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.
Statistics for Data Science with Python
Statistics for Data Science with Python
This course is part of Data Science Fundamentals with Python and SQL Specialization
Instructors: Murtaza Haider +1 more
35,125 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.5
(409 reviews)
14 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
91%
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What you'll learn
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.
Skills you'll gain
- Category: Probability And Statistics
- Category: Basic Descriptive Statistics
- Category: Statistical Hypothesis Testing
- Category: Data Visualization
- Category: Regression Analysis
Details to know
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Assessments
6 quizzes, 6 assignments
Taught in English
Build your subject-matter expertise
This course is part of the Data Science Fundamentals with Python and SQL Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 9 modules in this course
Welcome!
What's included
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.
What's included
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.
What's included
4 videos2 quizzes1 app item
This module will introduce the basic concepts and application of probability and probability distributions.
What's included
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.
What's included
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.
What's included
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.
What's included
8 readings1 peer review2 app items
What's included
1 assignment
Cheat sheet for Statistics in Python
What's included
1 reading1 assignment1 plugin
Instructors
Instructor ratings
4.4 (153 ratings)Murtaza Haider
IBM
3 Courses41,315 learners
Aije Egwaikhide
IBM
6 Courses651,792 learners
Offered by
IBM
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Frequently asked questions
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