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Introduction to Computational Thinking and Data Science

Eric Grimson, John Guttag, and Ana Bell

4.5

Based on 32 ratings

, see reviews

Eric Grimson, John Guttag, and Ana Bell

6.00.2x will teach you how to use computation to accomplish a variety of goals and provides you with a brief introduction to a variety of topics in computational problem solving . This course is aimed at students with some prior programming experience in Python and a rudimentary knowledge of computational complexity. You will spend a considerable amount of time writing programs to implement the concepts covered in the course. For example, you will write a program that will simulate a robot vacuum cleaning a room or will model the population dynamics of viruses replicating and drug treatments in a patient's body.

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6.00.2x will teach you how to use computation to accomplish a variety of goals and provides you with a brief introduction to a variety of topics in computational problem solving . This course is aimed at students with some prior programming experience in Python and a rudimentary knowledge of computational complexity. You will spend a considerable amount of time writing programs to implement the concepts covered in the course. For example, you will write a program that will simulate a robot vacuum cleaning a room or will model the population dynamics of viruses replicating and drug treatments in a patient's body.

Topics covered include:

Advanced programming in Python 3
Knapsack problem, Graphs and graph optimization
Dynamic programming
Plotting with the pylab package
Random walks
Probability, Distributions
Monte Carlo simulations
Curve fitting
Statistical fallacies

What's inside

Learning objectives

Plotting with the pylab package
Stochastic programming and statistical thinking

Monte carlo simulations

Plotting with the pylab package
Stochastic programming and statistical thinking
Monte carlo simulations

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Taught by experienced instructors Grimson, Guttag, and Bell, who are recognized for their work in computer science education and computational problem-solving

Provides hands-on learning through programming exercises and simulations, which strengthens problem-solving skills

Covers advanced Python programming concepts, making it suitable for learners with a basic understanding of the language

Introduces computational techniques like dynamic programming, curve fitting, and statistical fallacies, which are valuable in various fields

Emphasizes stochastic programming and statistical thinking, which are essential for data analysis and predictive modeling

Provides a strong foundation in computational problem-solving, preparing learners for further studies or industry applications

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Reviews summary

Solid introduction to computational data science

According to learners, this course provides a strong foundation in computational thinking and various data science topics. Many appreciate the challenging but practical assignments and problem sets, finding them crucial for learning. While the `lectures` are generally clear, some note the pace is fast and the content can be quite dense, requiring significant effort. Prior Python programming experience is essential; the course is not recommended for absolute beginners in coding. Overall, it's considered a highly valuable step for those with prerequisites looking to deepen their understanding of computational approaches to data problems.

Assumes comfort with intermediate Python programming.

"Definitely requires solid Python skills before starting. It builds on existing programming knowledge."

"If you are new to Python, take the intro courses first. This is not where you learn to code."

"The course moves assuming you are already functional in Python."

Instructors explain complex topics clearly.

"The instructors do an excellent job explaining complex ideas in a clear manner."

"Lectures are well-structured and easy to follow, even with the dense material."

Provides a robust base in data science and computation.

"Gives a solid introduction to computational thinking and data science fundamentals."

"I feel I have a much better grasp of simulations, probability, and analyzing data after this."

"Excellent overview of key topics like Monte Carlo, plotting, and basic stats in a computational context."

Problem sets are demanding but highly effective for learning.

"The problem sets were difficult but very rewarding. They force you to implement the concepts."

"I learned the most from the assignments. They are hard, but that's where the real understanding happens."

"Be prepared to spend a lot of time on the homeworks. They are challenging!"

"The assignments are rigorous and require applying the material deeply, which is great."

Lectures are packed with information and move quickly.

"The lectures are great but cover a lot of ground very quickly. Requires focus."

"I had to pause and rewatch sections of the lectures often because it was so dense."

"The pace is challenging, especially if you're not familiar with some of the math concepts."

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 Introduction to Computational Thinking and Data Science with these activities:

Read Introduction to Computation and Programming Using Python

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Refresh your Python skills to prepare for the course topics.

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Begin reading Chapter 1
Complete Chapter 1 exercises
Review and summarize the key concepts of Chapter 1
Create a list of any questions you have about the material

Attend Python User Group Meetups

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Connect with other Python enthusiasts and learn from their experiences.

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Find local Python user groups
Attend meetups and participate in discussions

Python Coding Exercises

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Reinforce your Python coding abilities.

