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Programming for Data Science
Dr Jonathan Ward
and
Hassan Izanloo
Dr Jonathan Ward
and
Hassan Izanloo
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/dpxgi0/programming
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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Syllabus
First steps with Python
This module introduces Python and Jupyter Notebook, as well as the concepts of variables, assignment and basic mathematical operators.
Read more
Data Types in Python
This module introduces the fundamental data types in Python, namely numbers, strings, Booleans and None. It also introduces structured data types, including lists, tuples, sets, dictionaries and classes.
Control Structures and Functions
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
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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Activities
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Programming for Data Science.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Programming for Data Science will develop knowledge and skills
that may be useful to these careers:
Data Scientist
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.
Machine Learning Engineer
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.
Business Analyst
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.
Statistician
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.
Software Engineer
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.
Data Analyst
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.
Data Engineer
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.
Quantitative Analyst
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.
Actuary
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.
Research Scientist
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.
For more career information including salaries, visit:
OpenCourser.com/course/dpxgi0/programming
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.
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.
Provides a thorough introduction to data science concepts and techniques, using Python as the programming language. It covers topics such as data cleaning, wrangling, analysis, and visualization. This book great choice for anyone who wants to learn the basics of data science.
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Practical guide to using Python for data analysis. It covers the basics of Python, as well as more advanced topics such as data manipulation and visualization. This book valuable resource for anyone who wants to learn how to use Python for data analysis.
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Provides a thorough introduction to the Python programming language. It covers the basics of Python, as well as more advanced topics such as object-oriented programming and functional programming. This book valuable resource for anyone who wants to learn how to use Python effectively.
Practical guide to machine learning using Python. It covers the basics of machine learning, as well as more advanced topics such as deep learning. This book valuable resource for anyone who wants to learn how to use machine learning for real-world applications.
Comprehensive introduction to machine learning using Python. It covers the basics of machine learning, as well as more advanced topics such as deep learning and natural language processing. This book valuable resource for anyone who wants to learn how to use machine learning for a variety of applications.
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Collection of recipes for solving common programming problems in Python. It covers a wide range of topics, from basic tasks such as file I/O to more advanced topics such as web development and data science. This book valuable resource for anyone who wants to learn how to use Python to solve real-world problems.
Provides a comprehensive overview of the Python standard library. It covers all of the modules that are included in the standard library, as well as how to use them. This book valuable resource for anyone who wants to learn how to use the Python standard library to solve real-world problems.
Comprehensive introduction to the Python programming language. It covers all of the major features of the language, as well as how to use them. This book valuable resource for anyone who wants to learn Python in depth.
Save
Provides a fun and engaging introduction to the Python programming language. It uses a visual approach to teaching, with lots of diagrams and examples. This book great choice for beginners who want to learn Python quickly and easily.
Fast-paced introduction to the Python programming language. It covers the basics of Python, as well as more advanced topics such as data analysis and machine learning. This book great choice for beginners who want to learn Python quickly and easily.
Comprehensive introduction to natural language processing using Python. It covers the basics of natural language processing, as well as more advanced topics such as machine translation and text classification. This book valuable resource for anyone who wants to learn how to use natural language processing for a variety of applications.
Practical guide to deep learning using Python. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks and recurrent neural networks. This book valuable resource for anyone who wants to learn how to use deep learning for real-world applications.
Save
Provides a deep dive into the Python programming language. It covers advanced topics such as metaprogramming and code optimization. This book valuable resource for anyone who wants to master the Python programming language.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/dpxgi0/programming
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Effort
3 weeks of study, 2 hours per week
Level
Introductory
Via
Coursera
Institution
University of Leeds
Instructors
Dr Jonathan Ward
Hassan Izanloo
Language
English
Good to know
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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