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Data Science Course Plan

Sprint 1: Google Sheets

Preparing Data

  • Module Overview: DS Preparing Data
  • Getting Set Up For Preparing Data [Slides]
  • Integrated Project: Access To Drinking Water (Overview) [Slides]

Data Sources And Access

  • Lesson Overview: Data Sources And Access
  • An Introduction To Spreadsheets [Slides]
  • Intro To The Sheets Interface [Walk-Through]
  • Importing Data From Files [Slides]
  • Importing Data In Sheets [Walk-Through]
  • Importing Fails And How To Fix Them [Walk-Through]
  • An Introduction To Data Governance, Legislation, And Ethics [Slides]
  • Reference Card: Google Sheets Interface [PDF]
  • Reference Card: Importing And Exporting Data In Sheets [PDF]

An Introduction To Using Data

  • Lesson Overview: An Introduction To Using Data
  • Types Of Data [Video]
  • Reference Card: Types Of Data [PDF]
  • Selecting Data In Sheets [Walk-Through]
  • Practical Data Types [Walk-Through]
  • Data Visibility [Walk-Through]
  • Introduction To Formulas [Walk-Through]
  • Useful Formulas And Where To Use Them [Slides]
  • Row Calculations [Walk-Through]
  • Column Calculations [Walk-Through]
  • Reference Card: Useful Formulas And Where To Use Them [PDF]

Data Aggregations And Descriptive Statistics

  • Lesson Overview: Data Aggregations And Descriptive Statistics
  • Descriptive Statistics [Video]
  • Summarising Data [Slides]
  • Summarising Data [Walk-Through]
  • Measures Of Central Tendency [Video]
  • Measures Of Central Tendency [Walk-Through]
  • Measures Of Spread [Video]
  • Measures Of Spread [Walk-Through]
  • Pivot Tables [Slides]
  • An Introduction To Pivot Tables [Walk-Through]
  • Pivot Tables: Creating Subcategories [Walk-Through]
  • Pivot Tables: Sorting, Ordering And Filtering [Walk-Through]
  • Reference Card: Descriptive Statistics [PDF]

Sprint 2: SQL

Database Concepts

  • Lesson Overview: Database Concepts
  • Data Structures [Video]
  • What Is A Database? [Video]
  • Relational Database Management Systems [Slides]
  • Installing MySQL Workbench On Mac [Walk-Through]
  • Installing MySQL Workbench On Windows [Walk-Through]
  • Create A Database And Table Using The GUI [Walk-Through]
  • Creating Our First MySQL Connection [Walk-Through]
  • Reference Card: Navigating MySQL Workbench [PDF]

SQL Basics

  • Lesson Overview: SQL Basics
  • What Is SQL? [Video]
  • SQL Data Types [Video]
  • SQL Syntax And Best Practices [Slides]
  • Data Definition Language (DDL) [Slides]
  • Create And Alter A Database Table Using DDL [Walk-Through]
  • Data Manipulation Language (DML) [Slides]
  • Create, Insert, And Delete Data Using DML [Walk-Through]
  • Truncate Table, Import CSV Data And Update Records [Walk-Through]
  • Reference Card: DDL And DML Commands [PDF]

Sprint 3: Power BI

Introduction to Power BI

  • Module Overview: Introduction to Power BI

Sprint 4: Python

Introduction to Python

  • Module Overview: Introduction to Python

Sprint 5: Regression

Linear Models

  • Lesson Overview: Linear Models
  • The Line Of Best Fit [Video]
  • The Line Of Best Fit Equation [Video]
  • Introduction To Simple Linear Regression [Examples]
  • Least Squares Method [Video]
  • Least Squares Regression [Examples]
  • Simple Linear Regression Using Least Squares [Exercise]
  • Additional Resources: Linear Models [Markdown]

Model Performance

  • Lesson Overview: Model Performance
  • An Introduction To Model Accuracy And Metrics [Slides]
  • Understanding Common Challenges To Model Accuracy [Slides]
  • Reference Card: Accuracy [PDF]
  • The Train-Test Split [Examples]
  • Testing Model Performance [Exercise]
  • Additional Resources: Model Performance [Markdown]