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Find online coding exercises or use platforms like HackerRank or LeetCode
Practice solving coding problems regularly
Review and analyze your solutions to identify areas for improvement

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Study Group Discussions

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Engage with peers and enhance your understanding through discussions

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Form or join a study group
Meet regularly to discuss course materials
Solve problems, share perspectives, and support each other's learning

Algorithms and Data Structures Workshop

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Deepen your understanding of algorithms and data structures.

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Find and enroll in a workshop
Actively participate and ask questions
Practice and implement the concepts learned

Data Visualization Dashboard

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Gather data from web or trusted sources
Clean and pre-process the data
Create data visualizations using libraries like Matplotlib, Plotly, or Seaborn
Design and develop an interactive dashboard using Flask and Streamlit

Monte Carlo Simulations Tutorial

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Find and follow online tutorials on Monte Carlo Simulations
Apply the techniques to solve a practical problem

Blog Entry: Applying Dynamic Programming

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Enhance your understanding of dynamic programming and improve your writing and communication skills

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Choose a real-world problem that can be solved using Dynamic Programming
Research the problem and existing solutions
Design and implement a solution using Dynamic Programming in Python
Write a blog entry explaining the problem, your solution, and the benefits of using Dynamic Programming

Career center

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

Statistician

Statisticians collect, analyze, and interpret data to provide insights and make predictions. This course is highly relevant to the field as it provides a strong foundation in statistical modeling, data analysis, and probability theory. The course covers topics like plotting with the pylab package, random walks, probability distributions, Monte Carlo simulations, and statistical fallacies. These skills are essential for Statisticians, as they enable them to develop and implement statistical models to solve real-world problems.

See salaries and explore the career path for Statistician

Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course is highly relevant to the field as it provides a strong foundation in probability, statistics, and financial modeling. The course covers topics like advanced programming in Python 3, plotting with the pylab package, random walks, probability distributions, Monte Carlo simulations, and statistical fallacies. These skills are essential for Actuaries, as they enable them to develop and implement mathematical models to assess risk and uncertainty.

See salaries and explore the career path for Actuary

Risk Analyst

Risk Analysts use data and analysis to identify and manage risks. This course is highly relevant to the field as it provides a strong foundation in risk management, data analysis, and statistical modeling. The course covers topics like advanced programming in Python 3, plotting with the pylab package, dynamic programming, random walks, probability distributions, Monte Carlo simulations, and statistical fallacies. These skills are essential for Risk Analysts, as they enable them to develop and implement risk management strategies to protect organizations from potential losses.

See salaries and explore the career path for Risk Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course is highly relevant to the field as it provides a strong foundation in data analysis, statistical modeling, and machine learning. The course covers topics like advanced programming in Python 3, plotting with the pylab package, dynamic programming, random walks, probability distributions, Monte Carlo simulations, and statistical fallacies. These skills are essential for Quantitative Analysts, as they enable them to develop and implement quantitative models for financial analysis.

See salaries and explore the career path for Quantitative Analyst

Data Visualization Specialist

Data Visualization Specialists use data and analysis to create visual representations of data. This course is highly relevant to the field as it provides a strong foundation in data visualization, programming, and design. The course covers topics like advanced programming in Python 3, plotting with the pylab package, dynamic programming, and statistical fallacies. These skills are essential for Data Visualization Specialists, as they enable them to develop and implement data visualization solutions that effectively communicate insights.

See salaries and explore the career path for Data Visualization Specialist

Data Analyst

Data Analysts use programming, statistical analysis, and machine learning to uncover actionable insights from data. This course is highly relevant to the field as it provides a strong foundation in Python programming, data analysis techniques, and statistical modeling. The course covers topics like advanced programming in Python 3, plotting with the pylab package, random walks, probability distributions, Monte Carlo simulations, and statistical fallacies. These skills are essential for Data Analysts, as they enable them to clean, analyze, and interpret data effectively.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models to solve real-world problems. This course is highly relevant to the field as it provides a strong foundation in machine learning, programming, and data analysis. The course covers topics like advanced programming in Python 3, plotting with the pylab package, dynamic programming, random walks, probability distributions, Monte Carlo simulations, and statistical fallacies. These skills are essential for Machine Learning Engineers, as they enable them to build and maintain machine learning systems.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use their expertise in machine learning, statistics, and programming to solve complex business problems. This course is highly relevant to the field as it provides a solid foundation in data analysis, machine learning, and statistical modeling. The course covers topics like advanced programming in Python 3, plotting with the pylab package, dynamic programming, random walks, probability distributions, Monte Carlo simulations, and statistical fallacies. These skills are essential for Data Scientists, as they enable them to develop and implement data-driven solutions.

