Harvard University is excited to offer a unique opportunity for lifelong learners: 12 free online courses designed to enhance your knowledge and skills across various fields.
Whether you’re interested in data science, biostatistics, or programming, these courses provide an accessible way to engage with cutting-edge content from a prestigious institution—all from the comfort of your home.
With topics ranging from data visualization to statistical modeling, there’s something for everyone in this winter session. Don’t miss out on these exceptional learning experiences that can shape your future—enroll today and embrace the power of education!
1. CS50’s Introduction to Programming with Scratch
This course offers a beginner-friendly introduction to programming, using Scratch—a visual programming language designed by MIT’s Media Lab. Instead of typing out complex code, students build their programs by dragging and dropping graphical blocks, making it accessible to those with no prior coding experience.
Scratch is also a foundational tool for learning programming concepts that apply to traditional languages like Python and Java.
Core Principles: Functions, variables, conditions, loops, and events.
Engaging Exercises: Create animations, games, interactive art, and stories using a visual approach that helps grasp the logic of programming.
Skill Development: Develop an understanding of fundamental programming concepts that are essential for transitioning into text-based programming languages and more advanced computer science courses.
Career Applications: This course serves as an entry point into programming, preparing students for careers in tech by building a strong foundation in computational thinking and problem-solving.
Learn more about this course here.
2. CS50: Introduction to Computer Science
Harvard University’s CS50x is an entry-level course designed to introduce learners to the foundational concepts of computer science and the art of programming.
Suitable for both majors and non-majors, with or without prior experience, this course emphasizes algorithmic thinking and problem-solving across various domains.
Core Principles: Algorithms, data structures, abstraction, software engineering, security, resource management, and web development.
Engaging Exercises: Tackle real-world problems in fields like biology, finance, cryptography, and gaming through programming assignments and a final project.
Skill Development: Learn to code in C, Python, SQL, JavaScript, CSS, and HTML, gaining a broad understanding of the key programming languages and concepts in computer science.
Career Applications: Prepares students for a variety of tech roles, from software development to data analysis and cybersecurity, with essential skills for building robust, secure applications.
Learn more about this course here.
3. CS50’s Web Programming with Python and JavaScript
Building on the foundations laid in CS50, this course delves deeper into web development, focusing on the design and implementation of dynamic web applications. Learners engage with advanced technologies and frameworks used in modern web development.
Core Principles: HTML, CSS, Python, Django, JavaScript, SQL, and Git.
Engaging Exercises: Develop web applications using hands-on projects that incorporate APIs, interactive user interfaces, and cloud services like GitHub and Heroku.
Skill Development: Master database design, security, scalability, and user experience while learning to deploy web applications from scratch.
Career Applications: Ideal for aspiring web developers and software engineers, providing the essential skills to create, design, and launch robust web applications in real-world environments.
Learn more about this course here.
4. CS50’s Introduction to Artificial Intelligence with Python
This course offers a comprehensive introduction to artificial intelligence, with a focus on machine learning using Python. Learners explore core AI concepts and algorithms that drive innovations such as self-driving cars and recommendation systems.
Core Principles: Graph search algorithms, reinforcement learning, machine learning, neural networks, and AI principles.
Engaging Exercises: Hands-on projects where you apply AI techniques to real-world problems like game engines, handwriting recognition, and machine translation.
Skill Development: Gain experience with AI libraries in Python and learn to design intelligent systems, mastering the theoretical foundations and practical applications of AI.
Career Applications: Perfect for anyone aiming to break into AI and machine learning fields, offering critical skills for roles in tech, healthcare, finance, and more.
Learn more about this course here.
5. Technology Entrepreneurship: Lab to Market
This course explores how entrepreneurs transform breakthrough technologies into successful businesses, focusing on how to move innovations from the lab to the marketplace.
Participants will learn key entrepreneurship strategies, including evaluating market readiness, aligning business models, and securing funding.
