Data Science A-Z™: Hands-On Exercises & ChatGPT Bonus [2023] Course

Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!

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 Data Science A-Z™: Hands-On Exercises & ChatGPT Bonus 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 visualization 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 Data Science A-Z™: Hands-On Exercises & ChatGPT Bonus 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.

Best Seller Course: The Data Science Course 2023: Complete Data Science Bootcamp

What you’ll learn

• Successfully perform all steps in a complex Data Science project
• Create Basic Tableau Visualizations
• 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.

Recommended Course: Math 0-1: Calculus for Data Science & Machine Learning

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