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