Computational Thinking and Big Data from edX

Computational thinking is an invaluable skill that can be used across every industry, as it allows you to formulate a problem and express a solution in such a way that a computer can effectively carry it out.

In this course, part of the Big Data MicroMasters program, you will learn how to apply computational thinking in data science. You will learn core computational thinking concepts including decomposition, pattern recognition, abstraction, and algorithmic thinking.

You will also learn about data representation and analysis and the processes of cleaning, presenting, and visualizing data. You will develop skills in data-driven problem design and algorithms for big data.

The course will also explain mathematical representations, probabilistic and statistical models, dimension reduction and Bayesian models.

You will use tools such as R and Java data processing libraries in associated language environments.

What's inside

Learning objectives

Understand and apply advanced core computational thinking concepts to large-scale data sets
Use industry-level tools for data preparation and visualisation, such as r and java

Apply methods for data preparation to large data sets
Understand mathematical and statistical techniques for attracting information from large data sets and illuminating relationships between data sets

Understand and apply advanced core computational thinking concepts to large-scale data sets
Use industry-level tools for data preparation and visualisation, such as r and java
Apply methods for data preparation to large data sets
Understand mathematical and statistical techniques for attracting information from large data sets and illuminating relationships between data sets

Syllabus

Section 1: Data in R Identify the components of RStudio; Identify the subjects and types of variables in R; Summarise and visualise univariate data, including histograms and box plots.

Section 2: Visualising relationships Produce plots in ggplot2 in R to illustrate the relationship between pairs of variables; Understand which type of plot to use for different variables; Identify methods to deal with large datasets.

Section 3: Manipulating and joining data Organise different data types, including strings, dates and times; Filter subjects in a data frame, select individual variables, group data by variables and calculate summary statistics; Join separate dataframes into a single dataframe; Learn how to implement these methods in mapReduce.

Section 4: Transforming data and dimension reduction Transform data so that it is more appropriate for modelling; Use various methods to transform variables, including q-q plots and Box-Cox transformation, so that they are distributed normally Reduce the number of variables using PCA; Learn how to implement these techniques into modelling data with linear models.

Section 5: Summarising data Estimate model parameters, both point and interval estimates; Differentiate between the statistical concepts or parameters and statistics; Use statistical summaries to infer population characteristics; Utilise strings; Learn about k-mers in genomics and their relationship to perfect hash functions as an example of text manipulation.

Section 6: Introduction to Java Use complex data structures; Implement your own data structures to organise data; Explain the differences between classes and objects; Motivate object-orientation.

Section 7: Graphs Encode directed and undirected graphs in different data structures, such as matrices and adjacency lists; Execute basic algorithms, such as depth-first search and breadth-first search.

Section 8: Probability Determine the probability of events occurring when the probability distribution is discrete; How to approximate.

Section 9: Hashing Apply hash functions on basic data structures in Java; Implement your own hash functions and execute, these as well as built-in ones; Differentiate good from bad hash functions based on the concept of collisions.

Section 10: Bringing it all together Understand the context of big data in programming.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches core computational thinking concepts that are used in data science, including decomposition, pattern recognition, abstraction, and algorithmic thinking

Taught by recognized instructors in the field of data science

Develops skills in data-driven problem design and algorithms for big data

Examines mathematical representations, probabilistic and statistical models, dimension reduction, and Bayesian models

Uses industry-level tools for data preparation and visualization, such as R and Java

Provides hands-on labs and interactive materials to reinforce learning

Reviews summary

Intriguing big data course

Learners say Computational Thinking and Big Data is an exciting course related to science education.

Students find the focus on big data intriguing.

"I just begin the course and have not strategic yet.. So very excited with this topic since it relates to science education"

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 Computational Thinking and Big Data with these activities:

Compile a Glossary of Key Computational Thinking Concepts

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Enhance your understanding of core computational thinking concepts by compiling a comprehensive glossary of terms and their definitions.

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Review the course material and identify key computational thinking concepts.
Define each concept in your own words, ensuring clarity and accuracy.
Organize the definitions alphabetically or by category for easy reference.
Share your glossary with peers or instructors for feedback.

Review data representation

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Review and practice data representation techniques to enhance understanding of the course concepts.

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Revise concepts of arrays, linked lists, and trees
Practice implementing data structures in Python or Java
Solve exercises on data representation and manipulation

Review Introduction to Algorithms, Third Edition

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Reinforce your understanding of computational thinking principles by reviewing this classic text, which provides a comprehensive overview of fundamental algorithms and their applications.

