Data-Science-Journey

Data Science Self-Study Syllabus: From Fundamentals to Mastery

Last Updated: September 14, 2025
Author: Ze-Fn (Zelvy Fauzan, M.Pd.)
Repository: [https://github.com/Ze-Fn/Data-Science-Journey]

Welcome to my personal journey in mastering Data Science! This syllabus outlines a structured, self-paced curriculum inspired by top Bachelor’s-level programs (e.g., from universities like Northeastern, Stanford, and UC Berkeley). I’ll document every step—notes, code, projects, and reflections—right here in this repo. Each course has a dedicated folder with Jupyter notebooks, datasets, and progress trackers. Feel free to fork, star, or contribute ideas!

Overview

This program is divided into three levels: Fundamental (math & programming basics), Intermediate (data handling & analysis), and Advanced (applied ML & big data). Expect 6-12 months to complete, assuming 10-15 hours/week. Track your progress with weekly commits and badges (e.g., via Shields.io).

Why this syllabus? It’s designed for real-world applicability, blending theory with hands-on projects. By the end, you’ll have a portfolio showcasing end-to-end data science workflows.

Learning Objectives

By completing this syllabus, you’ll be able to:

• Apply mathematical foundations to data problems.
• Clean, analyze, and visualize datasets using Python tools.
• Build and deploy machine learning models.
• Handle big data ethically and scalably.
• Communicate insights through visualizations and reports.

Prerequisites

• Basic computer literacy.
• Access to a machine with Python 3.10+ (use Anaconda for easy setup).
• GitHub account for version control and showcasing.

Schedule Suggestion

• Weeks 1-8: Fundamentals (2 courses/week).
• Weeks 9-20: Intermediate (2-3 courses/week).
• Weeks 21-32: Advanced (1-2 courses/week + capstone).
• Ongoing: Weekly reviews and portfolio updates.

Use this Notion template or Trello for personal tracking, and mirror updates in GitHub issues.

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Fundamental Level: Building the Foundation

Focus: Math and programming essentials. Estimated time: 8 weeks.

Calculus I

Description: Covers limits, derivatives, and integrals with applications to data rates (e.g., optimization in models).
Duration: 4 weeks.
Folder: /fundamentals/calculus-i/

Relevant Materials:

• Textbook: Calculus by Michael Spivak or OpenStax Calculus Volume 1 (free).
• Online: 3Blue1Brown Essence of Calculus (videos); Khan Academy Calculus 1 (interactive).

Practice Sessions:

• Daily: 20 problems on limits/derivatives from Pauls Online Math Notes.
• Weekly: Khan Academy unit tests on motion and optimization.
• Tool: SymPy for symbolic computation—try notebooks in repo.

Portfolios/Projects:

• Project 1: Jupyter notebook modeling population growth with derivatives. Visualize with Matplotlib. Link to notebook.
• Reflection: Commit a blog post (Markdown) on how calculus applies to gradient descent in ML.

Linear Algebra

Description: Vectors, matrices, eigenvalues—key for dimensionality reduction and neural nets.
Duration: 4 weeks.
Folder: /fundamentals/linear-algebra/

Relevant Materials:

• Textbook: Linear Algebra with Applications by Otto Bretscher.
• Online: MIT OCW Linear Algebra (Gilbert Strang); Coursera: Linear Algebra for ML (free audit).

Practice Sessions:

• Hands-on: NumPy exercises from freeCodeCamp.
• Problems: Matrix decompositions on GeeksforGeeks.
• Weekly: 3Blue1Brown video + implementation challenges.

Portfolios/Projects:

• Project: PCA on Iris dataset with scikit-learn. Notebook. Deploy a simple Streamlit app.
• Reflection: Video demo (record via Loom) explaining eigenvectors in recommendations.

Probability and Statistics

Description: Probability distributions, hypothesis testing—foundation for inference in data.
Duration: 4 weeks.
Folder: /fundamentals/prob-stats/

Relevant Materials:

• Textbook: Introduction to Probability by Blitzstein.
• Online: Bertsekas & Tsitsiklis PDF (free); Coursera: Statistics with Python.

Practice Sessions:

• Coding: SciPy hypothesis tests on StrataScratch.
• Book exercises: Think Stats PDF.
• Interactive: DataCamp guided stats projects.

Portfolios/Projects:

• Project: A/B test on e-commerce data with p-values. Notebook + Viz.
• Reflection: Markdown report on assumptions and limitations.

Introduction to Programming (Python)

Description: Basics of Python syntax, libraries like NumPy/Pandas for data tasks.
Duration: 4 weeks.
Folder: /fundamentals/python-intro/

Relevant Materials:

• Textbook: Python Data Science Handbook (free).
• Online: freeCodeCamp Python for DS; DataCamp Intro.

