Data Science courses which can be done simultaneously with some other courses

By ridhigrg |Email | Nov 25, 2019 | 1308 Views

Data Science: Wrangling
By Harvard University
About this course
In this course, part of our Professional Certificate Program in Data Science, we cover several standard steps of the data wrangling process like importing data into R, tidying data, string processing, HTML parsing, working with dates and times, and text mining. Rarely are all these wrangling steps necessary in a single analysis, but a data scientist will likely face them all at some point.

Very rarely is data easily accessible in a data science project. It's more likely for the data to be in a file, a database, or extracted from documents such as web pages, tweets, or PDFs. In these cases, the first step is to import the data into R and tidy the data, using the tidyverse package. The steps that convert data from its raw form to the tidy form is called data wrangling.

This process is a critical step for any data scientist. Knowing how to wrangle and clean data will enable you to make critical insights that would otherwise be hidden.

What you'll learn
  • Importing data into R from different file formats
  • Web scraping
  • How to tidy data using the tidyverse to better facilitate analysis
  • String processing with regular expressions (regex)
  • Wrangling data using dplyr

Data Science: R Basics
By Harvard University
Build a foundation in R and learn how to wrangle, analyze, and visualize data.
About this course
The first in our Professional Certificate Program in Data Science, this course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so you'll use a real-world dataset about crime in the United States. You will learn the R skills needed to answer essential questions about differences in crime across the different states.

We'll cover R's functions and data types, then tackle how to operate on vectors and when to use advanced functions like sorting. You'll learn how to apply general programming features like "if-else," and "for loop" commands, and how to wrangle, analyze and visualize data.

Rather than covering every R skill, you might need, you'll build a strong foundation to prepare you for the more in-depth courses later in the series, where we cover concepts like probability, inference, regression, and machine learning. We help you develop a skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux, version control with git and GitHub, and reproducible document preparation with RStudio.

The demand for skilled data science practitioners is rapidly growing, and this series prepares you to tackle real-world data analysis challenges.

What you'll 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

Professional Certificate in Data Science
By Harvard University
What you will learn
  • Fundamental R programming skills
  • Statistical concepts such as probability, inference, and modeling and how to apply them in practice
  • Gain experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr
  • Become familiar with essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio
  • Implement machine learning algorithms
  • In-depth knowledge of fundamental data science concepts through motivating real-world case studies

MicroMasters Program in Data Science
The University of California, San Diego
What you will learn
  • How to load and clean real-world data
  • How to make reliable statistical inferences from noisy data
  • How to use machine learning to learn models for data
  • How to visualize complex data
  • How to use Apache Spark to analyze data that does not fit within the memory of a single computer

Program in Statistics and Data Science
Massachusetts Institute of Technology
What you will learn
  • Master the foundations of data science, statistics, and machine learning
  • Analyze big data and make data-driven predictions through probabilistic modeling and statistical inference; identify and deploy appropriate modeling and methodologies in order to extract meaningful information for decision making
  • Develop and build machine learning algorithms to extract meaningful information from seemingly unstructured data; learn popular unsupervised learning methods, including clustering methodologies and supervised methods such as deep neural networks
  • Finishing this MicroMasters program will prepare you for job titles such as Data Scientist, Data Analyst, Business Intelligence Analyst, Systems Analyst, Data Engineer

Source: HOB