Statistics for Data Science with Python from Coursera

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.

What's inside

Syllabus

Course Introduction and Python Basics

Welcome!

Introduction & Descriptive Statistics

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.

Data Visualization

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.

Introduction to Probability Distributions

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

Hypothesis testing

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.

Regression Analysis

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.

Project Case: Boston Housing Data

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.

Final Exam

Other Resources

Cheat sheet for Statistics in Python

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Ideal for those looking to start their journey in data and statistics-driven roles such as Data Scientists, Data Analysts, Business Analysts, Statisticians, and Researchers

Develops an intuitive understanding and proficiency in statistical analysis using Python and Jupyter Notebooks

Strengthens an existing foundation for intermediate learners in statistics

Teaches measures of central tendency and measures of dispersion to grouped and ungrouped data

Provides hands-on experience with statistical analysis using Python and Jupyter Notebooks - the tools of choice for Data Scientists and Data Analysts

Builds a strong foundation for learners with no computer science or statistics background

Reviews summary

Easy to understand data science statistics

Learners say this beginner-friendly course is well structured, easy to follow and well explained. It provides engaging assignments and a practical final project. They describe it as an excellent introduction to statistics in data science but note that it does not go into great depth and assumes some knowledge of statistics. The course materials are well organized and the labs are helpful, but some learners found the explanations to be too brief and missed having downloadable lecture slides. Additionally, there were occasional issues with the IBM Cloud environment. Overall, learners who are new to statistics or want a refresher will find this course largely positive.

Materials are organized and easy to follow.

"A well structured course, simple and direct to the point, with a little of exercising you'll come out with a huge understanding of the statistical concepts."

"It is few of the Data Science courses in my learning series. This is one of the Best in Series."

Suitable for learners with limited statistics knowledge.

"Excellent introduction to basic statistics for data science."

"Quick basic statistics with python."

"It was an excellent course with fundamentals of statistics."

"This is an absolutely useful course to introduce the student in the topics of normal distribution, calculation of probabilities and hypothesis testing applying Python."

"As a Mechanical Engineer, I already knew Normal Distribution but don't know the T-test, ANOVA, etc."

Assignments and labs help reinforce learning.

"Excellent course with a step by step explanation and complete final assignment."

"I especially like the final assignment as it give me a feel for what being a Data Scientist is like."

"I'm particuliarly thankful for the step by step labs and excercises available on IBM."

"Assignments in week 7 of the course are completely unbalanced. The main questions are at the beginning , and the source data and the necessary libraries are at the end of course. There is no sequence , which increase in the time spent on the work."

"The final assignment is very well designed, I was able to review the entire course material and consolidate the learning. I have now a good understanding of hypothesis testing."

Environment can be unstable and unavailable.

"Partially becuase the environment is at times unavailable when needed."

"In addtion the environment has been undergoing upgrades and changes, and the course materials are not up to date with the changes in the cloud environment."

"This felt boring and a bit outdated. Some guides were no longer up to date or relevant."

Some concepts and tools may not be thoroughly explained.

"I found some of the explanations to be quite poor. Often the instructor starts off by detailing the steps for a certain test before you even know what the test is or why you would do it."

"The theoretical part is not clearly explained, I missed the structure, in particular, a more generic intro to every lecture and connection between lectures/slides would make it easier to follow."

"The videos, readings, and labs were not sufficient for me to feel prepared for the assessments."

"There was really no explanation of why you would use certain tools or the underlying statistics principles; the course assumes a lot of the learner (both in statistics and Python) considering it's aimed at beginners."

May not be suitable for complete beginners.

"The course is super useful, but I'm not a fan of the peer-reviewed portion for the project."

"Being a Mechanical Engineer, I already knew Normal Distribution but don't know the T-test, ANOVA, etc. This course covers pretty much about doing statistics using Python but you should know statistics before doing this course."

"This course is rather fit as a refresher."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Statistics for Data Science with Python with these activities:

Probability and Statistics Refresher

Show steps

Review fundamental concepts in probability and statistics to strengthen your foundation and prepare for the course material.

Browse courses on Probability

Show steps

Go through your previous notes or textbooks on probability and statistics.
Solve practice problems to test your understanding and identify areas where you need reinforcement.
Attend online refresher sessions or workshops offered by platforms like Coursera or edX.
Seek guidance from a tutor or mentor if necessary.
Revisit key statistical concepts such as probability distributions, hypothesis testing, and regression analysis.

Learn Python Fundamentals

Show steps

Get started with Python by following guided tutorials to establish a strong foundation in Python programming concepts.

