Statistics for Analytics and Data Science

Learn Statistics and Build your Career in Analytics and Data Science
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Statistics for Analytics and Data Science.

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4.8/5 Stars

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Course Outline

A comprehensive list of all sections & lectures for this course can be found below.

Introduction

About the course - 03:08 [Play]

Data Types - 11:46 [Play]

Frequency Types - 04:47

Distributional Shapes - 03:16

Central Tendency - 12:01

Describing Spread - 14:02

Chapter Summary - 01:38

Correlation and Causation - 15:03 [Play]

Introduction to Simple Linear Regression - 05:44

Implementing Simple Linear Regression - 22:05

Assumptions of Simple Linear Regression - 10:46

Chapter Summary - 02:09

Section Breakdown - 00:53 [Play]

Introduction to Probability and Randomness - 03:33

Sets, Independence, Conditional Probabilities - 11:42

Joint Probabilities & Bayes Law - 10:30

Chapter Summary - 01:44

Introduction to Random Variables - 02:21 [Play]

Introducing Probability Distributions - 01:18

Discrete Distributions - 07:14

Continuous Distributions - 07:08

Cumulative Probabilities and Discrete - 03:26

Cumulative Continuous - 04:24

Chapter Summary - 01:58

Introduction - 00:52 [Play]

Why Sample? - 16:48

Sampling Estimators - 08:26

Chapter Summary - 01:52

Introduction to Confidence Intervals - 10:22 [Play]

Example 1 - 04:12

Example 2 - 06:12

Example 3 - 03:58

Sample Sizes & Inference - 03:06

Chapter Summary - 01:23

Introduction to Hypothesis Testing - 04:07 [Play]

The Hypothesis Testing Process - 04:25

Assumptions Formulae and Examples - 04:36

Example 1 - 04:18

Example 2 - 03:10

Example 3 - 03:23

Confidence Intervals - 02:30

Errors - 03:36

Chapter Summary - 01:39

Course Description

Effectively working with numbers is now becoming one of the most valuable skills in the job market. Organizations are now dependent on data analysis to make sure every integral part of the business is running at optimal efficiency. If the numbers don’t add up, costly mistakes can easily occur. This is true in almost any industry including supply chain management, production and manufacturing.

Statistics encompasses the collection, analysis, and interpretation of data and provides a framework for thinking about data. Statistics is used in many areas of scientific and social research. It is critical to business and manufacturing, and provides the mathematical foundation for machine learning and data mining.

Students will gain a comprehensive introduction to the concepts and techniques of statistics as applied to a wide variety of disciplines.

This course covers basic statistics, such as calculating averages, medians, modes, and standard deviations. With easy-to-understand examples combined with real-world applications from the worlds of business, sports, education, entertainment, and more. 

Students are taught the skills and knowledge needed to start analyzing data. We explore how to use data and apply statistics to real-life problems and situations

Furthermore, this course offers an in-depth look into interpreting the relationships between different types of data to make effective decisions. Students will work with several key concepts including:

  • Descriptive Statistics
  • Measures of Central Tendency
  • Correlation
  • Linear Regression
  • Probability Theory
  • Discrete and Continuous Probability Distributions
  • Sampling Distributions
  • Confidence Intervals
  • Significance Tests

  • Module 1:

    Here, students explore one variable analysis. The module starts with discussing the various descriptive statistics and methods to represent data. Students are introduced to frequency tables, and move on to learning essential measures of central tendency and their interpretations. 

    Module 2:

    Focuses on Correlation and OLS Regression and shows how these can be calculated in Excel using various formulae. Students also explore the interpretation of these results.

    Module 3:

    Students explore Probability theory like sample space, events, randomness and basic set theory. The course then moves onto discussing conditional probability and introduces the concept of mutually exclusive events.

    Module 4:

    This module examines Probability Distributions and explores the difference between a continuous and a discrete distribution focusing on Normal and Binomial distributions.

    Module 5:

    Module five focuses on Sampling Distributions and introduces the concept of Central Limit Theorem and rationalizes the importance of sampling. We will also link the Central Limit theorem to the normal distribution.

    Module 6:

    Students explore confidence intervals and the use of intervals to determine the center of a distribution as opposed to a point estimate such as the mean. We will also discuss the relationship between the significance level (alpha) and the confidence level.

    Module 7:

    The last module will introduce the concepts of Significance Tests - what they are and aren't (i.e. you can't prove anything); how to define the null and alternate hypotheses correctly; and getting the direction and 'tails' of your distribution correct. Finally, we explore Type I and Type II errors with examples and interpretations.


    Statistics for Analytics and Data Science

    All course reviews are written by students who have completed the course or are currently enrolled.

    Course Instructor - Sachin Dean

    Sachin Dean
    Teaching 4 Courses

    Sachin Dean is currently teaching 4 courses. All courses are currently open for enrollment.

    10,235 Enrollments

    Sachin Dean currently has 10,235 global enrollments across 4 courses that are active on the platform.

    4.5 Star Rating

    Sachin Dean has an average rating of 4.5/5 stars, across 4 courses.

    Whether you're looking to launch your first career initiative or you are looking to enhance your library of skills, we offer an extensive range of data analysis courses to skyrocket your career in analytics!



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