Phoenix TS

Analytics Training

This course introduces students to the data analysis process and provides attendees with the core skills necessary to handle any data intensive analytic project and the clarity, insight and confidence in order to make sound and measurable business decisions.

Course Overview

Our 5-day, instructor-led Analytics Training course will focus on using MS Excel functionalities for data analysis and ultimately decision making. This course will provide students with knowledge to:

  • Summarize data effectively
  • Use advanced problem solving techniques to develop a thorough understanding of the data
  • Calculate and select the most appropriate central tendency measures
  • Identify and avoid hidden bias
  • Standard normal distribution
  • Extracting a random sample from a population
  • Identifying the reliability of an estimate using confidence intervals
  • Identifying relationships between variables

An understanding of Excel prior to this training course will be helpful for most students. This understanding can be developed through courses such as Excel Level 1, Excel Level 2, and Excel Level 3.


Analytics Training

11/01/21 - 11/05/21 (5 days)

8:30AM - 4:30PM EST

Tysons Corner, VA
11/15/21 - 11/19/21 (5 days)

8:30AM - 4:30PM EST

11/15/21 - 11/19/21 (5 days)

8:30AM - 4:30PM EST

Columbia, MD

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Course Outline

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 

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

Excel for Data Analysis

  • Introduction to excel
  • Sort/filter/conditional formatting
  • Pivot tables
  • Data visualization

Breakeven Analysis

  • Linear functions
  • Revenue and cost models
  • Exponential functions
  • Curve fitting
  • What-if analysis / goal seek 

Time Value of Money

  • Simple interest
  • Compound interest 

Probability Models

  • Basic principles
  • Conditional probability
  • Discrete random variables
  • Continuous random variables
  • Normal distribution
  • Z-score
  • Outlier detection method

Statistical Inferences

  • Sampling types / survey errors
  • Confidence intervals
  • t-distribution
  • Introduction to hypothesis testing
  • Single sample t-test
  • Type I/II errors

Linear Regression – Part 1

  • Correlation
  • Simple linear regression
  • Multiple linear regression
  • Fit measures

Predictive Modeling Basics

  • Data preparation
  • Integrating data from multiple sources

Linear Regression – Part 2

  • Regression for prediction
  • Performance evaluation 

Classification Models

  • Distance measures
  • K-nearest neighbors
  • Performance evaluation
  • Other methods

Segmentation Modeling / Cluster Analysis

  • Introduction to segmentation
  • Cluster analysis
  • Clusters interpretation

Spreadsheet Models / Optimization

  • Linear optimization models
  • Maximizing profit / minimizing cost 

Data Analysis using R

  • Introduction to R
  • Data analysis using R 

Decision Analysis (optional)

  • Introduction to decision making under uncertainty
  • Decision analysis without probabilities
  • Decision analysis with probabilities
  • Decision trees

Analytics Training FAQs

Who should take this class?

This course is designed for beginners who want to develop their foundation knowledge for data analytics, those experienced with statistics, and professionals seeking more advanced methods and skills to further their career. The particular job positions who take this course include Organizational Analysts, Functional Managers, IT Specialists, Statisticians, Business Intelligence Professionals

What do students have to say about this course?

“I like the fact that instructor was giving a lot of time on hands on training, he was very informative, knew his material thoroughly.” – Student from November 2018

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