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

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

By ridhigrg |Email | Nov 25, 2019 | 4167 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

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