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ih_ab_da_ft_may_2023's Introduction

Data Analytics

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


[Presentation] Python Functions


[Notebook] Python Functions

[Presentation] Python Lists Comprehension


[Notebook] Python Lists Comprehension

[Lab] Python Functions

[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:


Elevator pitches

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
Monday Tuesday Wednesday Thursday Friday

It is Friday!! 🥳😎✌️


[Presentation] Feature Selection


[Notebook] Feature Selection


[Notebook] Feature Selection using P-Value



[LAB] Model_Comparison

[Presentation] KNN


[Presentation] Logistic Regression


[Presentation] Evaluating Classification Models


[Notebook] KNN


[Notebook] Logistic Regression


[LAB] Logistic Regression

[Presentation] Decision Trees


[Notebook] Decision Trees


[Lab] Decision_Trees

[Presentation ] Cross Validation


[Presentation] Bias & Variance


[Notebook] Cross Validation


[Notebook] Handling Imbalanced Data sets


[Data] Imbalanced Data Set


[Lab] Cross Validation & Resampling

Kahoot


[Presentation] ML Frequent Problems


[Presentation] Ensemble Methods


[Presentation] Weekly Recap


[Notebook] Random Forest


[Notebook] Hyper Parameter Tuning


[Weekly Retro]


[Lab] Random Forest & Hyper Parameter Tuning

Final Project Kick off


📅   Week 8 - Advanced-ML & Final Project

Week 8

Week 8 | Day 1 Learning Objectives:

  • Final Project Presentation Example.
  • NLP
Monday Tuesday Wednesday Thursday Friday

It is Friday!! 🥳😎✌️

[Presentation] Natural Language Processing


[Notebook] NLP


[Data] NLP Data

Final Project Research Final Project Elevator Pitches Daily Standup


Final Project Plan Submission

Daily Standup
📅  Week 9 - Final Project - Hackshow

ih_ab_da_ft_may_2023's People

Contributors

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