Today

09:00 - 09:30	09:30 - 10:10		10:20 - 11:30		11:40 - 12:30	12:30 - 13:45	13:45 - 15:00	15:00 - 18:00
Warm up	Lab Review/Lecture	10 mins Break	Lecture	10 mins Break	Lab	Lunch Break	Lecture	Lab

👉 Table of Contents

📅 Week 1

Week 1 | Day5 Key Objectives:

Map function
Intro to Pandas.
Weekly Recap
Week 1 | Day4 Key Objectives:
- Programming Tips and coding efficiency.
- Lambda functions.
- Data Analysis Intro and Process
- Numpy Arrays
  Week 1 | Day3 Key Objectives:
  - Python functions
  - Python List comprehension
  - Pre-work review
    Week 1 | Day2 Key Objectives:
    - Conda: Package and Environment Manager
    - Python Data Structures: Lists, Tuples, dictionaries, Sets
    - Python String Operations
      Week 1 | Day1 Key Objectives:
      - Housekeeping Issues and Bootcamp Expectation
      - Command Line
      - Git & GitHub
      - Jupyter Notebooks and Markdown

Monday

Tuesday

Wednesday

Thursday

Friday

It is Friday!! 🥳😎✌️

[Presentation] Intro

[Activity] Command Line

[Presentation] Git

[Presentation] Jupyter Notebooks

[Cheat Sheet] Mac Command

[Cheat Sheet] Windows Command Line

[Cheat Sheet] Git Cheat Sheet

[Cheat sheet] Markdown Cheat Sheet

[LAB] Git

[LAB] Jupyter Notebook

[LAB] (Optional) Bash

[Presentation] Conda

[Activity] Conda Environment

[Cheat Sheet] Conda Cheat Sheet

[Presentation] Python Built-In Data Structures

[Notebook] Python Built-In Data Structures

[Presentation] Python String Operations

[Notebook] Python String Operations

[Lab] Python Built-In Data Structures

[Lab] Python Strings

[Presentation] Python Functions

[Notebook] Python Functions

[Presentation] Python Lists Comprehension

[Notebook] Python Lists Comprehension

[Lab] Pre-Work Review

[Presentation] Programming Tips

[Presentation] Programming Code Simplicity

[Presentation] Lambda Function

[Presentation] Data Analysis Intro

[Presentation] Data Analysis Process

[Presentation] Numpy Arrays

[Cheat Sheet] Numpy Arrays

[Notebook] Numpy Arrays

[Lab] Numpy Arrays

[Presentation] Python Map

[Presentation] Intro to Pandas

[Code Along] Intro to Pandas

Weekly Recap

Weekly Retro

[Lab] Pandas Exercises

📅 Week 2 - EDA

📅 Week 2

Week 2 | Day1 Key Objectives:

Pandas continued (filtering,applying functions, concatenating, IO operations)
HealthCare For All Case Study
Data Cleaning using Pandas
Week 2 | Day2 Key Objectives:
- HealthCare For All Case Study
- Data Cleaning using Pandas
- Statistics basics ( samples, probability, distributions, random variables, samples, measures of central tendency and dispersion).
  Week 2 | Day3 Key Objectives:
  - Correlation and correlation Matrix
  - Plotting using Matplotlib and seaborn
  - Exploratory Data Analysis
    Week 2 | Day4 Key Objectives:
    - Data Pipelining
    - Linear Regression
      Week 2 | Day5 Key Objectives:
      - Weekly Recap
      - Pandas Group By
      - Pandas Merging
      - Pandas Best Practices

Monday

Tuesday

Wednesday

Thursday

Friday

It is Friday!! 🥳😎✌️

[Code Along] Pandas_Part_2

[Healthcare For All Case Study]

[Code_Along] Healthcare For All Case Study

[Lab] EDA_Round_1

[Presentation] Basic Statistical Concepts

[Lab] EDA_Round_2

[Presentation] Correlation of Numerical Features

[Presentation] EDA with plotting

[Notebook] EDA with plotting

[Cheat Sheet] Matplotlib

[Cheat Sheet] Seaborn

[Lab] EDA_Round_3

[Linear Regression Overview]

[Code_Along] Data_Pipelining

[Lab] EDA_Round_4

[Presentation] Pandas Joining, Grouping

[Notebook] Pandas contd

Weekly Recap

Weekly Retro

Kahoot

[Case Study Presentations]

[Lab Pandas Group By]

📅 Week 3 - Databases - Visualizations

📅 Week 3

Week 3 | Day 5 Learning Objectives:

Business Intelligence
Tableau
Weekly Recap
Week 3 | Day 4 Learning Objectives:
- Having clause
- Temporary Table
- Data Warehousing
- Data Visualization
- Intro Tableau
  Week 3 | Day 3 Learning Objectives:
  - Subqueries
    Week 3 | Day 2 Learning Objectives:
    - ERDs
    - Joins
      Week 3 | Day 1 Learning Objectives:
      - Relational Databases
      - SQL Queries

