Statistics for Data Science Course in Hyderabad
Master the statistics behind data analysis and machine learning
The Statistics for Data Science course gives you the statistical foundation that analysts and data scientists rely on. You start with descriptive statistics, data types, distributions and visualisation, then study probability, conditional probability and Bayes' theorem, random variables and common distributions such as binomial, Poisson, normal and exponential. You learn sampling, the central limit theorem, confidence intervals, hypothesis testing with t-tests, chi-square tests and ANOVA, p-values and their limits, effect sizes and statistical power, correlation and linear regression with diagnostics, logistic regression, non-parametric tests, A/B testing and an introduction to Bayesian thinking, time series basics and the statistics behind machine learning model evaluation. Every topic is practised with Python (NumPy, pandas, SciPy, statsmodels) on real datasets, and you learn to report results clearly and avoid common mistakes. The course suits graduates, analysts and aspiring data scientists; school-level maths and basic Python are enough. Classes run in Ameerpet, Hyderabad, and in live online batches.
Syllabus
Data Types & Descriptive Statistics
- •Types of data and measurement scales
- •Mean, median, mode, variance, standard deviation, quartiles and IQR
- •Skewness, kurtosis and outlier detection
- •Lab: Summarise a dataset with pandas
- •Outcome: Describe data accurately with the right measures
Data Visualisation for Statistics
- •Histograms, density plots, box plots and bar charts
- •Scatter plots, pair plots and correlation heatmaps
- •Reading QQ plots and diagnosing distribution shape
- •Lab: Create an exploratory data analysis report
- •Outcome: See patterns and problems before testing
Probability Fundamentals
- •Probability rules, independence and combinatorics
- •Conditional probability and Bayes' theorem
- •Simulation of probability problems with Python
- •Lab: Simulate medical test and false-positive scenarios
- •Outcome: Reason correctly about uncertainty
Random Variables & Distributions
- •Discrete and continuous random variables
- •Binomial, Poisson, uniform, normal and exponential distributions
- •Expectation, variance, z-scores and the empirical rule
- •Lab: Fit and compare distributions to real data
- •Outcome: Model real-world randomness
Sampling & the Central Limit Theorem
- •Random, stratified and cluster sampling; sampling bias
- •Sampling distributions, standard error and the central limit theorem
- •Bootstrap resampling
- •Lab: Demonstrate the CLT by simulation
- •Outcome: Understand why sample statistics are reliable
Confidence Intervals & Estimation
- •Point and interval estimation for means and proportions
- •Margin of error and sample size planning
- •Interpreting intervals correctly
- •Lab: Estimate a population proportion from survey data
- •Outcome: Quantify uncertainty in estimates
Hypothesis Testing
- •Hypothesis testing framework, Type I and Type II errors
- •z-tests and t-tests, paired and independent samples
- •p-values, effect sizes, power and multiple testing
- •Lab: Test whether a process change improved results
- •Outcome: Run and interpret common tests correctly
Categorical Data & ANOVA
- •Chi-square tests of independence and goodness of fit
- •One-way ANOVA and post-hoc comparisons
- •Assumptions and diagnostics
- •Lab: Compare conversion across several campaigns
- •Outcome: Compare groups beyond two samples
Correlation & Linear Regression
- •Correlation measures and their pitfalls
- •Simple and multiple linear regression with statsmodels
- •Residual analysis, multicollinearity and influential points
- •Lab: Build and diagnose a pricing regression model
- •Outcome: Fit and defend a regression model
Logistic Regression & Non-Parametric Methods
- •Logistic regression, odds ratios and classification metrics
- •Non-parametric tests when assumptions fail
- •Choosing the right test
- •Lab: Model a yes or no outcome and interpret odds ratios
- •Outcome: Handle binary outcomes and non-normal data
A/B Testing, Bayesian Thinking & Time Series Basics
- •Designing and analysing A/B tests, sample size and stopping rules
- •Bayesian thinking: priors, posteriors and credible intervals
- •Time series basics: trend, seasonality and autocorrelation
- •Lab: Analyse an A/B test with frequentist and Bayesian methods
- •Outcome: Make sound decisions from experiments
Statistics for Machine Learning & Capstone
- •Statistical view of model evaluation: bias-variance, cross-validation and confidence in metrics
- •Common statistical mistakes and how to avoid them
- •Capstone: full statistical analysis of a business dataset with a written report
- •Outcome: A portfolio analysis and a reusable statistics toolkit
Tools Covered
Projects Included
- Exploratory Data Analysis Report: Profile a dataset with statistics and plots; deliverable is a notebook and a one-page summary.
- Hypothesis Testing Case Study: Test a business claim using the right test and report effect size; deliverable is a notebook and decision memo.
- Regression Pricing Model: Build and diagnose a regression to explain prices; deliverable is a model with diagnostics and interpretation.
- A/B Test Analysis: Analyse an experiment with confidence intervals and Bayesian updating; deliverable is a report with recommendations.
- Capstone Statistical Analysis: Complete an end-to-end analysis of a business dataset; deliverable is a written report and a GitHub repository.
Start Your Journey Today
Interested in Statistics for Data Science? Fill in your details and we'll get back to you.
Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the Statistics for Data Science course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on Statistics for Data Science.
Certificate of Completion
Statistics for Data Science
This certifies that
[Your Name]
has successfully completed the
Statistics for Data Science
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured Statistics for Data Science course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
What does the Statistics for Data Science course syllabus cover?
Is Statistics for Data Science very mathematical?
Do I need programming knowledge?
How is this different from the Data Science course?
Why is statistics important for machine learning?
Does the course cover Bayesian statistics?
Is R used?
Which jobs can I target after this course?
How long is the Statistics for Data Science course at GloryTecks, and how many hours a week?
Is the Statistics for Data Science course available as live online training?
What is the fee for the Statistics for Data Science course in Hyderabad?
Will I get a certificate after the Statistics for Data Science course?
What placement support do Statistics for Data Science students get?
Best Statistics for Data Science Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include Data Analyst, Data Scientist (entry level), Business Analyst, Research or Statistical Analyst; support is guidance with the job search, not a guarantee of a job.