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Statistics for Data Science using Python

Statistics you need in the office: Core of Statistics, Inferential & Descriptive statistics, Hypothesis testing,
Rating: 3.9 out of 53.9 (148 ratings)
20,136 students
Created by Shan Singh
Last updated 11/2020
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Understand the fundamentals of statistics
  • Understand the Stats concepts needed for data science using Python
  • Distinguish and work with different types of distributions
  • Calculate the measures of central tendency, asymmetry, and Skewness in Data
  • Under-stand Hypothesis Testing & its use-cases too
  • Get hands-on stats
  • if you do have a math background, you’ll definitely enjoy this fun, hands-on method too.

Requirements

  • There is no pre-requisite of this course ,we just want dedication.
  • We will start from the basics and gradually build up your knowledge.

Description

If You want to be a Data Scientist or Data Analyst then brushing up on your statistics skills is something you need to do.

But it's just hard to get started with Data Science in most of the course you will find theoritical knowledge on stats not having practical knowledge

I have explained Each topic in a easiest way as well as its implementation in Python from Scratch (most demanding language of Data Science Industry)

That's exactly why I have created this course for you!

Here you will quickly get the  essential stats knowledge for a Data Scientist or Analyst.

I have included real-world use-cases of business challenges to show you how you could apply Stats knowledge to boost your career.

At the same time you can master topics such as Descriptive Stats, distributions, z-test, the Central Limit Theorem, hypothesis testing,  & many more!

So what are you waiting for?

Enroll now and & get a transition into Data Science


Why should you take this course?

  • This course is the one course you take in statistic that is equipping you with the actual knowledge you need in statistics if you work with data

  • This course is taught by an actual mathematician that is in the same time also working as a data scientist.

  • This course is balancing both: theory & practical real-life example.

  • After completing this course you ll have everything you need to master the fundamentals in statistics & probability need in data science or data analysis.

Who this course is for:

  • People who want a career in Data Science whether from Technical as well as non-Technical domain
  • People who want to start learning statistics For Data Science
  • Anybody who wants to get hands-on experience with stats

Course content

6 sections • 47 lectures • 8h 1m total length

  • Preview01:22

  • Preview06:46
  • Preview05:43
  • What is Python & need of Python in Data Science!
    07:35
  • How Python works
    06:45
  • Installation of Anaconda Navigator
    08:32

  • Preview07:48
  • Types of Statistics
    09:20
  • What are Outliers & Measures of Central Tendancy(Mean,Median,Mode) ?
    12:38
  • Mean,Mode Median implementation using Python
    05:41
  • Measures of Spread (Variance,Standard Dev. ,Range,Inter-Quantile Range)
    24:32
  • Outliers Detection and Removal using Python
    11:57
  • Skewness in Data
    13:26

  • Frequency Tables & Histogram
    05:11
  • Frequency Tables & Histogram in Python
    07:38
  • Types of Analysis
    17:44
  • Types of Analysis in Python
    09:53
  • Covariance and Co-relation
    14:24
  • Co-relation using Python
    05:03

  • Intro to Probability
    08:59
  • What is Prob. Density Function(PDF) & Cumalative Demnsity Function(CDF) ?
    11:17
  • Bayes Theorem
    12:32
  • What is a Distribution and why we use it?
    05:03
  • Bionomial Distribution
    09:41
  • Binomial Dist. in Python
    07:01
  • Poisson's Distribution
    06:29
  • Poisson Distribution in Python
    04:18
  • Normal Distribution
    15:06
  • Normal Distribution in Python
    03:18
  • Implementation of Z-score(Standarization) in python
    08:35
  • Log-Normal Distribution and Heavy-Tailed Distribution
    08:50
  • Log-Normal and Heavy Tailed Dist.. in Python
    06:44
  • Q-Q Plot
    05:24
  • Central Limit Theorem
    05:27
  • Central Limit Theorem implemntation in Python
    13:36
  • Chebyshew's Inequality
    09:30
  • Estimation Problem based on Z-stats
    14:37

  • Hypothesis testing
    09:17
  • 1-tailed and 2-Tailed Test
    08:24
  • Critical_Region
    15:30
  • Hypothesis Table
    20:30
  • Level of Significance
    04:20
  • P-value
    05:30
  • T-test and various types of T-test
    11:09
  • T-test in Python
    21:43
  • Chi-Square Test
    15:43
  • Anova Test
    31:19

Instructor

Shan Singh
Data Scientist
Shan Singh
  • 4.3 Instructor Rating
  • 710 Reviews
  • 85,913 Students
  • 9 Courses

Professionally, I am a Data Scientist having experience of 6 years in finance, retail and transport.From my courses you will straight away notice how I combine my own experience to deliver content in a easiest fashion. To sum up, I am absolutely passionate about Data Analytics and I am looking forward to sharing my own knowledge with you!

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