Statistics for Data Science using Python

Statistics for Data Science using Python
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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