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Programming for Data Science

Explore the basics of programming and familiarise yourself with the Python language. After completing this course, you will be able to write Python programs in Jupyter Notebook and describe basic programming.

In this course, you will learn everything you need to start your programming journey. You will discover the different data types available in Python and how to use them, learn how to apply conditional and looping control structures, and write your own functions.

This course provides detailed descriptions of new concepts and background information for additional context. The quizzes available will help you to develop your understanding. You will also complete exercises using Jupyter Notebook on your computer. By using Jupyter Notebook, you will be able to combine your notes with useful examples so that you develop the resources you need to program independently in the future.

This course is a taster of the Online MSc in Data Science (Statistics) but it can be completed by learners who want an introduction to programming and explore the basics of Python.

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What's inside

Syllabus

First steps with Python

This module introduces Python and Jupyter Notebook, as well as the concepts of variables, assignment and basic mathematical operators.

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Suitable for those interested in Data Science, Statistics, and programming

No prior programming knowledge is required

Teaches real-world applications of Python programming

Provides interactive exercises using Jupyter Notebook

Covers Python fundamentals and basic coding concepts

Instructor Dr. Jonathan Ward is a Lecturer in Statistics at the University of Glasgow

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Reviews summary

Foundational python for data science novices

According to students, this course is an excellent starting point for absolute beginners who want to learn Python for data science. Many appreciate the clear explanations and well-structured modules that break down complex concepts. The hands-on exercises using Jupyter Notebook are frequently highlighted as a major strength, providing invaluable practical experience. While it builds a strong foundational understanding, some learners with prior programming experience found the pace too slow and the coverage of advanced topics like functions somewhat light, suggesting it's primarily suited for novices rather than intermediate programmers seeking deep dives.

Hands-on coding in Jupyter Notebook enhances learning.

"The Jupyter Notebook exercises are incredibly helpful for hands-on learning."

"The Jupyter Notebook integration was a big plus for me."

"The hands-on coding in Jupyter was very effective and helped me solidify my understanding."

Concepts are clearly explained and modules are well-organized.

"The explanations are very clear, and the instructor's pace is perfect."

"The modules are well-structured, and the practical examples using Jupyter are invaluable."

"I really enjoyed the detailed descriptions provided for new concepts."

Excellent for those with no prior programming experience.

"This course is absolutely fantastic for beginners! I had zero programming experience and now I feel confident writing Python code."

"As someone transitioning careers, this course was exactly what I needed. It broke down complex concepts into manageable chunks."

"Perfect for a complete novice. The course is very well structured and easy to follow."

May be too slow for learners with prior programming knowledge.

"It's an okay course, but if you have some prior programming experience, it might feel a bit slow."

"I found this course to be very basic; if you know any programming language, this won't offer much new."

"This course is truly for absolute beginners and might bore those who have programmed before."

Provides basics but lacks depth in advanced data science programming.

"My only minor gripe is that it could go a bit deeper into some topics for intermediate learners."

"The content on functions was a bit light for my taste, and I felt I needed to supplement with external resources."

"It's a good starting point, but don't expect to become a data science programmer overnight."

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 Programming for Data Science with these activities:

Read and review 'Automate the Boring Stuff with Python'

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Gain a deeper understanding of Python programming by reading and reviewing a recommended book.

View Automate the Boring Stuff with Python, 3rd... on Amazon

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Purchase or borrow a copy of 'Automate the Boring Stuff with Python'.
Read and review the chapters, taking notes and highlighting important concepts.
Complete the exercises and activities in the book to reinforce your understanding.

Refresh your understanding of basic math operations

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Refresh your understanding of basic math operations to strengthen your foundation for Python programming.

Browse courses on Math

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Review the concepts of addition, subtraction, multiplication, and division.
Practice solving simple math problems involving these operations.

Participate in online discussions or study groups with other students

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Engage with other students to discuss course concepts, share knowledge, and learn from each other.

Browse courses on Python

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Find or create an online discussion forum or study group related to Python.
Participate in discussions, ask questions, and share your own insights.

Four other activities

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Practice writing Python code in Jupyter Notebook

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Reinforce your understanding of Python syntax and improve your coding skills by practicing in Jupyter Notebook.

Browse courses on Python

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Create a new Jupyter Notebook.
Write Python code to perform simple tasks, such as printing text or performing basic calculations.
Save and run your code to see the results.

Follow tutorials on Python data types and structures

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Expand your knowledge of Python data types and structures by following guided tutorials.

Browse courses on Python

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Find tutorials on Python data types and structures, such as numbers, strings, lists, and dictionaries.
Follow the tutorials step-by-step and try out the examples provided.

Develop a simple Python program to solve a specific problem

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Apply your Python skills to solve a specific problem and create a working program.

Browse courses on Python

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Identify a problem that can be solved using Python.
Design and implement a Python program to solve the problem.
Test and refine your program to ensure it works correctly.

Contribute to open-source Python projects

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Enhance your Python skills and contribute to the wider Python community by participating in open-source projects.

