/IH_RH_DA_FT_AUG_2021

student-facing content for the DA bootcamp

Primary LanguageJupyter Notebook

Data Analytics Bootcamp - Berlin

Day 2

9:00 -

09:15

09:15 - 09:50 09:50 -

10:15

10:15 - 10:25 10:25 - 11:50 11:50 - 12:00 12:00 - 12:25 13:00 - 14:15 14:15 - 18:00 15:30 - 16:00 16:00 -17:00

Azure

Warm up Final Project

Q&A

Lecture Break Lecture + 10 min Q&A Break Lecture Lunch Break Final Project Elevator Pitches

Table of Contents

Week 8

Week 8 | Day 2 `s Learning Objectives:

  • NLP
  • Agile, MVP
  • Final Project Ideas review
  • Neural Network *
  • Azure*

    Week 8 | Day 1 `s Learning Objectives:

    • Ensemble Methods.
    • Project Intro.
    • ML frequent Problems
    • No Free Lunch Theorem
Day 1 Day 2 Day 3 Day 4 Day 5
[Presentation] Ensemble Methods [Presentation] Agile/ Project Management Final Project Final Project Final Project
[Presentation] ML Frequent Problems

No Free Lunch Theorem

[Presentation] Natural Language Processing
[Lab] Random Forest Azure* / AWS*
Final Project Kick off Neural Network Overview*
Object Oriented Programming* Final Project Elevator Pitches
Week 7

Week 7 | Day 5 `s Learning Objectives:

  • Cross Validation.
  • Extended Recap.

    Week 7 | Day 4 `s Learning Objectives:

    • Feature selection using p-value
    • Two-Sample Test
    • Decision Trees
    • Cross Validation

      Week 7 | Day 3 `s Learning Objectives:

      • Hypothesis Testing
      • P-Value, Confidence level, Significance level
      • One-Sample Z-Test, T-Test

        Week 7 | Day 2 `s Learning Objectives:

        • Logistic Regression
        • Handling Imbalanced Data Sets.

          Week 7 | Day 1 `s Learning Objectives:

          • Feature Selection
          • PCA
          • KNN
          • Bias and Variance Tradeoff
Day 1 Day 2 Day 3 Day 4 Day 5
[Presentation] Feature Selection [Presentation]

Logistic Regression

[Presentation] Hypothesis Testing [Presentation]

Two Sample T-Test

Chi

[Presentation] Weekly Recap
[Presentation]

PCA

[Notebook] Logistic Regression

[Notebook] Handling Imbalanced Data sets

[Notebook] Hypothesis One Sample Test [Presentation] Decision Trees

[Presentation ] Cross Validation

[Weekly Retro]
[Presentation]

KNN

[Presentation] Evaluating Classification Models [Lab] Hypothesis Testing [Notebook] Hypothesis Two Sample Test
[Presentation] Bias & Variance [LAB] Logistic Regression, Imbalance Sets [Notebook] Feature Selection using P-Value

[Notebook] Decision Trees

[Notebook] Feature Selection

[Notebook] KNN

[Notebook] PCA

[Lab] Decision_Trees
[LAB] Model_Comparision

[LAB] PCA

Week 6

Week 6 | Day 5 `s Learning Objectives:

  • Weekly Recap
  • Projects Presentations

    Week 6 | Day 4 `s Learning Objectives:

    • Unsupervised Learning
    • K-means Algorithm
    • Saving/Loading Model using Pickle

      Week 6 | Day 3 `s Learning Objectives:

      • APIs.
      • Spotify API.
      • JSON format overview.
      • Restful APIs

        Week 6 | Day 2 `s Learning Objectives:

        • Web Scraping multiple pages.
        • Beautiful Soap
        • Project Prototypes
        • Python modules.

          Week 6 | Day 1 `s Learning Objectives:

          • Web Scraping
          • HTML, CSS
          • Beautiful Soap
          • Python modules.
Day 1 Day 2 Day 3 Day 4 Day 5
[Case Study]

Gnod Song Recommender

[Notebook] Web Scraping Multiple Pages Code Along [Presentation] APIs [Presentation] Clustering using K-means [Presentation] Weekly Recap
[Presentation]

Web Scraping

[Code Along] Python Modules with VS Code [Presentation] Spotipy [LAB] Song Recommender Project [Weekly Retro]
[Activity] HTML

[Activity] CSS Selector

[LAB] Song Recommender Project [Notebook] APIs [Notebook] K-Means Code Along [LAB] Song Recommender Project
[Notebook] Web Scraping Code Along [Notebook] Spotipy [Presentation] K-Means with Scikit-Learn Presentations
[Presentation] Project Roadmap [LAB] Song Recommender Project
[LAB] Song Recommender Project
Week 5

Mid-Term Project

Day 1 Day 2 Day 3 Day 4 Day 5
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 4

Week 4 | Day 5`s Learning Objectives:

  • Tableau Table Calculations
  • Tableau different data sources.
  • Chart Types
  • Storytelling with Data.

    Week 4 | Day 4 `s Learning Objectives:

    • Intro to Tableau GUI
    • Tableau Dimensions, Measures, Geo fields
    • Chart Types
    • Visualization practices.

