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2-Week Data Science Crash Training

Dataset Guide

KirkYagami/2-Week-Data-Science-Crash-Training

2-Week Data Science Crash Training

KirkYagami/2-Week-Data-Science-Crash-Training

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Week 01 — Foundations
Week 01 — Foundations
- Overview
- Day 01 — Python
  Day 01 — Python
  - Part 1 — Python Basics
    Part 1 — Python Basics
    
    Agenda
    
    Python Introduction
    
    Variables & Data Types
    
    Control Flow
    
    Functions
    
    Lists, Tuples & Dicts
    
    Practice Problems
    
    Interview Questions
    
    Cheat Sheet
  - Part 2 — Advanced Python
    Part 2 — Advanced Python
    
    Agenda
    
    OOP Basics
    
    File Handling
    
    Modules & Packages
    
    Exception Handling
    
    Python Best Practices
    
    Mini Exercises
    
    Interview Questions
- Day 02 — NumPy & Pandas
  Day 02 — NumPy & Pandas
  - Part 1 — NumPy Fundamentals
    Part 1 — NumPy Fundamentals
    
    Agenda
    
    Introduction to NumPy
    
    Array Creation
    
    Indexing & Slicing
    
    Vectorization
    
    Broadcasting
    
    NumPy Exercises
    
    Cheat Sheet
  - Part 2 — Pandas Basics
    Part 2 — Pandas Basics
    
    Agenda
    
    Series & DataFrames
    
    Reading CSV & Excel
    
    Filtering & Sorting
    
    Basic Data Analysis
    
    Practice Exercises
    
    Interview Questions
- Day 03 — Pandas Advanced & Visualization
  Day 03 — Pandas Advanced & Visualization
  - Part 1 — Pandas Advanced
    Part 1 — Pandas Advanced
    
    GroupBy
    
    Merge & Join
    
    Apply Functions
    
    Missing Values
    
    Real-World Data Cleaning
    
    Exercises
  - Part 2 — Data Visualization
    Part 2 — Data Visualization
    
    Matplotlib Basics
    
    Line, Bar & Histogram
    
    Seaborn Basics
    
    Heatmaps
    
    Visualization Best Practices
    
    Mini Project
- Day 04 — Statistics
  Day 04 — Statistics
  - Part 1 — Statistics Basics
    Part 1 — Statistics Basics
    
    Mean, Median & Mode
    
    Variance & Std Dev
    
    Probability Basics
    
    Distributions
    
    Cheat Sheet
    
    Practice Questions
  - Part 2 — Inferential Statistics
    Part 2 — Inferential Statistics
    
    Hypothesis Testing
    
    P-Value
    
    Confidence Intervals
    
    Correlation
    
    Statistical Tests
    
    Interview Prep
- Day 05 — SQL & EDA
  Day 05 — SQL & EDA
  - Part 1 — SQL for Data Science
    Part 1 — SQL for Data Science
    
    SELECT & WHERE
    
    GROUP BY & HAVING
    
    JOINs
    
    Subqueries
    
    CTEs
    
    Window Functions
    
    SQL Case Studies
    
    Interview Questions
  - Part 2 — EDA
    Part 2 — EDA
    
    Data Cleaning
    
    Outlier Detection
    
    Feature Understanding
    
    Univariate Analysis
    
    Bivariate Analysis
    
    EDA Workflow
    
    Mini Case Study
Week 02 — Machine Learning
Week 02 — Machine Learning
- Overview
- Day 01 — ML Basics & Regression
  Day 01 — ML Basics & Regression
  - Part 1 — Machine Learning Basics
    Part 1 — Machine Learning Basics
    
    Agenda
    
    What is Machine Learning?
    
