Introduction to Statistics for Data Analysts
A practical guide to recognizing, understanding, and managing uncertainty in data
Here's What You'll Get
With The Course
More than an hour of self-paced training videos
Resource pack that includes data source files and R scripts
One Final Assessment
Real world data is frequently ambiguous
- We build practical understanding of the most important statistical tools used to differentiate the one from the other, whether the data is quantitative or categorical.
- Very little theory is presented; rather, the focus is on selecting appropriate tools and correctly interpreting the results.
Details Of What You Will Learn
During This Course
- Have strong intuition into variability and bias in real-world sample data
- Understand the fundamental logic of statistical inference
- Possess a toolkit of essential techniques for analyzing categorical and quantitative variables
- Recognize statistical abuse and be able to avoid it in practice
- Be prepared to quickly learn any of the various specialized inference techniques they might encounter in their future work with data
This Course Module is Tailored for:
Data Analysts
Reporting Managers
Excel Users
Anyone working with data and attempting to draw constructive, generalizable conclusions from them.
Participants of this course must have a basic knowledge and understanding of variable types and descriptive and inferential statistics. No other advanced preparation is needed to take this course. Although the demonstrations in the course are done in R, they are intended to be generalizable to Python or any other statistical software you choose to use. Knowledge of R is not a prerequisite for taking this course.
LEARN HOW TO
Manage uncertainty in data
- Bias and Variability
- Understanding Confidence intervals
- Practice with Significance Testing
- Statistical Use Misuse and Abuse
- Significance Testing for Proportions
- Goodness of Fit Practice
Course Curriculum
1. Introduction
- Introduction and Overview
- The Challenge of Inference
- Bias and Variability
2. Reproducing Work in R
- Reproducing Work in R
3. Confidence Intervals
- Understanding Confidence intervals
- Confidence Interval Practice
- Confidence Interval Demonstration
4. Significance Testing
- Significance Testing
- Errors in Significance Testing
- Practice with Significance Testing
5. Statistical Use Misuse and Abuse
- Statistical Use Misuse and Abuse
6. Proportions
- Confidence Intervals for Proportion
- Significance Testing for Proportions
- Proportions Practice
7. Goodness of Fit
- Goodness of Fit
- Goodness of Fit Practice
7. Sample Size and Power
- Sample Size and Power
Your Instructor
Dr. Andrew Gard
- Creator of the popular YouTube channel, Equitable Equations, which teaches practical statistics, data science, and R programming
- Professor of mathematics and computer science at Lake Forest College, located near Chicago, USA
- Author of the R package fqar, which facilitates the analysis of large floristic quality data sets
- PhD in mathematics from The Ohio State University
- Area of specialization: data analysis with R. I integrate both domain expertise and technical data science to provide deep answers to real-world data questions while respecting and quantifying the uncertainty inherent in the data.
Frequently Asked Questions
- How long do I have access to the course? You have access to this course as long as you have Enterprise DNA On-Demand Subscription.
- Can I purchase a single course instead of the Enterprise DNA On-Demand? The option to purchase a single course has been discontinued. To access any of our courses, you will need to upgrade to full On-Demand Subscription here.
- Do you offer one-off support or coaching? All support around Power BI and Enterprise DNA's online training content now occurs at the Enterprise DNA Forum. You must have an Enterprise DNA On-Demand Subscription or For Business access to receive support.
- What if I need to train my team? We recommend exploring Enterprise DNA For Business platform. To learn more, see here.
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