Probability & Statistics Certified Course

Course Description:

This course Probability & Statistics offers an introduction to both probability theory and statistical methods, focusing on real-world applications and computational tools. After covering the fundamental principles of probability—including counting techniques, random variables, and distribution theory—it transitions to statistical inference methods such as Bayes’ theorem, confidence intervals, hypothesis testing, regression analysis, and both frequentist and Bayesian approach.

Key Features of Course Divine:

• Collaboration with E‑Cell IIT Tirupati
• 1:1 Online Mentorship Platform
• Credit-Based Certification
• Live Classes Led by Industry Experts
• Live, Real-World Projects
• 100% Placement Support
• Potential Interview Training
• Resume-Building Activities

Career Opportunities After Probability & Statistics:

• Data Scientist
• Statistician
• Actuary
• Quantitative Analyst
• Market Research Analyst
• Investment Analyst
• Database Administrator
• Machine Learning Engineer

Essential Skills you will develop Probability & Statistics: 

• Data Interpretation and Analysis
• Probability Theory
• Statistical Inference
• Regression and Correlation
• Critical Thinking and Problem-Solving

Tools Covered:

• SPSS (Statistical Package for the Social Sciences)Used for analyzing survey data and performing complex statistical tests.
• Minitab Focuses on quality improvement and statistical analysis.
• MATLAB Numerical computing and probability modeling in engineering/statistics

Syllabus:

Module 1: Descriptive & Exploratory Data Analysis Data types (categorical vs. quantitative) Summary measures (mean, median, mode, variance, standard deviation) Visual tools: histograms, box plots, bar charts, scatter plots.

Module 2: Probability Foundations Sample spaces and events Axioms of probability Conditional probability, independence, Bayes’ theorem.

Module 3: Random Variables & Distributions Discrete and continuous variables Probability mass functions (PMFs), probability density functions (PDFs), cumulative distribution functions (CDFs) Expectation, variance, moment-generating functions.

Module 4: Common Probability Models Bernoulli, Binomial, Geometric, Poisson Uniform, Normal, Exponential distributions.

Module 5: Multivariate & Joint Distributions Joint, marginal, and conditional distributions Covariance and correlation Independence vs. correlation.

Module 6: Law of Large Numbers & Central Limit Theorem Law of Large Numbers (LLN) Central Limit Theorem (CLT) and implications for sampling.

Module 7: Sampling Distributions & Estimation Sampling distributions of sample means and proportions Point estimation: bias, consistency, efficiency.

Module 8: Confidence Intervals Constructing confidence intervals for means, proportions, variances Interpretation, margin of error, determining sample size.

Module 9: Hypothesis Formulating null and alternative hypo these Type I and II errors, significance levels, p-values Tests: z-test, t-test, chi-square tests, ANOVA, inference in regression.

Module 10: Regression & Correlation Analysis Simple linear regression: estimation, prediction, inference Multiple regression: model assumptions, interaction effects, goodness-of-fit.

Industry Projects:

• Quality Control in Manufacturing
• Stock Market Analysis
• Clinical Trials Data Analysis
• Reliability Testing in Engineering
• Risk Assessment in Insurance

Who is this program for?

• Undergraduate and postgraduate students
• Aspiring data scientists, analysts
• Researchers and academicians
• Working professionals
• Competitive exam aspirants

How To Apply:

Mobile: 9100348679

Email: coursedivine@gmail.com