    Supervised vs Unsupervised
    
    Train/Test Split & Leakage
    
    Scikit-learn Workflow
    
    Exercises
  - Part 2 — Regression Algorithms
    Part 2 — Regression Algorithms
    
    Agenda
    
    Regression Overview
    
    Linear Regression
    
    Ridge, Lasso & ElasticNet
    
    Tree-Based Regression
    
    Regression Metrics
    
    Exercises
- Day 02 — Classification & Clustering
  Day 02 — Classification & Clustering
  - Part 1 — Classification Algorithms
    Part 1 — Classification Algorithms
    
    Agenda
    
    Classification Overview
    
    Logistic Regression
    
    KNN & Naive Bayes
    
    Trees, Forests & Boosting
    
    Classification Metrics
    
    Exercises
  - Part 2 — Clustering Techniques
    Part 2 — Clustering Techniques
    
    Agenda
    
    Clustering Overview
    
    K-Means
    
    Hierarchical Clustering
    
    DBSCAN
    
    Evaluation & Scaling
    
    Exercises
- Day 03 — Feature Engineering & Evaluation
  Day 03 — Feature Engineering & Evaluation
  - Part 1 — Feature Engineering
    Part 1 — Feature Engineering
    
    Agenda
    
    Overview
    
    Numeric Features
    
    Categorical Features
    
    Datetime & Text Features
    
    Pipelines & Leakage
    
    Exercises
  - Part 2 — Model Evaluation
    Part 2 — Model Evaluation
    
    Agenda
    
    Evaluation Overview
    
    Cross-Validation
    
    Regression Evaluation
    
    Classification Evaluation
    
    Model Selection & Tuning
    
    Exercises
- Day 04 — Deep Learning & NLP
  Day 04 — Deep Learning & NLP
  - Part 1 — Intro to Deep Learning
    Part 1 — Intro to Deep Learning
    
    Agenda
    
    Neural Network Intuition
    
    Training Neural Networks
    
    Keras Quickstart
    
    Overfitting & Regularization
    
    Exercises
  - Part 2 — NLP Basics
    Part 2 — NLP Basics
    
    Agenda
    
    NLP Overview
    
    Text Preprocessing
    
    BoW & TF-IDF
    
    Sentiment Classification
    
    Transformers Overview
    
    Exercises
- Day 05 — Project & Interview
  Day 05 — Project & Interview
  - Part 1 — End-to-End Mini Project
    Part 1 — End-to-End Mini Project
    
    Agenda
    
    Project Brief
    
    EDA & Cleaning
    
    Feature Engineering
    
    Modeling Pipeline
    
    Evaluation & Report
    
    Submission Checklist
  - Part 2 — Mock Interview & Resume Review
    Part 2 — Mock Interview & Resume Review
    
    Agenda
    
    Resume Checklist
    
    Portfolio & GitHub
    
    Technical Interview Questions
    
    Case Study Practice
    
    Mock Interview Script
Projects
Projects
- Titanic Survival Prediction
  Titanic Survival Prediction
- House Price Prediction
  House Price Prediction
- Movie Recommendation System
  Movie Recommendation System
- Customer Churn Prediction
  Customer Churn Prediction
  - Overview
  - Dataset Guide Dataset Guide
    Table of contents
    
    Overview
    
    Notes
    
    Examples
    
    Practice
    
    Interview Questions
  - EDA
  - Feature Engineering
  - Model Building
  - Evaluation
  - Interview Questions
- Sales Forecasting
  Sales Forecasting
- Sentiment Analysis on Tweets
  Sentiment Analysis on Tweets
Cheat Sheets
Cheat Sheets
Interview Prep
Interview Prep
- Overview
- Python
  Python
- SQL
  SQL
- Statistics
  Statistics
- Machine Learning
  Machine Learning
- Deep Learning
  Deep Learning
- NLP
  NLP
- System Design
  System Design
- Case Studies
  Case Studies
- Behavioral
  Behavioral
Assignments
Assignments
- Week 01
- Week 02
Resources
Resources

Dataset Guide¶

Overview¶

Notes¶

Examples¶

Practice¶

Interview Questions¶