Browse courses on Python

Show steps

Choose a reputable online resource or tutorial platform. Explore options such as Coursera, edX, or DataCamp.
Create an account and enroll in a beginner-friendly Python course.
Follow the tutorials diligently, completing coding exercises and quizzes.
Dive into the basics of data types, variables, operators, control flow, and functions.
Practice regularly to solidify your understanding of Python syntax and concepts.

Organize Course Materials

Show steps

Enhance comprehension and retention by organizing and reviewing course materials, including notes, assignments, and quizzes, promoting active learning and knowledge consolidation.

Show steps

Create a dedicated folder or notebook for course materials.
Organize materials by topic or module.
Review materials regularly to reinforce learning.

Nine other activities

Expand to see all activities and additional details

Show all 12 activities

Review Probability Concepts

Show steps

Refresh understanding of probability theory, probability distributions, and random variables, providing a strong foundation for hypothesis testing and statistical inference.

Browse courses on Probability

Show steps

Review notes and textbooks on probability concepts.
Solve practice problems involving probability calculations.
Discuss probability concepts with peers or a mentor.

Attend a Data Science Networking Event

Show steps

Expand professional network and gain insights from industry experts by attending data science networking events, fostering connections and staying abreast of current trends.

Show steps

Identify and attend relevant data science networking events.
Engage in conversations with professionals in the field.
Exchange ideas and learn about new technologies and trends.

Calculating measures of central tendency

Show steps

Practice calculating mean, median, and mode to strengthen foundational understanding of descriptive statistics and their relevance in data analysis.

Browse courses on Measures of Central Tendency

Show steps

Review the concepts of mean, median, and mode.
Solve practice problems involving the calculation of these measures for various datasets.
Analyze the results and interpret the meaning of the calculated measures.

Data Visualization with Seaborn

Show steps

Gain hands-on experience in visualizing data effectively using Seaborn, a popular Python library for data visualization.

Browse courses on Data Visualization

Show steps

Install the Seaborn library in your Python environment if not already installed.
Explore the Seaborn documentation and tutorials to familiarize yourself with its features.
Load a dataset of your choice or use a publicly available dataset.
Practice creating various types of visualizations such as scatter plots, histograms, and boxplots.
Experiment with different visualization parameters to enhance the clarity and impact of your visualizations.

Data Visualization Presentation

Show steps

Develop communication and data storytelling abilities by creating a presentation that effectively conveys insights derived from data visualization, enhancing understanding of visual representation techniques.

Browse courses on Data Visualization

Show steps

Choose a dataset and create visualizations to highlight key findings.
Design a presentation that clearly communicates the story behind the data.
Practice delivering the presentation to receive feedback and improve communication skills.

Visualizing Data with Python

Show steps

Enhance data interpretation skills by following tutorials on creating informative visualizations using Python, fostering a practical understanding of data storytelling.

Browse courses on Data Visualization

Show steps

Explore online tutorials on data visualization with Python libraries like Matplotlib and Seaborn.
Practice creating different types of charts and graphs, such as histograms, scatterplots, and bar charts.
Analyze the effectiveness of various visualizations in conveying insights from data.

Attend a Regression Analysis Workshop

Show steps

Expand practical knowledge and skills in regression analysis through hands-on workshops, fostering a deeper understanding of modeling relationships between variables.

Browse courses on Regression Analysis

Show steps

Identify and register for a relevant regression analysis workshop.
Attend the workshop and actively participate in the activities.
Apply the learned techniques to analyze real-world datasets.

Hypothesis Testing Project

Show steps

Apply statistical reasoning and hypothesis testing techniques to solve real-world problems, solidifying understanding of inferential statistics and their application in decision-making.

Browse courses on Hypothesis Testing

Show steps

Identify a research question and formulate a hypothesis.
Collect and analyze data relevant to the hypothesis.
Conduct appropriate hypothesis tests and interpret the results.
Write a report summarizing the findings and conclusions.

Data Analysis Project

Show steps

Apply your statistical and data analysis skills to solve a real-world data analysis problem, solidifying your understanding and demonstrating your proficiency.

Show steps

Identify a topic or problem that interests you and has a data-driven aspect.
Gather and clean the necessary data from reliable sources.
Perform exploratory data analysis to understand the data and identify patterns.
Apply statistical methods and algorithms to analyze the data and draw meaningful conclusions.
Present your findings in a clear and concise report or presentation.

