Data Scientist — Foundational Statistics & Probability

Summarizing Data - Description: This day focuses on descriptive statistics, which are used to summarize and describe the main features of a dataset. You will learn about measures of central tendency (mean, median, mode) and measures of dispersion (range, variance, standard deviation). You'll also learn how to calculate these values. - Resources/Activities: - Expected Outcomes: Be able to calculate and interpret measures of central tendency and dispersion, and understand what they tell us about the data.

Charts and Graphs - Description: Data visualization is a critical skill for understanding and communicating data insights. You'll learn about different types of charts and graphs, such as histograms, bar charts, pie charts, and scatter plots, and when to use each type. - Resources/Activities: - Expected Outcomes: Understand different types of data visualization and the types of insights can be conveyed with each type of chart.

Understanding Chance - Description: This day introduces the core concepts of probability. You'll learn about sample spaces, events, probability calculations, and basic probability rules (e.g., addition rule, multiplication rule). - Resources/Activities: - Expected Outcomes: Define probability, calculate basic probabilities, and understand key probability rules.

Key Distributions - Description: Learn about key probability distributions, specifically the concept of discrete and continuous distributions. Introduce the binomial and normal distributions. - Resources/Activities: - Expected Outcomes: Understand the difference between discrete and continuous distributions, and the basic properties of the binomial and normal distributions.

Introduction to Statistical Inference - Description: Introduce hypothesis testing – the process of using sample data to evaluate a claim about a population. Explain null and alternative hypotheses, p-values, and the basics of significance testing. - Resources/Activities: - Expected Outcomes: Understand the basic steps of hypothesis testing and its purpose, understanding null and alternative hypothesis, and grasp the meaning of p-values.

• Description: Review key concepts covered during the week. Explore how statistics is used in real-world data science problems. - Resources/Activities: - Expected Outcomes: Reinforce understanding of the week's concepts and gain awareness of how they are used in data science. Identify areas for further learning.