Practice Sessions:

• Challenges: 50+ from Dataquest.
• Problems: GeeksforGeeks Python.
• Repo: Clone and solve 78 DS problems.

Portfolios/Projects:

• Project: Web scraper + sentiment analysis dashboard. Streamlit App.
• Reflection: Code review pull request to a sample repo.

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Intermediate Level: Data Wrangling and Insights

Focus: Handling real data. Estimated time: 12 weeks.

Data Structures and Algorithms

Description: Efficient data handling with trees, graphs—optimizes DS code.
Duration: 4 weeks.
Folder: /intermediate/dsa/

Relevant Materials:

• Textbook: Grokking Algorithms.
• Online: Google DSA Guide; LeetCode DS.

Practice Sessions:

• Implement: W3Schools DSA.
• Challenges: HackerRank.
• Projects: Codegnan DSA.

Portfolios/Projects:

• Project: Hash table search engine. Code + Benchmarks.
• Reflection: Time complexity analysis in a blog post.

Database Management Systems

Description: SQL/NoSQL for querying and storing data.
Duration: 4 weeks.
Folder: /intermediate/databases/

Relevant Materials:

• Textbook: Database System Concepts.
• Online: LearnSQL; Coursera SQL for DS.

Practice Sessions:

• Queries: LeetCode Database.
• Design: ProjectPro SQL.
• Sims: DataWars.

Portfolios/Projects:

• Project: E-commerce DB with queries. Schema + Queries.
• Reflection: ER diagram in Draw.io, committed as image.

Data Visualization

Description: Telling stories with charts using Matplotlib/Seaborn/Tableau.
Duration: 4 weeks.
Folder: /intermediate/viz/

Relevant Materials:

• Textbook: Storytelling with Data.
• Online: Tableau Resources; DataCamp Viz.

Practice Sessions:

• Projects: 10 from DataCamp.
• Interactive: Plotly Tutorials.
• Exercises: Coursera viz projects.

Portfolios/Projects:

• Project: COVID dashboard in Tableau. Public Viz Link.
• Reflection: A/B test on viz effectiveness.

Data Mining

Description: Extract patterns via clustering, association rules.
Duration: 4 weeks.
Folder: /intermediate/data-mining/

Relevant Materials:

• Textbook: Introduction to Data Mining.
• Online: Statistical Learning PDF; Coursera Mining.

Practice Sessions:

• Rules: Analytics Vidhya.
• Clustering: DataWars.
• Kernels: Kaggle mining.

Portfolios/Projects:

• Project: Market basket analysis. Apriori Notebook.
• Reflection: Insights report as PDF.

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Advanced Level: Scaling and Application

Focus: Production-ready DS. Estimated time: 12 weeks.

Machine Learning

Description: Supervised/unsupervised models, evaluation metrics.
Duration: 6 weeks.
Folder: /advanced/ml/

Relevant Materials:

• Textbook: Hands-On ML.
• Online: Andrew Ng Coursera; fast.ai.

Practice Sessions:

• Projects: 50 from ProjectPro.
• Nets: DataCamp ML.
• Comps: Kaggle ML.

Portfolios/Projects:

• Project: House price predictor with XGBoost. Flask Deploy.
• Reflection: Model tuning journal.

Big Data Analytics

Description: Spark/Hadoop for large-scale processing.
Duration: 4 weeks.
Folder: /advanced/big-data/

Relevant Materials:

• Textbook: Data Science for Business.
• Online: edX Big Data; Databricks tutorials.

Practice Sessions:

• Spark: ProjectPro.
• Guides: Dataquest.
• Dask: GeeksforGeeks.

Portfolios/Projects:

• Project: Twitter sentiment with PySpark. Notebook.
• Reflection: Scalability comparison.

Data Science Capstone Project

Description: Integrate everything in a full pipeline.
Duration: 2 weeks.
Folder: /advanced/capstone/

Relevant Materials:

• Textbook: Doing Data Science.
• Online: UCSD Guide; Kaggle workflows.

Practice Sessions:

• Iterations: DataPen.
• Mocks: Coursera guided.

Portfolios/Projects:

• Project: Titanic end-to-end (EDA to deploy). GitHub Pages Site.
• Reflection: Medium article linking repo.

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Additional Resources

• Tools Stack: Python, Jupyter, Git, Streamlit, Tableau Public.
• Communities: r/datascience, Dataquest forums, Kaggle discussions.
• Progress Tracking: Use GitHub Projects for kanban; add badges like .
• Ethics Note: Always consider bias/privacy—dedicated notebook in /ethics/.

Contributing & Contact

Star this repo if it helps! Open issues for feedback. Follow my progress via commits. Questions? E-mail.

License: MIT—feel free to adapt!

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