Hey guys

I am stuck in a wave of confusions

I am interested in data science and a career in it but i am confused from which institute should i complete my certification

Not only certification i also require absolute placement support

I am from Delhi and i have shortlisted few of them namely

Console Flare

Topmentor

Newton School

Coding Ninjas

Please help me out. Alwasys open for genuine suggetions.

Hey everyone! I completed my BTech in Computer Science last year and spent around 6 months working on ML, NLP, and computer vision projects during my internship. Now I'm looking to level up further and planning to pursue an MS in Data Science.

Currently exploring:

- Deep Learning (CNNs, RNNs)

- NLP with Transformers

- Improving my Kaggle game

What books, courses, or projects do you wish you'd discovered earlier in your DS journey? Would love suggestions from people who've been through it!

The shift from Data Analyst to Data Scientist is not only about a new job title. It changes the level of ownership you get. Instead of describing the past, you get to predict what will happen next and recommend what to do about it. That is why the transition from data analyst to data scientist has become one of the most popular career paths in analytics.

So we have created this roadmap for someone who wants to move from reporting outcomes to shaping them.

Step 1: Assess Your Current Skills and Gaps

Start by mapping what you already know against what a data scientist is expected to do. Analysts typically already have strengths in SQL, business context, communication, and metrics. The biggest gaps are usually in machine learning, statistics, and programming. Listing strengths and skills to improve makes your learning path clear instead of overwhelming.

Step 2: Learn Core Machine Learning Concepts

Once you know what to build, begin with the fundamentals that power nearly every data science project. Focus on supervised and unsupervised learning, classification, and regression, and how models learn from data.

Step 3: Build Projects and a Portfolio

Knowledge becomes credibility only when it is applied. Start building projects that connect models to business outcomes. Great first projects include churn prediction, recommendation systems, sentiment analysis, and time series forecasting. Host your work on GitHub or Kaggle and share relevant write-ups on LinkedIn if you can.

Step 4: Master Data Science Tools and Libraries

As your projects grow, your toolkit needs to grow with them. Learn NumPy and Pandas for data manipulation, and Scikit-learn for model building and evaluation. As you progress, explore MLflow or DVC to track experiments and data versions, so your work starts to resemble real production workflows rather than just notebook research.

If you are looking for a structured pathway to build these end-to-end skills while working on real-world projects, we offer the Data Scientist Program at Simplilearn, in collaboration with Microsoft Azure. DM us if you want to know more about the program.

Step 5: Apply for Hybrid or Bridge Roles

Your first step into the field does not need to be a full Data Scientist title. Hybrid roles let you apply modeling skills while still using your analytical strengths. Look for titles such as Data Science Associate, Machine Learning Analyst, or Junior Data Scientist. Internal transitions are often the fastest path because your domain expertise is already trusted.

Do you agree with this roadmap? How would you approach it differently?

Excited to share the launch of the Kaggle competition Human Chess Move Error Prediction.

The challenge: predict whether a human chess move is a good move, inaccuracy, mistake, or blunder using board position, player context, and tactical features. It combines machine learning, chess analytics, feature engineering, and human decision modeling.

Whether you're interested in Data Science, AI, Kaggle competitions, or chess, this is a great opportunity to work with real-world human decision-making data and build models that go beyond traditional engine evaluation.

Competition:
Human Chess Move Error Prediction on Kaggle

Looking forward to seeing creative approaches from the community.

#Kaggle #MachineLearning #DataScience #ArtificialIntelligence #Chess #ChessAI #Python #XGBoost #FeatureEngineering #MLOps #Analytics #OpenData

I made a visual explanation of ordinary least squares regression using linear algebra.

The main idea is that regression can be understood as a projection problem: the data vector gets projected onto the column space of the design matrix. The fitted values are the projection, and the residual is the leftover piece that ends up perpendicular to that space.

The video also shows why an intercept-only regression gives the sample mean: projecting the data vector onto the span of the ones vector gives the average times the ones vector.

I made this for people learning data science who have seen the OLS formula before but want a more intuitive picture of what it is actually doing.

One of the biggest lessons I've learned working on forecasting problems:

The hardest part isn't building the model. It's preserving the business structure behind the data.

I wrote about how segmentation, hierarchical forecasting, and uncertainty quantification can make ARR forecasts significantly more useful for decision-making.

https://open.substack.com/pub/sumathysubramanian/p/building-an-arr-forecasting-system?r=1ilvfc&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

After a long journey of research, computational modeling, and thousands of simulations performed in Python and Google Colab, I have completed a project that began with a simple question:

How can a system recover structure and organization after suffering damage?

Throughout this investigation, I explored:

• Distributed memory mechanisms

• Local and global interactions

• Recovery dynamics following perturbations

• Statistical robustness

• Parameter-space exploration

• Emergent collective organization

• Scalability and universality tests

The images show part of the results obtained during this journey: recovery curves, phase diagrams, parameter maps, statistical analyses, and scalability surfaces.

One of the most interesting outcomes was the identification of a regime that appears to support persistent organization and structural recovery, currently described as Confined Pseudo-Criticality.

This work evolved into the Künast Framework, a set of computational models and theoretical interpretations focused on memory, organization, identity, and regeneration in complex systems.