Career center

Learners who complete Statistics for Data Science with Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

As a Data Scientist, you will use your knowledge of statistics and Python to analyze data and draw conclusions. This course will provide you with the skills you need to succeed in this role, including how to gather data, summarize data using descriptive statistics, display and visualize data, examine relationships between variables, and conduct hypothesis testing.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

As a Machine Learning Engineer, you will use your knowledge of statistics and Python to develop and implement machine learning models. This course will provide you with the skills you need to succeed in this role, including how to gather data, summarize data using descriptive statistics, display and visualize data, examine relationships between variables, and conduct hypothesis testing.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

As a Business Analyst, you will use your knowledge of statistics and Python to analyze data and draw conclusions. This course will provide you with the skills you need to succeed in this role, including how to gather data, summarize data using descriptive statistics, display and visualize data, examine relationships between variables, and conduct hypothesis testing.

See salaries and explore the career path for Business Analyst

Statistician

As a Statistician, you will use your knowledge of statistics and Python to analyze data and draw conclusions. This course will provide you with the skills you need to succeed in this role, including how to gather data, summarize data using descriptive statistics, display and visualize data, examine relationships between variables, and conduct hypothesis testing.

See salaries and explore the career path for Statistician

Software Engineer

As a Software Engineer, you will use your knowledge of statistics and Python to develop and implement software applications. This course will provide you with the skills you need to succeed in this role, including how to gather data, summarize data using descriptive statistics, display and visualize data, examine relationships between variables, and conduct hypothesis testing.

See salaries and explore the career path for Software Engineer

Data Analyst

As a Data Analyst, you will use your knowledge of statistics and Python to analyze data and draw conclusions. This course will provide you with the skills you need to succeed in this role, including how to gather data, summarize data using descriptive statistics, display and visualize data, examine relationships between variables, and conduct hypothesis testing.

See salaries and explore the career path for Data Analyst

Data Engineer

As a Data Engineer, you will use your knowledge of statistics and Python to design and build data pipelines. This course will provide you with the skills you need to succeed in this role, including how to gather data, summarize data using descriptive statistics, display and visualize data, examine relationships between variables, and conduct hypothesis testing.

See salaries and explore the career path for Data Engineer

Quantitative Analyst

As a Quantitative Analyst, you will use your knowledge of statistics and Python to analyze data and draw conclusions. This course will provide you with the skills you need to succeed in this role, including how to gather data, summarize data using descriptive statistics, display and visualize data, examine relationships between variables, and conduct hypothesis testing.

See salaries and explore the career path for Quantitative Analyst

Researcher

As a Researcher, you will use your knowledge of statistics and Python to analyze data and draw conclusions. This course will provide you with the skills you need to succeed in this role, including how to gather data, summarize data using descriptive statistics, display and visualize data, examine relationships between variables, and conduct hypothesis testing.

See salaries and explore the career path for Researcher

Product Manager

As a Product Manager, you will use your knowledge of statistics and Python to analyze data and draw conclusions. This course will provide you with the skills you need to succeed in this role, including how to gather data, summarize data using descriptive statistics, display and visualize data, examine relationships between variables, and conduct hypothesis testing.

See salaries and explore the career path for Product Manager

Consultant

As a Consultant, you will use your knowledge of statistics and Python to analyze data and draw conclusions. This course will provide you with the skills you need to succeed in this role, including how to gather data, summarize data using descriptive statistics, display and visualize data, examine relationships between variables, and conduct hypothesis testing.

See salaries and explore the career path for Consultant

Epidemiologist

As an Epidemiologist, you will use your knowledge of statistics and Python to analyze data and draw conclusions. This course will provide you with the skills you need to succeed in this role, including how to gather data, summarize data using descriptive statistics, display and visualize data, examine relationships between variables, and conduct hypothesis testing.

See salaries and explore the career path for Epidemiologist

Economist

As an Economist, you will use your knowledge of statistics and Python to analyze data and draw conclusions. This course will provide you with the skills you need to succeed in this role, including how to gather data, summarize data using descriptive statistics, display and visualize data, examine relationships between variables, and conduct hypothesis testing.

See salaries and explore the career path for Economist

Actuary

As an Actuary, you will use your knowledge of statistics and Python to analyze data and draw conclusions. This course will provide you with the skills you need to succeed in this role, including how to gather data, summarize data using descriptive statistics, display and visualize data, examine relationships between variables, and conduct hypothesis testing.

See salaries and explore the career path for Actuary

Financial Analyst

As a Financial Analyst, you will use your knowledge of statistics and Python to analyze data and draw conclusions. This course will provide you with the skills you need to succeed in this role, including how to gather data, summarize data using descriptive statistics, display and visualize data, examine relationships between variables, and conduct hypothesis testing.

See salaries and explore the career path for Financial Analyst