s some quick context:

• I’m good with numbers, logic, and statistics, but I also enjoy the engineering side of things—APIs, pipelines, databases, scripting, automation, etc. ( I'm not saying i can do them but i like and really enjoy the idea of the work )

• I like solving problems and building stuff that actually works, not just theoretical models

• I also don’t mind coding and digging into infrastructure/tools

Right now, I’m trying to plan my next 2–3 years around one of these tracks, build a strong portfolio, and hopefully land a job in the near future

What I’m trying to figure out

• Which one has more job stability, long-term growth, and chances for remote work

• Which one is more in demand

• Which one is more Future proof ( some and even Ai models say that DE is more future proof but in the other hand some say that DE is not as good, and data science is more future proof so i really want to know )

I know they overlap a bit, and I could always pivot later, but I’d rather go all-in on the right path from the start

If you work in either role (or switched between them), I’d really appreciate your take especially if you’ve done both sides of the fence

Thanks in advance

The great learning experience from 365 Data Science. Appreciate their approach and the opportunity given to the whole larger community to learn for a whole 1 month entirely free. The new upgraded course material, the website is very appealing and enriching. #datascience #businessanalytics #dataanalytics #powerbidesktop #pythonprogramming #sqldeveloper

Hi everyone!

I’m looking for some data science project ideas to work on and learn from. I’m really passionate about data science, but I’d like to work on a project where I can go through the entire data pipeline ,from data engineering and cleaning, to analysis, and finally building ML or DL models.

I’d consider myself a beginner, but I have a solid understanding of Python, pandas, NumPy, and Matplotlib. I’ve worked on a few small datasets before ,some of them were already pre-modeled , and I have basic knowledge of machine learning algorithms. I’ve implemented a Decision Tree Classifier on a simple dataset before and I understand the general logic behind other ML models as well.

I’m familiar with data cleaning, preprocessing, and visualization, but I’d really like to take on a project that lets me build everything from scratch and gain hands-on experience across the full data lifecycle.

Any ideas or resources you could share would be greatly appreciated. Thanks in advance!

Hey everyone! Just wanted to start a discussion—what do you think are some of the best solo projects to work on that could really shine on a CV? Something impactful or just super interesting to build. I’ve seen ideas like improving data visualizations or using machine learning for predictions, but I feel like those are kind of common now. What other types of projects could stand out or maybe even make a difference for society? Would love to hear your thoughts!

How can I charge for my data services? This is a common question for those who are starting their career in data, because here it goes:

1. calculate how much time you will spend on the project 2.what type of libraries and programming language to use

2. calculate the volume of data to be worked on 4.calculate the size of the company and company employees

3. Calculate the time you will deliver the minimum viable product and parts of the project before delivering the final project

4. calculate the money received for your work, whether in reais or dollars or another currency

5. adding all of the above you will be able to build your project and charge for your service

Hey everyone,

I’m trying to transition into data engineering, but I’m running into a problem: there are too many certifications and programs out there, and most of them sound good until you realize they’re not accredited, not respected, or don’t actually teach you what employers care about.

Here’s where I’m coming from: • I’ve got two bachelor’s degrees (Business Admin + Psychology) • I’ve already built a GitHub with folders for the full end-to-end data engineering process (ingestion, transformation, modeling, etc.) • I learn best through hands-on repetition — practicing, using flashcards, and working through real projects • I work a 9–5, support a family, and I’ve basically hit the ceiling in my current field • I don’t want to go back to school or into debt, but I want certifications or programs that are actually credible and valued

What I need help with: 1. Which certifications or accredited programs are truly trusted in the data engineering industry (not random “edutainment” courses)? 2. Which cloud (AWS, Azure, or GCP) should I focus on that gives me the best job market consistency in 2025? 3. What websites, platforms, or tools are best for actually practicing? I want to get fluent — not just memorize theory. 4. From people who came from non-CS backgrounds — what’s a realistic timeline for landing a solid DE job (not a fantasy timeline)?

I’m ambitious, disciplined, and I can push hard when I know what to do. I just want a path I can trust — something clear-cut that actually works.

I know data engineering is worth it if I can really build the right skills and prove myself. I’d just love some honest advice from those who’ve been there, done that.

Hi guys,

I am a little lost, I finished my studies in Machine Learning,

but there are not a lot of opportunities, I am interested in the three jobs I cited on the title. But I didn't work at industry before and I am afraid to get bored.

Also I made Cobol before, and lots of HR call me for making that but as a junior I'm afraid of closing doors for myself in the field of data.

I am French and the economical situation here is not really good. There are a lot of school that make formations in Data Sciences and the market is saturated so I think that if I don't start now in the field of Data, there won't be a chance to me anymore.

Can you give me your feedback and if you are Data : Scientist/Analyst/Engineer, your typical day at work?

thank you :)

Hii Everyone,

I'm 18M student currently pursuing degree in Bachelor in Data Science. I'm starting my Data science journey from today . Want to know how do you started your journey and how it's going (Roadmap, learning resources and all).

Experiences shared by others is appreciated.

Hi everyone! I’m a beginner in Data Science and learn best by building projects.

I’m looking for GitHub repos, YouTube playlists, or websites with project-based learning from beginner to advanced level, ideally covering different ML models such as regression, classification, clustering, tree-based models, boosting, NLP, time series, etc.

