This beginner-friendly course gives you a clear picture of how data analytics works in practice, from the first review of your data through analysis and decision making. Instead of diving straight into tools, it begins by unpacking key ideas like data distributions, descriptive and inferential statistics, and the ways organizations use analytics to spot patterns, predict outcomes, and plan their next move. You'll also look at how Big Data and modern analytics tools are reshaping the way decisions get made across industries.
Practical exercises run throughout, helping you put what you learn to work on realistic problems. You'll get comfortable with statistical concepts such as probability distributions, correlation, and regression, then move into forecasting with trendlines, moving averages, and scenario analysis. The course also covers data visualization and prescriptive techniques, including charts, Pivot Tables, and optimization tools like Solver, so you can turn your analysis into insights that lead to smarter decisions.
What You Will Learn
- Grasp core statistical concepts including measures of central tendency, data dispersion, and the normal curve
- Examine descriptive and inferential statistics, including probability distributions like binomial and Poisson
- Analyze and forecast data using correlation, linear regression, and multiple regression techniques
- Put predictive analytics to work with tools like trendlines, moving averages, and scenario modeling
- Build clear data visualizations using charts, histograms, icon sets, color scales, sparklines, and pivot tables
- Explore prescriptive analytics techniques such as Solver and linear programming to sharpen decision making
Course Outline
Basic Data Analysis
-
Measures of Central Tendency
-
Measures of Position
-
Measures of Dispersion
-
The Normal Curve
-
Descriptive Statistics
Predictive Analytics I
-
Forecasting
-
Series Forecast
Data Visualization I
-
Charts
-
Icon Sets
-
Histograms
-
Moving Average
Predictive Analytics
-
Correlation
-
Regression - overview
-
Regression - analysis
-
Linear regression
-
Multiple regression
Probability
-
Probability I
-
Probability II
-
Binomial Probability
-
Poisson Probability
Prescriptive Analytics I
-
What If Analysis
-
Data Table (3 variables)
-
Scenario Manager
-
Scenario Manager - Pivot
Data Visualization II
-
Sparklines
-
Color Scales
-
Drawing Shapes
-
Pivot Tables
-
Pivot Charts
Prescriptive Analytics II
-
Solver - overview
-
Linear Programming
-
The Solver model
-
Non-Linear Programming
-
Evolutionary Solver
This course is available for 27 days. You can choose when to start your access period. Once you activate, you will have 27 days to complete it (access the course materials, quizzes, projects and videos). You may request one extension of seven (7) days. Other extension requests will be evaluated on a case-by-case basis. Videos are not downloadable.
