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Data Science Syllabus

Roadmap to Data Scientist. The full Data Science syllabus: 9 subjects, 72 chapters, 251 topics and 476 sub-topics, with 490 flashcards written against it.

9Subjects
72Chapters
251Topics
476Sub-topics
490Flashcards

Data Science subjects

Data Science has 9 subjects. Each one is listed below with its chapters in syllabus order; open a subject for every topic and sub-topic inside them.

Mathematics and Statistics

10 chapters · 53 topics · 52 flashcards

  1. Linear Algebra · 5 topics
  2. Calculus · 6 topics
  3. Probability · 7 topics
  4. Discrete Mathematics · 5 topics
  5. Descriptive Statistics · 4 topics
  6. Inferential Statistics · 6 topics
  7. Regression Analysis · 6 topics
  8. Multivariate Statistics · 5 topics
  9. Time Series Analysis · 5 topics
  10. Non-Parametric Methods · 4 topics

Python Programming

9 chapters · 30 topics · 62 sub-topics · 73 flashcards

  1. Introduction to Python · 3 topics
  2. Data Types and Variables · 3 topics
  3. Control Flow · 2 topics
  4. Functions · 2 topics
  5. Modules and Packages · 3 topics
  6. File Handling · 3 topics
  7. Error and Exception Handling · 3 topics
  8. Object-Oriented Programming · 5 topics
  9. Data Science · 6 topics

Databases

7 chapters · 25 topics · 68 sub-topics · 50 flashcards

  1. Introduction to Databases · 2 topics
  2. Relational Databases · 4 topics
  3. NoSQL Databases · 5 topics
  4. Big Data and Distributed Databases · 4 topics
  5. Data Warehousing · 4 topics
  6. Database Security · 3 topics
  7. Advanced sub-topics · 3 topics

Data Wrangling and Exploration

7 chapters · 33 topics · 50 flashcards

  1. Data Collection · 4 topics
  2. Data Cleaning · 5 topics
  3. Data Transformation · 5 topics
  4. Data Integration · 4 topics
  5. Data Exploration · 5 topics
  6. Tools and Libraries · 5 topics
  7. Best Practices · 5 topics

Data Visualization

7 chapters · 20 topics · 60 sub-topics · 51 flashcards

  1. Introduction to Data Visualization · 3 topics
  2. Principles of Data Visualization · 3 topics
  3. Tools and Technologies · 3 topics
  4. Creating Effective Visualizations · 3 topics
  5. Advanced Data Visualization Techniques · 3 topics
  6. Case Studies and Real-World Applications · 3 topics
  7. Ethics and Best Practices · 2 topics

Machine Learning

11 chapters · 29 topics · 105 sub-topics · 56 flashcards

  1. Introduction to Machine Learning · 2 topics
  2. Mathematics for Machine Learning · 4 topics
  3. Data Preprocessing · 3 topics
  4. Supervised Learning · 2 topics
  5. Unsupervised Learning · 3 topics
  6. Reinforcement Learning · 2 topics
  7. Model Evaluation and Optimization · 2 topics
  8. Deep Learning · 4 topics
  9. Natural Language Processing · 3 topics
  10. Time Series Analysis · 2 topics
  11. Deployment and Production · 2 topics

Deep Learning

6 chapters · 21 topics · 63 sub-topics · 52 flashcards

  1. Introduction to Deep Learning · 2 topics
  2. Mathematical Foundations · 4 topics
  3. Deep Learning Frameworks · 3 topics
  4. Types of Neural Networks · 4 topics
  5. Advanced sub-topics in Deep Learning · 4 topics
  6. Practical Implementation · 4 topics

Natural Language Processing (NLP)

7 chapters · 21 topics · 57 sub-topics · 51 flashcards

  1. Introduction to Natural Language Processing (NLP) · 3 topics
  2. Basic Linguistics for NLP · 3 topics
  3. Text Preprocessing · 3 topics
  4. Feature Extraction · 3 topics
  5. NLP Algorithms · 3 topics
  6. Advanced NLP Techniques · 3 topics
  7. NLP in Practice · 3 topics

Big Data Technologies

8 chapters · 19 topics · 61 sub-topics · 55 flashcards

  1. Big Data Fundamentals · 2 topics
  2. Data Storage Technologies · 3 topics
  3. Data Processing Frameworks · 3 topics
  4. Data Ingestion and ETL · 2 topics
  5. Data Analytics and Visualization · 2 topics
  6. Machine Learning and AI in Big Data · 3 topics
  7. Big Data Security and Governance · 2 topics
  8. Big Data Use Cases and Applications · 2 topics

Data Science flashcards

490 question-and-answer cards across 9 subject decks, each shown in full on its subject page.

Data Science syllabus FAQ

What subjects are in the Data Science syllabus?

Data Science covers 9 subjects: Mathematics and Statistics, Python Programming, Databases, Data Wrangling and Exploration, Data Visualization, Machine Learning, Deep Learning, Natural Language Processing (NLP) and Big Data Technologies.

How many chapters and topics are in the Data Science syllabus?

72 chapters and 251 topics, broken down further into 476 sub-topics, across 9 subjects. Mathematics and Statistics is the largest subject, with 53 topics in 10 chapters.

Are there Data Science flashcards?

Yes — 490 question-and-answer flashcards in 9 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.

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