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This 3 -day instructor led course introduces the necessary core quantitative methods and the foundations for statistical methodologies used in data analytics. Statistical software and the use of spreadsheets are integrated throughout so that students better comprehend the importance of using technological tools for effective model building and decision-making. This course is data-oriented, exposing students to basic statistical methods, their conceptual underpinning, such as variability and uncertainty, and their use in the real world. Topics include data collection, descriptive statistics, elementary probability rules and distributions, statistical inferences, break-even analysis, regression analysis, and introduction to predictive modeling and optimization models.
There are no prerequsites for this course.
At the end of this training course, participants will be able to do the following:
- To improve analytical thinking and develop effective problem-solving strategies and validation techniques for different problem situations.
- To familiarize students with useful, efficient, and proper methodologies for summarizing and communicating quantitative and qualitative data in Excel.
- To build statistical models replicating the real-life situation as closely as possible and to formulate appropriate hypothesis given the context
- To help students acquire effective modeling skills in designing and implementing readable and reliable spreadsheet models.
- To teach students how to interpret the results of statistical tests and decision models, and to use them in making decisions.
- To develop one’s ability and confidence in effectively communicate analytical, quantitative, and statistical concepts.
Currently, there are no public classes scheduled. Please contact a Phoenix TS Training Consultant to discuss hosting a private class at 240-667-7757.
Module 1: Introduction to The Course
• Introduction to Analytics
• Different Types of Analytics
• Why are There So Many Different Methods?
• Terminology and Notation
• Core Ideas in Data Analytics
• The Steps in Data Analytics Projects
Module 2: Data Exploration• Introduction to Statistics
• Variable Types
• Summarizing Data
• Descriptive Statistics: Measures of Central Tendency
• Descriptive Statistics: Measures of Variation
• Statistical Displays: Histograms and Boxplots
Module 3: Excel for Data Analysis
• Introduction to Excel
• Sort/Filter/Conditional Formatting
• Pivot Tables
• Data Visualization
Module 4: Breakeven Analysis
• Linear Functions
• Revenue and Cost Models
• Exponential Functions
• Curve Fitting
• What-If Analysis / Goal Seek
Module 5: Time Value of Money
• Basics of lending and borrowing.
• Present and future value of investments
• Simple Interest
• Compound Interest
Module 6: Probability Models
• Basic Principles
• Conditional Probability
• Discrete Random Variables
• Continuous Random Variables
• Normal Distribution
• Outlier Detection Method
Module 7: Statistical Inferences
• Sampling Types / Survey Errors
• Confidence Intervals
• Introduction to Hypothesis Testing
• Single Sample t-Test
• Type I/II Errors