Top Online Harvard University Data Science Courses
Data Science, Machine Learning, Deep Learning, and Artificial Intelligence are among the most in-demand skills at this moment and offer lucrative careers with higher salaries. Harvard University offers free data science and AI courses on the online learning platform edX. The article covers top data science Harvard university online courses, along with their benefits and learning outcomes.
Learn more about data science, read our blog – What is data science?
You may also be interested in exploring:
Harvard University Online Courses in Data Science
These handpicked data science courses from Harvard University are at beginner, intermediate and advanced levels. They cover a defined data science syllabus and usually span over a few weeks. take a look –
Best-suited Personal Development courses for you
Learn Personal Development with these high-rated online courses
Data Science: Visualization
Duration – 8 Weeks
Level – Beginner
You will learn
- Data visualization principles
- How to communicate data-driven findings
- How to use ggplot2 to create custom plots
- The weaknesses of several widely-used plots and why you should avoid them
CS50’s Introduction to Artificial Intelligence with Python
Duration – 7 Weeks
Level – Beginner
You will learn
- Graph search algorithms
- Adversarial search
- Knowledge representation
- Logical inference
- Probability theory
- Bayesian networks
- Markov models
- Constraint satisfaction
- Machine learning
- Reinforcement learning
- Neural networks
- Natural language processing
Data Science Linear Regression
Duration – 8 Weeks
Level – Beginner
You will learn
- How linear regression was originally developed by Galton
- What is confounding and how to detect it
- How to examine the relationships between variables by implementing linear regression in R
To learn more about data science, read our blog on – What is data science?
Data Science: R Basics
Duration – 8 Weeks
Level – Beginner
You will learn
- Basic R syntax
- Foundational R programming concepts such as data types, vectors arithmetic, and indexing
- How to perform operations in R including sorting, data wrangling using dplyr, and making plots
Data Science: Visualization (using R)
Duration – 8 Weeks
Level – Beginner
You will learn
- Data visualization principles
- How to communicate data-driven findings
- How to use ggplot2 to create custom plots
- The weaknesses of several widely-used plots and why you should avoid them
Data Science: Capstone
Duration – 2 Weeks
Level – Introductory
You will learn
- How to apply the knowledge base and skills learned throughout the series to a real-world problem
- How to independently work on a data analysis project
Data Science: Probability
Duration – 8 Weeks
Level – Introductory
You will learn
- Important concepts in probability theory including random variables and independence
- How to perform a Monte Carlo simulation
- The meaning of expected values and standard errors and how to compute them in R
- The importance of the Central Limit Theorem
Data Science: Inference and Modeling
Duration – 8 Weeks
Level: Introductory
You will learn
- The concepts necessary to define estimates and margins of errors of populations, parameters, estimates and standard errors in order to make predictions about data
- How to use models to aggregate data from different sources
- The very basics of Bayesian statistics and predictive modeling
Data Science: Wrangling
Duration – 8 Weeks
Level: Introductory
You will learn
- Importing data into R from different file formats
- Web scraping
- Tidy data using the tidy verse to facilitate analysis
- String processing with regular expressions (regex)
- Wrangling data using dplyr
- How to work with dates and times as file formats
- Text mining
Data Science: Productivity Tools
Duration – 8 Weeks
Level: Introductory
You will learn
- Using Unix/Linux to manage your file system
- Performing version control with git
- Starting a repository on GitHub
- Leveraging the many useful features provided by RStudio
Data Science: Machine Learning
Duration – 8 Weeks
Level: Introductory
You will learn
- The basics of machine learning
- How to perform cross-validation to avoid overtraining
- Several popular machine learning algorithms
- How to build a recommendation system
- What is regularization and why is it useful?
