Statistics Foundations

This course is part of multiple programs. Learn more

Instructor: Brandi Robinson

What you'll learn

  •   The basic principles of descriptive and inferential statistics
  •   Use statistical analyses to make data-driven decisions
  •   How to formulate and test hypotheses and take action based on the outcome
  • Skills you'll gain

  •   Spreadsheet Software
  •   Analytics
  •   Statistical Analysis
  •   Statistical Modeling
  •   Statistical Hypothesis Testing
  •   Bayesian Statistics
  •   Sampling (Statistics)
  •   Tableau Software
  •   Marketing Analytics
  •   Quantitative Research
  •   Statistics
  •   Descriptive Analytics
  •   Data Analysis Software
  •   Statistical Inference
  •   Time Series Analysis and Forecasting
  •   Probability & Statistics
  •   Data Modeling
  •   Data Analysis
  •   Descriptive Statistics
  •   Statistical Methods
  • There are 5 modules in this course

    Many of the mistakes made by data analysts today are due to a lack of understanding the concepts behind the tests they run, leading to incorrect tests or misinterpreting the results. This course is tailored to provide you with the necessary background knowledge to comprehend the "what" and "why" of your actions in a practical sense. By the end of this course you will be able to: • Understand the concept of dependent and independent variables • Identify variables to test • Understand the Null Hypothesis, P-Values, and their role in testing hypotheses • Formulate a hypothesis and align it to business goals • Identify actions based on hypothesis validation/invalidation • Explain Descriptive Statistics (mean, median, standard deviation, distribution) and their use cases • Understand basic concepts from Inferential Statistics • Explain the different levels of analytics (descriptive, predictive, prescriptive) in the context of marketing • Create basic statistical models for regression using data • Create time-series forecasts using historical data and basic statistical models • Understand the basic assumptions, use cases, and limitations of Linear Regression • Fit a linear regression model to a dataset and interpret the output using Tableau • Explain the difference between linear and multivariate regression • Run a segmentation (cluster) analysis • Describe the difference between observational methods and experiments This course is designed for people who want to learn the basics of descriptive and inferential statistics.

    Inferential Statistics

    Designing Experiments and Testing Hypotheses

    Data Modeling

    Using Statistics in Real-World Settings

    Explore more from Marketing

    ©2025  ementorhub.com. All rights reserved