Month 1 Python (Basics and libraries like Pandas, NumPy, Matplotlib) Data Structures & Algorithms (lists, dictionaries, sets, and practical algorithms such as sorting, searching.)
Next Month
Month 2 Statistics & Probability: Basic math concepts (Linear Algebra, Calculus) and stats concepts (mean, median, variance, distributions, hypothesis testing).
Next Month
Month 3 SQL: Learn to query databases, especially for data extraction and aggregation.
Next Month
Month 4 Data Cleaning and Preparation: Learn techniques for handling missing data, outliers, and data normalization. Git/GitHub: Master version control for collaboration and code management.
Next Month
Month 5 Exploratory Data Analysis (EDA): Learn data visualization with Matplotlib, Seaborn, and statistical analysis.
Next Month
Month 6 Machine Learning (ML): Regression, classification, clustering) using Scikit-learn. Feature Engineering and different types of ML models such as Supervised, Unsupervised
Next Month
Month 7 Deep Learning (DL): Introduction to DL using TensorFlow or PyTorch (build basic neural networks).
Next Month
Month 8 Natural Language Processing (NLP): Basic NLP techniques (tokenization, sentiment analysis) using spaCy, NLTK, or Hugging Face Transformers.
Next Month
Month 9 Cloud Platforms: Familiarize with AWS Sagemaker, GCP AI Platform, or Azure ML for deploying ML models Understand concepts like data warehouse, data lake, data mesh & fabric architecture.
Next Month
Month 10 Model Deployment: Learn about basics of MLOps and model deployment using Flask, FastAPI, and Docker.
Next Month
Month 11 Large Language Models (LLM): Explore how LLMs like GPT and BERT are used for NLP tasks.
Next Month