Course Includes
 Recorded Lessons: 37
 Recorded Hours: 3
 Duration: 3 days (Avg)
Course Features
 Access on mobile
 TDP Assessment Test
Top Skills Covered
Overview
Course Description
"Statistics and Hypothesis Testing for Data Science" is a comprehensive course designed to equip learners with the fundamental statistical knowledge and data analysis skills essential for success in the field of data science.
The course begins by emphasizing the crucial role of statistics in deriving datadriven insights, laying the foundation for understanding and interpreting information effectively. Through practical examples and handson exercises, participants will develop proficiency in Python, a key tool for data manipulation and visualization in the industry.
Participants will learn to categorize data effectively, facilitating meaningful analysis. Key statistical measures such as mean, median, and mode will be covered, enabling learners to summarize data accurately. Additionally, concepts such as range, variance, and standard deviation will be explored to understand the variability inherent in datasets.
The course delves into understanding relationships between variables through correlation and covariance analysis. Techniques like quartiles and percentiles will be employed to grasp the shape and distribution of data, providing insights into its characteristics.
Participants will also learn to standardize data and calculate zscores, essential for comparative analysis across different datasets. Probability theory will be introduced, starting with foundational concepts in set theory and progressing to practical applications, including Bayesian probability.
The course will enable learners to solve complex counting problems effortlessly and understand the role of random variables in probability calculations. Various probability distributions, such as the normal distribution and binomial distribution, will be explored, along with their realworld applications in data science scenarios.
Ultimately, the course aims to empower learners with the knowledge and skills necessary to analyze data effectively, make informed decisions, and apply statistical methods confidently in a data science context. Whether participants are beginners or seeking to deepen their statistical expertise, this course serves as a comprehensive gateway to mastering statistics for data science. Enroll now and embark on your journey to becoming a proficient data scientist!
What you'll learn
 Fundamental concepts and importance of statistics in various fields.
 How to use statistics for effective data analysis and decisionmaking.
 Introduction to Python for statistical analysis, including data manipulation and visualization.
 Different types of data and their significance in statistical analysis.
 Measures of central tendency, spread, dependence, shape, and position.
 How to calculate and interpret standard scores and probabilities.
 Key concepts in probability theory, set theory, and conditional probability.
 Understanding Bayes' Theorem and its applications.
 Permutations, combinations, and their role in solving realworld problems.
 Practical knowledge of various statistical tests, including ttests, chisquared tests, and ANOVA, for hypothesis testing and inference.
Requirements
 Access to a computer with internet connectivity.
 A basic understanding of mathematics, including algebra and arithmetic.
 Familiarity with fundamental concepts in data analysis and problemsolving.
 A willingness to learn and engage with statistical concepts and Python programming.
 Basic knowledge of Python is a plus but not mandatory.
Course Content
31 Lessons  1 Downloadable material  5 Quiz  3:00 Total hours
Introduction to Statistics

Resources  Statistics And Hypothesis Testing For Data Science

Introduction to Statistics and its importance
00:05:58 
Explain the role of statistics in data analysis

Introduction to Python for Statistical Analysis
00:05:31 
Quiz on Introduction to Statistics
Introduction to Descriptive Statistics

Types of Data
00:07:58 
Measures of Central Tendency
00:06:18 
Measures of Spread
00:04:37 
Measures of Dependence
00:05:06 
Measures of Shape and Position
00:09:37 
Measures of Standard Scores
00:04:56 
Quiz on Descriptive Statistics
Introduction to Basic and Conditional Probability

Introduction to Basic Probability

Introduction to Set Theory

Introduction to Conditional Probability
00:05:12 
Introduction to Bayes Theorem
00:08:23 
Introduction to Permutations and Combinations

Introduction to Random Variables

Introduction to Probability Distribution Functions
00:13:43 
Quiz on Basic and Conditional Probability
Introduction to Inferential Statistics

Introduction to Normal Distribution
00:13:32 
Introduction to Skewness and Kurtosis
00:09:01 
Introduction to Statistical Transformations
00:12:46 
Introduction to Sample and Population Mean
00:04:57 
Introduction to Central Limit Theorem
00:05:08 
Introduction to Bias and Variance
00:07:28 
Introduction to Maximum Likelihood Estimation
00:06:53 
Introduction to Confidence Intervals

Introduction to Correlations
00:18:27 
Introduction to Sampling Methods

Quiz on Inferential Statistics
Introduction to Hypothesis Testing

Fundamentals of Hypothesis Testing
00:07:09 
Introduction to T Tests
00:09:00 
Introduction to Z Tests
00:05:37 
Introduction to Chi Squared Tests

Introduction to Anova Tests

Quiz on Hypothesis Testing
Frequently asked questions
Who is this course for?
Professionals looking to improve their skills in using statistics for making datadriven decisions.
What are the benefits of taking this course?
Develop skills for hypothesis testing and making datadriven decisions. Gain a solid foundation in statistical concepts crucial for data science.
What are the prerequisites for this course?
You should have a basic understanding of mathematics (such as algebra) and familiarity with programming languages like Python or R, as they are commonly used in data science.
What are the key topics covered in this course?
This course covers essential topics such as descriptive statistics, probability distributions, inferential statistics, hypothesis testing, pvalues, confidence intervals, ANOVA, correlation, and regression analysis, among others.
Do I need a math background for this course?
A basic understanding of algebra and probability will be helpful, but the course is designed to make statistical concepts accessible to beginners in data science with stepbystep guidance.
About the instructor
Meritshot Zetta Edutech Private Limited
Institute
4 Courses
2+ Lesson
4 Students enrolled