Introduction to Data Analysis

Organizations need to make business decisions more quickly and accurately than ever before. Basing these decisions on data and best practice analysis techniques and less on gut feel or «the way we have always done things» is how today’s corporate management is demanding information. A solid foundation of data analysis for business decision making is a critical skill you should have regardless of whether your motive is to obtain or sustain a competitive advantage or simply better steward your resources to serve customers. In this course, you will learn to use data analytics to create actionable recommendations, as well as identify and manage opportunities where data-based decisions can be used to change the way you do business.

This course provides many of the common data analysis tools used to gather, analyze and adapt your data to feed business decisions. You do not need heavy Excel or data analysis experience. This course includes introductory exercises on Excel add-ins, standard deviation, random sampling, and an introduction to pivot tables and charts. These exercises will show you how to effectively demonstrate basic data analysis functions and reporting in Excel or Google Spreadsheets. We will simplify math jargon and complex symbols and equations to concentrate on what your data can tell you and your organization. In addition, you will learn how to present to those executives, managers and subject matter experts who need to quickly make decisions that drive your organization.


Terms, jargon, and impact of business intelligence and data analytics
Scope and application of data analysis
Impact of analytics on gaining competitive advantage and decision support
Measure the performance of and improvement opportunities for business processes
Need for tracking and identifying the root causes of deviation or failure
Basic principles, properties, and application of probability theory and the normal distribution
Introduction to different methods for summarizing information and presenting results including charts
Statistical inference and drawing conclusions about the population
Sample sizes and confidence intervals, and how they influence the accuracy of your analysis
Forecasting and an introduction to simple linear regression analysis
Interpret your results and draw sound and relevant conclusions on business
Methods and algorithms for forecasting future results and to reduce current and future risk
Process improvement and analysis skills
Where powerful reference material exists and how to leverage to enhance your decision-making

Cloud computing

Disponible en formato e-learning

Disponible en formato presencial

Disponible en formato a distancia

Subvención disponible
A través de Fundae, cumpliendo requisitos.

10 horas

  • Dificultad 50% 50%
  • Nivel alcanzado 80% 80%

Dirigido a

Individuals involved in operations, project management, business analysis, or management, who need an introduction to data analysis

Conocimientos requeridos


1. Course Introduction

Logistics, materials, and course expectations
Agile and integrated (A&I™) set of tools and best practices
References and resources
2. Introduction to Data Analysis and Analytics

Definition and history
Current technology, the growing availability of data, and increasing challenges
Applications for gaining competitive advantages
3. Rethinking the Value and Usage of Data

The impact of vast volumes of available data especially for decision making
Data difficulties and limitations: ROI vs. effort/expense, incomplete and inconclusive data
Dealing with data uncertainty
Getting real value out of your data: The data continuum
Effective and responsible data ownership
Advantages and disadvantages of qualitative and quantitative data types
Solutions and best practices to transform the way your organization accesses and uses data
Organizing the entire organization’s data for maximum efficiency using easily available tools
Taking advantage of the expertise of the entire organization
4. Introduction to Data Mining and Data Warehousing

Data Mining concepts and application
Application benefits of data warehousing
5. Data Distribution and Variance

Decision making under uncertainty
Data distribution
Standard deviation
6. Information Needs

Operational and executive information classes
Key functional transactions and documents
Map information needs to underlying data
Executive information needs and the balanced scorecard
Role of the business analyst and data analyst
How to use simple pivot tables in Excel or Google Sheets to analyze and present your data
Tracking and managing business process performance
Learning from data
7. Data Exploration Concepts and Methods

Basic concepts
Descriptive measures of a sample
Statistical hypothesis and inference
Dependence and correlation
Moving beyond data and decision uncertainty – managing risk
8. Forecasting

Forecasting methods and models
Time series analysis
Linear regression
Establishing trends and business cycles (i.e., seasonality)
Selecting independent variables for predictive models including regression techniques
9. Review, Best Practices, and Next Steps

Data analysis and transformation
Best practices revisited
Next steps options
10. Course Closeout: Putting It All Together. The Value of Powerful Data

11. Additional Resources and Exercises

Solicita información del curso

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