Foundations of Data Analytics II
This course is intended as a continuation of Foundations of Data Analytics I. In this course, you’ll conduct more advanced analysis and data manipulation using spreadsheets. You will also expand your data analytics toolkit by learning the basics of the programming language Python, enabling you to solve a wider range of data problems. Additionally, this course introduces predictive models.
Course Topics
Bayesian Thinking in Real-world Scenarios
Understanding and applying the principles and intuition behind Bayes’ Rule, enabling students to interpret real-world situations probabilistically.
Hypothesis Testing and Significance
Mastering the fundamentals of the null hypothesis, leveraging distributions, and determining significance using p-values.
A/B Testing Fundamentals
Designing and implementing simple A/B tests to validate hypotheses, ensuring actionable insights for business decisions.
Predictive Modeling with Linear Regression
Delving into the creation and interpretation of predictive models, using linear regression as a foundational tool.
Bias Detection and Mitigation in Data Analytics
Recognizing and addressing biases in both data analysis and visualization, fostering more objective and reliable interpretations.
Roles and Presentation Skills in Data Professions
Understanding the distinct responsibilities of data scientists, data analysts, and business analysts, while mastering the art of presenting data-driven insights with minimal bias.
100
Disclosure
This course is delivered online through an institution of the Lower Cost Models Consortium (LCMC) that is different than your degree-granting institution that awards the academic credit for the course.