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Read the assigned chapters and take notes on key concepts.
Work through the practice exercises and coding challenges provided in the book.
Summarize the main ideas and algorithms covered in each chapter.
Discuss your insights with classmates or a study group.

11 other activities

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Review Java tutorials

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Reviewing Java tutorials will help you solidify your understanding of the basics of Java, object-oriented programming concepts, and coding principles, which will be essential for your success in this course.

Browse courses on Java Basics

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Find reputable online tutorials or courses on Java basics.
Go through the tutorials, taking notes and practicing the concepts.
Complete any coding exercises or assignments provided in the tutorials.

Organize a Study Group for Course Review

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Enhance your understanding and retention by forming a study group with peers to review course concepts and discuss challenging topics.

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Identify a group of peers who are interested in collaborating.
Establish regular meeting times and create a shared agenda.
Review course materials, discuss concepts, and solve problems together.
Provide mutual support and motivation for continuous learning.

Machine learning with scikit-learn

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Follow online tutorials to enhance understanding of machine learning concepts and gain practical experience with scikit-learn library.

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Explore scikit-learn documentation and tutorials
Build and train machine learning models using scikit-learn
Evaluate the performance of models
Apply machine learning techniques to solve practical problems

Follow Data Science Tutorials in R and Java

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Expand your proficiency in data science techniques by following online tutorials that provide practical examples and hands-on exercises in R and Java.

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Identify online tutorials that align with the course topics and your learning goals.
Follow the tutorials step-by-step, replicating the code and experimenting with different parameters.
Document your findings and identify any areas where you need further clarification.
Share your experiences and insights with peers or instructors.

Algorithmic problem-solving exercises

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Engage in algorithmic problem-solving exercises to strengthen problem-solving skills and computational thinking.

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Identify the problem and define the input and output
Design an algorithm to solve the problem
Implement the algorithm in Python or Java
Test and debug the solution
Analyze the efficiency and complexity of the solution

Complete the Java programming challenges

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Strengthen your Java programming skills by working through the practice challenges, which provide opportunities to apply the concepts covered in the course.

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Review the Java syntax and concepts covered in the course material.
Attempt to solve the Java programming challenges provided in the course week
Compare your solutions with the provided solutions and debug any errors.
Reflect on your progress and make adjustments to your learning strategies.

Data visualization project

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Create interactive data visualizations to demonstrate understanding of data analysis and presentation techniques.

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Choose a dataset and explore the data
Select appropriate data visualization techniques
Implement the visualizations using Python or R libraries
Present the visualizations and discuss insights

Design and Implement a Custom Data Visualization Dashboard

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Showcase your ability to apply computational thinking and data visualization techniques by designing and implementing a custom dashboard that presents data insights.

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Identify a dataset and relevant metrics for your dashboard.
Choose appropriate visualization techniques to represent the data effectively.
Design and implement the dashboard using your preferred programming language and visualization library.
Test the dashboard to ensure it is user-friendly and conveys insights clearly.
Share your dashboard with peers or instructors for feedback and evaluation.

Data-driven problem-solving project

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Apply computational thinking and data analysis techniques to solve a real-world problem and communicate the findings.

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Identify a problem and gather data
Analyze the data and identify patterns
Develop a solution using computational thinking
Implement the solution
Evaluate the results and communicate the findings

Contribute to an Open-Source Data Science Project

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Enhance your real-world experience and solidify your understanding by contributing to a collaborative data science project in an open-source environment.

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Identify an open-source data science project that aligns with your interests.
Review the project documentation and identify opportunities to contribute.
Join the project's community and connect with other contributors.
Make meaningful contributions to the project by improving code, adding features, or addressing issues.
Collaborate with team members to ensure the project's success.

Develop a White Paper on a Computational Thinking Application

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Synthesize your knowledge by writing a white paper that explores a specific application of computational thinking in a real-world context.

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Choose a specific industry or domain where computational thinking can be applied.
Research and analyze existing applications of computational thinking in that domain.
Identify a potential new application or improvement area for computational thinking.
Write a white paper outlining your research findings, proposed application, and potential impact.
Share your white paper with industry professionals or academic researchers.

Career center

Learners who complete Computational Thinking and Big Data will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists mine, process, and interpret large volumes of data, providing insights that inform decision-making across various industries. Computational Thinking and Big Data builds a foundation in computational thinking and data analysis, equipping you with the necessary skills to succeed in this field. You'll gain proficiency in data representation and analysis, data-driven problem design, and algorithms for big data, empowering you to extract meaningful insights and make data-driven recommendations.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts translate raw data into actionable insights, enabling organizations to make informed decisions. This course provides a comprehensive introduction to computational thinking and data analysis, which forms the cornerstone of data analytics. By mastering core concepts such as pattern recognition, abstraction, and algorithmic thinking, you'll develop the skills to uncover patterns, identify trends, and draw meaningful conclusions from large datasets.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. Computational Thinking and Big Data provides a comprehensive introduction to computational thinking and data analysis, which are fundamental to quantitative analysis. You'll learn about data representation, statistical techniques, and machine learning algorithms, empowering you to develop and implement sophisticated models for risk assessment, portfolio optimization, and financial forecasting.

