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 data-driven insights, laying the foundation for understanding and interpreting information effectively. Through practical examples and hands-on 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 z-scores, 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 real-world 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 decision-making.
- 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 real-world problems.
- Practical knowledge of various statistical tests, including t-tests, chi-squared 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 problem-solving.
- 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 data-driven decisions.
What are the benefits of taking this course?
Develop skills for hypothesis testing and making data-driven 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, p-values, 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 step-by-step guidance.
About the instructor
Meritshot Zetta Edutech Private Limited
Institute
4 Courses
2+ Lesson
4 Students enrolled