learn Essential Statistics for Data science
6 months
(3 hours/day)
44 students/ class
₹ 45,000
(Books are included)
course syallabus
Introduction
Statistics forms the backbone of data science, providing the foundation for analyzing data, identifying patterns, and making informed decisions. The Essential Statistics for Data Science course focuses on equipping participants with the statistical concepts and techniques necessary for data analysis, machine learning, and predictive modeling.
This course covers key topics like descriptive and inferential statistics, probability, hypothesis testing, correlation, regression analysis, and data distributions. Through practical examples and hands-on exercises, participants will learn to interpret data, identify trends, and draw meaningful conclusions. Special emphasis is placed on applying statistical techniques in data science workflows using Python or R.
By the end of this course, participants will have a solid understanding of statistical tools and techniques essential for solving real-world data science problems. Whether you're preparing data, building predictive models, or validating results, this course will empower you to apply statistics effectively in your data science projects.
Objectives
After this course student will be able to:
- Understand the role of statistics in data science and its applications.
- Learn descriptive statistics for summarizing and visualizing data.
- Explore data distributions, including normal, binomial, and Poisson distributions.
- Master probability theory and its use in modeling uncertainty.
- Perform hypothesis testing and understand confidence intervals.
- Analyze relationships using correlation and regression analysis.
- Understand p-values and their significance in hypothesis testing.
- Apply statistical techniques to data science projects using Python or R.
- Develop the ability to interpret and communicate statistical findings.
- Learn statistical methods for feature selection in machine learning.
Certification
Participants who complete the course successfully will receive an Essential Statistics for Data Science Certification. This certification validates your knowledge of statistical concepts and their application in data science workflows, making you stand out in roles requiring data-driven decision-making.
Who can apply for this course?
- Aspiring data scientists who need a solid foundation in statistics.
- Analysts or professionals transitioning into data science roles.
- Machine learning enthusiasts looking to understand the statistical concepts behind algorithms.
- Students or graduates in computer science, mathematics, or engineering.
- Business professionals who work with data-driven decision-making processes.
- Anyone interested in applying statistical methods to real-world problems.
Training Methodology
- Instructor-Led Sessions: Live lessons covering both theory and practical applications of statistics in data science.
- Hands-On Practice: Exercises using datasets to apply statistical methods in Python or R.
- Tool Training: Learn to use libraries like Pandas, NumPy, SciPy, and Statsmodels for statistical analysis.
- Case Studies: Solve real-world problems using statistical techniques.
- Assessments: Periodic quizzes and assignments to ensure understanding of key concepts.
- Capstone Project: Analyze a dataset using statistical methods to draw actionable insights.
- Collaborative Learning: Group activities to discuss and validate statistical results.