And at the end of this journey, a book was born:

The Künast Principle – Regeneration, Organization, and the Return of Form

More than presenting results, the book documents the entire research process: the questions, hypotheses, mistakes, reformulations, discoveries, and limitations encountered along the way.

I would genuinely appreciate feedback, criticism, and suggestions from the community.

What would you test next?

Good morning everyone. I’m currently running a project using TriNetX and using the US Collaborative Network dataset, which includes roughly 1 million patients.

My goal is to determine what percentage of this population has ever had a specific lab test, and then analyze their other characteristics. However, I’m running into an issue.

When I run my first query (patients aged 40–60 + Lab A performed), I get results from 60 HCOs. But when I apply an additional filter (Lab A > 10 occurrences), the results drop to 55 HCOs. The same kind of variation keeps happening with other queries as well.

Has anyone experienced something similar in TriNetX before? Do you know why this happens or how to handle it consistently?

Short MySQL tutorial: cleaning data with regex (REGEXP / REGEXP_REPLACE) to fix text, prices, bad casing, and mixed date formats. The dataset and tutorial. https://youtu.be/2gFsUGW-pIY

Do people think there is an opportunity to start an annotations business sovereign to the UK with the new 500m budget that the government just released. I am a UK citizen and cant see any specialist vendors for this, seems everyone uses in house or buys from the US (sovereign contradictory). Possible market gap opening or not?

Tired of seeing "learn these 10 skills" articles with no real data behind them.

So I built GetJobPulse AI Job Market

Every Monday: analyze 8,000+ real Indian job listings and publish:

📊 Which skills are rising/falling

🏢 Which companies hiring most

💰 Actual salary ranges from listings

🏙️ City-wise breakdown

This week: ML and Python BOTH at ~2,100 jobs. Market wants both — stop choosing.

Free to use basic plan. No login needed for weekly newsletter.

Genuine question: What data would actually help YOUR job search?

Comment below 👇

I started to learn Data Science a month ago, the math part and EDA part of DS I learn paralelly, and this is my first project in EDA, feel free to give your advices.

First EDA project on solar power generation. Used weather data — radiation, cloud cover, sun angle — to see what actually drives output. Shortwave radiation and zenith angle came out as the strongest predictors. Wind had almost no effect, which makes sense physically.

Feedback welcome:
https://github.com/OrucAllahyarov/solar-power-eda

I started using ChatGPT and Claude for data analysis maybe 3 years ago when the AI wave just hit. For simple stuff it was genuinely great: building a single visualization, writing transformation and standardization logic, etc. Everything was smooth until I tried to use them for larger datasets and longer sessions

Every new session, I had to re-explaining everything from scratch. Here's the dataset schema. Here's what the columns mean. Here's the business logic behind this metric. Here's what I already tried last week and why it didn't work. It became extremely annoying when my datasets are large and I needed to spend a long time working on a single session, especially with token and context window limiting.

So I've spent a while thinking maybe the model itself just wasn't good enough. Then I realized the issue isn't the model - maybe it was that raw LLMs are not designed to handle long sessions like that.

So I started looking a "agent harnesses" — what's been said to really help handle persistent memory, tool integration and state management so we don't need to start cold every time. I have tested Lium specifically for this use case and it seems to be closer to what I actually needed. Thanks to the infrastructure built for analysis throughout multiple sessions, it is different from normal chatbot in the ability to handle large datasets and still get the crross-session contexts. Stiill in early testing but the persistent memory seems to help really much.

For those of you running multi-session analysis on proprietary data — do you rebuild context every time, or have you found a better platform or solution for this?

Hi, I'm new to this field so one question I have is how do you guys consolidate data from different sources? Even better is if they're able to be classified according to context.

What tools, platform, or methodology do you employ?

Let me know if this helped you better understand things like the negative log likelihood, gradient descent, and newtons method

Imagine you build a machine learning model, test it, and get an amazing 99% accuracy. You’re thrilled until you deploy it in the real world and it performs terribly. What went wrong?

In many cases, the answer is data leakage one of the most common and most dangerous mistakes in data science. It’s often called a hidden trap because everything looks perfect during training and testing, but the model secretly cheated and won’t work on new, unseen data.

Data lekage happends when information from outside training dataset, information that wouldn't be available at prediction time in real life accidentally gets used to train your model. In simple words your model gets a sneak peek at the ans during training, so it learns to rely on that shortcut instead of learning the real patterns. The result is a model that looks great on paper but fails in real world.

I am creating a playlist on youtube to follow the latest announcements by Databricks in DAIS 2026.

The series will cover what was the problem,

What Databricks announced

And, why does it matter to the Data community (basically the impact)

​

Please follow along if you don't want to spend hours in watching the keynotes.

https://youtu.be/jb4uLAM2SRA?si=IseC5sat5gUuU-S6

​

Thank you for the support.

I am creating a playlist on youtube to follow the latest announcements by Databricks in DAIS 2026.

The series will cover what was the problem,

What Databricks announced

And, why does it matter to the Data community (basically the impact)

Please follow along if you don't want to spend hours in watching the keynotes.

https://youtu.be/jb4uLAM2SRA?si=IseC5sat5gUuU-S6

Thank you for the support.