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.
The field of Business intelligence depends largely on Data analysis tools and techniques iIntroduction to Data Analysisn order to inform effective decision-making. In fact, the disciplines are so intertwined that some often confuse the two. Therefore, we begin our introduction by examining the history of Business intelligence, its relationship to data analysis, and why the two are needed to help businesses deliver a complete assembly of their 'data puzzle'. This module also addresses some of the hurdles businesses face when dealing with data overload and suggests some possible solutions to the problem.
With the explosion of big data, businesses recognize there is a greater need for employing someone who is qualified to correctly analyze the data. In this module, we explore the qualifications for the data analyst as well as the analytic tools associated with the position. It is unfortunate that there is such a dearth of data analysts. With a projected shortage of 190,000 data science jobs into 2020, it is no wonder that businesses are scrambling to recruit talent.
- Learn the terms, jargon, and impact of business intelligence and data analytics.
- Gain knowledge of the scope and application of data analysis.
- Explore ways to measure the performance of and improvement opportunities for business processes.
- Be able to describe the need for tracking and identifying the root causes of deviation or failure.
- Review the basic principles, properties, and application of Probability Theory.
- Discuss data distribution including Central Tendency, Variance, Normal Distribution, and non-normal distributions.
- Learn about Statistical Inference and drawing conclusions about a Data Population.
- Learn about Forecasting, including introduction to simple Linear Regression analysis.
- Learn about Sample Sizes and Confidence Intervals and Limits, and how they influence the accuracy of your analysis.
- Explore different methods and easy algorithms for forecasting future results and to reduce current and future risk.
Who Can Benefit
Anyone involved in operations, project management, business analysis, or management who needs an introduction to Data Analysis, would benefit from this class:
- Business Analyst, Business Systems Analyst, Staff Analyst
- Those interested in CBAP®, CCBA®®, or other business analysis certifications
- Systems, Operations Research, Marketing, and other Analysts
- Project Manager, Team Leads, Project Leads, Project Assistants, Project Coordinators
- Those interested in PMP®, CAPM®, or other project management certifications
- Program Managers, Portfolio Managers, Project Management Office (PMO) staff
- Data Modelers and Administrators, DBAs
- Technical & other Subject Matter Experts (SMEs)
- IT Staff, Manager, VPs
- Finance Staff, Manager
- Operations Analyst, Supervisor
- External and Internal Consultants
- Risk Managers, Operations Risk Professionals
- Operations Managers, Line Managers, Operations Staff
- Process Improvement, Compliance, Audit, & other Governance Staff
- Thought Leaders, Transformation & Change Champions, Change Manager
- Executives, Directors, & other senior staff exploring cost reduction and process improvement options
- Executive and Administrative Assistants and Coordinators
- Job seekers and those who want to show dedication to data analysis and process improvement
- Leaders at all levels who wish to increase their Data Analysis capabilities
Although it is not mandatory, students who have completed the self-paced Introduction to R eLearning course have found it very helpful when completing this course.
Part 1: Data and Information
- Data in the Real World
- Data vs. Information
- The Many “Vs” of Data
- Structured Data and Unstructured Data
- Types of Data
Part 2: Data Analysis Defined
- Why do we analyze data?
- Data Analysis Mindset
- Data Analysis Steps
- Data Analysis Defined
- Descriptive Statistics vs Inferential Statistics
Part 3: Types of Variables
- Categorical vs Numerical
- Nominal Variables
- Ordinal Variables
- Interval Variables
- Ratio Variables
Part 4: Central Tendency of Data
- (Arithmetic) Mean
Part 5: Basic Probability
- Probability Uses In Business
- Ways We Can Calculate Probability
- Probability Terms
- Calculating Probability
- Calculating Probability from a Contingency Table
- Conditional Probability
- Frequency Distribution
Part 6: Distributions, Variance, and Standard Deviation
- Discrete Distributions
- Continuous Distributions
- Standard Deviation
- Population vs. Sample
- Application of the Standard Deviation
- Standard Deviation and the Normal Distribution
- Sigma (σ) Values (Standard Deviations)
- Bimodal distribution
- Skew and Summary
- Other Distributions
- Poisson Distribution
- Exponential Distribution
- Pareto Distribution (“80/20”)
- Log Normal Distribution
- Distributions in Excel
Part 7: Fitting Data
- Bivariate Data (Two Variables)
- Covariance and Correlation
- Simple Linear Regression
- Linear Regression
- Fitting Functions
- Linear Fit
- Polynomial Fit
- Power-Law Fit
Part 8: Predictive Analytics Overview