Examius
🇮🇳 India · IT & Programming
Data Science & Machine Learning Syllabus
Course covering Python, statistics, ML algorithms and tools like Pandas, NumPy and scikit-learn. The full Data Science & Machine Learning syllabus: 8 subjects, 32 chapters, 110 topics and 212 sub-topics, with 416 flashcards written against it.
Data Science & Machine Learning subjects
Data Science & Machine Learning has 8 subjects. Each one is listed below with its chapters in syllabus order; open a subject for every topic and sub-topic inside them.
Python Programming for Data Science
- Python Fundamentals · 4 topics
- Data Structures in Python · 4 topics
- Object-Oriented and Modular Programming · 4 topics
- File Handling and Environment · 3 topics
Mathematics and Statistics for Machine Learning
- Linear Algebra Essentials · 3 topics
- Calculus for Optimization · 3 topics
- Descriptive Statistics · 4 topics
- Probability Theory · 4 topics
- Inferential Statistics · 4 topics
Data Manipulation with NumPy and Pandas
- Numerical Computing with NumPy · 4 topics
- Pandas Data Structures · 3 topics
- Data Wrangling · 5 topics
- Data Cleaning and Feature Preparation · 3 topics
Exploratory Data Analysis and Visualization
- Exploratory Data Analysis Workflow · 3 topics
- Visualization with Matplotlib · 3 topics
- Statistical Visualization with Seaborn · 3 topics
- Interactive and Dashboard Visualization · 3 topics
Supervised Machine Learning
- Machine Learning Foundations · 3 topics
- Regression Algorithms · 3 topics
- Classification Algorithms · 4 topics
- Tree-Based and Ensemble Methods · 3 topics
- Model Evaluation and Tuning · 4 topics
Unsupervised Learning and Feature Engineering
- Clustering Techniques · 3 topics
- Dimensionality Reduction · 3 topics
- Feature Engineering · 4 topics
Deep Learning and Natural Language Processing
- Neural Network Fundamentals · 3 topics
- Deep Learning Frameworks · 4 topics
- Natural Language Processing · 4 topics
Data Engineering, Deployment and Projects
- Databases and SQL for Data Science · 3 topics
- Big Data and Data Pipelines · 3 topics
- Model Deployment and MLOps · 3 topics
- Capstone Projects and Best Practices · 3 topics
Data Science & Machine Learning flashcards
416 question-and-answer cards across 8 subject decks, each shown in full on its subject page.
- Python Programming for Data Science — 60 cards
- Mathematics and Statistics for Machine Learning — 52 cards
- Data Manipulation with NumPy and Pandas — 51 cards
- Exploratory Data Analysis and Visualization — 50 cards
- Supervised Machine Learning — 51 cards
- Unsupervised Learning and Feature Engineering — 51 cards
- Deep Learning and Natural Language Processing — 50 cards
- Data Engineering, Deployment and Projects — 51 cards
Data Science & Machine Learning syllabus FAQ
What subjects are in the Data Science & Machine Learning syllabus?
Data Science & Machine Learning covers 8 subjects: Python Programming for Data Science, Mathematics and Statistics for Machine Learning, Data Manipulation with NumPy and Pandas, Exploratory Data Analysis and Visualization, Supervised Machine Learning, Unsupervised Learning and Feature Engineering, Deep Learning and Natural Language Processing and Data Engineering, Deployment and Projects.
How many chapters and topics are in the Data Science & Machine Learning syllabus?
32 chapters and 110 topics, broken down further into 212 sub-topics, across 8 subjects. Mathematics and Statistics for Machine Learning is the largest subject, with 18 topics in 5 chapters.
Are there Data Science & Machine Learning flashcards?
Yes — 416 question-and-answer flashcards in 8 subject decks. Each deck is printed in full on its subject page here, and the same cards are in the Examius app with spaced repetition. The free plan covers one exam of your choice, including its flashcards; Examius Pro, a one-time purchase, unlocks every other exam and deck.