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Statistics And Hypothesis Testing For Data Science

"Mastering Data Analysis and Making Informed Decisions with Statistical Hypothesis Testing in Data Science".

Meritshot Zetta Edutech Private Limited

Institute

Included with Data Science & ML

What you will 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.

This Course Includes

  • Recorded Lessons:  37
  • Recorded Hours:  3
  • Duration:   3 days (Avg)
  • TD Assessment Available
  • Access on Mobile

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. 

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

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.

Course Content

31 Lessons | 1 Document | 5 Quiz | 4hr 15min

Frequently Asked Questions

Professionals looking to improve their skills in using statistics for making data-driven decisions.

Develop skills for hypothesis testing and making data-driven decisions. Gain a solid foundation in statistical concepts crucial for data science.

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.

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.

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.
Course Preview
(4.7)
4hr 15min
₹399 ₹5000
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