STATISTICS
Specialization | 24 Course Series | 11 Mock Tests

This Statistical Analysis course equips learners with the knowledge and skills to uncover meaningful insights from data through advanced statistical techniques. Designed to cover concepts typically taught at graduate and postgraduate levels, the course provides a comprehensive foundation in both fundamental and applied statistics.

Participants will develop expertise in areas such as data manipulation, merging and creating datasets, defining and working with variables, calculating variance, standard deviation, covariance, and correlation, and visualizing data using histograms and scatter plots. The course also covers essential statistical principles, including probability, probability distributions, Bayes' theorem, random variables, discrete and continuous distributions, exponential distribution, and expected value calculations. Practical problem-solving examples, such as the Monte Hall problem and applications of distributions like binomial and normal, are included, along with advanced techniques such as ANOVA and the Chi-square test.

In addition, the course emphasizes Predictive Modeling to help learners apply statistical methods for forecasting and decision-making across diverse business contexts. Key techniques include time series analysis, linear regression, and other predictive modeling approaches. These skills can be leveraged to analyze customer behavior, predict churn, forecast financial market trends and stock prices, or study the impact of medical treatments in healthcare and pharmaceutical domains.

This course combines theory with hands-on practice, empowering learners to apply statistical analysis and predictive modeling effectively in real-world scenarios.

• Basic comfort with mathematics is required; prior expertise is not necessary, but understanding core concepts is essential. Familiarity with probability will make learning easier.
• Enjoyment of mathematics at the high school level is a good indicator of readiness for this course.
• Working knowledge of at least one programming language is recommended. Learners should have hands-on experience with a language such as C, Java, C#, or a similar language.
• No strict prerequisites beyond the above; learners from any academic background or professional domain can successfully engage with the course content if these criteria are met.

• Students and professionals from diverse fields, including engineering, science, commerce, management, and medicine.
• Individuals pursuing or holding degrees like B.Tech, M.Tech, BCA, MCA, MBA, B.Sc, BS, MS, and similar programs.
• Entry-level professionals seeking foundational knowledge in statistical analysis.
• Experienced professionals, managers, and business leaders looking to enhance analytical and data-driven decision-making skills.
• Graduate students aiming to strengthen their statistical and predictive modeling expertise.
• Anyone meeting the course prerequisites who wants to build practical skills in statistical analysis and analytics.