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.

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Read about what's good

what should give you pause

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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

Foundational statistics with python for data science

According to learners, this course provides an excellent foundation in statistics for data science, particularly for those new to the field. Students appreciate the practical hands-on approach using Python and Jupyter Notebooks, making abstract statistical concepts more accessible. It's often described as ideal for beginners, building core knowledge in descriptive statistics, probability, and hypothesis testing. While it offers a strong start for career-focused individuals aiming for roles like Data Analyst, some noted that for deeper understanding or advanced topics, supplementary resources may be beneficial. The inclusion of a real-life project and practical demonstrations is frequently highlighted as a major positive.

Applies learned concepts to a real-world data science problem.

"The Boston Housing Project was a fantastic way to apply everything I learned in a realistic scenario."

"I really valued the final project; it brought all the different modules together in a meaningful way."

"Working with a real dataset for the project made the concepts click and feel much more tangible."

Accessible for those with little to no prior statistics or programming background.

"As someone new to statistics, I found the explanations easy to follow and the pace just right."

"The course truly assumes no prior knowledge, which was a huge relief for me as a beginner."

"It's a great starting point for anyone looking to enter the data science field without a stats background."

Practical use of Python and Jupyter for statistical analysis.

"The hands-on coding in Jupyter Notebooks really helped solidify my understanding of the concepts."

"I appreciated learning how to apply statistical methods directly using Python, a crucial skill for data science."

"The practical examples using Python made the theory much easier to follow and apply."

Builds a solid understanding of core statistical principles.

"This course gave me a very clear understanding of descriptive statistics and probability distributions."

"I finally grasped hypothesis testing, which was always confusing to me before this course."

"It perfectly covers the basic principles needed to start analyzing data effectively."

Provides broad fundamental coverage but less on advanced topics.

"While excellent for basics, I felt it didn't go deep enough into advanced statistical modeling techniques."

"If you already have some stats background, parts of this course might feel a bit too introductory."

"I needed to supplement the course with additional readings for more complex topics like advanced ANOVA."

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

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

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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

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

Browse courses on Python

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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

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

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

Nine other activities

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Review Probability Concepts

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

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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

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

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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

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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

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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

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

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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

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

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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

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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

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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

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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

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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

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

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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

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

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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

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

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

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

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

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

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.

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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.

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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.

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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.

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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.

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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.

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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

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