Applied Statistical Methods for Data Science and Analysis

This course is designed for beginner Data Analysts, IT professionals, and marketing or financial specialists looking to enhance their data analysis skills. It covers fundamental statistical methods, data management, and visualization techniques. Participants will learn to develop data-driven solutions and make informed business decisions.

Learn essential statistical methods and data analysis techniques for data-driven projects!

Understand key concepts like frequency interpretation, measures of central tendency (mean, median, mode), sampling, and hypothesis testing. Master data preparation, cleaning, and handling missing data, and explore data analysis approaches for social science, big data, and data mining.
Grasp data visualization using tools like PowerBI, Tableau, and Excel, and gain experience with SPSS, JASP, and multivariate analysis techniques. Learn how to communicate data insights through infographics, reports, and presentations.
As businesses increasingly rely on data-driven decisions, this knowledge enables more accurate forecasting, trend identification, and problem-solving, meeting the growing demand for data professionals.

• Statistics Basics: Learn key concepts like central tendency, sampling, and hypothesis testing.
• Data Preparation: Explore data profiling, cleaning, and handling missing data.
• Analysis Techniques: Apply methods for big data, social sciences, and multivariate analysis.
• Visualization & Tools: Use PowerBI, Tableau, JASP, SPSS, and Excel for data analysis.
• Effective Communication: Present insights through reports, dashboards, and infographics.

• The course will be held in English as well as all projects and questions will be submitted in English.
• E-learning materials, self-paced: Access interactive digital materials and guided coding videos to study at your own pace, with one year of rewatching available.
• Learn-by-doing approach, weekly schedule: Apply your knowledge through weekly practice exercises, requiring 8-12 hours of study each week.
• Constant mentoring, live sessions: Receive feedback on projects, ask questions anytime, and join live sessions for personalized support.
• Exam, certificate: Complete an exam and/or hand in your final project at the end of the course to earn a certificate for your CV and LinkedIn profile.