Statistics : Statistical Analysis Online course

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Statistics: Statistical Analysis is a comprehensive course designed for learners seeking mastery in statistical methods, probability, data analysis, and predictive modeling. This course covers fundamental statistical concepts, including descriptive statistics, data variability, central tendency, distributions, and hypothesis testing. Students will gain hands-on experience with regression analysis, Bayesian methods, and practical analytics applications. By combining theory with real-world examples, this course equips learners with the skills to interpret data accurately, apply statistical algorithms, and make informed decisions. Perfect for beginners, professionals, and anyone interested in data-driven insights, this course emphasizes practical learning and modern statistical approaches to improve analysis outcomes.

Learning Outcomes:

1. Understand key statistical concepts and their real-world applications effectively.
2. Analyze data variability to summarize information with precision and accuracy.
3. Apply probability theories to solve practical statistical problems confidently.
4. Conduct hypothesis testing to make informed data-driven decisions successfully.
5. Use regression analysis and predictive models to forecast trends effectively.
6. Implement Bayesian techniques to refine predictions using past experiences accurately.

Course Link: Statistics: Statistical Analysis

Course Curriculum:

Module 01: The Realm of Statistics – Introduction to statistics and its applications.

Module 02: Basic Statistical Terms – Learn key terms like mean, median, mode, and variance.

Module 03: The Center of the Data – Explore central tendency and data summaries.

Module 04: Data Variability – Measure dispersion, standard deviation, and variance effectively.

Module 05: Binomial and Normal Distributions – Understand probability distributions and their uses.

Module 06: Introduction to Probability – Basics of probability, events, and likelihood calculations.

Module 07: Estimates and Intervals – Learn confidence intervals and statistical estimates techniques.

Module 08: Hypothesis Testing – Test hypotheses using standard statistical methods accurately.

Module 09: Regression Analysis – Explore linear, multiple regression, and predictive modeling techniques.

Module 10: Algorithms, Analytics and Predictions – Apply statistical algorithms to data-driven insights.

Module 11: Learning From Experience: The Bayesian Way – Use Bayesian analysis for improved predictions.

Module 12: Doing Statistics: The Wrong Way – Understand common errors and pitfalls in statistics.

Module 13: How We Can Do Statistics Better – Best practices for accurate, reliable statistical analysis.

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Disclaimer:

This is an online course with pre-recorded lessons. You will get access to the course within 48 hours after your enrolment.