See salaries and explore the career path for Quantitative Analyst

Biostatistician

Biostatisticians apply statistical methods to medical research. Computational Thinking and Big Data provides a solid foundation in computational thinking and data analysis, which are essential for success in biostatistics. You'll gain proficiency in data representation, statistical techniques, and data visualization, enabling you to analyze complex medical data, design clinical trials, and make evidence-based recommendations for patient care.

See salaries and explore the career path for Biostatistician

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. Computational Thinking and Big Data provides a solid foundation in the fundamentals of computational thinking and big data analysis, which are essential for success in machine learning. You'll learn about data representation, analysis, and algorithms, empowering you to build robust machine learning models that can solve complex problems and drive innovation.

See salaries and explore the career path for Machine Learning Engineer

Data Architect

Data Architects design and manage data systems and infrastructure. This course provides a comprehensive introduction to computational thinking and data analysis, which are crucial for success in data architecture. You'll learn about data representation, data modeling, and data integration, empowering you to design and implement scalable, reliable, and secure data systems that meet the needs of modern organizations.

See salaries and explore the career path for Data Architect

Statistician

Statisticians collect, analyze, interpret, and present data. This course provides a strong foundation in computational thinking and data analysis, which are essential for success in statistics. You'll gain proficiency in data representation, statistical techniques, and data visualization, enabling you to extract meaningful insights from data and communicate them effectively to a variety of audiences.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. This course provides a solid foundation in computational thinking and data analysis, which are highly sought-after skills in operations research. By mastering concepts such as optimization, simulation, and data modeling, you'll be equipped to develop innovative solutions that improve efficiency, productivity, and decision-making.

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Actuary

Actuaries assess risk and uncertainty in financial, insurance, and other business contexts. Computational Thinking and Big Data provides a solid foundation in computational thinking and data analysis, which are highly valued in the actuarial profession. By mastering concepts such as probability, statistics, and financial modeling, you'll be equipped to quantify and manage risk, develop insurance products, and make informed decisions that protect individuals and organizations.

See salaries and explore the career path for Actuary

Data Engineer

Data Engineers build and maintain data systems and infrastructure, ensuring the availability and integrity of data. This course introduces core concepts in computational thinking and data analysis, which are crucial for data engineering. You'll gain insights into data representation, analysis, and algorithms, enabling you to design and implement efficient data pipelines and storage solutions.

See salaries and explore the career path for Data Engineer

Epidemiologist

Epidemiologists investigate the causes and patterns of health and disease in populations. Computational Thinking and Big Data provides a valuable foundation in computational thinking and data analysis, which are becoming increasingly important in epidemiology. You'll learn how to analyze large health datasets, identify risk factors, and develop data-driven strategies for preventing and controlling diseases.

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Database Administrator

Database Administrators manage and maintain database systems. Computational Thinking and Big Data provides a solid foundation in computational thinking and data analysis, which are valuable skills for Database Administrators. You'll gain insights into data representation, data structures, and query optimization, enabling you to ensure the efficient and reliable operation of database systems.

See salaries and explore the career path for Database Administrator

Business Analyst

Business Analysts bridge the gap between business and technology, using data to drive decision-making. Computational Thinking and Big Data provides a valuable foundation in computational thinking and data analysis, which are essential for success in business analysis. You'll learn how to analyze data, identify trends, and communicate insights, empowering you to make data-driven recommendations that improve business outcomes.

See salaries and explore the career path for Business Analyst

Financial Analyst

Financial Analysts evaluate and make recommendations on investment opportunities. Computational Thinking and Big Data provides a strong foundation in data analysis and problem-solving, which are essential for success in financial analysis. You'll learn how to analyze financial data, identify trends, and develop data-driven investment strategies, empowering you to make informed decisions and maximize returns.

See salaries and explore the career path for Financial Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. While not directly related to computational thinking and big data, this course may be useful for Software Engineers who wish to expand their skill set and gain proficiency in data analysis and problem-solving. The concepts of abstraction, algorithmic thinking, and data representation covered in this course can enhance your ability to design and implement software solutions that effectively handle and process data.

See salaries and explore the career path for Software Engineer