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7 Best Analytics Courses on Coursera

In the last blog we told you how you can make your Career in Analytics and we mentioned about the courses from Coursera. We are sure that you were very busy and could not take out time for browsing courses so to make things easy for you we bring you the list of courses that will boost your Analytics career.

Something about Coursera

Coursera’s website is an educational platform that has partnered with top organizations and universities across the globe to offer online courses for anyone for free. You can select any course that suits your profile and sign up for it and learn with your own convenience.
Best Analytics Courses online

R Programming

R is a programming language that is used in graphics and statistical computing. It is majorly used by data miners and statisticians for data analysis and statistical software. The popularity of R programming has increased tremendously in the recent years.

About the Course

In the R Programming Course at Coursera you’ll be learning:

  • Programming in R and using it for efficient data analysis.
  • Installing and Configuring Software
  • Reading data into R
  • Accessing R packages
  • Writing R functions
  • Debugging
  • Profiling R code
  • Organizing & Commenting R code

Getting & Cleaning the data

Before you start working on data and carry out any analysis, it is important that you have the data in the first place. Through this course you’ll be able to learn that.

About the Course

In Getting & Cleaning the data course you’ll be learning:

  • Obtaining data from APIs
  • Obtaining data from databases and from colleagues in various formats
  • Basics of data cleaning
  • Components of a complete data set including raw data, processing instructions, codebooks and processed data.
  • Basics of sharing the data

Reproducible Research

Reproducible research from coursera is the idea that data analyses and scientific claims are published with their software code and data so that others may authenticate the findings and build upon them. The need for reproducible research is growing day by day as data analysis is becoming complex.

About the Course

Through Reproducible Research Course, you’ll be learning:

  • To write a document using R markdown
  • To integrate live R code into a literate statistical program
  • To Compile R markdown documents using knitr and other related tools.
  • To organize the data so that it is accessible and reproducible to others.

Statistical Inference

According to the definition by Wikipedia, “Statistical inference is the process of deducing properties of an underlying distribution by analysis of data.”

It is the process of drawing conclusions of scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses.

About the Course

In Statistical Inference course at coursera, you’ll be learning:

  • Fundamentals of statistical inference
  • Goals, assumptions and modes of performing statistical inference
  • Performing inferential tasks in highly targeted settings
  • Broad directions of statistical inference that will be helpful for making informed choice while analyzing data.

The Data Scientist Toolbox

There are several tools and ideas that one needs to apply while conducting data analysis. In this course you’ll be learning about them in detail.

About the Course

Through the Data Scientist Toolbox course you’ll be learning:

  • Basics about all the main tools in data scientist toolbox
  • To identify and classify data science problems
  • To create a Github account
  • To create your first repository
  • To push your markdown file to your Github account

Regression Models

Regression Models are a part of statistics. It is a statistical process for estimating the relationships among variables. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit.

About the Course

In Regression Models course , you’ll be learning:

  • To fit the regression models
  • To interpret coefficients
  • To investigate residuals and variability
  • Special cases of regression model

Practical Machine Learning

These days one of the most common and important task performed by data analyst and data scientist is machine learning. Machine learning explores the construction and study of algorithms that can learn from and make predictions on data.

About the Course

Through the Practical Machine Learning Course, you’ll be learning:

  • To Build and Apply prediction functions on practical applications
  • To apply multiple basic machine learning tools
  • To build prediction functions including data collection, feature creation, algorithms and evaluation.
  • Components of a machine learning algorithm

That’s not all, there are many more courses available, we mentioned some of the best ones. You can check more on Coursera’s website .