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Statistics Essentials with Python

Home Courses Statistics Essentials with Python
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Course Overview

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

14
2 - 6 hours 3h 23m | 24 Videos | 41210 Views | Intermediate  Intermediate
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Statistics for Data Science 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.

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

Target Customers:

  • Anyone who wants to learn about data and analytics
  • Data Engineers
  • Analysts
  • Architects
  • Software Engineers
  • IT operations
  • Technical managers

Pre-Requisites:

  • No prior knowledge of machine learning required
  • Basic knowledge of Python

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Curriculum

1. Introduction

  • Introduction to Data Science

2. Calculating Mode

  • Calculating Mode
  • Calculating Dispersion Measures
  • Histogram Calculation
  • Correlation Function
  • Basic Concept of Statistics
  • Pandas Data Frame

3. Basic Techniques

  • Basic Reveration Techniques
  • Using Numphy Techniques
  • Summation of Elements

4. Testing Method

  • Hypothetical Testing Method
  • Differences in Numphy Package
  • Calculating the Denominator

5. Exclusive Events

  • Using Exclusive Events
  • Finding the Measurement
  • Implementing Test Scenarios

6. Statistics for Data Science

  • Ordinary Least Square Regression Techniques
  • Analyzing the Test Statistics
  • Output of the Variables
  • Multiple Explanatory Variables
  • Fitting the Model
  • Fitting the Model Continues
  • Curve Fitting and Regression Fit Line
  • Co efficient and Intercept Value
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