Data Science A-Z™: Hands-On Exercises & ChatGPT Bonus 
Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!
Created by Kirill Eremenko, Ligency Team | 21 hours on-demand video course
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.
What you’ll 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
Machine Learning A-Z™: AI, Python & R + ChatGPT Bonus 
Deep Learning A-Z™ 2023: Neural Networks, AI & ChatGPT Bonus
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