INTRODUCTION FOR DATA ANALYTICS FOR FASTER DECISION MAKING
Data Analysis is an ever-evolving discipline with lots of focus on new predictive modeling techniques coupled with rich analytical tools that keep increasing our capacity to handle big data. However, in order to chart a coherent path forward, it is necessary to understand where the discipline has come from since its inception.
Today, more organizations are using data analysis tools and techniques in order to inform effective decision-making. This module also addresses some of the hurdles an organization faces when dealing with data overload and suggests some possible solutions to the problem. It is unfortunate that there is such a dearth of data analysts.
COURSE OBJECTIVES
In this course, the participants will learn:
- Types of data and how to group them
- Basics of data organization
- Basic statistics
- Central limit theorem
- Predict a process performance
- Comparing between populations
- Measure any changes in trend
- Explore ways to measure the performance of and improvement opportunities
- Discuss data distribution including Central Tendency, Variance, Normal Distribution, and non-
normal distributions. - Explore different methods and easy algorithms for forecasting future results and to reduce
current and future risk.
COURSE CONTENT
What is Data Analytics?
- Definition of DA
- Data Here, There, and Everywhere!
- Possible solutions to data overflow problems
- Got Data? The Unique Role of the Data Analyst
- Role of a Data Analyst
- Skill set required to be an effective Data Analyst
- Exercise
Fact-Based Decision-Making Process
- How to collect & use appropriate data?
- Attributive vs variable data
- Transform data to information
- Means & Standard Deviation
- Statistical method for decision making
- Large & small sample theory
- Data from industries : specs, tolerance, variance, process capability
- Activity
- The two types of Decision Models Businesses use
- The Benefits of Fact-Based Decision Making
- Rational Decision Model: Six- Step Method
- Exercise
Big Data Anatomy
- The Attributes of Big Data
- Definition of Big Data
- The 4 V’s of Big Data
- Structured versus Unstructured Data
- The Challenges of Big Data
- Exercise
Getting to Know Your Data
- Data Types: Qualitative versus Quantitative
- Taking a Closer Look: Data Measurement
- Four Types of Data Variables
- Definition and examples of Nominal Variables
- Definition and examples of Ordinal Variables: Order Matters
- Definition and examples of Interval Variables
- Definition and examples of Ratio Variables
- Summary of Statistics/Operations that can be performed on each type
- Exercise
The Fundamental Ways we use data Visualization techniques
- The five ways we use data visualization techniques
- Displaying Tabular Data in Excel
- Using Charts and Graphs to Communicate Data
- How to create Pie, Column, and Line charts using Excel
- Communicating effectively using different chart types
- How to choose the correct chart to display the correct data type
- Exercise
Using Numerical Descriptives to Summarize Data
- Measures of Centrality: Mean, Median, Mode
- Format of Data Values: Grouped Discrete and Grouped Continuous
- Formulas for the Mean
- Examples: Applying 3M’s to Grouped Discrete and Grouped Continuous Data
- Measures of Spread: Standard Deviation, Range, Inter-quartile Range Examples
Probability: Quantifying Uncertainty
- Origin of Probability
- Probability: Examples of Business Applications
- The traditional definition of Probability
- Applying probability to calculate the expected value
- Using Expected Value in Decision Making
- Exercise
The Normal Distribution
- Characteristics of the Normal Distribution
- Interpreting the Empirical Rule
- Components of the Normal Distribution: Probabilities and X values
- Using the NORMDIST function in Excel to calculate probability from a normal distribution
- Using the NORM. INV function in Excel to calculate X values related to a normal distribution
- Exercise
Correlation and Regression
- Definition of Correlation and Regression
- The relationship between Correlation and Regression
- Correlation Coefficient: Values
- Examples of Correlation
- Interpretation of a Regression Equation
- Step-by-Step example of How to Do a Regression Analysis
- Exercise
