Statistics And Hypothesis Testing For Data Science

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

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.

Description

Welcome to "Statistics and Hypothesis Testing for Data Science" – a comprehensive Udemy course that will empower you with the essential statistical knowledge and data analysis skills needed for success in the world of data science. Here's what you'll learn:Delve into the world of data-driven insights and discover how statistics plays a pivotal role in shaping our understanding of information.Equip yourself with the essential Python skills required for effective data manipulation and visualization.Learn to categorize data, setting the stage for meaningful analysis.Discover how to summarize data with measures like mean, median, and mode.Explore the variability in data using concepts like range, variance, and standard deviation.Understand relationships between variables with correlation and covariance.Grasp the shape and distribution of data using techniques like quartiles and percentiles.Learn to standardize data and calculate z-scores.Dive into probability theory and its practical applications.Lay the foundation for probability calculations with set theory.Explore the probability of events under certain conditions.Uncover the power of Bayesian probability in real-world scenarios.Solve complex counting problems with ease.Understand the concept of random variables and their role in probability.Explore various probability distributions and their applications.This course will empower you with the knowledge and skills needed to analyze data effectively, make informed decisions, and apply statistical methods in a data science context. Whether you're a beginner or looking to deepen your statistical expertise, this course is your gateway to mastering statistics for data science. Enroll now and start your Journey!

Overview

Section 1: Introduction to Statistics

Lecture 1 Introduction to Statistics and its importance

Lecture 2 Explain the role of statistics in data analysis

Lecture 3 Introduction to Python for Statistical Analysis

Section 2: Introduction to Descriptive Statistics

Lecture 4 Types of Data

Lecture 5 Measures of Central Tendency

Lecture 6 Measures of Spread

Lecture 7 Measures of Dependence

Lecture 8 Measures of Shape and Position

Lecture 9 Measures of Standard Scores

Section 3: Introduction to Basic and Conditional Probability

Lecture 10 Introduction to Basic Probability

Lecture 11 Introduction to Set Theory

Lecture 12 Introduction to Conditional Probability

Lecture 13 Introduction to Bayes Theorem

Lecture 14 Introduction to Permutations and Combinations

Lecture 15 Introduction to Random Variables

Lecture 16 Introduction to Probability Distribution Functions

Section 4: Introduction to Inferential Statistics

Lecture 17 Introduction to Normal Distribution

Lecture 18 Introduction to Skewness and Kurtosis

Lecture 19 Introduction to Statistical Transformations

Lecture 20 Introduction to Sample and Population Mean

Lecture 21 Introduction to Central Limit Theorem

Lecture 22 Introduction to Bias and Variance

Lecture 23 Introduction to Maximum Likelihood Estimation

Lecture 24 Introduction to Confidence Intervals

Lecture 25 Introduction to Correlations

Lecture 26 Introduction to Sampling Methods

Section 5: Introduction to Hypothesis Testing

Lecture 27 1. Fundamentals of Hypothesis Testing

Lecture 28 Introduction to T Tests

Lecture 29 Introduction to Z Tests

Lecture 30 Introduction to Chi Squared Tests

Lecture 31 Introduction to Anova Tests

Students or professionals in various fields, including business, science, social sciences, and healthcare, who want to enhance their data analysis skills.,Data analysts, researchers, and scientists seeking to strengthen their statistical foundations and Python programming skills.,Anyone interested in gaining a deeper understanding of statistical concepts and their practical applications.,Beginners with no prior statistical knowledge but with a curiosity to learn and apply statistical methods.,Professionals looking to advance their career by acquiring valuable statistical and data analysis skills.,Individuals preparing for standardized tests or exams that include statistical and data analysis components.