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October 7, 2024
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This course is part of Data Science Fundamentals with Python and SQL Specialization
Instructors: Murtaza Haider
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(425 reviews)
Write Python code to conduct various statistical tests including a T test, an ANOVA, and regression analysis.
Interpret the results of your statistical analysis after conducting hypothesis testing.
Calculate descriptive statistics and visualization by writing Python code.
Create a final project that demonstrates your understanding of various statistical test using Python and evaluate your peer's projects.
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This Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. After completing this course you will have practical knowledge of crucial topics in statistics including - data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA (analysis of variance), regression and correlation analysis. You will take a hands-on approach to statistical analysis using Python and Jupyter Notebooks – the tools of choice for Data Scientists and Data Analysts.
At the end of the course, you will complete a project to apply various concepts in the course to a Data Science problem involving a real-life inspired scenario and demonstrate an understanding of the foundational statistical thinking and reasoning. The focus is on developing a clear understanding of the different approaches for different data types, developing an intuitive understanding, making appropriate assessments of the proposed methods, using Python to analyze our data, and interpreting the output accurately. This course is suitable for a variety of professionals and students intending to start their journey in data and statistics-driven roles such as Data Scientists, Data Analysts, Business Analysts, Statisticians, and Researchers. It does not require any computer science or statistics background. We strongly recommend taking the Python for Data Science course before starting this course to get familiar with the Python programming language, Jupyter notebooks, and libraries. An optional refresher on Python is also provided. After completing this course, a learner will be able to: ✔Calculate and apply measures of central tendency and measures of dispersion to grouped and ungrouped data. ✔Summarize, present, and visualize data in a way that is clear, concise, and provides a practical insight for non-statisticians needing the results. ✔Identify appropriate hypothesis tests to use for common data sets. ✔Conduct hypothesis tests, correlation tests, and regression analysis. ✔Demonstrate proficiency in statistical analysis using Python and Jupyter Notebooks.
Welcome!
2 videos2 readings1 app item
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.
4 videos2 quizzes1 app item
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.
4 videos2 quizzes1 app item
This module will introduce the basic concepts and application of probability and probability distributions.
5 videos2 readings2 quizzes1 app item
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.
5 videos2 assignments1 app item
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.
4 videos2 assignments1 app item
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.
8 readings1 peer review2 app items
1 assignment
Cheat sheet for Statistics in Python
1 reading1 assignment1 plugin
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
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Reviewed on May 2, 2022
It is few of the Data Science courses in my learning series. This is one of the Best in Series. Thanks to the team.
Reviewed on Nov 30, 2021
Excellent Course! Clear and didactical explanations, objectives exercices and very oriented subjects! For those who are interested in data analytics, this the trainning you should take!
Reviewed on Apr 4, 2021
I highly recommend this course for anyone that is having problems with basic statisitcs.
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