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127 Courses

Course

Case Study: Analyzing City Time Series Data in R

  • IntermediateSkill Level
  • 4.8+
  • 147 reviews

Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.

Probability & Statistics

4 hours

Course

Market Basket Analysis in R

  • IntermediateSkill Level
  • 4.8+
  • 95 reviews

Explore association rules in market basket analysis with R by analyzing retail data and creating movie recommendations.

Data Manipulation

4 hours

Course

Intermediate Portfolio Analysis in R

  • IntermediateSkill Level
  • 4.8+
  • 73 reviews

Advance you R finance skills to backtest, analyze, and optimize financial portfolios.

Applied Finance

5 hours

Course

Fraud Detection in R

  • IntermediateSkill Level
  • 4.7+
  • 38 reviews

Learn to detect fraud with analytics in R.

Machine Learning

4 hours

Course

ChIP-seq with Bioconductor in R

  • IntermediateSkill Level
  • 4.7+
  • 52 reviews

Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.

Probability & Statistics

4 hours

Course

Bayesian Regression Modeling with rstanarm

  • AdvancedSkill Level
  • 4.8+
  • 72 reviews

Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.

Probability & Statistics

4 hours

Course

Scalable Data Processing in R

  • AdvancedSkill Level
  • 4.7+
  • 27 reviews

Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.

Software Development

4 hours

Course

Analyzing US Census Data in R

  • IntermediateSkill Level
  • 4.8+
  • 38 reviews

Learn to rapidly visualize and explore demographic data from the United States Census Bureau using tidyverse tools.

Exploratory Data Analysis

4 hours

Course

Dimensionality Reduction in R

  • BasicSkill Level
  • 4.7+
  • 98 reviews

Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.

Machine Learning

4 hours

Course

Sentiment Analysis in R

  • IntermediateSkill Level
  • 4.7+
  • 97 reviews

Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.

Machine Learning

4 hours

Course

Practicing Statistics Interview Questions in R

  • AdvancedSkill Level
  • 4.7+
  • 23 reviews

In this course, youll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.

Probability & Statistics

4 hours

Course

Hyperparameter Tuning in R

  • AdvancedSkill Level
  • 4.8+
  • 63 reviews

Learn how to tune your models hyperparameters to get the best predictive results.

Machine Learning

4 hours

Course

GARCH Models in R

  • AdvancedSkill Level
  • 4.8+
  • 100 reviews

Specify and fit GARCH models to forecast time-varying volatility and value-at-risk.

Applied Finance

4 hours

Course

Equity Valuation in R

  • IntermediateSkill Level
  • 4.8+
  • 65 reviews

Learn the fundamentals of valuing stocks.

Applied Finance

4 hours

Course

Joining Data with data.table in R

  • IntermediateSkill Level
  • 4.8+
  • 73 reviews

This course will show you how to combine and merge datasets with data.table.

Data Manipulation

4 hours

Course

Building Dashboards with flexdashboard

  • IntermediateSkill Level
  • 4.7+
  • 51 reviews

In this course youll learn how to create static and interactive dashboards using flexdashboard and shiny.

Reporting

4 hours

Course

Analyzing Social Media Data in R

  • IntermediateSkill Level
  • 4.8+
  • 90 reviews

Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.

Data Manipulation

4 hours

Course

Optimizing R Code with Rcpp

  • IntermediateSkill Level
  • 4.8+
  • 14 reviews

Use C++ to dramatically boost the performance of your R code.

Software Development

4 hours

Course

Intermediate Regular Expressions in R

  • IntermediateSkill Level
  • 4.8+
  • 37 reviews

Manipulate text data, analyze it and more by mastering regular expressions and string distances in R.

Software Development

4 hours

Course

Parallel Programming in R

  • IntermediateSkill Level
  • 4.7+
  • 78 reviews

Unlock the power of parallel computing in R. Enhance your data analysis skills, speed up computations, and process large datasets effortlessly.

Software Development

4 hours

Course

Bayesian Modeling with RJAGS

  • AdvancedSkill Level
  • 4.8+
  • 52 reviews

In this course, youll learn how to implement more advanced Bayesian models using RJAGS.

Probability & Statistics

4 hours

Course

Forecasting Product Demand in R

  • IntermediateSkill Level
  • 4.7+
  • 30 reviews

Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example.

Probability & Statistics

4 hours

Course

Business Process Analytics in R

  • IntermediateSkill Level
  • 4.8+
  • 44 reviews

Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.

Reporting

4 hours

FAQs

What is data science?

Data science is an area of expertise focused on gaining information from data. Using programming skills, scientific methods, algorithms, and more, data scientists analyze data to form actionable insights.

How can I learn data science?

You’ll need to learn a programming language such as Python or R and master the principles of math and statistics. Knowledge of data analysis methods and data science tools is also essential. There are many ways to learn data science. As well as formal means of education, such as a degree or university study, there are plenty of other resources to help you learn at your own pace. As well as online courses and tutorials, there are books, videos, and more.

What skills are required for data science?

As well as knowledge of mathematics and statistics, data scientists need programming skills in languages such as Python, R, and SQL. Additionally, data science requires the ability to work with large data sets, knowledge of data visualization, data wrangling, and database management. Skills in machine learning and deep learning can also be useful.

What can I use data science for?

In a professional capacity, almost every industry can use data science to some degree. Healthcare organizations use data science to detect and cure diseases, while finance companies use it to detect and prevent fraud. All kinds of industries use data science for marketing, such as building recommendation systems and analyzing customer churn.

Is data science a good career?

Yes, data science is among the fastest-growing sectors in the US and worldwide. It’s also one of the best-paid careers out there. According to data from Payscale, experience data scientists earn an average of $97,609 and have a satisfaction rating of four stars out of five in the US.

Is it difficult to become a data scientist?

There are a few things to consider here. First, data science degrees can be competitive to get onto, often requiring consistently high grades. Similarly, many of the skills required for data science require a lot of study and patience. It can take several months to master all of the necessary basics, as well as a lot of practical experience to secure an entry-level position.

Does data science require coding?

Yes, you’ll need some coding experience in languages such as Python, R, SQL, Java, and C/C++. However, due to its relatively simple syntax, Python programming language is often the preferred choice among newcomers.

How long does it take to become a data scientist?

For a person with no prior coding experience and/or mathematical background, it can typically take 7 to 12 months of intensive studies to be at the level of an entry-level data scientist. However, it is important to remember that learning only the theoretical basis of data science may not make you a real data scientist.

What topics can I study within data science?

Once you’ve mastered the foundations of data science, you can then specialize in a variety of areas, including machine learning, artificial intelligence, big data analysis, business analytics and intelligence, data mining, and more.

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