See salaries and explore the career path for Data Scientist

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. This course is highly relevant to the field as it provides a strong foundation in optimization, modeling, and simulation. The course covers topics like advanced programming in Python 3, plotting with the pylab package, dynamic programming, and statistical fallacies. These skills are essential for Operations Research Analysts, as they enable them to develop and implement mathematical models to optimize business processes.

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UX Researcher

UX Researchers use data and analysis to improve the user experience of products and services. This course is moderately relevant to the field as it provides a solid foundation in data analysis, programming, and human-computer interaction. The course covers topics like advanced programming in Python 3, plotting with the pylab package, dynamic programming, and statistical fallacies. These skills are helpful for UX Researchers, as they enable them to analyze data and develop insights to improve the user experience.

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Software Engineer

Software Engineers design, develop, and maintain software applications. This course is moderately relevant to the field as it provides a solid foundation in programming, data structures, and algorithms. The course covers topics like advanced programming in Python 3, plotting with the pylab package, dynamic programming, and statistical fallacies. These skills are helpful for Software Engineers, as they enable them to develop and maintain robust software applications.

See salaries and explore the career path for Software Engineer

Data Engineer

Data Engineers design, build, and maintain data pipelines to ensure that data is available, reliable, and secure. This course is moderately relevant to the field as it provides a solid foundation in data analysis, programming, and database management. The course covers topics like advanced programming in Python 3, plotting with the pylab package, dynamic programming, and statistical fallacies. These skills are helpful for Data Engineers, as they enable them to develop and maintain data pipelines that meet the needs of the business.

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Business Analyst

Business Analysts use data and analysis to identify and solve business problems. This course is moderately relevant to the field as it provides a solid foundation in data analysis, programming, and business intelligence. The course covers topics like advanced programming in Python 3, plotting with the pylab package, dynamic programming, and statistical fallacies. These skills are helpful for Business Analysts, as they enable them to analyze data and develop insights to improve business operations.

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Financial Analyst

Financial Analysts use financial data and analysis to make investment recommendations and provide financial advice. This course is moderately relevant to the field as it provides a solid foundation in data analysis, statistics, and financial modeling. The course covers topics like advanced programming in Python 3, plotting with the pylab package, dynamic programming, and statistical fallacies. These skills are helpful for Financial Analysts, as they enable them to analyze financial data and develop investment recommendations.

See salaries and explore the career path for Financial Analyst

Cybersecurity Analyst

Cybersecurity Analysts use data and analysis to identify and protect against cyber threats. This course is moderately relevant to the field as it provides a solid foundation in data analysis, programming, and security. The course covers topics like advanced programming in Python 3, plotting with the pylab package, dynamic programming, and statistical fallacies. These skills are helpful for Cybersecurity Analysts, as they enable them to analyze data and develop strategies to protect against cyber threats.

See salaries and explore the career path for Cybersecurity Analyst

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 Introduction to Computational Thinking and Data Science.

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Rating

4.5

Effort

14 to 16 hours per week for 9 weeks

Level

Intermediate

Via

edX

Institution

Massachusetts Institute of Technology

Instructors

Eric Grimson

John Guttag

Ana Bell

Language

English

Transcript

English

This course belongs to a collection called Computational Thinking using Python. It's comprised of two courses:

Introduction to Computer Science and Programming Using Python
Introduction to Computational Thinking and Data Science (viewing)

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Taught by experienced instructors Grimson, Guttag, and Bell, who are recognized for their work in computer science education and computational problem-solving

Provides hands-on learning through programming exercises and simulations, which strengthens problem-solving skills

Covers advanced Python programming concepts, making it suitable for learners with a basic understanding of the language

Introduces computational techniques like dynamic programming, curve fitting, and statistical fallacies, which are valuable in various fields

Emphasizes stochastic programming and statistical thinking, which are essential for data analysis and predictive modeling

Provides a strong foundation in computational problem-solving, preparing learners for further studies or industry applications

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