      Week 4 | Day 3 `s Learning Objectives:

      • Linear regression review
      • Model Validation
      • Improve data Transformation
      • Intro to Tableau GUI

        Week 4 | Day 2 `s Learning Objectives:

        • One Hot/Label Encoding (categorical).
        • Linear regression
        • Model Validation

          Week 4 | Day 1 `s Learning Objectives:

          • Intro to Machine Learning
          • Probability
          • Sampling
          • Probability distributions
          • Data Transformation/Processing
Day 1 Day 2 Day 3 Day 4 Day 5
[Presentation]

Intro to Machine Learning

[Presentation] Linear Regression [Presentation]

Improving Model Accuracy

[Presentation] Intro to Tableau Guest Speaker, CTO
[Presentation]

Probability

[Activity] Modeling [LAB] Lab | Model Evaluation and Improving [Presentation] Data Visualisation [Presentation] Tableau
[Presentation]

Sampling

[LAB] Lab | Model Fitting and Evaluating [Activity] Tableau [Presentation] Storytelling with Data]
[Presentation]

Probability Distributions

[LAB] Lab | Tableau [Activity] Tableau
[Presentation]

Data Processing

Weekly Recap
[Activity] Distributions Midterm Project Intro/ Briefing
Weekly Retro Weekly Retro
[LAB] Lab | Data Transformation [LAB] Lab | Tableau
Week 3

Week 3 | Day 5 `s Learning Objectives:

  • Connect Python to Mysql
  • Data Cleaning using MySQL
  • MongoDB
  • Data Warehouses

    Week 3 | Day 4 `s Learning Objectives:

    • DDL
    • Stored Procedures
    • Select Case Statement.

      Week 3 | Day 3 `s Learning Objectives:

      • ERD
      • Sub Queries
      • Temporary Tables/ Views

        Week 3 | Day 2 `s Learning Objectives:

        • ERD
        • Joins

          Week 3 | Day 1 `s Learning Objectives:

          • Relational Databases
          • SQL Queries.
Day 1 Day 2 Day 3 Day 4 Day 5
[Presentation]

Relational Databases

[Presentation]

Joins & ERD

[Activity ERD] [Presentation]

DDL

[Presentation]

Connect Python into MySQL

[LAB] Lab | SQL Intro [Lab] Lab | Sql Join two tables [Presentation]

SQL Sub Queries

[Presentation] Stored Procedures [Notebook]

Connect Python into MySQL

[LAB] Lab | SQL Queries [Lab] Lab | Sql Join multiple tables [Presentation]

Temporary Table/ Views

[Presentation] Select Case Statement [Activity] No-SQL Databases MongoDB
[Lab] Lab | SQL Sub Queries [Lab] DDL Weekly Recap
[Lab] Lab | Group By [Presentation]

Data Warehousing

[Lab] (Optional) (Additional) Lab | Stored Procedures [Activity] Lab | Python to Mysql
[Lab] Lab | SQL Data Cleaning
Week 2

Week 2 | Day 5 `s Learning Objectives:

  • Recap
  • Sampling
  • Practicing Group By
  • Kahoot*
  • Presentation skills

    Week 2 | Day 4 `s Learning Objectives:

    • Organizing data transformations into a pipeline.
    • Using Matplotlib and Seaborn
    • Plotting types
      • Scatter plots
      • Box plots
      • Bar plots
      • Histograms
    • Descriptive Statistics Measures

      Week 2 | Day3 `s Learning Objectives:

      • Dataframe filtering
      • apply functions on Data Frames
      • using map function
      • using Lambda functions
      • dealing with missing values
      • Working with Categorical variables
      • Concatenating
      • working with DateTime using Pandas
      • Grouping using Pandas
      • Correlation Matrix
Day 1 Day 2 Day 3 Day 4 Day 5
Weekly Retro [Code Along] Intro to Pandas [Presentation] Correlation of Numerical Features [Presentation] EDA with plotting [Presentation] Basic Statistical Concepts
[Presentation]

Numpy Arrays

[Pandas Cheat Sheet] [Activity] Correlation Matrix [Notebook] EDA with plotting [Notebook] Basic Statistical Concepts
[Cheat Sheet] Numpy Arrays [Presentation] Pandas Joining, Grouping [Activity] Grouping, Cleaning using Pandas Health Care For All Case Study [Cheat Sheet] Matplotlib [Weekly Recap]
[Code Along] Numpy [Code Along] Pandas Joining, Grouping [Lab] Customer Analysis [Activity] Plotting [Weekly Retro]
[Healthcare For All Case Study] [Lab] Customer Analysis Case Study [Lab] EDA Kahoot*
[Lab] Healthcare for All Excel,Sheets [Lab Pandas Group By]
[Presentation] Intro to Pandas
[Lab] Numpy Arrays
Week 1
Day 1 Day 2 Day 3 Day 4 Day 5
[Cheat Sheet] Mac Command

[Cheat Sheet] Windows Command Line

[Presentation] Conda [Presentation] Python Functions [Presentation] Intro to Data Analysis [Presentation] Python Map, Filter, Reduce
[Activity] Command Line [Cheat Sheet] Conda Cheat Sheet [Notebook] Python Functions [Presentation] Data Analysis Process [Notebook] Python Map, Filter, Reduce
[Presentation] Git Concepts [Presentation] Python Object Types [Presentation] Programming Tips [Extra] Data Analysis [Presentation] Python Lists Comprehension
[Presentation] Git Commands [Notebook] Python Object Types [Presentation] Programming Practices [Activity] Data Analysis [Notebook] Python Lists Comprehension
[Cheat Sheet] Git Cheat Sheet [Activity] Conda Environment [Extra] Programming Useful Resources [Presentation] Python, Error Handling [Lab] Lab | Python Lists Comprehension
[Extra] Git Extra Resources [Lab] Lab | Python Sets, Tuples, Dicts [Presentation] Python String Operations [Notebook] Python, Error Handling [Lab] Lab | Prework Review
[Presentation] Jupyter Notebooks [Notebook] Python String Operations [Lab] Lab | Python Error Handling
[Cheat sheet] Markdown Cheat Sheet [Lab] Lab | Python Strings
[Extra] Jupyter Notebook Extra Resources
[LAB] Lab | Git
[LAB] Lab | Jupyter Notebook
[LAB] (Optional) Lab | Bash