Fundamentals of TinyML
Duration – 5 Weeks
Level – Beginner
You will learn
- Fundamentals of Machine Learning (ML)
- Fundamentals of Deep Learning
- How to gather data for ML
- How to train and deploy ML models
- Understanding embedded ML
- Responsible AI Design
Causal Diagrams: Draw Your Assumptions Before Your Conclusions
Duration – 9 Weeks
Level – Beginner
You will learn
- Translating expert knowledge into a causal diagram
- Drawing causal diagrams under different assumptions
- Using causal diagrams to identify common biases
- Using causal diagrams to guide data analysis
Principles, Statistical and Computational Tools for Reproducible Data Science
Duration – 8 Weeks
Level: Intermediate
You will learn
- Understand a series of concepts, thought patterns, analysis paradigms, computational and statistical tools
- Fundamentals of reproducible science using case studies that illustrate various practices
- Key elements for ensuring data provenance and reproducible experimental design
- Statistical methods for reproducible data analysis
- Computational tools for reproducible data analysis and version control (Git/GitHub, Emacs/RStudio/Spyder)
- Tools for reproducible data (Data repositories/Dataverse), reproducible dynamic report generation (Rmarkdown/R Notebook/Jupyter/Pandoc), and workflows.
- How to develop new methods and tools for reproducible research and reporting
- How to write your own reproducible paper
Statistical Inference and Modeling for High-throughput Experiments
Duration – 4 Weeks
Level – Intermediate
You will learn
- Organizing high throughput data
- Multiple comparison problem
- Family Wide Error Rates
- False Discovery Rate
- Error Rate Control procedures
- Bonferroni Correction
- q-values
- Statistical Modeling
- Hierarchical Models and the basics of Bayesian Statistics
- Exploratory Data Analysis for High throughput data
Introduction to Bioconductor
Duration – 5 Weeks
Level – Intermediate
You will learn
- What we measure with high-throughput technologies and why
- Introduction to high-throughput technologies
- Next-generation Sequencing
- Microarrays
- Preprocessing and Normalization
- The Bioconductor Genomic Ranges Utilities
- Genomic Annotation
Statistics and R
Duration – 4 Weeks
Level – Intermediate
You will learn
- Random variables
- Distributions
- Inference: p-values and confidence intervals
- Exploratory data analysis
- Non-parametric statistics
Applications of TinyML
Duration – 6 Weeks
Level – Intermediate
You will learn
- The code behind some of the most widely used applications of TinyML
- Real-word industry applications of TinyML
- Principles of Keyword Spotting
- Principles of Visual Wake Words
- Concept of Anomaly Detection
- Principles of Dataset Engineering
- Responsible AI Development
Calculus Applied!
Duration – 10 Weeks
Level – Intermediate
You will learn
- Learn how calculus is applied to problems in other fields
- Analyze mathematical models, including variables, constants, and parameters
- Learn about assumptions and complications that go into modeling real-world situations with mathematics
Deploying TinyML
Duration – 6 Weeks
Level – Intermediate
You will learn
- An understanding of the hardware of a microcontroller-based device
- A review of the software behind a microcontroller-based device
- How to program your own TinyML device
- How to write your code for a microcontroller-based device
- How to deploy your code to a microcontroller-based device
- How to train a microcontroller-based device
- Responsible AI Deployment
Advanced Bioconductor
Duration – 5 Weeks
Level – Advanced
You will learn
- Static and interactive visualization of genomic data
- Reproducible analysis methods
- Memory-sparing representations of genomic assays
- Working with multiomic cancer experiments
- Targeted interrogation of cloud-scale genomic archives
High-Dimensional Data Analysis
Duration – 4 Weeks
Level – Advanced
You will learn
- Mathematical Distance
- Dimension Reduction
- Singular Value Decomposition and Principal Component Analysis
- Multiple Dimensional Scaling Plots
- Factor Analysis
- Dealing with Batch Effects
- Clustering
- Heatmaps
- Basic Machine Learning Concepts
If you have recently completed a professional course/certification, click here to submit a review.
FAQs
How is machine learning different from data science?
Machine learning as well as statistical principles are a small part of data science. Algorithms applied in machine learning are data dependent and apply a training set so as to fine-tune a model for algorithmic parameters. Most of them comprise of techniques like regression, naive Bayes or supervised clustering.
Is it worth learning about data science?
Of course, Data Science is an ever-growing industry with a lot of scope so yes, it's worth learning. Data Scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge.
Rashmi is a postgraduate in Biotechnology with a flair for research-oriented work and has an experience of over 13 years in content creation and social media handling. She has a diversified writing portfolio and aim... Read Full Bio
Top Picks & New Arrivals