Monday

Tuesday

Wednesday

Thursday

Friday

It is Friday!! 🥳😎✌️

[Presentation] Relational Databases

[LAB] Lab | SQL Intro

[LAB] Lab | SQL Queries

[Presentation] Joins & ERD

[Lab] Sql Join two tables

[Lab] (Optional) Sql Join multiple tables

[Activity ERD]

[Presentation] SQL Sub Queries

[Lab] SQL Sub Queries

[SQL Having clause]

[Presentation] Data Warehousing

[Presentation] Temporary Table/ Views

[Presentation] Intro to Tableau

[LAB] Tableau

[Lab] (Optional) SQL Group By

[Presentation] Tableau

[Presentation] Business Intelligence

[Activity] KPIs

Weekly Recap

Weekly Retro

[LAB] Tableau Dashboard

📅 Week 4 - Stats - Regression

📅 Week 4

Week 4 | Day 5 Learning Objectives:

Hypothesis Testing
Mid term project briefing
Recap
Week 4 | Day 4 Learning Objectives:
- Model Validation
- Revisit StandardScaler fit and transform functions
- Using the model to predict on a new unseen record.
- Hypothesis Testing
  Week 4 | Day 3 Learning Objectives:
  - Linear Regression.
  - Model Validation.
    Week 4 | Day 2 Learning Objectives:
    - Linear Regression.
    - Model Validation.
      Week 4 | Day 1 Learning Objectives:
      - Storytelling with Data
      - Data Visualization
      - Machine Learning Intro.
      - Data Transformation.

Monday

Tuesday

Wednesday

Thursday

Friday

It is Friday!! 🥳😎✌️

[Presentation] Data Visualization

[Presentation] Storytelling with Data]

[Presentation] Intro to Machine Learning

[Presentation] Probability Distributions

[Presentation] Data Processing

[LAB] Lab | Data Transformation

[Lab] [Optional] Resume using Tableau

[Presentation] Linear Regression

[Notebook] Linear Regression

[LAB] Lab | Model Fitting and Evaluating

[Presentation] Improving Model Accuracy

[Notebook] Linear Regression

[LAB] Model Evaluation and Improving

[Presentation] Sampling Distributions

[Presentation] Hypothesis Testing

[Notebook] Hypothesis One Sample Test

[LAB] Model Evaluation and Improving

[Lab] Hypothesis Testing

Kahoot

[Presentation] A/B Testing

[Notebook] A/B Testing

Weekly Recap

Weekly Retro

Midterm Project Intro/ Briefing

[Lab] Hypothesis Testing

📅 Week 5 - Mid Term Project

📅 Week 5 📅 Mid-Term Project
Monday	Tuesday	Wednesday	Thursday	Friday It is Friday!! 🥳😎✌️
Submitting project plans	Work on the project	Work on the project	Work on the project	Work on the project
Work on the project				Presentations

📅 Week 6 - Song Recommender Product

Week 6

Week 6 | Day 5 Learning Objectives:

Working on the product
Week 6 | Day 4 Learning Objectives:
- Unsupervised Learning
- K-means Algorithm
- Saving/Loading Model using Pickle
  Week 6 | Day 3 Learning Objectives:
  - APIs.
  - Spotify API.
  - JSON format overview.
  - Restful APIs
    Week 6 | Day 2 Learning Objectives:
    - Web Scraping multiple pages
    - Python modules
      Week 6 | Day 1 Learning Objectives:
      - Git ignore
      - Web Scraping
      - HTML, CSS
      - Beautiful Soup

Monday

Tuesday

Wednesday

Thursday

Friday

It is Friday!! 🥳😎✌️

[Case Study] Gnod Song Recommender

[Presentation] Web Scraping

[Activity] CSS Selector

[Notebook] Web Scraping Code Along

[Presentation] Project Roadmap

[LAB] Song Recommender Project

[Notebook] Web Scraping Multiple Pages Code Along

[LAB] Song Recommender Project

[Presentation] APIs

[Presentation] Spotipy

[Notebook] APIs

[Notebook] Spotipy

[LAB] Song Recommender Project

[Presentation] Clustering using K-means

[Presentation] K-Means with Scikit-Learn

[Notebook] K-Means Code Along

[LAB] Song Recommender Project

[Presentation] Weekly Recap

[LAB] Song Recommender Project

Song Recommender Presentations

📅 Week 7 - Machine Learning - Advanced

Week 7

Week 7 | Day 5 Learning Objectives:

Random Forest
Hyper Parameter Tuning
ML Frequent Problems
Recap
Week 7 | Day 4 Learning Objectives:
- Cross Validation
- Handling Imbalanced Data
- Bias and Variance Tradeoff
  Week 7 | Day 3 Learning Objectives:
  - Decision Trees
    Week 7 | Day 2 Learning Objectives:
    - KNN
    - Logistic regression
    - Evaluating Classification models
      Week 7 | Day 1 Learning Objectives:
      - Feature Selection