Statistics for Data Science
Master the fundamentals of Statistics tailored for Data Science applications. Learn how to analyze data, draw insights, and make data-driven decisions. Gain hands-on experience with real-world datasets and statistical tools. Build a strong foundation in probability, hypothesis testing, and regression. Turn raw data into powerful business intelligence.
Course Overview
You’ll start with the basics—data types, descriptive statistics, and probability.
Then, move on to hypothesis testing, regression, and inferential statistics.
Each module combines theory with hands-on exercises using real-world datasets.
You’ll also learn how to apply statistical methods with Python and key data tools.
By the end, you’ll have the skills to analyze, interpret, and communicate data effectively.
what you will learn
- Build a strong foundation in descriptive and inferential statistics for data-driven decision making.
- Understand probability, distributions, and sampling essential for data science applications.
- Perform hypothesis testing, correlation, and regression analysis to draw meaningful insights.
- Analyze and visualize real-world datasets using Python, NumPy, Pandas, and Matplotlib.
- Apply statistical methods to solve business problems and support predictive modeling.
- Gain confidence in interpreting data, testing assumptions, and validating results.
- Develop an analytical mindset aligned with Data Analyst and Data Scientist job roles.
- Prepare for advanced machine learning and AI topics with a solid statistical background.
Next Cohort Starts On 30 Nov
Skilled Covererd
- Strengthen your eligibility for roles such as Data Analyst, Data Scientist, or Business Intelligence Specialist.
- Gain in-demand statistical and analytical skills valued across industries.
- Build confidence in working with real-world data and solving business problems.
- Enhance your technical proficiency with hands-on experience in Python-based data analysis.
- Lay a strong foundation for advanced learning in Machine Learning and AI.
- Increase your career growth potential with skills that employers consistently seek.
- Stand out in interviews and professional assessments with proven statistical knowledge.
- Future-proof your career by mastering data-driven decision-making techniques.
Benefits
The certification can help you land lucrative roles in IT, manufacturing, finance, healthcare, and other exciting industries certified project managers drive better project performance and are often rewarded with substantial pay raises as shown below.
Designation
Annual Salary
Hiring Companies
Course Curriculum
Introduction to Data Science
What is Data Science?
Applications of Data Science
Data Science Process
Tools & Roles
Python for Data Science
Statistics and Probability
Data Wrangling and Cleaning
Data Visualization
Exploratory Data Analysis (EDA)
Machine Learning Basics
Model Evaluation and Selection
Big Data Concepts
Capstone Project and Case Studies
Statistics for Data Science FAQ
No prior programming knowledge is required. A basic understanding of mathematics (like averages, percentages, and simple algebra) is helpful but not mandatory.
You’ll primarily use Python along with libraries such as NumPy, Pandas, and Matplotlib for data analysis and visualization.
Yes, this course is designed for beginners and professionals looking to build a strong foundation in statistics for data science applications.
Absolutely. The course includes multiple exercises, case studies, and projects using real-world datasets to reinforce learning.
You’ll gain essential statistical and analytical skills required for roles like Data Analyst, Data Scientist, and Business Intelligence professional — boosting both your confidence and employability.
Eligibility
- Statistical Analysis and Data Interpretation
- Probability, Hypothesis Testing, and Regression Modeling
- Data Manipulation and Visualization using Python (NumPy, Pandas, Matplotlib)
- Data-Driven Problem Solving and Decision Making
- Analytical Thinking and Insight Generation
- Understanding of Statistical Foundations for Machine Learning
Pre-requisites
- Basic understanding of mathematics (especially algebra and arithmetic).
- Familiarity with Microsoft Excel or any basic data-handling tool (helpful but not mandatory).
- No prior programming experience required — Python fundamentals will be introduced in the course.
- A curious and analytical mindset with a willingness to learn data-driven problem-solving.
Contact Us
1800-212-7688
(Toll Free)
Why join this program
Develop skills for real career growth
Cutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
Learn from experts active in their field, not out-of-touch trainers
Leading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
Learn by working on real-world problems
Capstone projects involving real world data sets with virtual labs for hands-on learning
Structured guidance ensuring learning never stops
24×7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts
