Business Data Analysis - Princeton Academy

Business Data Analysis

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Gain a solid understanding of statistics and basic probability, using Excel, and build on your data analysis and data science foundation. 

 

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.

What you’ll learn

  • Descriptive statistics
  • Basic probability
  • Random variables
  • Sampling and confidence intervals
  • Hypothesis testing

 

Course Syllabus

Module 1: Descriptive Statistics 
  • Describe data using charts and basic statistical measures.
  • Full use will be made of the
  • new histograms, Pareto charts, Boxplots, and Treemap and Sunburst charts in Excel 2016.
Module 2: Basic Probability 
  • basic probability including the law of complements,
  • independent events,
  • conditional probability and
  • Bayes Theorem.
Module 3: Random Variables 
  • Find the mean and variance of random variables
  • Binomial,
  • Poisson, and
  • Normal random variables.
  • Discussion of the beautiful and important Central Limit Theorem.
Module 4: Sampling and Confidence Intervals
  • Learn the mechanics of sampling,
  • point estimation, and
  • interval estimation of population parameters.
Module 5: Hypothesis Testing 
  • Null and alternative hypotheses,
  • Type I and Type II error,
  • One sample tests for means and proportions,
  • Tests for difference between means of two populations and
  • Chi Square Test for Independence.