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Basics of Statistical Analysis

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By Daniel Ford

By Daniel Ford

Cloud computing and automation instructor.

This course introduces core statistical concepts, including probability, confidence intervals, and hypothesis testing. Participants will learn how to perform simple statistical analyses that guide decision-making in real-world scenarios.

Why It’s Worth It

Unlock real value — from fast results to long-term transformation.

Gain a solid understanding of core statistical concepts that are critical in various fields.

Learn to analyze data effectively, enabling better decision-making in professional and personal contexts.

Develop confidence in using statistics, making you a more competent and informed individual in data-driven environments.

Your Learning Roadmap

See everything included in your journey — from quick wins to deep dives.

Introduction to Statistical Concepts

Students will be introduced to the fundamental ideas of statistical analysis, including what statistics is, its historical evolution, and its application in modern decision-making processes. This module sets the stage for all subsequent content by emphasizing statistical thinking and basic terminologies. Overview of Statistics Types of Data and Measurement Scales Statistical Thinking and Process

Data Collection and Summarization

This module focuses on the methods of collecting accurate data and techniques to summarize this data using various descriptive statistics. Participants will explore different data sources, learn the importance of data quality, and use visual and numerical methods to summarize information. Data Collection Methods Organizing and Visualizing Data Measures of Central Tendency and Dispersion

Introduction to Probability

This module demystifies probability by explaining its basic principles and applications. Learners will understand how probability forms the backbone of statistical inference and decision-making. The module covers fundamental rules and introduces common probability distributions. Basic Probability Concepts Conditional Probability and Independence Common Probability Distributions

Confidence Intervals and Estimation

In this module, participants will understand the process of estimating population parameters and learn how to construct confidence intervals. This module emphasizes the mathematical reasoning behind estimation and discusses factors like margin of error and sample size. Real-life examples are used to illustrate these concepts in practice. Understanding Estimates and Parameters Constructing Confidence Intervals Margin of Error and Sample Size

Hypothesis Testing

This module introduces the framework for hypothesis testing, focusing on the steps required to test assumptions about a data set. Participants will learn how to formulate clear hypotheses, choose appropriate tests, and interpret results. The lesson emphasizes the use of p-values and error types in reaching conclusive insights. Formulating Hypotheses Selecting the Appropriate Test Interpreting p-values and Conclusions

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What Users Are Saying

Feedback from people exploring the learning experience
This course was a game-changer for me! The concepts were clearly explained, and I feel much more confident in my statistical skills.
Carlos Mendes
I enjoyed the course overall. The lessons were easy to follow and applicable to my work in market research.
Aisha Khan
Fantastic course! The real-world examples helped me understand how to apply statistical analysis to my business decisions.
Oluwaseun Adebayo
An excellent introduction to statistics. I appreciated the focus on hypothesis testing, it made it very practical.
Maya Zhang
The course was very informative. I now feel equipped to conduct my own analyses – highly recommended!
John Mwanga
The course was good, but I felt some topics could have been covered in more depth. Overall, a solid introduction to statistics.
Elena Rossi

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