**Statistics for Data Science using Python**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 3.5 Hours | 1.28 GB

eLearning | Skill level: All Levels

This training is an introduction to the concept of statistical concepts that are very important to Data science domain and its application using Python.

Many of us have heard it that statistics is one the next sexy jobs that is coming up in the career opportunities (this fact is vouched by even Hal Varian). Almost five years Tim O’Reilly said that data is the next big thing to happen in the world. But what exactly is data and why is it so important? And why is there so much importance being given to statistics and data in the world today?

The web is full of apps that are driven by data. All the e-commerce apps and websites are based on data in the complete sense. There is database behind a web front end and middleware that talks to a number of other databases and data services. But the mere use of data is not what comprises of data science. A data application gets its value from data and in the process creates value for itself. This means that data science enables the creation of products that are based on data.

What Will I Learn?

- Software Engineers
- IT operations
- Technical managers

**+ Table of Contents**

**Introduction**

1 Introduction to Data Science

**Calculating Mode**

2 Calculating Mode

3 Calculating Dispersion Measures

4 Histogram Calculation

5 Correlation Function

6 Basic Concept of Statistics

7 Pandas Data Frame

**Basic Techniques**

8 Basic Reveration Techniques

9 Using Numphy Techniques

10 Summation of Elements

**Testing Method**

11 Hypothetical Testing Method

12 Differences in Numphy Package

13 Calculating the Denominator

**Exclusive Events**

14 Using Exclusive Events

15 Finding the Measurement

16 Implementing Test Scenarios

**Statistics for Data Science**

17 Ordinary Least Square Regression Techniques

18 Analyzing the Test Statistics

19 Output of the Variables

20 Multiple Explanatory Variables

21 Fitting the Model

22 Fitting the Model Continues

23 Curve Fitting and Regression Fit Line

24 Co efficient and Intercept Value

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