Statistical Analysis - I

Course details

"Statistical Analysis - I" lays the groundwork for understanding the fundamental principles of statistics and their application in various domains. From descriptive statistics to inferential techniques, this course introduces students to the tools necessary to interpret, analyze, and make informed decisions based on data.

Start Date

TBD

Duration

14-15 weeks

Learning Objectives

Grasp the foundational concepts of statistics.
Understand the importance of statistical measures in data interpretation.
Master hypothesis testing and its real-world applications.
Learn various statistical distributions and their significance.
Develop analytical skills to make data-driven decisions.

Key Features

Expert-led classes delving deep into statistical concepts.
Hands-on sessions for practical data interpretation.
Interactive tutorials focusing on real-world scenarios.
Collaborative projects to encourage team-based analysis.
Comprehensive learning resources via the LMS.

Learning path

Unit 1: Introduction to Statistics (4)
Unit 2: Descriptive Statistics (6)
Unit 3: Probability Basics (4)
Unit 4: Discrete Probability Distributions (4)
Unit 5: Continuous Probability Distributions (4)
Unit 6: Inferential Statistics (6)

Topics covered

The Role of Statistics in Data Science
Types of Data: Qualitative vs. Quantitative
Levels of Measurement
Introduction to Sampling and Sampling Distributions

Central Tendency: Mean, Median, Mode
Measures of Dispersion: Variance, Standard Deviation
Skewness and Kurtosis
Box Plots and Outliers
Data Visualization: Histograms, Pie Charts
Z-scores and Data Standardization

Introduction to Probability
Basic Probability Rules
Conditional Probability and Independence
Bayes' Theorem

Binomial Distribution
Poisson Distribution
Hypergeometric Distribution
Expected Value and Variance for Discrete Random Variables

Normal Distribution
Exponential Distribution
Application of the Standard Normal Distribution
Sampling Distribution of the Mean

Introduction to Hypothesis Testing
Confidence Intervals
t-Distribution
Comparing Two Means
Chi-Square Tests
Analysis of Variance (ANOVA)

Grading:

Class Participation: 10%
Bi-weekly Assignments: 30%
Group Project: 20%
Mid-Term Examination: 20%
Final Examination: 20%

Reading Material: Core textbooks reflecting the foundational principles of statistics, complemented by recent research articles. Additional resources available on the LMS.

Assignments: A mix of theoretical problem-solving and practical data analysis to reinforce understanding.

Faculty: A leading academic with a blend of theoretical expertise and real-world experience in statistical analysis.

Teaching Instructors: TBD