Understanding the World Through Data from edX

Speech recognition, drones, and self-driving cars – things that once seemed like pure science fiction – are now widely available technologies, and just a few examples of how humans have taught machines to analyze data and make decisions. In this hands-on, introductory course, you will examine all the forms in which data exists, learn tools that uncover relationships between data, and leverage basic algorithms to understand the world from a new perspective.

Whether you're a high school student or someone switching careers, all you need to get started in this course is a curiosity about the topic of machine learning and a willingness to tinker around with your computer.

The course is taught by modules. Within each module, you'll have access to videos, short exercises, and a final capstone project. In Module 1, you'll begin by looking at different kinds of data. To help you explore the data, you'll dive right into some programming with the Python programming language. You don't need to have any programming background, we will guide you on how to leverage Python to explore and visualize any data.

One kind of data you'll work with is data that relates one variable to another. Coming up with a relationship between two variables—one depending on the other—is at the center of Module 2. In that module, you'll build up some core concepts before seeing your first machine learning algorithm. The goal is to use programming to create models that describe mathematical relationships between data. You'll be able to see how good the model is and use it to make predictions about new data.

In Module 3, you'll see a discussion about where imperfections in collected data might come from. You rarely have perfectly “clean” data sets, so it's important to understand how imperfections impact the model that an algorithm might come up with. To this end, we will introduce the notion of data distributions and build up to the concepts of biased and unbiased noise.

Another kind of data you'll work with is data that belongs in different groups (or classes). Creating a model that predicts what group data belongs in is at the center of Module 4. You'll work through different ways of thinking about this problem and see three different ways of approaching making such groupings (classification).

What's inside

Learning objectives

Python programming and the colab notebook programming environment
Dependent and independent variables
Coming up with relationships between data using linear and polynomial regression models
Recognizing how data is distributed

How to observe noise in distributions and when to ignore it
Categorize data into groups with classification models
And more!

Python programming and the colab notebook programming environment
Dependent and independent variables
Coming up with relationships between data using linear and polynomial regression models
Recognizing how data is distributed
How to observe noise in distributions and when to ignore it
Categorize data into groups with classification models
And more!

Syllabus

Module 1: How to represent and manipulate data

Examples of numerical data

The Python programming language and the Colab notebook programming environment

Loading datafiles in Colab as dataframes and performing simple operations (selecting rows or columns, filtering data by specific conditions, grouping data, applying functions on the resulting groups)

Finding the correlation between columns of the dataframe

Visualizing the data using line plots, scatter plots, histograms, correlation matrix

Module 2: Reverse engineering nature

Dependent and independent variables and how they correspond to real life scenarios

Intuition for what a linear model is

Intuition for what a polynomial model is

Python libraries that can perform the linear regression on data

Compare the quality of different models (mean-squared-error and R^2 values)

Fitting higher order polynomials

Overfitting

Uniform distributions

Gaussian distributions

Distribution mean and standard deviation

Noise in distributions (biased and unbiased noise)

Categorizing data based on particular conditions being met

Using linear regression to classify a new datapoint as above or below the best fit line

Using a support vector classifier to separate two groups of data and classifying a new datapoint into a group

Using logistic regression to classify data into two groups and finding the probabilities of a new datapoint falling into each group

Understanding how to divide data into training and test sets

Module 3: Distributions and Latent Variables

Module 4: How machines think

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Relies on Python, which is standard in industry

Teaches linear regression, which is used in machine learning

Teaches polynomial regression, which is used to model non-linear relationships

Provides hands-on activities that reinforce the concepts

Builds a foundation in data analysis and machine learning

Has modules on data distributions and noise

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 Understanding the World Through Data with these activities:

Organize and review course materials

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Review and organize your notes, course material, and assignments to effectively prepare for the course.

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Gather and整理course notes, assignments, quizzes, and exams
Review and consolidate notes, highlighting important concepts
Organize and categorize materials into logical sections
Create a study schedule and plan for regular review

Engage in peer discussions

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Enhance your understanding through discussions with peers, exchanging ideas, and clarifying concepts.

