Programming for Data Science from edX

There is a rising demand for people with the skills to work with Big Data sets and this course can start you on your journey through our Big Data MicroMasters program towards a recognised credential in this highly competitive area.

Using practical activities you will learn how digital technologies work and will develop your coding skills through engaging and collaborative assignments.

You will learn algorithm design as well as fundamental programming concepts such as data selection, iteration and functional decomposition, data abstraction and organisation. In addition to this you will learn how to perform simple data visualisations using Processing and embed your learning using problem-based assignments.

This course will test your knowledge and skills in solving small-scale data science problems working with real-world datasets and develop your understanding of big data in the world around you.

What's inside

Learning objectives

How to analyse data and perform simple data visualisations using processing
Understand and apply introductory programming concepts such as sequencing, iteration and selection

Equip you to study computer science or other programming languages

How to analyse data and perform simple data visualisations using processing
Understand and apply introductory programming concepts such as sequencing, iteration and selection
Equip you to study computer science or other programming languages

Syllabus

Section 1: Creative code - Computational thinkingUnderstanding what you can do with Processing and apply the basics to start coding with colour; Learn how to qualify and express how algorithms work.

Section 2: Building blocks - Breaking it down and building it upUnderstand how data can be represented and used as variables and learn to manipulate shape attributes and work with weights and shapes using code.

Section 3: Repetition - Creating and recognising patternsExplain how and why using repetiton can aid in creating code and begin using repetition to manipulate and visualise data.

Section 4: Choice - Which path to followHow to create simple and complicated choices and how to create and use decision points in code.

Section 5: Repetition - Going furtherDiscussing advantages of repetition for data visualisation and applying and reflecting on the power of repetitions in code. Creating curves, shapes and scale data in code.

Section 6: Testing and DebuggingUnderstanding why and how to comprehensively test your code and debug code examples using line tracing techniques.

Section 7: Arranging our dataExploring how and why arrays are used to represent data and how static and dynamic arrays can be used to represent data.

Section 8: Functions - Reusable codeUnderstand how functions work in Processing and demonstate how to deconstruct a problem into useable functions.

Section 9: Data Science in practiceExploring how data science is used to solve programming problems and how to solve big data problems by applying skills and knowledge learned throughout the course.

Section 10: Where next?Understand the context of big data in programming and transform a problem description into a complete working solution using the skills and knowledge you've learned throughout the course, and explore how you can expand the skills learned in this course by participating in future courses.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops algorithm design, data abstraction, and functional decomposition, which are core skills for computer science

Teaches how to perform simple data visualisations using Processing, which is standard in industry

Taught by Katrina Falkner, Claudia Szabo, Nick Falkner, who are recognized for their work in this field

Suitable for those seeking to study computer science or other programming languages

Starts you on your journey through our Big Data MicroMasters program towards a recognised credential in this highly competitive area

This course requires students to come in with basic software skills

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 'Data Science for Business'

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Gain a comprehensive understanding of the role of data science in business decision-making

View Data Science para Negócios on Amazon

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Read the book and take notes
Summarize key concepts and ideas
Discuss the book with others

Review the concepts of data science and big data

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Refresh your knowledge on the fundamentals of data science and big data before starting the course.

Browse courses on Big Data

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Read the course syllabus and review the learning objectives.
Review your notes or textbooks from previous data science courses.
Watch online videos or tutorials on data science and big data concepts.

Review coding exercises

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Practice coding by solving challenges that cover core concepts crucial for this course

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Choose a topic you want to practice
Find a list of exercises
Start solving the exercises

Eight other activities

Expand to see all activities and additional details

Show all 11 activities

Join a study group or online forum

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Engage with peers to discuss course concepts and reinforce your understanding

Browse courses on Collaboration

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Find a study group or online forum that aligns with your interests
Participate in discussions
Share your knowledge and insights with others

Follow tutorials on machine learning algorithms

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Enhance your understanding of machine learning algorithms through guided, hands-on exercises

Browse courses on Machine Learning

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Find a reputable online learning platform
Choose a machine learning course or tutorial
Follow the tutorials and complete the exercises
Experiment with different algorithms and datasets

Practice writing Processing code and manipulating data

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Build your coding skills by following tutorials and practicing writing Processing code.

