Data Science & Machine Learning Mastery
A comprehensive, project-based learning path designed to take you from foundational statistics to deploying production-ready ML models. Includes Python, SQL, visualization, and MLOps fundamentals.
What You'll Learn
Statistical Foundations
Master probability, distributions, hypothesis testing, and A/B testing frameworks.
Python for Data Science
Proficient usage of NumPy, Pandas, SciPy, and Matplotlib/Seaborn for analysis.
Machine Learning Algorithms
Implement regression, classification, clustering, and ensemble methods with scikit-learn.
SQL & Data Engineering
Extract, transform, and query relational databases efficiently for ML pipelines.
Prerequisites
- Basic programming knowledge (Python recommended)
- High school level mathematics (algebra & basic statistics)
- A computer with internet access and Python 3.9+ installed
8 Lessons 1. Foundations of Data Science
- What is Data Science & The Data Lifecycle 12:45
- Setting Up Your Environment (Anaconda, VS Code, Jupyter) 18:20
- Python Fundamentals for DS (Lists, Dicts, Functions) 24:10
- Introduction to NumPy Arrays & Broadcasting 21:30
- Pandas DataFrames & Series 28:15
- Data Wrangling & Missing Value Imputation 19:40
- Exploratory Data Analysis (EDA) Framework 25:00
- Module Project: Titanic Survival Prediction 35:20
6 Lessons 2. Statistical Thinking & Probability
- Descriptive Statistics & Distributions 22:10
- Probability Theory & Bayes' Theorem 26:45
- Hypothesis Testing & P-Values 20:30
- Confidence Intervals & Effect Size 18:15
- A/B Testing Methodology 24:00
- Module Project: E-Commerce A/B Test Analysis 40:10
9 Lessons 3. Machine Learning with Scikit-Learn
- Supervised vs Unsupervised Learning 15:20
- Linear & Logistic Regression 28:40
- Decision Trees & Random Forests 25:15
- Support Vector Machines (SVM) 22:30
- Model Evaluation & Cross-Validation 20:50
- Hyperparameter Tuning (GridSearch, Randomized) 24:10
- Feature Engineering & Selection 19:45
- Pipeline Construction 21:00
- Capstone: End-to-End Churn Prediction 45:30
5 Lessons 4. Data Visualization & Storytelling
- Matplotlib & Seaborn Fundamentals 23:10
- Interactive Dashboards with Plotly 26:45
- Design Principles for Data Comms 18:20
- Executive Reporting Framework 20:15
- Final Presentation & Peer Review 30:00
The statistical module finally clicked for me. The instructor breaks down complex concepts into digestible, practical examples. Highly recommend for career switchers.
Great curriculum structure. The capstone project was challenging but extremely rewarding. Would love more deep dives into deployment in the next update.