Data Science is a hot field but one that not many people understand how it works. This is because scientific analysis requires specialized research skills. The good news is, the skills required are ones you already have. With this knowledge, your next logical step would be to look for a guide that shows you how you can use these skills to benefit yourself, right? Well you came to the right place! This book teaches students what tools they can use to solve data science problems and how to effectively apply those tools.

## What you’ll learn in Data Science A-Z™: Real-Life Data Science Exercises Included

1. Complete a complex Data Science project successfully.
2. Tableau Visualizations for Beginners
3. In Tableau, conduct data mining.
4. Understand the Chi-Squared statistical test and how to apply it.
5. Create Linear Regressions Using the Ordinary Least Squares Method
6. For all types of models, evaluate R-Squared.
7. For all types of models, look at the Adjusted R-Squared.
8. Make an SLR (Simple Linear Regression).
9. Make an MLR (Multiple Linear Regression).
10. Dummy Variables should be created.
11. MLR coefficients should be interpreted in a certain way.
12. For created models, read the output of statistical software.
13. To create statistical models, employ the methods of backward elimination, forward selection, and bidirectional elimination.
14. Make a Logistic Regression diagram.
15. Recognize a Logistic Regression from the start.
16. Recognize the difference between False Positives and False Negatives.
17. Consult a Matrix of Confusion.
18. Make a Geodemographic Segmentation Model that is Robust
19. For modeling purposes, convert independent variables.
20. For modeling purposes, generate new independent variables.
21. Using VIF and the correlation matrix, look for multicollinearity.
22. Recognize multicollinearity’s underlying logic
23. Models should be evaluated using the Cumulative Accuracy Profile (CAP).
24. In Excel, create a CAP curve.
25. To build robust models, use Training and Test data.
26. Use the CAP curve to gain insight.
27. Recognize the Ratio of Odds
28. The coefficients of a logistic regression can be used to gain business insight.
29. Learn how to recognize the signs of model deterioration.
30. To keep your model from deteriorating, use three levels of maintenance.
31. SQL Server: How to Set Up and Use
32. Microsoft Visual Studio Shell is a program that allows you to install and use Microsoft Visual Studio.
33. Look for anomalies in the data and clean it up.
34. To import data into a database, use SQL Server Integration Services (SSIS).
35. In SSIS, you can make conditional splits.
36. Text Qualifier Errors in RAW Data: How to Deal With Them
37. In SQL, you can make scripts.
38. Data Science projects can benefit from SQL.
39. SQL procedures can be created.

## Requirements

• Only a strong desire to succeed will get you anywhere.
• This course’s software is either free or available in a demo version.

## Description

Extremely practical… unbelievably real!
This isn’t one of those fluff classes where everything goes as planned and your training is a breeze; instead, you’ll be thrown into the deep end.
This course will expose you to all of the PAIN that a Data Scientist faces on a daily basis, including corrupt data, anomalies, and irregularities.

This course has pre-designed pathways that allow you to navigate the course and combine sections to create YOUR OWN journey that will provide you with the skills you require.
You can also complete the entire course and prepare yourself for a successful Data Science career.

## Who this course is for:

• 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
File name: Data-Science-A-Z™-Real-Life-Data-Science-Exercises-Included.rar 12.14 gb 21 hours Kirill Eremenko , Ligency Team Anglais