Browse courses on Data Manipulation

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Find online tutorials or resources that teach Processing coding.
Follow the tutorials and write your own Processing code.
Experiment with different data sets and visualizations.

Practice data manipulation tasks

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Strengthen your understanding of data analysis techniques by applying them to real-world datasets

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Find a dataset that interests you
Load the dataset into a programming environment
Perform data cleaning and exploration tasks
Visualize the data
Write a report summarizing your findings

Seek guidance from experienced data scientists

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Connect with individuals who can provide valuable insights and support your learning journey

Browse courses on Mentorship

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Identify potential mentors within your network or industry
Reach out to them and express your interest in connecting
Set up regular meetings or calls to discuss your progress and seek advice

Solve coding challenges and practice data science algorithms

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Reinforce your understanding of data science algorithms and coding concepts by solving practice problems.

Browse courses on Coding Challenges

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Find online coding challenges or practice problems.
Solve the problems using Processing or other programming languages.
Review your solutions and identify areas for improvement.

Build a data visualization dashboard

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Demonstrate your ability to present data insights effectively by creating an interactive dashboard

Browse courses on Data Visualization

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Choose a dataset and identify the key insights you want to convey
Select a visualization tool
Design and develop the dashboard
Test and refine the dashboard
Share the dashboard with others

Participate in a data science hackathon

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Gain hands-on experience working on real-world data science challenges and collaborating with others

Browse courses on Data Science

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Find a hackathon that aligns with your interests
Form a team or join an existing one
Develop a solution to the hackathon challenge
Present your solution to the judges

Career center

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

Software Engineer

A Software Engineer applies the principles of computer science and software design to the design, development, implementation, testing, deployment and maintenance of software systems.

See salaries and explore the career path for Software Engineer

Computer Programmer

A Computer Programmer writes and maintains the source code for computer programs. With this course, you will learn algorithm design as well as fundamental programming concepts, which are key to success as a Computer Programmer.

See salaries and explore the career path for Computer Programmer

Data Engineer

A Data Engineer designs and builds the infrastructure and tools that enable data scientists and analysts to access and manage data.

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Web Developer

A Web Developer is responsible for the design and development of websites. This course provides the foundational programming knowledge needed in this field as it introduces you to data selection, iteration and functional decomposition.

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Statistician

A Statistician collects, analyzes, interprets and presents data, and advises on the appropriate use of statistical methods.

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Data Architect

A Data Architect designs and manages the architecture of data systems.

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Data Analyst

A Data Analyst analyzes data to extract meaningful insights and help organizations make informed decisions. This course may be useful for aspiring Data Analysts, as it covers data analysis and data visualisation techniques.

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Actuary

An Actuary uses mathematical and statistical methods to assess risk and uncertainty.

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Operations Research Analyst

An Operations Research Analyst uses analytical methods to solve complex problems in business and industry.

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Financial Analyst

A Financial Analyst researches and interprets financial data to make investment recommendations.

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Risk Analyst

A Risk Analyst identifies and assesses risks to businesses and organizations.

See salaries and explore the career path for Risk Analyst

Business Analyst

A Business Analyst identifies and documents the needs of a business and develops solutions to meet those needs. This course may be useful for those seeking a career in business analysis, as it provides a foundation in data analysis and problem-solving.

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Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical models to analyze financial data and make investment decisions.

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Machine Learning Engineer

A Machine Learning Engineer designs, builds and deploys machine learning models to solve business problems. This course may be useful for aspiring Machine Learning Engineers as it provides a foundation in programming and data analysis.

See salaries and explore the career path for Machine Learning Engineer

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 for someone looking to enter this career as it helps build a foundation in programming concepts and data analysis.

See salaries and explore the career path for Data Scientist