Course
Trust and Security with Google Cloud
- BasicSkill Level
- 4.8+
- 154
Trust and Security with Google Cloud
Cloud
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Course
Sentiment Analysis in R
- IntermediateSkill Level
- 4.5+
- 154
Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.
Machine Learning
Course
Case Studies: Building Web Applications with Shiny in R
- IntermediateSkill Level
- 4.7+
- 153
Practice your Shiny skills while building some fun Shiny apps for real-life scenarios!
Reporting
Course
GARCH Models in R
- AdvancedSkill Level
- 4.5+
- 153
Specify and fit GARCH models to forecast time-varying volatility and value-at-risk.
Applied Finance
Course
Differential Expression Analysis with limma in R
- AdvancedSkill Level
- 4.5+
- 151
Learn to use the Bioconductor package limma for differential gene expression analysis.
Probability & Statistics
Course
Dimensionality Reduction in R
- BasicSkill Level
- 4.5+
- 147
Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.
Machine Learning
Course
Modernize Infrastructure and Applications with Google Cloud
- BasicSkill Level
- 4.8+
- 146
Modernize Infrastructure and Applications with Google Cloud
Cloud
Course
Advanced NLP with spaCy
- IntermediateSkill Level
- 5.0+
- 145
Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.
Machine Learning
Course
Credit Risk Modeling in R
- IntermediateSkill Level
- 4.4+
- 145
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Applied Finance
Course
Cleaning Data in Java
- IntermediateSkill Level
- 4.8+
- 142
Master data cleaning in Java using statistical methods, transformations, and validation for reliable apps.
Importing & Cleaning Data
Course
R For SAS Users
- BasicSkill Level
- 4.6+
- 140
Learn how to translate your SAS knowledge into R and analyze data using this free and powerful software language.
Software Development
Course
Discrete Event Simulation in Python
- AdvancedSkill Level
- 4.6+
- 138
Discover the power of discrete-event simulation in optimizing your business processes. Learn to develop digital twins using Pythons SimPy package.
Probability & Statistics
Course
Pandas Joins for Spreadsheet Users
- IntermediateSkill Level
- 4.5+
- 138
Learn how to effectively and efficiently join datasets in tabular format using the Python Pandas library.
Data Manipulation
Course
Python for MATLAB Users
- BasicSkill Level
- 4.5+
- 138
Transition from MATLAB by learning some fundamental Python concepts, and diving into the NumPy and Matplotlib packages.
Software Development
Course
Introduction to Natural Language Processing in R
- IntermediateSkill Level
- 4.5+
- 138
Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.
Machine Learning
Course
Support Vector Machines in R
- IntermediateSkill Level
- 4.4+
- 137
This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
Machine Learning
Course
Feature Engineering in R
- IntermediateSkill Level
- 4.5+
- 136
Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.
Machine Learning
Course
Analyzing Police Activity with pandas
- IntermediateSkill Level
- 4.0+
- 131
Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.
Data Manipulation
Course
Analyzing Social Media Data in R
- IntermediateSkill Level
- 4.5+
- 130
Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.
Data Manipulation
Course
Foundations of Functional Programming with purrr
- IntermediateSkill Level
- 4.5+
- 130
Learn to easily summarize and manipulate lists using the purrr package.
Software Development
Course
Practicing Statistics Interview Questions in R
- AdvancedSkill Level
- 4.4+
- 125
In this course, youll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.
Probability & Statistics
Machine Learning
Course
DataLab with SQL
- BasicSkill Level
- 4.3+
- 124
Elevate your analysis with this hands-on course using SQL with DataLab workbooks.
Reporting
Course
Importing and Managing Financial Data in R
- IntermediateSkill Level
- 4.5+
- 123
Learn how to access financial data from local files as well as from internet sources.
Applied Finance
Course
Data Transformation with Polars
- IntermediateSkill Level
- 4.8+
- 121
Take Polars further with text manipulation, rolling statistics, DataFrame joins, and advanced analytics.
Data Manipulation
Course
Loan Amortization in Google Sheets
- IntermediateSkill Level
- 3.6+
- 121
Learn how to build an amortization dashboard in Google Sheets with financial and conditional formulas.
Applied Finance
Course
Business Process Analytics in R
- IntermediateSkill Level
- 4.5+
- 120
Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.
Reporting
Course
Intermediate Regular Expressions in R
- IntermediateSkill Level
- 4.8+
- 119
Manipulate text data, analyze it and more by mastering regular expressions and string distances in R.
Software Development
Course
Life Insurance Products Valuation in R
- BasicSkill Level
- 4.6+
- 117
Learn the basics of cash flow valuation, work with human mortality data and build life insurance products in R.
Applied Finance
Course
Case Study: Ecommerce Analysis in Tableau
- IntermediateSkill Level
- 4.0+
- 116
In ecommerce, increasing sales and reducing expenses are top priorities. In this case study, youll investigate data from an online pet supply company.
Data Visualization
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.