Top Online Harvard University Data Science Courses

# Top Online Harvard University Data Science Courses

Rashmi Karan
Manager - Content
Updated on Jul 5, 2022 10:06 IST

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.

You may also be interested in exploring:

 Popular Data Science Basics Online Courses & Certifications Popular Machine Learning Online Courses & Certifications Popular Statistics for Data Science Online Courses & Certifications Popular Python for data science Online Courses & Certifications

## 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 –

## 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
• 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

### 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

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

Duration – 5 Weeks

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

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

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## 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.