How to Complete Data Science in 200 Days: From Beginner to Intermediate (with Projects, Tools)

Data Science is one of the most in-demand and exciting fields today, with applications ranging from business intelligence to artificial intelligence. But how do you start if you're a complete beginner? And how long will it take? In this blog, I’ll walk you through a 200-day structured learning path to go from zero to intermediate-level Data Scientist — with real-world projects, top tools, and handpicked affiliate products (like laptops and books) to help you along the way.

⌛️ Overview of the 200-Day Plan

Phase
Duration
Focus
Phase 1
Day 1-30
Python Programming Basics
Phase 2
Day 31-60
Statistics & Math Foundations
Phase 3
Day 61-90
Data Handling with Pandas & NumPy
Phase 4
Day 91-120
Data Visualization
Phase 5
Day 121-160
Machine Learning
Phase 6
Day 161-200
Real Projects & Portfolio Building

🔢 Phase 1: Python Programming Basics (Day 1–30)

Python is the backbone of Data Science. It’s simple, versatile, and beginner-friendly.

Topics to Cover:

  • Python syntax and variables

  • Loops and conditionals

  • Functions and modules

  • Lists, Tuples, Dictionaries

  • File handling

  • Object-Oriented Programming (OOP)

Tools:

  • Jupyter Notebook

  • Google Colab (free and cloud-based)

Recommended Course:

Mini Project:

  • Build a simple contact book or to-do list app

σ Phase 2: Math & Statistics (Day 31–60)

You need strong foundations in statistics and mathematics to understand data and build models.

Topics to Cover:

  • Mean, Median, Mode

  • Standard Deviation & Variance

  • Probability Basics

  • Linear Algebra (vectors, matrices)

  • Descriptive and Inferential Statistics

  • Correlation and Regression

Tools:

  • Excel or Google Sheets

  • Python libraries: statistics, scipy

Recommended Book:

Mini Project:

  • Analyze marks of 100 students and derive patterns using regression

📈 Phase 3: Data Handling with Pandas & NumPy (Day 61–90)

Now it’s time to manipulate and clean real-world data!

Topics to Cover:

  • Importing datasets (CSV, Excel, JSON)

  • DataFrames and Series

  • Filtering and sorting

  • Missing values and duplicates

  • GroupBy, Pivot Tables, Aggregations

Tools:

  • Jupyter Notebook / Colab

  • Pandas and NumPy libraries

Recommended Course:

Mini Project:

  • COVID-19 India Tracker with Pandas

📊 Phase 4: Data Visualization (Day 91–120)

Visualization helps you explain data stories and trends clearly.

Topics to Cover:

  • Bar charts, line graphs, histograms

  • Pie charts, scatter plots

  • Heatmaps, pairplots

  • Advanced: Interactive Dashboards (Plotly, Dash)

Tools:

  • Matplotlib

  • Seaborn

  • Plotly (optional)

Mini Project:

  • Create a Sales Performance Dashboard

🧐 Phase 5: Machine Learning (Day 121–160)

Time to make your system learn and predict!

Topics to Cover:

  • Supervised vs Unsupervised Learning

  • Algorithms: Linear/Logistic Regression, KNN, Decision Trees, SVM, Naive Bayes

  • Clustering: K-Means, Hierarchical

  • Train-test split, cross-validation

  • Overfitting, bias-variance tradeoff

Tools:

  • Scikit-learn

  • NumPy, Pandas

Recommended Book:

Mini Projects:

  • Titanic Dataset Prediction (Classification)

  • Iris Flower Dataset (Clustering)

📅 Phase 6: Real Projects & Portfolio (Day 161–200)

This phase makes your learning job-ready. You’ll work on complete end-to-end projects.

Projects to Build:

  1. Movie Recommendation System using Cosine Similarity

  2. Customer Segmentation using K-Means

  3. Spam Email Detection with NLP

  4. Stock Market Forecasting using ARIMA

  5. House Price Prediction using Regression

Platforms to Showcase:

  • GitHub (Upload projects)

  • Kaggle (Compete & practice)

  • Medium/Blogspot (Write articles about your projects)

🚀 Tools You’ll Use Throughout

  • Google Colab / Jupyter Notebook

  • Git & GitHub

  • Scikit-learn

  • Pandas / NumPy / Seaborn / Matplotlib

  • Excel / Sheets

  • Kaggle (datasets + competitions)

💻 Top 5 Laptops for Data Science (Affiliate)

Laptop
Specs
Buy Link
Apple MacBook Air M2 (2024)
8GB RAM, 512GB SSD
Buy on Amazon
ASUS ROG Strix G16
i7, 16GB RAM, RTX 4060
Buy on Amazon
Dell Inspiron 15
i5 12th Gen, 16GB RAM
Buy on Amazon
HP Victus Ryzen 5
8GB RAM, GTX GPU
Buy on Amazon
Lenovo IdeaPad Slim 5
Ryzen 7, 512GB SSD
Buy on Amazon

📚 Best Books to Learn Data Science (Affiliate)

  1. Python for Data Analysis by Wes McKinney

  2. Data Science from Scratch by Joel Grus

  3. Hands-On ML with Scikit-Learn, Keras & TensorFlow

  4. Storytelling with Data by Cole Nussbaumer

  5. The Art of Statistics by David Spiegelhalter

📆 Bonus: Weekly Time Table Example (Optional)

Day Task
Mon Watch tutorial + take notes
Tue Practice coding problems
Wed Read 1-2 chapters of book
Thu Work on small project or mini task
Fri Review & revise concepts
Sat Attend online community, ask questions
Sun Rest or build side project

📄 Conclusion

Data Science is not about rushing; it’s about building deep, applied knowledge step by step. This 200-day plan is designed to help beginners like you become confident in data skills, portfolio building, and project experience.

🚀 Start now, stay consistent, and share your journey.

If you'd like a printable PDF of this roadmap or want help setting up your GitHub portfolio or blog, let me know in the comments or contact me!

#DataScience #Python #BCA #LearnToCode #TechBlog #Affiliate #GitHub #MachineLearning

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