Data Science A-Z™: Hands-On Exercises & ChatGPT Bonus (2023)

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About Course

Unlock the secrets of Data Science with this comprehensive and hands-on course, completely free on Theetay! Get ready to dive deep into the real-world challenges and complexities that Data Scientists face daily.

This course, originally from Udemy, will equip you with the skills to clean, prepare, visualize, model, and analyze data, culminating in a confident ability to present your findings. You’ll gain practical experience with essential tools like SQL, SSIS, Tableau, and Gretl, tackling thought-provoking exercises that will challenge your understanding and build your expertise.

Explore pre-planned pathways to personalize your learning journey, focusing on the skills most relevant to your career goals. Or, embark on the entire course and build a solid foundation for a successful career in Data Science.

This is your chance to learn from Kirill Eremenko, a renowned instructor, and join a vibrant community of learners. Start your Data Science journey today – it’s completely free on Theetay!

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What Will You Learn?

  • Successfully perform all steps in a complex Data Science project
  • Create Basic Tableau Visualisations
  • Perform Data Mining in Tableau
  • Understand how to apply the Chi-Squared statistical test
  • Apply Ordinary Least Squares method to Create Linear Regressions
  • Assess R-Squared for all types of models
  • Assess the Adjusted R-Squared for all types of models
  • Create a Simple Linear Regression (SLR)
  • Create a Multiple Linear Regression (MLR)
  • Create Dummy Variables
  • Interpret coefficients of an MLR
  • Read statistical software output for created models
  • Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models
  • Create a Logistic Regression
  • Intuitively understand a Logistic Regression
  • Operate with False Positives and False Negatives and know the difference
  • Read a Confusion Matrix
  • Create a Robust Geodemographic Segmentation Model
  • Transform independent variables for modelling purposes
  • Derive new independent variables for modelling purposes
  • Check for multicollinearity using VIF and the correlation matrix
  • Understand the intuition of multicollinearity
  • Apply the Cumulative Accuracy Profile (CAP) to assess models
  • Build the CAP curve in Excel
  • Use Training and Test data to build robust models
  • Derive insights from the CAP curve
  • Understand the Odds Ratio
  • Derive business insights from the coefficients of a logistic regression
  • Understand what model deterioration actually looks like
  • Apply three levels of model maintenance to prevent model deterioration
  • Install and navigate SQL Server
  • Install and navigate Microsoft Visual Studio Shell
  • Clean data and look for anomalies
  • Use SQL Server Integration Services (SSIS) to upload data into a database
  • Create Conditional Splits in SSIS
  • Deal with Text Qualifier errors in RAW data
  • Create Scripts in SQL
  • Apply SQL to Data Science projects
  • Create stored procedures in SQL
  • Present Data Science projects to stakeholders

Course Content

Get Excited

  • A Message from the Professor
  • Welcome to Data Science AZ™
    04:41

What is Data Science

Part 1 Visualisation

Introduction to Tableau

How to use Tableau for Data Mining

Advanced Data Mining With Tableau

Part 2 Modelling

Stats Refresher

Simple Linear Regression

Multiple Linear Regression

Logistic Regression

Building a robust geodemographic segmentation model

Assessing your model

Drawing insights from your model

Model maintenance

Part 3 Data Preparation

Business Intelligence BI Tools

ETL Phase 1 Data Wrangling before the Load

ETL Phase 2 Stepbystep guide to uploading data using SSIS

Handling errors during ETL Phases 1 & 2

SQL Programming for Data Science

ETL Phase 3 Data Wrangling after the load

Handling errors during ETL Phase 3

Part 4 Communication

Working with people

Presenting for Data Scientists

Homework Solutions

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