Data Science A-Z- Hands-On Exercises ChatGPT Prize [2024]

Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2024], available at $129.99, has an average rating of 4.58, with 235 lectures, 1 quizzes, based on 34111 reviews, and has 218932 subscribers.

You will learn about 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 This course is ideal for individuals who are Anybody with an interest in Data Science or Anybody who wants to improve their data mining skills or Anybody who wants to improve their statistical modelling skills or Anybody who wants to improve their data preparation skills or Anybody who wants to improve their Data Science presentation skills It is particularly useful for Anybody with an interest in Data Science or Anybody who wants to improve their data mining skills or Anybody who wants to improve their statistical modelling skills or Anybody who wants to improve their data preparation skills or Anybody who wants to improve their Data Science presentation skills.

Enroll now: Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2024]

Summary

Title: Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2024]

Price: $129.99

Average Rating: 4.58

Number of Lectures: 235

Number of Quizzes: 1

Number of Published Lectures: 217

Number of Published Quizzes: 1

Number of Curriculum Items: 236

Number of Published Curriculum Objects: 218

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Anybody with an interest in Data Science

Anybody who wants to improve their data mining skills

Anybody who wants to improve their statistical modelling skills

Anybody who wants to improve their data preparation skills

Anybody who wants to improve their Data Science presentation skills

Target Audiences

Anybody with an interest in Data Science

Anybody who wants to improve their data mining skills

Anybody who wants to improve their statistical modelling skills

Anybody who wants to improve their data preparation skills

Anybody who wants to improve their Data Science presentation skills

Extremely Hands-On Incredibly Practical Unbelievably Real!

This is not one of those fluffy classes where everything works out just the way it should and your training is smooth sailing. This course throws you into the deep end.

In this course you WILL experience firsthand all of the PAIN a Data Scientist goes through on a daily basis. Corrupt data, anomalies, irregularities – you name it!

This course will give you a full overview of the Data Science journey. Upon completing this course you will know:

How to clean and prepare your data for analysis

How to perform basic visualisation of your data

How to model your data

How to curve-fit your data

And finally, how to present your findings and wow the audience

This course will give you so much practical exercises that real world will seem like a piece of cake when you graduate this class. This course has homework exercises that are so thought provoking and challenging that you will want to cry But you won’t give up! You will crush it. In this course you will develop a good understanding of the following tools:

SQL

SSIS

Tableau

Gretl

This course has pre-planned pathways. Using these pathways you can navigate the course and combine sections into YOUR OWN journey that will get you the skills that YOU need.

Or you can do the whole course and set yourself up for an incredible career in Data Science.

The choice is yours. Join the class and start learning today!

See you inside,

Sincerely,

Kirill Eremenko

Course Curriculum

Chapter 1: Get Excited

Lecture 1: Welcome Challenge!

Lecture 2: Welcome to Data Science A-Z&

Lecture 3: Get the Datasets here

Chapter 2: What is Data Science?

Lecture 1: Intro (what you will learn in this section)

Lecture 2: Profession of the future

Lecture 3: Areas of Data Science

Lecture 4: IMPORTANT: Course Pathways

Lecture 5: EXTRA: Success Story

Lecture 6: EXTRA: ChatGPT For Data Science

Chapter 3: Part 1: Visualisation

Lecture 1: Welcome to Part 1

Chapter 4: Introduction to Tableau

Lecture 1: Intro (what you will learn in this section)

Lecture 2: Installing Tableau Desktop and Tableau Public (FREE)

Lecture 3: Challenge description + view data in file

Lecture 4: Connecting Tableau to a Data file – CSV file

Lecture 5: Navigating Tableau – Measures and Dimensions

Lecture 6: Creating a calculated field

Lecture 7: Adding colours

Lecture 8: Adding labels and formatting

Lecture 9: Exporting your worksheet

Lecture 10: Section Recap

Chapter 5: How to use Tableau for Data Mining

Lecture 1: Intro (what you will learn in this section)

Lecture 2: Get the Dataset + Project Overview

Lecture 3: Connecting Tableau to an Excel File

Lecture 4: How to visualise an AB test in Tableau?