Multiple Linear Regression

  • Lesson Overview: Multiple Linear Regression
  • Fitting A Multiple Linear Regression Model In Sklearn [Video]
  • Multiple Linear Regression: Advanced Regression Analysis [Video]
  • Multiple Linear Regression: Fitting A Model In Sklearn [Example]
  • Multiple Linear Regression: Advanced Regression Analysis 1 [Example]
  • Multiple Linear Regression: Advanced Regression Analysis 2 [Example]
  • Multiple Linear Regression [Exercise]
  • Additional Resources: Multiple Linear Regression [Markdown]

Variable Selection And Model Persistence

  • Lesson Overview: Variable Selection And Model Persistence
  • Variable Selection [Walk-Through]
  • Variables And Variable Selection [Examples]
  • Variables And Variable Selection [Exercise]
  • Additional Resources : Variables And Variable Selection [Markdown]
  • Saving And Restoring Models In Python [Walk-Through]
  • Saving And Restoring Models In Python [Examples]
  • Saving And Restoring Models In Python [Exercise]

Regularisation

  • Lesson Overview: Regularisation
  • Regularisation - Data Scaling [Examples]
  • Regularisation - Ridge [Video]
  • Regularisation - Ridge [Examples]
  • Regularisation - Lasso [Video]
  • Regularisation - LASSO [Examples]
  • Regularisation [Exercise]
  • Additional Resources: Regularisation [Markdown]
  • Multiple Linear Regression, Variable Selection And Regularisation [MCQ]

Decision Trees

  • Lesson Overview: Decision Trees
  • An Introduction To Decision Trees [Video]
  • Training A Decision Tree [Video]
  • Pruning A Decision Tree [Video]
  • Decision Trees With Sklearn [Examples]
  • Decision Trees [Exercise]

Ensemble Methods And Bootstrapping

  • Lesson Overview: Ensemble Methods And Bootstrapping
  • Parametric Methods, Ensembling And Boostrapping [Video]
  • Ensemble Methods [Video]
  • Understanding The Basics For Ensemble Methods [Examples]
  • Heterogeneous Ensemble Methods [Examples]
  • Homogeneous Ensemble Methods [Examples]
  • Ensemble Methods [Exercise]
  • Additional Resources: Parametric Models, Ensembling And Bootstrapping [Markdown]

Decision Tree Code Challenge

  • Decision Tree [Code Challenge]

Random Forests And Applying Our Knowledge

  • Lesson Overview: Random Forests And Applying Our Knowledge
  • The Random Forest [Video]
  • How The Random Forest Makes Predictions [Video]
  • Introduction To Random Forests [Examples]
  • The Random Forest [Exercise]
  • Additional Resources: Random Forests [Markdown]
  • Solving A Regression Problem [Examples]
  • Random Forest [Code Challenge]

Regression MCQ

  • Regression MCQ [Notebook]
  • Regression [MCQ]

Sprint Plan

Sprint 1: Google Sheets

Sprint Day Date Focus Daily Goals & Resources
Sheets 1 Apr 29 Getting Set Up For Preparing Data Dive into Integrated Project: Access To Drinking Water (Overview) [Slides]
Sheets 2 Apr 30 Importing Data in Sheets Understand Importing Data In Sheets [Walk-Through], Importing Fails And How To Fix Them [Walk-Through]
Sheets 3 May 1 An Introduction To Using Data Explore Types Of Data [Video], Selecting Data In Sheets [Walk-Through]
Sheets 4 May 2 Data Aggregations And Descriptive Statistics Study Descriptive Statistics [Video], Summarising Data [Slides]
Sheets 5 May 3 An Introduction To Data Visualisation Learn Data Visualisation Introduction [Video], Data Visualisation: Comparison [Video]
Sheets 6 May 4 Integrated Project: Access To Drinking Water (Part 1) Work on Integrated Project: Understanding The Data [Slides], Integrated Project: Access To Drinking Water (Part 1 - Understanding The Data) [MCQ]
Sheets 7 May 5 Data Formatting Understand Data Quality [Video], Formatting [Slides]

