Data Scientist
From Python basics to production ML models — a structured path to becoming a data scientist in 2026.
Python Fundamentals
Learn Python from the ground up: variables, control flow, functions, OOP, file I/O, list comprehensions, and virtual environments. Python is the lingua franca of data science.
Mathematics & Statistics
Build your mathematical foundation: linear algebra, probability, distributions, hypothesis testing, Bayesian statistics, and the math behind machine learning algorithms.
Data Manipulation (Pandas & NumPy)
Master the essential data science libraries: NumPy arrays, Pandas DataFrames, data selection, groupby operations, merging datasets, and efficient vectorized computations.
Data Cleaning & Preprocessing
Real-world data is messy. Learn to handle missing values, outlier detection, feature encoding, normalization, data type conversion, and reproducible preprocessing pipelines.
Data Visualization
Communicate insights visually: Matplotlib, Seaborn, Plotly for interactive charts, statistical plots, geographic maps, and dashboard design principles for stakeholder communication.
SQL for Data Science
Query and analyze data at scale: complex joins, window functions, CTEs, subqueries, aggregation, and connecting SQL databases to Python notebooks for analysis.
Machine Learning (Scikit-learn)
Learn core ML algorithms: linear/logistic regression, decision trees, random forests, SVMs, k-NN, cross-validation, hyperparameter tuning, and model evaluation metrics.
Exploratory Data Analysis Project
Milestone project: perform a full EDA on a real dataset — clean it, visualize distributions, discover correlations, test hypotheses, and present actionable findings.
Deep Learning (TensorFlow)
Dive into neural networks: perceptrons, CNNs, RNNs, activation functions, backpropagation, transfer learning, and building models with TensorFlow and Keras.
Natural Language Processing
Process and understand text data: tokenization, embeddings, sentiment analysis, text classification, named entity recognition, and transformer architectures (BERT, GPT).
Computer Vision
Analyze images and video: image preprocessing, CNNs for classification, object detection (YOLO), image segmentation, data augmentation, and OpenCV fundamentals.
Build an ML Pipeline
Milestone project: build an end-to-end ML pipeline — data ingestion, feature engineering, model training, evaluation, serialization, and serving predictions via an API.
AI Ethics & Responsible AI
Understand the ethical dimensions: bias detection, fairness metrics, model interpretability (SHAP, LIME), privacy regulations (GDPR), and responsible deployment practices.
Kaggle Competition
Capstone project: compete in a Kaggle competition or solve a real-world problem — apply everything you've learned to achieve a top leaderboard ranking.