My Data Science 4-Months Roadmap

Overview

This repository contains documents that has to do with my roadmap. The roadmap was created by my mentor to follow as a guide to become a better and more advanced data scientist.

I have been following through from July and now in September and it is meant to end in October as it is a 4 month journey. Month 1 contains basic data analysis tools like matplotlib, Numpy, Pandas understanding jupyter notebooks, SQL and Data manipulation and cleaning. Month 2 comprises of different forms of statistics, inferential, descriptive and also some EDA, Month 3 are machine Learning Fundamentals (Supervised learning, Model development, preprocessing and also unsupervised Learning).

It has been a beautiful and eye opening journey. I hope to grow more and get better. I hope you like it as you go through it.

Contents

Data Science 4-Month Roadmap

Month 1: Data Foundations & Python Ecosystem

Python for Data Science 23rd June, 2025

• NumPy: Arrays, broadcasting, vectorization, linear algebra operations
• Pandas: DataFrames, data cleaning, merging, groupby, time series
• Matplotlib/Seaborn: Statistical plots, customization, best practices
• Jupyter Notebooks: Workflow, markdown, magic commands

SQL for Data Science 29th June, 2025

• Core SQL: SELECT, WHERE, GROUP BY, HAVING, JOINs, subqueries
• Window Functions: ROW_NUMBER, RANK, LAG/LEAD, running totals
• Advanced: CTEs, CASE statements, date functions
• Practice Platform: HackerRank SQL, LeetCode Database

Data Manipulation & Cleaning - 14th Be done by 23rd July completed on 7th August by 14 days

• Missing Data: Detection, imputation strategies
• Outlier Detection: IQR, Z-score, isolation methods
• Data Types: Converting, categorical encoding
• Feature Engineering: Creating new variables, transformations

Month 2: Statistics & Exploratory Data Analysis

Descriptive Statistics - To be done on 14th of August

• Central Tendency: Mean, median, mode --
• Variability: Standard deviation, variance, range --
• Distribution: Skewness, kurtosis, percentiles
• Correlation: Pearson, Spearman, covariance

Inferential Statistics - 21st as final date make it 16th

• Hypothesis Testing: t-tests, chi-square, ANOVA
• Confidence Intervals: Construction and interpretation
• P-values: Understanding and limitations
• Effect Size: Cohen's d, eta-squared

Exploratory Data Analysis (EDA) - make it 23rd

• Univariate Analysis: Histograms, box plots, summary statistics
• Bivariate Analysis: Scatter plots, correlation matrices
• Multivariate Analysis: Pair plots, heatmaps
• EDA Best Practices: Structured approach, storytelling

Month 3: Machine Learning Fundamentals

Supervised Learning - finish 16th

• Linear Regression: Simple, multiple, assumptions, diagnostics
• Regularization: Ridge, Lasso, Elastic Net
• Polynomial Regression: Feature interactions, overfitting
• Evaluation Metrics: MSE, RMSE, MAE, R²

Supervised Learning - Classification 20th

• Logistic Regression: Binary, multiclass, interpretation
• Decision Trees: Splitting criteria, pruning, visualization
• Random Forest: Ensemble methods, feature importance
• Evaluation Metrics: Accuracy, precision, recall, F1, ROC-AUC

Model Development Process - 27th

• Train/Validation/Test Split: Proper data splitting
• Cross-Validation: K-fold, stratified, time series
• Hyperparameter Tuning: Grid search, random search
• Scikit-learn: Pipeline, preprocessing, model selection

Unsupervised Learning - 31st

• K-Means Clustering: Algorithm, elbow method, silhouette score
• Hierarchical Clustering: Dendrograms, linkage methods
• PCA: Dimensionality reduction, explained variance
• Association Rules: Market basket analysis (basic)

Month 4: Advanced ML & Production Skills

Advanced Machine Learning

• Gradient Boosting: XGBoost, feature importance, tuning
• Support Vector Machines: Kernels, hyperparameter tuning
• Naive Bayes: Assumptions, text classification
• Ensemble Methods: Voting, stacking, blending

Model Evaluation & Selection

• Bias-Variance Tradeoff: Understanding overfitting/underfitting
• Learning Curves: Diagnosing model performance
• Feature Selection: Univariate, recursive, LASSO-based
• Model Interpretation: SHAP values, feature importance

Time Series Analysis (Industry Essential)

• Components: Trend, seasonality, noise
• Stationarity: ADF test, differencing
• ARIMA Models: Basic forecasting
• Evaluation: MAE, MAPE for forecasts

Production & Tools

• Git/GitHub: Version control, collaboration
• Docker: Basic containerization for ML models
• Cloud Platforms: AWS S3, EC2 basics (or GCP/Azure equivalent)
• MLOps Basics: Model versioning, monitoring concepts

Business Skills

• A/B Testing: Design, statistical significance, interpretation
• Metrics Definition: KPIs, business impact measurement
• Communication: Technical findings to non-technical stakeholders
• Project Structure: End-to-end ML project workflow

Weekly Practice Requirements

• 2 Kaggle competitions (participate, don't aim to win)
• 1 end-to-end project per month with GitHub documentation
• Daily coding practice (30-60 minutes)
• 1 technical blog post per month explaining concepts learned

Industry-Standard Tools to Master

• Python: pandas, numpy, scikit-learn, matplotlib, seaborn
• SQL: PostgreSQL or MySQL
• Jupyter: Notebooks and Lab
• Git: Version control
• Cloud: Basic AWS/GCP/Azure familiarity

Key Datasets for Practice

• Titanic (Classification)
• Boston Housing (Regression)
• Iris (Multi-class classification)
• Time series: Stock prices or weather data
• Text data: Movie reviews or news articles