Introduction to Statistics

ABOUT THE COURSE:In today's data-driven era, the ability to analyze and derive meaningful insights from data has become increasingly important. To achieve this, there are four fundamental steps: data collection, data summarization, data visualization, and data analysis. My course is designed to guide students through each of these steps, ensuring a comprehensive understanding. It begins by elucidating the data collection process, followed by techniques for effective data summarization and visualization. Additionally, students will learn to discern the appropriate summarization or visualization technique for different types of data variables. Prior to advancing to the analysis stage, students will acquire essential knowledge of the sampling distribution of single sample and two sample mean, variance, and proportion, a necessary tool in the field. Moving forward, the course delves into the analysis stage, encompassing critical topics such as methods for point and interval estimation. Lastly, the course concludes with an extensive exploration of hypothesis testing methods for means, variances, and proportions.INTENDED AUDIENCE: Science and Engineering stream students at the undergraduate level.PREREQUISITES: Student must be pursuing an undergraduate degree and have done a course on Probability at this level.INDUSTRY SUPPORT: Goldman Sachs, Microsoft, Adobe, Flipkart, SamsungR&D, Salesforce.

Week 1:Collecting dataWeek 2:Summarizing dataWeek 3:Visualizing dataWeek 4:Analysis tool – Sampling Distribution (one sample problem)Week 5:Analysis tool – Sampling Distribution (two sample problem)Week 6:Analyzing Data – Point EstimationWeek 7:Analyzing Data – Point Estimation for missing dataWeek 8:Analyzing Data – Testing of HypothesisWeek 9:Analyzing Data – Testing of Hypothesis (continued)Week 10:Analyzing Data – Bootstrap Hypothesis TestingWeek 11:Analyzing Data – Confidence Interval EstimationWeek 12:Analyzing Data – Bootstrap Confidence Interval, Inference on missing data