Key Concepts:
- Systematic approach to technology entrepreneurship.
- Matching customer needs with technological innovations.
- Evaluating technology for market fit and readiness.
- Aligning business and operational models.
- Positioning for funding.
Using real-world examples, this course provides practical insights into the venture creation process, offering a blueprint for commercializing technology. Ideal for aspiring entrepreneurs in technology sectors.
6. CS50 for Lawyers
This course is a specialized version of Harvard University’s renowned CS50 course, tailored specifically for lawyers and law students. It bridges the gap between law and technology, offering a top-down approach to understanding high-level concepts in computer science, rather than focusing on low-level technical details.
The course equips legal professionals with the skills to analyze and make informed decisions regarding the legal implications of technological innovations.
Core Principles: Computational thinking, algorithms, data structures, programming languages (Python and SQL), cryptography, and cybersecurity.
Practical Applications: Case studies and technical instruction explore how technology affects legal frameworks. You’ll learn how to interpret technological decisions from a legal perspective, preparing you to contribute to tech-driven legal conversations.
Skill Development: Hands-on experience with programming and databases enables you to mine data and better understand the underlying technologies that affect your clients.
Career Applications: Ideal for legal professionals working in tech-related fields, such as cybersecurity, business law, and intellectual property, where understanding the intersection of law and technology is crucial.
Learn more about this course here.
7. Introduction to Data Science with Python
In this course, led by Harvard University instructor Pavlos Protopapas, participants gain practical experience using Python to analyze and solve real-world data science problems.
It covers essential libraries such as Pandas, NumPy, Matplotlib, and SKLearn, and provides hands-on practice with key data science and machine learning concepts.
Key Concepts:
- Python coding for data analysis, modeling, and storytelling.
- Use of regression models (Linear, Multilinear, Polynomial) and classification models (kNN, Logistic).
- Techniques for preventing overfitting, regularization, and model evaluation.
- Understanding machine learning and AI foundations.
Prerequisites: Basic Python programming and statistics knowledge is recommended.
8. High-Dimensional Data Analysis
This 4-week online course offered by Harvard T.H. Chan School of Public Health focuses on techniques used for analyzing high-dimensional data, which are crucial in fields like genomics.
The course covers mathematical and statistical methods, helping participants understand how to reduce data complexity and deal with challenges like batch effects in genomic data.
What You’ll Learn:
- Mathematical Distance: Understanding the definition and application of distance in data analysis.
- Dimension Reduction: Techniques like Singular Value Decomposition (SVD) and Principal Component Analysis (PCA).
- Data Visualization: Use of multiple dimensional scaling plots and factor analysis.
- Batch Effects: Detecting and adjusting for the batch effect, a key challenge in genomics.
- Machine Learning: Introduction to clustering analysis (K-means, hierarchical clustering) and prediction algorithms (k-nearest neighbors).
Prerequisites: Intermediate knowledge of data science is expected.
9. Statistics and R
This 4-week online course by Harvard T.H. Chan School of Public Health focuses on introducing basic statistical concepts and R programming, tailored to life sciences. It provides learners with foundational skills necessary for analyzing biological data.
What You’ll Learn:
- Random Variables: Understanding the concept of randomness in data.
- Distributions: Learning about different statistical distributions.
- Inference: Computing p-values and confidence intervals for data analysis.
- Exploratory Data Analysis: Using R to explore datasets and determine statistical approaches.
- Non-parametric Statistics: Robust techniques when data do not meet standard assumptions.
Course Features:
- Hands-on R programming examples.
- Problem sets to test understanding.
- Visualization techniques for exploring new datasets.
- Exposure to reproducible research practices.
Instructors:
- Rafael Irizarry, Professor of Biostatistics, Harvard T.H. Chan School of Public Health.
- Michael Love, Assistant Professor, UNC Gillings School of Global Public Health.
Ideal for: Learners interested in data science for life sciences, with a basic understanding of statistics and R.
