Statistics for Data Science with Python

Go to Course: https://www.coursera.org/learn/statistics-for-data-science-python

Introduction

### Course Review: Statistics for Data Science with Python **Course Overview:** The course *Statistics for Data Science with Python* on Coursera is a comprehensive introduction to the foundational principles of statistics tailored for aspiring data scientists. In an era where data is the new oil, equipping yourself with statistical knowledge is paramount for effective data analysis. This course not only imparts theoretical understanding but also emphasizes practical application using Python, making it an invaluable resource for learners from various backgrounds. **Syllabus Breakdown:** 1. **Course Introduction and Python Basics:** The course begins with a warm welcome and a quick introduction to Python for those who are new to the programming language. This module ensures that all participants, regardless of their prior experience, are adequately prepared for the statistical concepts that follow. 2. **Introduction & Descriptive Statistics:** This module dives into descriptive statistics, covering essential measures such as mean, median, mode, variance, and standard deviation. By understanding these metrics, learners gain insights into data distribution and variability, which are critical in interpreting datasets effectively. 3. **Data Visualization:** Effective data communication hinges on visualization. This module explores various techniques for displaying data, helping students learn how to choose the right type of graph or chart to represent their findings. You will not only calculate these visualizations but also interpret their significance. 4. **Introduction to Probability Distributions:** Probability is the backbone of statistical analysis. In this section, learners are introduced to probability concepts and various distributions, enabling them to understand the likelihood of events in uncertain scenarios. 5. **Hypothesis Testing:** This module serves as a critical turning point in the course, illustrating how to conduct hypothesis tests correctly. Understanding the assumptions behind each test is highlighted, as well as the proper language for interpreting outcomes—an essential skill for any data scientist. 6. **Regression Analysis:** Transitioning to a more advanced topic, the course teaches regression analysis using Python. Here, students learn how to establish relationships between variables and assess differences in means, providing the foundational skills needed for predictive analytics. 7. **Project Case: Boston Housing Data:** The course culminates in a capstone project involving the Boston Housing dataset. This practical case study allows learners to apply their statistical knowledge to real-world data, conducting analyses and drawing insights using descriptive statistics and hypothesis testing. The peer review component further enhances the learning experience by fostering collaboration and critical feedback. 8. **Final Exam:** The course wraps up with a final exam that tests students on the concepts learned throughout the modules, ensuring a solid understanding of statistics in data science. 9. **Other Resources:** A handy cheat sheet for Statistics in Python is also provided, which serves as a useful reference as students navigate through their data science journey. ### Recommendations: *Statistics for Data Science with Python* is highly recommended for beginners and intermediates alike who want to deepen their understanding of statistical concepts and their applications in Python. Whether you are a student pursuing a career in data analytics, a professional looking to upskill, or a hobbyist interested in data science, this course offers a structured learning path with practical, hands-on projects. The combination of theoretical knowledge and practical application, coupled with the ease of learning through Python, makes it an ideal pick for anyone aiming for proficiency in data analysis. With its well-structured syllabus, engaging modules, and valuable peer interactions, this course sets a solid foundation in statistics that is essential for any data-driven role. Enroll today and take the first step toward harnessing the power of data through statistics!

Syllabus

Course Introduction and Python Basics

Welcome!

This module will focus on introducing the basics of descriptive statistics - mean, median, mode, variance, and standard deviation. It will explain the usefulness of the measures of central tendency and dispersion for different levels of measurement.

This module will focus on different types of visualization depending on the type of data and information we are trying to communicate. You will learn to calculate and interpret these measures and graphs.

This module will introduce the basic concepts and application of probability and probability distributions.

This module will focus on teaching the appropriate test to use when dealing with data and relationships between them. It will explain the assumptions of each test and the appropriate language when interpreting the results of a hypothesis test.

This module will dive straight into using python to run regression analysis for testing relationships and differences in sample and population means rather than the classical hypothesis testing and how to interpret them.

In the final week of the course, you will be given a dataset and a scenario where you will use descriptive statistics and hypothesis testing to give some insights about the data you were provided. You will use Watson studio for your analysis and upload your notebook for a peer review and will also review a peer's project. The readings in this module contain the complete information you need.

Cheat sheet for Statistics in Python