30-Day Python for Data Science Bootcamp (1.5 Hours/Day)

Equip yourself with essential data science skills using Python. Over 30 days, you’ll learn core Python, perform EDA, visualize data, build and tune machine learning models, deploy them as APIs, and create BI dashboards—culminating in a capstone project and certification.

Who Should Enroll?

• Aspiring data scientists and analysts
• Python developers transitioning into data roles
•  Business analysts seeking data-driven insights
• Students and self-learners craving a structured, daily regimen

Prerequisites

• Basic understanding of programming concepts
• Python 3.x installed (Anaconda recommended)
• Familiarity with command-line and package installation
• Code editor or IDE (VS Code, PyCharm)

Course Format & Requirements

• Daily Sessions: 1 hour of guided instruction
• Practice Time: 15–30 minutes of hands-on exercises each day
• Environment: Jupyter Notebook or Google Colab.
• Python Version: 3.7+
• Libraries Covered: NumPy, Pandas, Matplotlib, Seaborn, scikit-learn

Learning Outcomes

By the end of this bootcamp, you will be able to:

• Explain the data science lifecycle and industry applications
• Write Python scripts utilizing core data types, control flow, and functions
• Manipulate arrays with NumPy and tabular data with Pandas
• Clean and preprocess datasets: handle missing values, duplicates, and data types
• Conduct exploratory data analysis and generate insights
• Create and customize plots with Matplotlib and Seaborn
• Compute descriptive statistics, work with distributions, and perform hypothesis testing
• Query and join data using SQL
• Build, evaluate, and tune supervised models: regression and classification
• Implement clustering, dimensionality reduction, and feature engineering
• Serialize models and expose them via Flask APIs
• Design interactive dashboards in Power BI and Looker Studio
• Showcase a full data science project from data cleaning to deployment

Course Schedule

Detailed Daily Breakdown

Week 1: Python for Data Science Basics

• Day 1: Introduction to Data Science
  – What is Data Science?
  – Industry applications and case studies
  – Roadmap overview
• Day 2: Python Basics
  – Variables, data types, operators
  – Conditional statements & loops
  – Hands-on: Basic Python scripts
• Day 3: Functions & Data Structures
  – Built-in vs. custom functions
  – Lists, tuples, dictionaries, sets
• Day 4: Working with NumPy
  – Arrays, slicing, broadcasting
  – Hands-on: Numerical computations
• Day 5: Introduction to Pandas
  – Series & DataFrames
  – Reading CSV/Excel files; indexing, filtering, sorting
• Day 6: Data Cleaning with Pandas
  – Handling missing values & duplicates
  – Data type conversion; string operations
• Day 7: Exploratory Data Analysis (EDA)
  – describe(), value_counts(), groupby()
  – Hands-on EDA on a real dataset

Week 2: Data Visualization & Statistics

• Day 8: Matplotlib & Seaborn
  – Bar, line, histogram, boxplot, heatmap
  – Customizing styles and annotations
• Day 9: Statistics Basics
  – Mean, median, mode, variance, standard deviation
  – Intro to probability
• Day 10: Probability Distributions
  – Normal, binomial, Poisson distributions
  – Use cases and plotting
• Day 11: Hypothesis Testing
  – Null vs. alternative hypotheses
  – t-test, z-test, chi-square; hands-on A/B test
• Day 12: Correlation & Covariance
  – Pearson, Spearman coefficients; heatmaps
  – Hands-on: Correlation analysis
• Day 13: Introduction to SQL
  – SELECT, WHERE, GROUP BY, JOINs
  – Hands-on queries on sample DB
• Day 14: Mini Project 1 – EDA & Visualization
  – Complete data analysis report using Pandas + Seaborn

Week 3: Machine Learning Foundations

• Day 15: Introduction to Machine Learning
  – Supervised vs. unsupervised vs. reinforcement
  – ML lifecycle and scikit-learn overview
• Day 16: Data Preprocessing
  – Label encoding, one-hot encoding
  – Feature scaling; train-test split
• Day 17: Linear Regression
  – Simple & multiple regression
  – Metrics: R², MAE, RMSE; hands-on housing price prediction
• Day 18: Logistic Regression
  – Sigmoid function, confusion matrix
  – Precision, recall, F1-score; churn prediction
• Day 19: Decision Trees & Random Forest
  – Gini, entropy, overfitting; ensemble basics
  – Hands-on: Titanic/fraud detection
• Day 20: KNN & Naive Bayes
  – Distance metrics; Bayesian logic
  – Hands-on: Iris classification
• Day 21: Mini Project 2 – ML Classification
  – End-to-end pipeline: data → model → evaluation

Week 4: Advanced ML, Deployment & BI Tools

• Day 22: Clustering with K-Means
  – Elbow method, silhouette score
  – Hands-on: Customer segmentation
• Day 23: Dimensionality Reduction
  – PCA, t-SNE visualization hands-on
• Day 24: Feature Engineering
  – Creating interaction terms, handling outliers
• Day 25: Model Evaluation & Tuning
  – Cross-validation; GridSearchCV, RandomizedSearchCV
• Day 26: Model Deployment Intro
  – Pickle, Joblib; Flask for ML APIs basics
• Day 27: Power BI & Looker Studio
  – Connecting to CSV/BigQuery; building dashboards
• Day 28: Capstone Project – Regression or Classification
  – Full workflow: cleaning, modeling, visualization, reporting
• Day 29: Resume & Interview Prep
  – Data role resume tips; mock Q&A: Python, stats, ML, SQL
• Day 30: Final Presentation & Certification
  – Capstone demo; Q&A, feedback; certificate of completion

Tools & Technologies

• Python 3.x & Anaconda
• Jupyter Notebook / Google Colab
• NumPy, Pandas
• Matplotlib, Seaborn
• scikit-learn
• SQLite / MySQL (via Python)
• Flask
• Power BI, Looker Studio
• Git & GitHub

Ready to Get Started?

Enroll today and transform your front-end skill set with a month of focused, daily Angular learning.