Data Science, Machine Learning, Deep Learning, and Artificial intelligence are among the most in-demand skills at this moment and offer a lucrative career 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.

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__Harvard University Online Courses in ____Data Science__

__Harvard University Online Courses in__

__Data Science__

**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

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**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 better 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

**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

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