Written By: Ramakrushna
Right now, I feels like everyone is doing AI. Every job post, every resume, every course—it’s all about LLMs, transformers, deep learning.
For entry-level data scientists? It’s making things worse. What and which one to choose.
Here’s the truth: If you’re trying to break into data science, focusing on AI first is a waste of time. Before you even think about deep learning or generative AI, you need to master the foundations—because most entry-level data scientist roles don’t require AI at all.
Your first job as a data scientist will require you to work with data —not training a GPT-4 model -> 80% of your job will be data cleaning, transformation, and analysis.
✅ SQL – Extract, manipulate, and analyze structured data efficiently.
✅ Python (Pandas/Numpy) – Perform data wrangling, preprocessing, and numerical computations.
✅ Data Visualization – Use tools like Matplotlib, Seaborn, or Tableau to communicate insights clearly.
If you can’t take raw data, clean it, and extract meaningful insights, you’re not ready for machine learning yet.
Instead of jumping into deep learning, start with:
✅ Regression Models – Learn Linear Regression for predicting numerical values and Logistic Regression for classification problems.
✅ Tree-Based Models – Understand how Decision Trees, Random Forest, and XGBoost can handle complex patterns in data.