Duration: 4 weeks, self-paced, 2-4 hours per week.
10. Data Science: Visualization
This 8-week online course, offered by Harvard T.H. Chan School of Public Health, teaches basic principles of data visualization and how to apply them using ggplot2, an R package. It is part of the Professional Certificate in Data Science program and is suitable for beginners interested in data analysis.
What You’ll Learn:
- Data Visualization Principles: Key concepts and best practices.
- Communicating Data-Driven Findings: Effectively conveying insights through visual representation.
- Using ggplot2: Building custom plots and graphics.
- Avoiding Common Plotting Mistakes: Understanding the weaknesses of certain widely-used plot types.
Course Features:
- Introduction to exploratory data analysis.
- Case studies covering health, economics, and infectious disease trends.
- Focus on detecting errors, biases, and flaws within datasets through visual techniques.
Ideal for: Beginners looking to strengthen their data visualization skills and build a strong foundation in presenting data findings.
Instructors:
- Rafael Irizarry, Professor of Biostatistics, Harvard T.H. Chan School of Public Health.
Duration: 8 weeks, self-paced, 1-2 hours per week.
11. Statistical Inference and Modeling for High-throughput Experiments
This 4-week online course offered by Harvard T.H. Chan School of Public Health focuses on statistical techniques for analyzing high-throughput data. It is ideal for learners looking to deepen their understanding of statistical inference in the context of data from fields like genomics.
What You’ll Learn:
- Organizing High-Throughput Data: Best practices for data management.
- Multiple Comparison Problem: Understanding the challenges in testing multiple hypotheses.
- Family Wide Error Rates: Managing the error rates associated with multiple tests.
- False Discovery Rate: Techniques to control the rate of false discoveries.
- Error Rate Control Procedures: Strategies like Bonferroni Correction for adjusting p-values.
Course Features:
- Discussions on statistical modeling applied to high-throughput data.
- Coverage of parametric distributions such as binomial, exponential, and gamma.
- Introduction to maximum likelihood estimation and hierarchical models.
- Use of R programming for practical examples connecting concepts to implementation.
Instructors:
- Rafael Irizarry, Professor of Biostatistics, Harvard T.H. Chan School of Public Health.
- Michael Love, Assistant Professor, Departments of Biostatistics and Genetics, UNC Gillings School of Global Public Health.
Duration: 4 weeks, self-paced, 2-4 hours per week.
This course is perfect for those looking to enhance their skills in biostatistics and data analysis, particularly in the context of high-throughput experimental data.
12. Introduction to Linear Models and Matrix Algebra
This online course from Harvard T.H. Chan School of Public Health provides an introduction to linear models and matrix algebra, essential for data analysis in the life sciences.
Key Course Details:
- Duration: 4 weeks
- Time Commitment: 2-4 hours per week
- Start Date: November 29, 2023
- End Date: November 27, 2024
- Modality: Online, self-paced
- Price: Free (with an option to add a verified certificate for $219)
What You’ll Learn:
- Matrix Algebra Notation: Understanding the language of matrix operations.
- Matrix Algebra Operations: Performing calculations and manipulations of matrices.
- Application of Matrix Algebra to Data Analysis: Using matrix methods in practical data scenarios.
- Linear Models: Formulating and interpreting linear models for data analysis.
- Brief Introduction to QR Decomposition: Exploring this important factorization technique.
Course Description:
This course emphasizes the foundational role of matrix algebra in experimental design and the analysis of high-dimensional data. You will learn to represent linear models that are frequently used to model variations among experimental units and how to perform statistical inference on these differences, all while utilizing the R programming language.
Instructors:
- Rafael Irizarry, Professor of Biostatistics, Harvard T.H. Chan School of Public Health.
- Michael Love, Assistant Professor, Departments of Biostatistics and Genetics, UNC Gillings School of Global Public Health.
This course is particularly suited for individuals looking to gain skills in statistical modeling and data analysis using R, especially within the context of life sciences.
