Welcome to the world of data science! As we all know, the course you are about to discover is Math 229: Statistics for Data Science. This course is offered by colleges and universities across the United States, but together, we are going to make ours very special!

By the end of the course, you will have a firm understanding and appreciation of the power of statistics and how it is used or misused in real life. From understanding data trends to applying statistical models, this course will equip you with practical knowledge for analyzing and interpreting data.

UC Berkeley Data 8
Access the course website here
Textbook: Computational and Inferential Thinking: The Foundations of Data Science is a free online textbook that includes interactive Jupyter notebooks.

The use of probability techniques, hypothesis testing, and predictive techniques to facilitate decision-making. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-square and t-tests; and application of technology for statistical analysis, including the interpretation of the relevance of the statistical findings. Applications using data from disciplines including business, social sciences, psychology, life science, health science, and education.

• Quantify and Visualize Qualitative Data: Learn how to transform qualitative data into a visual representation using techniques such as bar charts, pie charts, and scatter plots.
• Uncover Patterns in Frequency Distributions: Develop an understanding of frequency distributions and learn how to create histograms, density plots, and box plots to visualize the distribution of your data.
• Master Measures of Center and Spread: Gain a deep understanding of measures of central tendency (mean, median, mode) and spread (range, variance, standard deviation) and learn how to calculate them.
• Pinpoint Outliers and Anomalies: Learn how to identify outliers and anomalies using visualizations such as box plots, scatter plots, and density plots.
• Correlation and Regression: Discover how to measure the strength and direction of relationships between variables using correlation coefficients, and learn how to create least-squares regression lines.
• Probability and Random Variables: Explore the world of probability theory and learn how to work with random variables, including Bernoulli trials and binomial distributions.
• Normal Distribution: Master the normal distribution, including the mean, standard deviation, and z-scores. Learn how to identify normality and apply it to real-world problems.
• Central Limit Theorem: Understand the concept of the central limit theorem and how it applies to sampling distributions.
• Confidence Intervals: Learn how to construct confidence intervals for means, proportions, and regression slopes, and understand the importance of confidence intervals in statistical inference.
• Hypothesis Testing: Develop an understanding of hypothesis testing, including one-sample tests, two-sample tests, and ANOVA. Learn how to use hypothesis testing to make informed decisions.
• Statistical Technology: Visualize and analyze the data effectively.

Throughout the term, the class will have different assignments and activities encouraging students to interact and collaborate with each other to promote a positive peer-to-peer learning environment.

• Sampling and Data
• Descriptive Statistics
• Probability Topics
• Discrete Random Variables
• Continuous Random Variables
• The Normal Distribution
• The Central Limit Theorem
• Hypothesis Testing with One Sample
• Hypothesis Testing with Two Samples
• The Chi-Square Distribution
• F Distribution and One-Way ANOVA
• Linear Regression and Correlation

This course stands out because it is taught using Python and R, two powerful and versatile programming languages designed specifically for statistical computing and data analysis. Python and R enable students to visualize data, conduct advanced statistical analyses, and gain practical experience with industry-standard statistical tools.