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Join or create a study group or online forum
Participate actively in discussions, sharing your insights and asking questions
Collaborate on assignments or projects, leveraging diverse perspectives
Provide feedback and support to fellow learners

Practice Python exercises

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Reinforce your Python programming skills by completing practice exercises to enhance your understanding and proficiency.

Browse courses on Python Programming

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Identify online resources or textbooks with Python exercises
Solve exercises covering data manipulation, visualization, and modeling
Debug and optimize your code for efficiency and accuracy
Seek assistance from online forums or mentors when needed

Two other activities

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Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'

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Supplement your course knowledge with this comprehensive book that provides practical insights into machine learning libraries and techniques.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read and understand the key concepts presented in each chapter
Work through the code examples and exercises to reinforce your understanding
Refer to the book for additional clarification or deeper exploration of topics

Develop a machine learning project

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Apply your machine learning knowledge and skills by creating a project that demonstrates your ability to solve real-world problems.

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Identify a problem or challenge that can be addressed with machine learning
Gather and prepare relevant data for your project
Develop and implement a machine learning model to address the problem
Evaluate the performance of your model and make necessary adjustments
Document and present your project, highlighting your approach and results

Career center

Learners who complete Understanding the World Through Data will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design, develop, and maintain machine learning models. A course like Understanding the World Through Data can help Machine Learning Engineers build a foundation in Python programming, data manipulation, and statistical modeling that's often required for this role. The course also covers topics such as linear and polynomial regression, classification models, and data distributions, which are all essential for building and deploying machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts work with data to extract insights and help businesses make better decisions. A course like Understanding the World Through Data can help Data Analysts build a foundation in Python programming, data manipulation, and statistical modeling that's often required for this role.

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Statistician

Statisticians collect, analyze, and interpret data to help businesses make better decisions. A course like Understanding the World Through Data can help Statisticians build a foundation in Python programming, data manipulation, and statistical modeling that's often required for this role.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. A course like Understanding the World Through Data can help Quantitative Analysts build a foundation in Python programming, data manipulation, and statistical modeling that's often required for this role.

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Data Scientist

Data Scientists use statistical methods and machine learning algorithms to uncover patterns in data. A course like Understanding the World Through Data can help Data Scientists build a foundation in Python programming, data manipulation, and statistical modeling that's often required for this role.

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Research Scientist

Research Scientists conduct research to develop new knowledge and solve problems. A course like Understanding the World Through Data can help Research Scientists build a foundation in Python programming, data manipulation, and statistical modeling that's often required for this role.

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Consultant

Consultants help businesses solve problems and improve their performance. A course like Understanding the World Through Data may be useful for Consultants who want to build a foundation in data analysis and machine learning.

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Data Engineer

Data Engineers build and maintain the infrastructure that stores and processes data. A course like Understanding the World Through Data may be useful for Data Engineers who want to build a foundation in data analysis and machine learning.

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Actuary

Actuaries use mathematical and statistical models to assess risk and make financial decisions. A course like Understanding the World Through Data may be useful for Actuaries who want to build a foundation in data analysis and machine learning.

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Data Journalist

Data Journalists use data to tell stories and inform the public. A course like Understanding the World Through Data may be useful for Data Journalists who want to build a foundation in data analysis and machine learning.

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Business Analyst

Business Analysts help businesses make better decisions by analyzing data and identifying trends. A course like Understanding the World Through Data may be useful for Business Analysts who want to build a foundation in data analysis and machine learning.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to improve the efficiency and effectiveness of business operations. A course like Understanding the World Through Data may be useful for Operations Research Analysts who want to build a foundation in data analysis and machine learning.

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Software Engineer

Software Engineers design, develop, and maintain software applications. A course like Understanding the World Through Data may be useful for Software Engineers who want to build a foundation in data analysis and machine learning.

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Financial Analyst

Financial Analysts analyze financial data to make investment decisions. A course like Understanding the World Through Data may be useful for Financial Analysts who want to build a foundation in data analysis and machine learning.

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Product Manager

Product Managers lead the development and launch of new products. A course like Understanding the World Through Data may be useful for Product Managers who want to build a foundation in data analysis and machine learning.

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