Browse courses on Python

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Find open-source Python projects that align with your interests.
Review the project's documentation and codebase.
Identify areas where you can contribute, such as bug fixes or feature enhancements.
Submit your contributions to the project and engage with the community.

Career center

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

Data Analyst

A Data Analyst gathers, analyzes, interprets, and presents data. This course may be useful because it provides a background in Python, a programming language commonly used by Data Analysts. Students will learn how to write Python programs in Jupyter Notebook, a common tool for data analysis. The course will also cover data types in Python, such as numbers, strings, Booleans and None, as well as structured data types, including lists, tuples, sets, dictionaries and classes.

See salaries and explore the career path for Data Analyst

Data Scientist

A Data Scientist uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course may be useful because it provides a background in Python, a programming language commonly used by Data Scientists. Students will learn how to write Python programs in Jupyter Notebook, a common tool for data science. The course will also cover data types in Python, such as numbers, strings, Booleans and None, as well as structured data types, including lists, tuples, sets, dictionaries and classes.

See salaries and explore the career path for Data Scientist

Software Engineer

A Software Engineer designs, develops, tests, deploys, maintains, and modifies the software or application programs that operate computers and computer-controlled devices. This course may be useful because it provides a background in Python, a programming language commonly used by Software Engineers. Students will learn how to write Python programs in Jupyter Notebook, a common tool for software development. The course will also cover data types in Python, such as numbers, strings, Booleans and None, as well as structured data types, including lists, tuples, sets, dictionaries and classes.

See salaries and explore the career path for Software Engineer

Statistician

A Statistician applies statistical theory and methods to collect, analyze, interpret, and present data. This course may be useful because it provides a background in Python, a programming language commonly used by Statisticians. Students will learn how to write Python programs in Jupyter Notebook, a common tool for statistical analysis. The course will also cover data types in Python, such as numbers, strings, Booleans and None, as well as structured data types, including lists, tuples, sets, dictionaries and classes.

See salaries and explore the career path for Statistician

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models. This course may be useful because it provides a background in Python, a programming language commonly used by Machine Learning Engineers. Students will learn how to write Python programs in Jupyter Notebook, a common tool for machine learning. The course will also cover data types in Python, such as numbers, strings, Booleans and None, as well as structured data types, including lists, tuples, sets, dictionaries and classes.

See salaries and explore the career path for Machine Learning Engineer

Quantitative Analyst

A Quantitative Analyst applies mathematical and statistical methods to financial data. This course may be useful because it provides a background in Python, a programming language commonly used by Quantitative Analysts. Students will learn how to write Python programs in Jupyter Notebook, a common tool for financial analysis. The course will also cover data types in Python, such as numbers, strings, Booleans and None, as well as structured data types, including lists, tuples, sets, dictionaries and classes.

See salaries and explore the career path for Quantitative Analyst

Research Scientist

A Research Scientist conducts research in a specific scientific field. This course may be useful because it provides a background in Python, a programming language commonly used by Research Scientists. Students will learn how to write Python programs in Jupyter Notebook, a common tool for scientific research. The course will also cover data types in Python, such as numbers, strings, Booleans and None, as well as structured data types, including lists, tuples, sets, dictionaries and classes.

See salaries and explore the career path for Research Scientist

Data Engineer

A Data Engineer designs, builds, and maintains data pipelines and infrastructure. This course may be useful because it provides a background in Python, a programming language commonly used by Data Engineers. Students will learn how to write Python programs in Jupyter Notebook, a common tool for data engineering. The course will also cover data types in Python, such as numbers, strings, Booleans and None, as well as structured data types, including lists, tuples, sets, dictionaries and classes.

See salaries and explore the career path for Data Engineer

Business Analyst

A Business Analyst identifies and analyzes business needs and develops solutions. This course may be useful because it provides a background in Python, a programming language commonly used by Business Analysts. Students will learn how to write Python programs in Jupyter Notebook, a common tool for business analysis. The course will also cover data types in Python, such as numbers, strings, Booleans and None, as well as structured data types, including lists, tuples, sets, dictionaries and classes.

See salaries and explore the career path for Business Analyst

Actuary

An Actuary assesses financial risk and develops solutions. This course may be useful because it provides a background in Python, a programming language commonly used by Actuaries. Students will learn how to write Python programs in Jupyter Notebook, a common tool for actuarial analysis. The course will also cover data types in Python, such as numbers, strings, Booleans and None, as well as structured data types, including lists, tuples, sets, dictionaries and classes.

See salaries and explore the career path for Actuary

Reading list

We've selected 14 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Programming for Data Science.

Python for Data Analysis

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Comprehensive introduction to Python programming for data science applications. It covers the basics of the Python language, as well as more advanced topics such as data manipulation, statistical modeling, and machine learning. This book valuable resource for anyone who wants to learn how to use Python for data science.

Programming for Data Science

Here's a deal for you

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Foundational python for data science novices

Activities

Career center

Reading list

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