Essential Statistics for Data Analysis using Excel
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About this course
If you’re considering a career as a data analyst, you need to know about histograms, Pareto charts, Boxplots, Bayes’ theorem, and much more. In this applied statistics course, the second in our Microsoft Excel Data Analyst XSeries, use the powerful tools built into Excel, and explore the core principles of statistics and basic probability—from both the conceptual and applied perspectives. Learn about descriptive statistics, basic probability, random variables, sampling and confidence intervals, and hypothesis testing. And see how to apply these concepts and principles using the environment, functions, and visualizations of Excel.
As a data science pro, the ability to analyze data helps you to make better decisions, and a solid foundation in statistics and basic probability helps you to better understand your data. Using real-world concepts applicable to many industries, including medical, business, sports, insurance, and much more, learn from leading experts why Excel is one of the top tools for data analysis and how its built-in features make Excel a great way to learn essential skills.
Before taking this course, you should be familiar with organizing and summarizing data using Excel analytic tools, such as tables, pivot tables, and pivot charts. You should also be comfortable (or willing to try) creating complex formulas and visualizations. Want to start with the basics? Check out Introduction to Data Analysis using Excel. As you learn these concepts and get more experience with this powerful tool that can be extremely helpful in your journey as a data analyst or data scientist, you may want to also take the third course in our series, Analyzing and Visualizing Data with Excel
This course includes excerpts from Microsoft Excel 2016: Data Analysis and Business Modeling from Microsoft Press and authored by course instructor Wayne Winston.
- Descriptive statistics
- Basic probability
- Random variables
- Sampling and confidence intervals
- Hypothesis testing
- Data Scientists
- Secondary school (high school) algebra
- Ability to work with tables, formulas, and charts in Excel
- Ability to organize and summarize data using Excel analytic tools such as tables, pivot tables, and pivot charts
- Excel 2016 is required for the full course experience. Excel 2013 will work but will not support all the visualizations and functions.
Self-Paced Learning Outline
Module 1 : Descriptive Statistics
- Defining Data
- Histograms and Skewness
- Descriptive Statistics with Analysis ToolPak
- Categorical Data, PivotTables, and PivotCharts
- Summarizing Hierarchical Data
- 80-20 Rule and Pareto Charts
- Module 1 Discussion
- Module 1 Quiz
Module 2 : Basic Probability
- Introduction to Probability
- Law of Complements
- Mutually Exclusive and Independent Events
- Conditional Probability
- Law of Total Probability and Bayes Rule
- Additional Reading and Review
- Module 2 Discussion
- Module 2 Quiz
Module 3 : Random Variables
- Random Variable Definitions
- Mean, Variance, and Standard Deviation of a Random Variable
- Mean, Variance, and Standard Deviation for Sum of Random Variables
- Binomial Random Variable
- Poisson Random Variable
- Normal Random Variable
- Central Limit Theorem
- Z Scores
- Module 3 Discussion
- Module 3 Quiz
Module 4 : Sampling and Confidence Intervals
- Populations and Samples
- Point Estimation of a Population Mean and Proportion
- The Standard Normal
- Confidence Interval Estimation
- Sample Size Determination
- The Finite Correction Factor
- Additional Reading
- Module 4 Discussion
- Module 4 Quiz
Module 5 : Hypothesis Testing
- Defining Hypotheses
- Type I and Type II Error
- One Sample Z-Test
- One Sample T-Test
- Single Sample Test for Population Proportion
- Testing Equality of Variances
- Testing the Difference Between Two Population Means
- Chi-Squared Test for Independence
- Additional Reading
- Module 5 Discussion
- Module 5 Quiz
- Final Exam
|Learning Style||Self-Paced Learning|
|Course Duration||24 Hours|
|VPA Discount||VPA Discount|
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