Methodology

All analytical methods and solutions are elaborated with step-by-step case studies with practical, hands on experiences. An exhaustive documentation will cover analytical topics with an exclusive face-to-face comparison between SAS, SPSS, STATISTICA, Excel, R and Python.

Course Objectives

By the end of the course, participants will be able to:

Understand and structure data for effective analysis

Evaluate solutions for Data Analysis versus Machine Learning

Distinguish between predictive models and pattern-detection models

Make informed choices between proprietary and open-source technologies

Map the modern data workflow from raw sources to finalized reports

Oversee Data Science projects using project management best practices

Target Audience

This course is for specialists who aspire to become accustomed with data science components, and how they can be applied coordinately to solve data and business problems, as well as research issues. The course is specifically suited for managers and persons involved in marketing, CRM, research, manufacturing, quality control, app developers and IT analysts from almost any sector, such as banks, insurance companies, retail, governments, manufacturers, healthcare, telecom and transport

Target Competencies

Business data analysis

Data analytic validity

Judging AI algorithms

Evaluating IoT platforms

Comparing big data results