Statistical Foundations for Data Science

"Builds strong foundations in data, probability, and inference."

📊 Statistical Foundations for Data Science

🔵 This course builds a strong base in statistics for data science learning.

🟢 It helps learners understand how data is collected, organized, and analyzed.

🟣 The course introduces core statistical concepts in a simple and structured way.

🟡 It prepares students for advanced studies in statistics and data science.

🔴 Equal focus is given to theory and practical understanding.

🟠 Learners gain confidence in statistical thinking and problem-solving.

🟤 The course connects statistics to real-world data and applications.

📊What You’ll Learn Here

🟩 How to represent data using tables and graphs

🟦 Key ideas of descriptive statistics and data summaries

🟩 Basics of probability and probability distributions

🟦 Concepts of sampling and sampling distributions

🟩 Methods of estimation and confidence intervals

🟦 Fundamentals of hypothesis testing

🟩 Understanding ANOVA (Analysis of Variance)

🟦 Introduction to regression analysis and relationships between variables

🟩 How to apply statistical methods to real-world problems

📊 Statistical Foundations for Data Science – Course Outline

🟩 Module 1: Introduction to Statistics

🟠 Data Science Overview

🟢 Data and Statistics

🟩 Module 2: Descriptive Statistics

🟣 Tabular and Graphical Displays

🟣 Numerical Measures

🟩 Module 3: Introduction to Probability

🟢 Probability Concepts and Applications

🟡 Bayes’ Theorem

🟩 Module 4: Probability Distributions

🔵 Discrete Probability Distributions

🔵 Continuous Probability Distributions

🟩 Module 5: Sampling Techniques and Sampling Distributions

🟢 Central Limit Theorem (CLT)

🟩 Module 6: Inferential Statistics

🟣 Interval Estimation

🟣 Hypothesis Testing

🟡 Inference About Means and Proportions (Two Populations)

🔵 Analysis of Variance (ANOVA)

🟢 Regression Analysis

📊 Statistics Fundamentals Course – Description

🔵 Chapter 1: Data and Statistics

This chapter introduces the concept of data and statistics, types of data (qualitative and quantitative), variables, and levels of measurement. Real-world examples are used to show how data is collected, organized, and applied in decision-making.

🟣 Chapter 2: Descriptive Statistics – Tabular and Graphical Displays

This chapter focuses on summarizing data using frequency tables and visual tools such as bar charts, histograms, and pie charts. It helps learners interpret patterns, trends, and distributions effectively.

🟠 Chapter 3: Descriptive Statistics – Numerical Measures

This chapter covers numerical summaries of data, including measures of central tendency (mean, median, mode) and measures of dispersion (range, variance, standard deviation). These measures help describe data concisely.

🟢 Chapter 4: Introduction to Probability

This chapter introduces probability as a measure of uncertainty. It covers basic concepts, sample space, events, and fundamental probability laws with simple illustrations.

🟣 Chapter 5: Discrete Probability Distributions

This chapter discusses discrete random variables and their probability distributions. Important distributions such as Binomial and Poisson are explained along with their properties and applications.

🟡 Chapter 6: Continuous Probability Distributions

This chapter introduces continuous random variables and probability density functions. Key distributions such as Uniform and Normal distributions are studied with emphasis on interpretation and real-life applications.

🔵 Chapter 7: Sampling and Sampling Distributions

This chapter explains different sampling techniques and the concept of sampling distributions. It highlights the importance of sample statistics and the Central Limit Theorem.

🟠 Chapter 8: Interval Estimation

This chapter focuses on estimating population parameters using confidence intervals. Confidence intervals for population means and proportions are developed and interpreted.

🟢 Chapter 9: Hypothesis Testing

This chapter introduces statistical hypothesis testing, including null and alternative hypotheses, test statistics, significance levels, and decision rules.

🟣 Chapter 10: Inference About Means and Proportions (Two Populations)

This chapter deals with statistical inference involving two populations. It includes comparison of means and proportions using appropriate tests and interpretations.

🟠 Chapter 11: Analysis of Variance (ANOVA)

This chapter explains ANOVA as a method for comparing the means of more than two populations. The concept, assumptions, and interpretation of results are discussed.

🔵 Chapter 12: Regression Analysis

This chapter introduces simple linear regression for studying relationships between variables. Model estimation, interpretation of coefficients, and prediction are emphasized.