Sprint 2: SQL

Sprint Day Date Focus Daily Goals & Resources
SQL 1 May 6 Lesson Overview: Data Sources And Access Explore Sources Of Data [Slides], An Introduction To Spreadsheets [Slides]
SQL 2 May 7 An Introduction To Using Data Study Types Of Data [Video], Reference Card: Types Of Data [PDF]
SQL 3 May 8 Data Aggregations And Descriptive Statistics Learn Descriptive Statistics [Video], Summarising Data [Slides]
SQL 4 May 9 Data Visualisation Dive into Data Visualisation Introduction [Video], Data Visualisation: Comparison [Video]
SQL 5 May 10 Integrated Project: Access To Drinking Water (Part 1) Work on Integrated Project: Understanding The Data [Slides], Integrated Project: Access To Drinking Water (Part 1 - Understanding The Data) [MCQ]
SQL 6 May 11 Data Formatting Understand Data Quality [Video], Formatting [Slides]
SQL 7 May 12 Data Cleaning Explore Cleaning Techniques [Slides], NaN And Nulls [Walk-Through]

Sprint 3: Power BI

Sprint Day Date Focus Daily Goals & Resources
Power BI 1 May 13 Getting Started With Power BI Introduction to Power BI [Slides], Power BI Desktop [Walk-Through]
Power BI 2 May 14 Loading Data into Power BI Data Sources and Loading [Slides], Importing Data into Power BI [Walk-Through]
Power BI 3 May 15 Transforming Data in Power BI Data Transformation in Power BI [Slides], Data Transformation with Power Query [Walk-Through]
Power BI 4 May 16 Data Modeling in Power BI Data Modeling and Relationships in Power BI [Slides], Creating Relationships in Power BI [Walk-Through]
Power BI 5 May 17 Visualization Basics in Power BI Introduction to Data Visualization [Slides], Building Basic Visualizations [Walk-Through]
Power BI 6 May 18 Advanced Visualizations in Power BI Advanced Visualizations in Power BI [Slides], Building Advanced Visualizations [Walk-Through]
Power BI 7 May 19 Sharing and Publishing in Power BI Sharing and Publishing Reports in Power BI [Slides], Publishing Reports [Walk-Through]

Sprint 4: Python

Sprint Day Date Focus Daily Goals & Resources
Python 1 May 20 Getting Started with Python Introduction to Python [Slides], Setting Up Python Environment [Walk-Through]
Python 2 May 21 Basic Syntax and Data Structures Python Syntax Basics [Video], Basic Data Types and Structures [Walk-Through]
Python 3 May 22 Control Structures and Loops Control Structures: If, Else, and Loops [Slides], Looping in Python [Walk-Through]
Python 4 May 23 Functions and Modules Functions and Modules in Python [Slides], Defining and Using Functions [Walk-Through]
Python 5 May 24 File Handling and Error Handling File Handling in Python [Slides], Error Handling with Try-Except [Walk-Through]
Python 6 May 25 Python Libraries Overview of Python Libraries [Slides], Installing and Importing Libraries [Walk-Through]
Python 7 May 26 Data Manipulation with Pandas Introduction to Pandas [Slides], Data Manipulation with Pandas [Walk-Through]

Sprint 5: Regression

Sprint Day Date Focus Daily Goals & Resources
Regression 1 May 27 An Introduction To Machine Learning Introduction to Machine Learning [Video], An Introduction To Predictive Modelling [Video]
Regression 2 May 28 Linear Models Introduction to Simple Linear Regression [Examples], Least Squares Method [Video]
Regression 3 May 29 Model Performance An Introduction To Model Accuracy And Metrics [Slides], Testing Model Performance [Exercise]
Regression 4 May 30 Multiple Linear Regression Fitting A Multiple Linear Regression Model In Sklearn [Video], Multiple Linear Regression: Fitting A Model In Sklearn [Example]
Regression 5 May 31 Variable Selection And Model Persistence Variable Selection [Walk-Through], Saving And Restoring Models In Python [Walk-Through]
Regression 6 Jun 1 Regularisation Regularisation - Data Scaling [Examples], Regularisation - Ridge [Video]
Regression 7 Jun 2 Decision Trees An Introduction To Decision Trees [Video], Training A Decision Tree [Video]