Data Science with Python
Learn data science with real-world projects & industry tools
⭐ 4.8 (100 reviews)👥 100 students⏱ 12-14 Weeks📈 Beginner → Advanced
Hands-on Projects
Industry Tools
Certificate
Portfolio
About the Course
Gain expertise in data science by mastering Python, statistics, machine learning, and data visualization. Work on real datasets and build portfolio-ready projects.
Tip: Hybrid mentorship pairs you with an expert to review code, unlock roadblocks, and plan your portfolio.
Prerequisites
- Basic Python programming
- Math & Statistics fundamentals
- Laptop + stable internet
- Interest in data-driven problem solving
Curriculum
▸Module 1 · Introduction to Data Science
- • What is Data Science?
- • Roles of Data Scientist, Analyst & Engineer
- • Data lifecycle & workflow
- • Case studies in data science
▸Module 2 · Python for Data Science
- • NumPy for arrays
- • Pandas for data handling
- • Matplotlib & Seaborn for visualization
- • Basic data wrangling
▸Module 3 · Data Cleaning & Preprocessing
- • Handling missing values
- • Data transformation & scaling
- • Encoding categorical variables
- • Outlier detection
▸Module 4 · Exploratory Data Analysis (EDA)
- • Summary statistics
- • Data distributions & visualization
- • Correlation analysis
- • Insights generation
▸Module 5 · Probability & Statistics
- • Descriptive vs Inferential statistics
- • Probability distributions
- • Hypothesis testing
- • ANOVA & chi-square tests
▸Module 6 · Regression & Classification
- • Linear & Logistic Regression
- • Decision Trees & Random Forests
- • Support Vector Machines (SVM)
- • Evaluation metrics
▸Module 7 · Clustering & Recommendation Systems
- • K-means clustering
- • Hierarchical clustering
- • Principal Component Analysis (PCA)
- • Recommendation systems basics
▸Module 8 · Feature Engineering & Model Tuning
- • Feature selection methods
- • Dimensionality reduction
- • Hyperparameter tuning (Grid Search, Random Search)
- • Cross-validation techniques
▸Module 9 · Introduction to Big Data & Deep Learning
- • Spark & PySpark basics
- • Intro to TensorFlow/Keras
- • Neural networks for data science
- • Use cases of deep learning in DS
▸Module 10 · Capstone Project
- • Select dataset
- • Apply data science pipeline
- • Model training & evaluation
- • Project report & presentation
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