Course Overview

Applied statistics is a fundamental skill for data analysis, decision-making, and predictive modeling, and Python provides a powerful ecosystem for performing statistical computations efficiently. Mastering statistical concepts helps in understanding data patterns, making informed business decisions, and validating assumptions in real-world scenarios. Whether you're working in data science, finance, or research, a strong foundation in statistics enhances your ability to interpret data accurately and derive meaningful insights.

This course begins with an introduction to essential statistical concepts, explaining their significance and how to build assumptions for data analysis. It then explores the Central Limit Theorem (CLT), its applications, and best practices. Learners will gain hands-on experience with skewness and kurtosis to assess data distribution. Applied hypothesis testing is covered in-depth, including Type I and Type II errors, p-values, and real-world applications. The course also delves into feature selection, emphasizing correlation and the chi-square test. Finally, statistical modeling is introduced, focusing on business use cases, problem framing, and dataset exploration, culminating in building statistical models using real-world scenarios.

By the end of this course, you will develop the ability to apply statistical techniques in Python, make data-driven decisions, and validate business assumptions using statistical modeling and hypothesis testing.

What You Will Learn

  • Master how to build assumptions
  • Get hands-on applied statistics tools with different data sets
  • Get to know step by step process to build statistical model
  • Deep dive into different aspect of applied statistics
  • Hand-on with real time client project dealing with around more than 10+ millions of data set

Program Curriculum

  • Why Statistics is Important?
  • Essential Statistical Concepts
  • How to Build Assumptions
  • $7 Million Cybersecurity Scholarship by EC-Council
  • Chapter 1 Quiz

  • What is CLT and Its Importance?
  • Essentials of CLT
  • Best Practices of CLT
  • Chapter 2 Quiz

  • What is Skewness?
  • Applied Skewness
  • What is Kurtosis?
  • Applied Kurtosis
  • Chapter 3 Quiz

  • What is Hypothesis Testing and Some Important Parameters
  • Type 1 Error and Type 2 Error
  • What is P-Value?
  • Applied Hypothesis Testing
  • Chapter 4 Quiz

  • Why Feature Selection is Important?
  • Reason Behind Feature Selection
  • Applied Correlation
  • What is Chi-Square Test?
  • Chapter 5 Quiz

  • What is Statistical Modeling?
  • Understanding the Business Use Case
  • Essential Skills to Frame Problem Statement
  • Deep Dive into Data Set
  • Chapter 6 Quiz

  • Understanding the Features and Building Assumptions
  • How to Apply Statistical Tools with Real-time Problem Statement
  • Feature Selection Based on Business Use Case Understanding
  • Looking Closure at Different Testing Using Applied Statistics and Validating Assumptions
  • Chapter 7 Quiz
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Instructor

Vivek Chaudhary chaudhary

Vivek Chaudhary currently works as a freelance data scientist and has worked with different product-based & EdTech startups. He has published one of the best-selling books on Amazon, “Data Investigation-EDA the right way”. His areas of expertise are applied statistics, EDA, data cleaning techniques, and feature engineering and process to building statistical models. According to him “Building Assumptions” is the important factor to apply statistical tools in real-time. If you can’t build assumptions, then no matter how much you learn at the end, it will be difficult to apply statistical techniques. He has mentored 200+ professionals to start their journey and helped them understand applied statistics & EDA.

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