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Basic Statistics and Regression in Python

Basic Statistics and Regression in Python

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Basic Statistics and Regression in Python Exam

The certification in Basic Statistics and Regression in Python introduces learners to the science of analyzing data and making predictions. Statistics focuses on summarizing and understanding data, while regression explores the relationship between variables to forecast future results. Python makes these tasks easier with ready-to-use tools and libraries designed for data handling.

This program provides practical knowledge of applying statistics and regression in day-to-day scenarios. From analyzing simple datasets to predicting future outcomes, learners gain hands-on skills that are the foundation of modern data analysis. It is an ideal starting point for anyone aiming to grow in data-driven careers.
 

Who should take the Exam?

This exam is ideal for:

  • Students & Beginners 
  • Data Analysts 
  • Business Analysts 
  • Researchers 
  • Software Developers 
  • Aspiring Data Scientists 

Skills Required

  • Basic knowledge of Python programming
  • Interest in working with numbers and data
  • Logical thinking and problem-solving ability
  • Curiosity about data patterns and predictions

Course Outline

  • Domain 1 - Introduction to Statistics and Python
  • Domain 2 - Descriptive Statistics
  • Domain 3 - Probability Fundamentals
  • Domain 4 - Inferential Statistics
  • Domain 5 - Introduction to Regression Analysis
  • Domain 6 - Multiple Regression Models
  • Domain 7 - Python for Regression
  • Domain 8 - Practical Applications

Key Features

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Basic Statistics and Regression in Python FAQs

Yes, statistics and regression are the foundation of ML models.

Yes, the course covers both statistics and regression modeling.

Students, analysts, developers, and professionals interested in data analysis.

No, only basic statistics and algebra are needed.

Yes, basic Python skills are recommended.

Learning statistics and regression through practical Python examples.

NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn.

Finance, healthcare, retail, technology, research, and marketing.

It predicts outcomes like sales growth, demand, or customer behavior.