Lecture 5: Working with Aliases

Lecture 6: Adding a Reference Line

Lecture 7: Looking for anomalies

Lecture 8: Handy trick to validate your approach / data

Lecture 9: Section Recap

Chapter 6: Advanced Data Mining With Tableau

Lecture 1: Intro (what you will learn in this section)

Lecture 2: Creating bins & Visualizing distributions

Lecture 3: Creating a classification test for a numeric variable

Lecture 4: Combining two charts and working with them in Tableau

Lecture 5: Validating Tableau Data Mining with a Chi-Squared test

Lecture 6: Chi-Squared test when there is more than 2 categories

Lecture 7: Quick Note

Lecture 8: Visualising Balance and Estimated Salary distribution

Lecture 9: Extra: Chi-Squared Test (Stats Tutorial)

Lecture 10: Extra: Chi-Squared Test Part 2 (Stats Tutorial)

Lecture 11: Section Recap

Lecture 12: Part Completed

Chapter 7: Part 2: Modelling

Lecture 1: Welcome to Part 2

Chapter 8: Stats Refresher

Lecture 1: Intro (what you will learn in this section)

Lecture 2: Types of variables: Categorical vs Numeric

Lecture 3: Types of regressions

Lecture 4: Ordinary Least Squares

Lecture 5: R-squared

Lecture 6: Adjusted R-squared

Chapter 9: Simple Linear Regression

Lecture 1: Intro (what you will learn in this section)

Lecture 2: Introduction to Gretl

Lecture 3: Get the dataset

Lecture 4: Import data and run descriptive statistics

Lecture 5: Reading Linear Regression Output

Lecture 6: Plotting and analysing the graph

Chapter 10: Multiple Linear Regression

Lecture 1: Intro (what you will learn in this section)

Lecture 2: Get the dataset

Lecture 3: Assumptions of Linear Regression

Lecture 4: Dummy Variables

Lecture 5: Dummy Variable Trap

Lecture 6: Understanding the P-Value

Lecture 7: Ways to build a model: BACKWARD, FORWARD, STEPWISE

Lecture 8: Backward Elimination – Practice time

Lecture 9: Using Adjusted R-squared to create Robust models

Lecture 10: Interpreting coefficients of MLR

Lecture 11: Section Recap

Chapter 11: Logistic Regression

Lecture 1: Intro (what you will learn in this section)

Lecture 2: Get the dataset

Lecture 3: Binary outcome: Yes/No-Type Business Problems

Lecture 4: Logistic regression intuition

Lecture 5: Your first logistic regression

Lecture 6: False Positives and False Negatives

Lecture 7: Confusion Matrix

Lecture 8: Interpreting coefficients of a logistic regression

Chapter 12: Building a robust geodemographic segmentation model

Lecture 1: Intro (what you will learn in this section)

Lecture 2: Get the dataset

Lecture 3: What is geo-demographic segmenation?

Lecture 4: Lets build the model – first iteration

Lecture 5: Lets build the model – backward elimination: STEP-BY-STEP

Lecture 6: Transforming independent variables

Lecture 7: Creating derived variables

Lecture 8: Checking for multicollinearity using VIF

Lecture 9: Correlation Matrix and Multicollinearity Intuition

Lecture 10: Model is Ready and Section Recap

Chapter 13: Assessing your model

Lecture 1: Intro (what you will learn in this section)

Lecture 2: Accuracy paradox

Lecture 3: Cumulative Accuracy Profile (CAP)

Instructors

Kirill Eremenko
DS & AI Instructor

SuperDataScience Team
Helping Data Scientists Succeed

Ligency Team
Helping Data Scientists Succeed

Rating Distribution

1 stars: 211 votes

2 stars: 505 votes

3 stars: 2787 votes

4 stars: 11342 votes

5 stars: 19265 votes

Frequently Asked Questions

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