Are you ready to move beyond just spreadsheets and start making data-driven decisions based on solid statistical evidence? If you know that a career in Data Science, Business Intelligence, or Analytics demands more than simple averages, this course is your complete guide to building that essential quantitative foundation.

Master the Statistical Foundations of Data Science and Business Analysis

This is the practical, hands-on course you’ve been looking for. We designed it for one purpose: to give you the practical skills to confidently handle data and make reliable statistical inferences.

By the end of this course, you will be able to:

• Build a solid foundation in descriptive statistics (mean, median, dispersion).

• Master core probability concepts like conditional probability and Bayes’ Theorem.

• Understand and apply key probability distributions (Binomial, Poisson, Normal).

• Perform real-world hypothesis testing (like T-tests) to validate business decisions with data.

Why is Statistical Fluency Your Career Superpower?

In the modern world, data is the new oil. But raw data is useless. The real value is in the insights extracted from it. Companies like Google, Netflix, and Amazon use statistical models as the backbone of their decision-making. If you want a career in data, you must speak the language of statistics.

This course is your translator. It bridges the gap between being a “Data User” (who just looks at dashboards) and a “Data Analyst” (who can build and question them). We ensure you have the conceptual clarity and the Python coding skills to work with data confidently and responsibly.

How This Course is Taught (Your Practical Toolkit)

We believe the only way to learn statistics is by doing. We’ll start from Lesson 1, “Introduction to Data and Variables,” and build your knowledge logically, module by module.

• Clear & Simple: We have broken down complex topics like Bayes’ Theorem, the Central Limit Theorem, and p-values into easy-to-follow steps.

• Real-World Focus: We emphasize practical application over abstract theory. We use real-world examples to discuss common pitfalls like sampling bias, effect sizes, and the limitations of statistical tests, ensuring you become an effective and ethical data analyst.

You will gain the skills to handle data quality issues, outliers, and missing values. You’ll learn to construct and interpret confidence intervals and execute one-sample and two-sample T-tests to test real hypotheses.

Ready to start your data science journey with a rock-solid statistical foundation?

Enroll now, watch the free preview lectures, and begin building the quantitative skills that employers demand!

Course provided by MTF Institute of Management, Technology and Finance

MTF is the global educational and research institute with HQ at Lisbon, Portugal, focused on business & professional hybrid (on-campus and online) education at areas: Business & Administration, Science & Technology, Banking & Finance.

MTF R&D center focused on research activities at areas: Artificial Intelligence, Machine Learning, Data Science, Big Data, WEB3, Blockchain, Cryptocurrency & Digital Assets, Metaverses, Digital Transformation, Fintech, Electronic Commerce, Internet of Things.

MTF is the official partner of: IBM, Intel, Microsoft, member of the Portuguese Chamber of Commerce and Industry.

MTF is present in 218 countries and has been chosen by more than 915 000 students.

Course Author:

Dr. Alex Amoroso is a seasoned professional with a rich background in academia and industry, specializing in research methodologies, strategy formulation, and product development. With a Doctorate Degree from the School of Social Sciences and Politics in Lisbon, Portugal, where she was awarded distinction and honour for her exemplary research, Alex Amoroso brings a wealth of knowledge and expertise to the table.

In addition to her doctoral studies, Ms. Amoroso has served as an invited teacher, delivering courses to a wide range of students from undergraduate level to business professionals and executives. Currently, as Head of the School of Business and Management at MTF, she leads the Product Development academic domain. At EIMT, where she also supervises doctoral students, Ms. Amoroso offers advanced instruction in research design and methodologies. Furthermore, she serves as a Research Consultant.

In synergy between academical and business experience, Ms. Amoroso achieved high results in business career, leading R&D activities, product development, strategic development, market analysis activities in wide range of companies. She implemented the best market practices in industries from Banking and Finance, to PropTech, Consulting and Research, and Innovative Startups.

Alex Amoroso’s extensive scientific production includes numerous published articles in reputable journals, as well as oral presentations and posters at international conferences. Her research findings have been presented at esteemed institutions such as the School of Political and Social Sciences and the Stressed Out Conference at UCL, among others.

With a passion for interdisciplinary collaboration and a commitment to driving positive change, Alex Amoroso is dedicated to empowering learners and professionals for usage of cutting edge methodologies for achieving of excellence in global business world.

Who this course is for:

• Beginners in Data Science or Machine Learning who need to build a strong, foundational understanding of Probability and Statistics
• Analysts or researchers who want to start using Python for reliable data exploration and hypothesis testing
• Students looking for a comprehensive and foundational course in statistical methods and inference
• Aspiring Data Scientists, Data Analysts, and Business Intelligence (BI) Professionals.
• Python developers who want to add statistical skills to their toolkit for Machine Learning.
• Business Analysts who want to move beyond basic Excel analysis and make reliable data inferences.
• Students or professionals from any field (finance, marketing, engineering) who need to work with data and validate decisions.
• Anyone who is curious about statistics but finds traditional textbooks boring and impractical.