Course
Inference for Numerical Data in R
- AdvancedSkill Level
- 4.9+
- 274
In this course youll learn techniques for performing statistical inference on numerical data.
Probability & Statistics
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127 Courses
Course
Inference for Linear Regression in R
- AdvancedSkill Level
- 4.8+
- 272
In this course youll learn how to perform inference using linear models.
Probability & Statistics
Course
A/B Testing in R
- IntermediateSkill Level
- 4.8+
- 262
Learn the basics of A/B testing in R, including how to design experiments, analyze data, predict outcomes, and present results through visualizations.
Probability & Statistics
Course
Case Study: Exploratory Data Analysis in R
- BasicSkill Level
- 4.9+
- 260
Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.
Exploratory Data Analysis
Course
Categorical Data in the Tidyverse
- BasicSkill Level
- 4.8+
- 253
Get ready to categorize! In this course, you will work with non-numerical data, such as job titles or survey responses, using the Tidyverse landscape.
Data Manipulation
Course
Interactive Maps with leaflet in R
- BasicSkill Level
- 4.7+
- 246
Learn how to produce interactive web maps with ease using leaflet.
Data Visualization
Course
Modeling with tidymodels in R
- IntermediateSkill Level
- 4.8+
- 231
Learn to streamline your machine learning workflows with tidymodels.
Machine Learning
Course
Joining Data with data.table in R
- IntermediateSkill Level
- 4.9+
- 229
This course will show you how to combine and merge datasets with data.table.
Data Manipulation
Course
Survival Analysis in R
- IntermediateSkill Level
- 4.8+
- 227
Learn to work with time-to-event data. The event may be death or finding a job after unemployment. Learn to estimate, visualize, and interpret survival models!
Probability & Statistics
Course
Sentiment Analysis in R
- IntermediateSkill Level
- 4.8+
- 200
Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.
Machine Learning
Course
Hyperparameter Tuning in R
- AdvancedSkill Level
- 4.8+
- 191
Learn how to tune your models hyperparameters to get the best predictive results.
Machine Learning
Course
Market Basket Analysis in R
- IntermediateSkill Level
- 4.8+
- 173
Explore association rules in market basket analysis with R by analyzing retail data and creating movie recommendations.
Data Manipulation
Course
Nonlinear Modeling with Generalized Additive Models (GAMs) in R
- IntermediateSkill Level
- 4.8+
- 172
GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.
Probability & Statistics
Course
Visualizing Geospatial Data in R
- IntermediateSkill Level
- 4.6+
- 169
Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.
Data Visualization
Course
Quantitative Risk Management in R
- BasicSkill Level
- 4.9+
- 167
Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.
Applied Finance
Machine Learning
Course
Building Dashboards with shinydashboard
- BasicSkill Level
- 4.7+
- 163
Learn to create interactive dashboards with R using the powerful shinydashboard package. Create dynamic and engaging visualizations for your audience.
Reporting
Course
Text Mining with Bag-of-Words in R
- IntermediateSkill Level
- 4.8+
- 162
Learn the bag of words technique for text mining with R.
Machine Learning
Course
Case Studies: Building Web Applications with Shiny in R
- IntermediateSkill Level
- 4.9+
- 159
Practice your Shiny skills while building some fun Shiny apps for real-life scenarios!
Reporting
Course
Machine Learning for Marketing Analytics in R
- IntermediateSkill Level
- 4.9+
- 159
In this course youll learn how to use data science for several common marketing tasks.
Machine Learning
Course
Credit Risk Modeling in R
- IntermediateSkill Level
- 4.8+
- 157
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Applied Finance
Course
Machine Learning in the Tidyverse
- IntermediateSkill Level
- 4.8+
- 156
Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.
Machine Learning
Course
Structural Equation Modeling with lavaan in R
- AdvancedSkill Level
- 4.8+
- 147
Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.
Probability & Statistics
Course
GARCH Models in R
- AdvancedSkill Level
- 4.8+
- 147
Specify and fit GARCH models to forecast time-varying volatility and value-at-risk.
Applied Finance
Course
Handling Missing Data with Imputations in R
- AdvancedSkill Level
- 4.7+
- 141
Diagnose, visualize and treat missing data with a range of imputation techniques with tips to improve your results.
Data Manipulation
Course
Interactive Data Visualization with plotly in R
- BasicSkill Level
- 4.8+
- 140
Learn how to use plotly in R to create interactive data visualizations to enhance your data storytelling.
Data Visualization
Course
Importing and Managing Financial Data in R
- IntermediateSkill Level
- 4.7+
- 140
Learn how to access financial data from local files as well as from internet sources.
Applied Finance
Course
Developing R Packages
- IntermediateSkill Level
- 4.8+
- 137
Learn to develop R packages and boost your coding skills. Discover package creation benefits, practice with dev tools, and create a unit conversion package.
Software Development
Course
Differential Expression Analysis with limma in R
- AdvancedSkill Level
- 4.8+
- 132
Learn to use the Bioconductor package limma for differential gene expression analysis.
Probability & Statistics
Course
Bond Valuation and Analysis in R
- IntermediateSkill Level
- 4.9+
- 120
Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.
Applied Finance
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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