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100 Days of Applied Statistics for Data Science, Machine Learning, and Analytics

Explore the 100 Days of Applied Statistics for Data Science, Machine Learning, and Analytics — a structured roadmap covering foundational statistics, probability, inference, regression, machine learning statistics, and applied analytics with hands-on Python (NumPy, Pandas, Matplotlib, Seaborn, SciPy, Statsmodels, Scikit-Learn) examples and real-world projects.

road map 100 days applied statistics

🟢 PHASE 1: Statistical Foundations (Days 1–20)

Week 1: Introduction to Statistics & Data

Day 1 – What is Statistics? Role in DS, ML, AI
Day 2 – Types of Data (Qualitative vs Quantitative)
Day 3 – Scales of Measurement (Nominal, Ordinal, Interval, Ratio)
Day 4 – Population vs Sample
Day 5 – Descriptive vs Inferential Statistics
Day 6 – Data Collection Methods
Day 7 – Bias, Errors, and Data Quality

Week 2: Descriptive Statistics

Day 8 – Mean, Median, Mode
Day 9 – Variance and Standard Deviation
Day 10 – Range, IQR, Percentiles
Day 11 – Skewness and Kurtosis
Day 12 – Summary Statistics using Python (NumPy, Pandas)
Day 13 – Real Dataset Practice
Day 14 – Mini Quiz + Lab

Week 3: Data Visualization

Day 15 – Why Visualization Matters
Day 16 – Bar Charts, Line Charts
Day 17 – Histograms & Density Plots
Day 18 – Box Plots & Violin Plots
Day 19 – Scatter Plots & Pair Plots
Day 20 – Visualization with Matplotlib & Seaborn (Lab)

🟢 PHASE 2: Probability & Randomness (Days 21–40)

Week 4: Probability Basics

Day 21 – What is Probability?
Day 22 – Sample Space & Events
Day 23 – Rules of Probability
Day 24 – Conditional Probability
Day 25 – Bayes’ Theorem
Day 26 – Real-Life Probability Examples
Day 27 – Python Probability Simulations

Week 5: Random Variables

Day 28 – Discrete Random Variables
Day 29 – Continuous Random Variables
Day 30 – Probability Mass Function (PMF)
Day 31 – Probability Density Function (PDF)
Day 32 – Cumulative Distribution Function (CDF)
Day 33 – Expectation & Variance
Day 34 – Practice + Quiz

Week 6: Probability Distributions

Day 35 – Bernoulli Distribution
Day 36 – Binomial Distribution
Day 37 – Poisson Distribution
Day 38 – Uniform Distribution
Day 39 – Normal Distribution
Day 40 – Distribution Visualization with Python

🟢 PHASE 3: Statistical Inference (Days 41–60)

Week 7: Sampling & Estimation

Day 41 – Sampling Techniques
Day 42 – Sampling Distributions
Day 43 – Central Limit Theorem
Day 44 – Point Estimation
Day 45 – Confidence Intervals
Day 46 – CI using Python
Day 47 – Practice Lab

Week 8: Hypothesis Testing

Day 48 – Hypothesis Testing Concept
Day 49 – Null & Alternative Hypothesis
Day 50 – Type I & Type II Errors
Day 51 – p-value & Significance Level
Day 52 – One-tailed vs Two-tailed Tests
Day 53 – Z-test
Day 54 – t-test

Week 9: Advanced Hypothesis Testing

Day 55 – Paired vs Independent t-test
Day 56 – Chi-Square Test
Day 57 – ANOVA
Day 58 – Hypothesis Testing in Python (SciPy)
Day 59 – Case Studies
Day 60 – Mid-Course Assessment

🟢 PHASE 4: Regression & Correlation (Days 61–75)

Week 10: Correlation Analysis

Day 61 – Covariance
Day 62 – Pearson Correlation
Day 63 – Spearman Rank Correlation
Day 64 – Correlation vs Causation
Day 65 – Correlation in Python

Week 11: Regression Basics

Day 66 – Introduction to Regression
Day 67 – Simple Linear Regression
Day 68 – Regression Assumptions
Day 69 – Model Evaluation (R², MSE)
Day 70 – Linear Regression using Python

Week 12: Multiple Regression

Day 71 – Multiple Linear Regression
Day 72 – Feature Interpretation
Day 73 – Multicollinearity
Day 74 – Residual Analysis
Day 75 – Regression Case Study

🟢 PHASE 5: Statistics for Machine Learning (Days 76–90)

Week 13: Statistical Thinking in ML

Day 76 – Statistics vs Machine Learning
Day 77 – Bias-Variance Tradeoff
Day 78 – Overfitting & Underfitting
Day 79 – Train-Test Split
Day 80 – Cross-Validation

Week 14: Classification Statistics

Day 81 – Logistic Regression (Statistics View)
Day 82 – Sigmoid Function
Day 83 – Odds & Log-Odds
Day 84 – Confusion Matrix
Day 85 – Precision, Recall, F1-Score

Week 15: Feature Engineering & Scaling

Day 86 – Feature Scaling
Day 87 – Normalization vs Standardization
Day 88 – Handling Missing Data
Day 89 – Outlier Detection
Day 90 – ML Preprocessing Lab

🟢 PHASE 6: Applied Analytics & Capstone (Days 91–100)

Week 16: Real-World Analytics

Day 91 – Exploratory Data Analysis (EDA)
Day 92 – Business Analytics Case Study
Day 93 – A/B Testing
Day 94 – Time-Series Basics
Day 95 – Dashboard-Driven Insights

Week 17: Capstone Project

Day 96 – Project Problem Selection
Day 97 – Data Cleaning & EDA
Day 98 – Statistical Analysis
Day 99 – Model Interpretation
Day 100 – Final Presentation & Evaluation 🎓

🧰 Tools Used

Python
NumPy, Pandas
Matplotlib, Seaborn
SciPy, Statsmodels
Scikit-Learn