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Phoenix TS

Data Analysis Level 2 Training

Course Overview

Our 3-day, instructor-led Data Analysis Level 2 training course provides you with the knowledge of  Data and Business Analytics: what is it, how it is used, when it should be used, what it tells you and how it affects our decisions? We will focus on how to deal with analytics and uncertainty, using data, making optimal decisions or creating real life scenarios.  The 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.  

You will Learn

  • To improve analytical thinking and develop effective problem solving strategies and validation techniques for different problem situations.
  • To familiarize students with usefule, efficient, and proper methodologies for summarizing and communicating quantitative and qualitative data in Excel.
  • To build statistical models replacing the real life situation as closely as possible and to formulate appropriate hypothesis given the content.
  • to help stsdents acquire effective modeling skills in designing and implementing readable and reliable spreadsheet models.
  • To trach students how to to interpret the results of statisitical tests and decision models, and to use them in making decsions.
  • To develop one’s ability and confidence in effectively communicate analytical, quantitative, and statistical concepts.

Schedule

Data Analysis Level 2 Training

date
location
3/09/21 - 3/11/21 (3 days)

8:30AM - 4:00PM EST

Columbia, MD
Sold Out
3/31/21 - 4/02/21 (3 days)

8:30AM - 4:30PM EST

Tysons Corner, VA
Open
4/07/21 - 4/09/21 (3 days)

8:30AM - 4:30PM EST

Columbia, MD
Open
4/07/21 - 4/09/21 (3 days)

8:30AM - 4:30PM EST

Online
Open
9/15/21 - 9/17/21 (3 days)

8:30AM - 4:30PM EST

Tysons Corner, VA
Open
9/22/21 - 9/24/21 (3 days)

8:30AM - 4:30PM EST

Online
Open
9/22/21 - 9/24/21 (3 days)

8:30AM - 4:30PM EST

Columbia, MD
Open
3/23/22 - 3/25/22 (3 days)

8:30AM - 4:30PM EST

Tysons Corner, VA
Open
3/30/22 - 4/01/22 (3 days)

8:30AM - 4:30PM EST

Online
Open
3/30/22 - 4/01/22 (3 days)

8:30AM - 4:30PM EST

Columbia, MD
Open
11/30/22 - 12/02/22 (3 days)

8:30AM - 4:30PM EST

Tysons Corner, VA
Open
12/07/22 - 12/09/22 (3 days)

8:30AM - 4:30PM EST

Online
Open
12/07/22 - 12/09/22 (3 days)

8:30AM - 4:30PM EST

Columbia, MD
Open
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Course Outline

Module 1: Predictive Modeling Basics

  • Data Preparation
  • Data Cleansing
  • Integrating Data from Multiple Sources
  • Common Issues

Module 2: Linear Regression

  • Predictive vs. Explanatory Modeling Using Regression
  • Overfitting vs. Underfitting
  • Splitting Data into Training/Validation subsets
  • Multicollinearity
  • Feature Subset Selection Models

Module 3: Classification Models

  • K- Nearest Neighbor
  • Distance Function
  • Similarity Functions
  • Combination Function
  • Choosing K
  • Advantages/Disadvantages

Module 4: Segmentation Modeling/ Cluster Analysis

  • Clustering
  • Clustering vs. Classification
  • K-Means Clustering
  • Clusters Interpretation
  • Hierarchical Clustering
  • Segmentation

Module 5: Spreadsheet Models / Optimization

  • Linear Optimization Models
  • Maximizing Profit/ Minimizing Cost

Module 6: Data Analysis Using R

  • Introduction to R
  • Data Analysis using R
  • Reading Data
  • Data Type in R
  • Clustering in R
  • Regression in R

Exercise and Software:

Within each module, students will be provided with lots of in-class hands on exercises to practice the materials on their own and/or with the guidance of the instructor. Class materials, including lecture notes and exercises will be provided to students. Students are required to have Microsoft Excel (and R for Level 2) installed on their laptops.

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