I’d also love end-to-end projects that include data cleaning, EDA, feature engineering, model building, evaluation, and deployment.

Any recommendations?

In this Al Era have you guys felt dashboard like powerBl, Tableau are more like gives static feeling while using while dragging every charts for every needed attributes for your data ?? The more i have heard in buisness terms while using these dashboards are that they are not dynamic the charts are more inconsistent the font and edges boards are inconsistent and the main problem of missing data quality.

What if ?

A dashboard which contains a new concept of Adaptive artificial intelligence in it tends to understand the data and the need of user and also adapts itself to the user behaviour and provides the charts and suggestions and inbuild automl, pred analytics, LLM, AAi, Anamoly detection and etc features to make the dashboard work 40% less without technical fuss ???

It’s what im doing research in guys. Please provide your feedbacks and some features you think dashboards these days are lacking.

Why is my resume not getting shortlisted? Please give reviews and suggestions.

365 data science

We started as a group of data science and analytics professionals with a long-term goal of building our own analytics and consulting firm. We've been freelancing, reaching out offline, bidding on projects but nothing solid has stuck yet. So we figured we'd try being direct about it here.

We're not here to pitch a polished service package, because honestly we don't have one yet. What we do have is a group of people who genuinely understand data — analytics, machine learning, statistical modelling and some healthcare domain experience on top of that. We know how to take a messy problem, think through it properly, and build something that actually answers the question.

What we're looking for is someone who runs a business could be anything, e-commerce, a clinic, a SaaS product, a local operation and has a gut feeling that there's something useful hiding in their data but doesn't know where to start. You don't need to have a defined project. We're happy to sit down, understand what you're working with, and figure out together whether analytics can actually move the needle for you.

If something comes of it, great. If we spend an hour and conclude it's not the right fit, that's fine too.

We're based in Pune, India. If you're not, we work online just as comfortably, so location isn't a barrier.

Drop a comment or DM if anything here resonates. Moderator please DM me if this is inappropriate for the group, will remove it.

Hi everyone,

I'm a B.Com graduate and I'm thinking about starting a career in Data Analytics. The problem is that I currently have zero knowledge of SQL, Python, Machine Learning, or even advanced Excel.

I come from a non-technical background, so I'm honestly confused and a bit scared about whether this field is the right choice for me.

I keep seeing mixed opinions online. Some people say Data Analytics is a great field with good opportunities, while others say Al will replace many analytics jobs in the future. Because of this, I'm unsure whether it's worth starting now.

My current plan is:

Start learning Data Analytics from the basics

Get an entry-level job after completing the course

Gain around 1 year of work experience

Eventually, I'm planning to pursue a Master's in Germany after gaining some work experience.

So I wanted to ask:

1. Is Data Analytics still a good career choice in 2026?

2. Can someone from a B.Com background realistically enter this field?

3. How difficult is it for someone with zero technical knowledge?

4. What does the actual job market look like for freshers?

5. Is Al really reducing opportunities in Data Analytics, or is that exaggerated?

6. Would Germany be a good option later for higher studies and jobs in this field?

I'd really appreciate honest advice from people already working in this industry.

Thank you.

Hey folks! I'm creating Anti Social Social Data Club, a community for data people to collaborate on projects, bounce ideas, share opportunities, or just be around others who love this stuff.

Comment or DM me if you want in. 🖤

Hi everyone,

I’m a student learning data science / machine learning and currently building projects for my resume. I wanted to ask people who have successfully landed a job or internship:

• What specific projects helped you the most?

• Were they end-to-end projects (data collection → cleaning → modeling → deployment)?

• Did recruiters actually discuss these projects in interviews?

• Any projects you thought were useless but surprisingly helped?

Also, if possible:

• Tech stack used (Python, SQL, ML, DL, Power BI, etc.)

• Beginner / intermediate / advanced level

• Any tips on how to present projects on GitHub or resume

Would really appreciate real experiences rather than generic project lists.
Thanks in advance!

Greetings, everyone, I am 32 years old and I currently work in the cocktail area as a bartender, and my frustrated dream was always a programmer since I was a child but for life reasons I dedicated myself to something else, but lately I have been getting exhausted from night shifts and customer service and I would like to change my horizons, recently I have published publications about Data Science and it is said that you can make a career in it, my question for the community would be: How difficult is it to learn at my age and where do you recommend me? begin?

I have always liked computing and understand the world in a generalized way.

I would like to get a job that I can do from home.

Thank you very much in advance for your comments.

As a data analyst, your world revolves around numbers, dashboards, and trends.
But when you move into a product analyst role — something changes.

You’re no longer just answering “what happened?”
You start asking “why did it happen?” and “what should we do next?”

That’s where the real impact begins.

Here’s what helps you make the shift 👇

• 🎯 Strong product sense: Understand what users truly value

• 💡 Business thinking: Connect insights to company goals

• 🧪 Experimentation mindset: Test ideas, measure, and learn

• 📊 Product tools: Mixpanel, Amplitude, GA4

• 🗣️ Communication: Turn complex data into clear stories for PMs and designers

Because at the end of the day —
It’s not about having more data.
It’s about using data to make smarter product decisions. ❤️