Data Analysis Boot Camp

In this course, you will address the competencies and skills essential to successfully analyzing data. You will gain the essential knowledge of probability theory and the skills necessary to apply the techniques and formulas employed in statistical analysis of data. Through individual and group exercises, you will gain insight into the practical application of the material in the analysis of business problems and in identifying and quantifying the risks associated with decision making and forecasting future results. You will also examine the future of analytics, the changing role of business analysts, and the role of the IT.

What You Will Learn

• Scope and impact of applying quantitative methods to the analysis of data
• Basic concepts of probability theory and statistical analysis techniques
• Apply statistical techniques to forecast future performance
• Use quantitative analysis to support key management decisions by identifying significant trends and correlation between business results and controllable and incontrollable variables
• Establish business process performance metrics and the mechanism to track business process performance
• Document the business needs for information and its impact on defining the data requirements
• Apply the concepts of a probability model and distribution, taking into account the impact of uncertainty
• Analyze the business environment in which your project occurs
• Extract information from a sample of data observations
• Concepts of statistical inference to draw conclusions about the shape and parameters of a probability distribution from the results of a sample
• Apply the required test to confirm the validity of the parameters obtained from a sample
• Establish confidence intervals on the estimated sample parameters
• Apply the statistical model to extract information on the impact of significant variables on the results of a selected performance variable (or dependent variable)

Audience

Business and systems analysts, product managers, operations research analysts, marketing analysts, data modelers and administrators, project or program managers, and database analysts (DBAs)

Prerequistes

Course Outline

1. Data Analysis and Analytics

• Definition and history
• Current technology environment and the growing availability of data
• Role of the business analyst
• Applications for gaining competitive advantages
  • Fact based decision making
  • Process tracking and control

2. Need for Information

• Identifying operational and executive information classes
  • Modeling key decisions and the needs for information
  • Key business transactions and documents
  • Map information needs to data requirements
• Executive information needs and the balanced scorecard
• Tracking and managing business process performance
  • Selecting measures and targets
  • Measuring performance and finding performance gaps
  • Root cause analysis

3. Application of Probability and Probability Distributions

• Key concepts and essentials
  • Decision making under uncertainty
  • Random variables
  • Population and sample
  • Discrete and continuous distributions
• Properties of a distribution
• Normal distribution
• Other distributions
  • Poisson
  • Exponential
  • Binomial
• Establishing confidence intervals

4. Data Exploration Concepts and Formulas

• Basic Concepts
  • Types of variables
  • Selecting dependent and independent variables
  • Sample vs. population
• Descriptive measures of a sample
  • Key sample parameters
  • Variability
  • Measures of shape
• Histograms
• Establishing and analyzing correlation among different variables
• Curve fitting and explanation of variance

5. Statistical Inference

• Sampling distributions
• Performing the t-test and degrees of freedom
• Establishing confidence interval for the mean and standard deviation
• Selecting sample size

6. Regression and Multiple Regression

• Formulating a hypothesis
• Regression and multiple regression analysis
• Inference and goodness of fit
• Testing adequacy of model and establishing confidence intervals

7. Forecasting

• Forecasting methods and models
  • Use of history and independent variables
  • Long and short term forecast
  • Heuristics
• Time series analysis
• Establishing trends and business cycles (i.e. seasonality) and confidence limits
• Selecting independent variables for predictive model

8. Decision Support

• Scope of the decision and measure of performance
• Controllable and uncontrollable variables
• Uncertainty and risk analysis
• What if analysis
• Identification and description of needed information types

9. Data Mining and Data Warehousing

• Data mining concepts and application
• Application benefits of data warehousing

Exercises:

Exercise 1: Execute Excel function to estimate parameters of a given probability distribution. From these results establish the expected value, standard deviation, and the confidence limits. Apply these properties in reviewing a given business situation to address a problem and establish the necessary business rules.

Exercise 2: Data sets will be provided to calculate a number of parameters with the help of Excel. Working in teams a given business scenario will be examined and the data entered on Excel to fit a curve and estimate the degree of correlation and for explaining the variance of the dependent variable. You will determine the significant variables impacting the chosen dependent variable and determine the performance metrics required for effective tracking of the performance of the given business area.

Exercise 3: Individual practices will be conducted for you to learn and execute the needed formulas available in Excel. A group exercise will apply these concepts to a business scenario to determine the value on a real business situation.

Exercise 4: A number of individual practices will provide you with knowledge of the functionality provided by Excel. Working in teams, you will apply the techniques required to calculate performance gaps and to determine root causes of the observed deviations. You will establish a model for establishing a performance target and tracking future performance and identify significant deviations. Formulate a model for tracking the performance of a business process and establish the method for identifying significant deviations with the certainty and time required

Exercise 5: Apply a number of forecasting techniques with the aid of Excel. Once this is complete, you will review a business scenario with data on history of actual results, past forecasts, and the correlation of the dependent variable on a number of external factors to prepare a forecast. You will revise the projection based on your judgment and expertise.

Exercise 6: This group exercise will provide you with information on the scope of a given decision and the additional facts required for you to document the controllable variables and the measure of performance. Additional information will be provided on the history of results and the occurrence of a set of independent variables to select the critical independent variables impacting the decision. You will estimate the uncertainty and describe the risks. Information will be provided to determine the degree of uncertainty and associated risks. The required information needed to support the given decision and the impact on the data requirements will be documented.

Course Labs