Course
Introduction to PySpark
- IntermediateSkill Level
- 4.6+
- 5.1K
Master PySpark to handle big data with ease—learn to process, query, and optimize massive datasets for powerful analytics!
Data Engineering
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Course
Intermediate SQL Server
- IntermediateSkill Level
- 4.7+
- 1.1K
In this course, you will use T-SQL, the flavor of SQL used in Microsofts SQL Server for data analysis.
Software Development
Course
Anomaly Detection in Python
- IntermediateSkill Level
- 4.7+
- 409
Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.
Probability & Statistics
Course
Experimental Design in R
- IntermediateSkill Level
- 4.3+
- 361
In this course youll learn about basic experimental design, a crucial part of any data analysis.
Probability & Statistics
Course
Case Study: HR Analytics in Tableau
- IntermediateSkill Level
- 4.5+
- 121
Explore HR data analysis in Tableau with this case study.
Data Visualization
Course
Parallel Programming in R
- IntermediateSkill Level
- 4.8+
- 66
Unlock the power of parallel computing in R. Enhance your data analysis skills, speed up computations, and process large datasets effortlessly.
Software Development
Course
Analyzing IoT Data in Python
- IntermediateSkill Level
- 4.7+
- 242
Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.
Data Manipulation
Course
Cleaning Data in Java
- IntermediateSkill Level
- 4.8+
- 140
Master data cleaning in Java using statistical methods, transformations, and validation for reliable apps.
Importing & Cleaning Data
Course
Understanding Excel
- BasicSkill Level
- 4.6+
- 1.6K
Master Excel basics quickly: navigate spreadsheets, apply formulas, analyze data, and create your first charts!
Data Manipulation
Course
String Manipulation with stringr in R
- IntermediateSkill Level
- 4.4+
- 524
Learn how to pull character strings apart, put them back together and use the stringr package.
Software Development
Course
Analyzing Social Media Data in R
- IntermediateSkill Level
- 4.5+
- 131
Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.
Data Manipulation
Course
Data Transformation with Polars
- IntermediateSkill Level
- 4.8+
- 110
Take Polars further with text manipulation, rolling statistics, DataFrame joins, and advanced analytics.
Data Manipulation
Course
HR Analytics: Predicting Employee Churn in Python
- IntermediateSkill Level
- 4.6+
- 93
In this course youll learn how to apply machine learning in the HR domain.
Machine Learning
Course
Data Manipulation in SQL
- BasicSkill Level
- 4.6+
- 11.5K
Master the complex SQL queries necessary to answer a wide variety of data science questions and prepare robust data sets for analysis in PostgreSQL.
Data Manipulation
Course
Data Manipulation in Snowflake
- BasicSkill Level
- 4.7+
- 1.1K
Master data manipulation and analysis techniques such as CASE statements, subqueries, and CTEs in Snowflake.
Data Manipulation
Course
Introduction to R for Finance
- BasicSkill Level
- 4.6+
- 1K
Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
Applied Finance
Course
Introduction to Polars
- BasicSkill Level
- 4.6+
- 641
Learn how to efficiently transform, clean, and analyze data using Polars, a Python library for fast data manipulation.
Data Manipulation
Course
Visualizations in Sigma
- BasicSkill Level
- 4.8+
- 420
Learn to build and customize Sigma charts to tell clear, compelling data stories—no coding required.
Data Visualization
Course
Business Process Analytics in R
- IntermediateSkill Level
- 4.5+
- 119
Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.
Reporting
Course
Data Preparation in Alteryx
- BasicSkill Level
- 4.6+
- 1.5K
Master data preparation, cleaning, and analysis in Alteryx Designer, whether you are a new or seasoned analyst.
Data Preparation
Course
Introduction to KNIME
- BasicSkill Level
- 4.3+
- 1.1K
Learn to use the KNIME Analytics Platform for data access, cleaning, and analysis with a no-code/low-code approach.
Data Preparation
Course
Connecting Data in Tableau
- BasicSkill Level
- 4.7+
- 990
Learn to connect Tableau to different data sources and prepare the data for a smooth analysis.
Data Preparation
Course
Case Study: Mortgage Trading Analysis in Power BI
- IntermediateSkill Level
- 4.7+
- 380
In this Power BI case study you’ll play the role of a junior trader, analyzing mortgage trading and enhancing your data modeling and financial analysis skills.
Applied Finance
Course
A/B Testing in R
- IntermediateSkill Level
- 4.3+
- 247
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
Data Preparation in Power BI
- BasicSkill Level
- 4.7+
- 7.3K
In this interactive Power BI course, you’ll learn how to use Power Query Editor to transform and shape your data to be ready for analysis.
Data Preparation
Course
Introduction to Python for Finance
- BasicSkill Level
- 4.7+
- 3.9K
Build Python skills to elevate your finance career. Learn how to work with lists, arrays and data visualizations to master financial analyses.
Applied Finance
Course
Data Literacy Case Study: Remote Working Analysis
- BasicSkill Level
- 4.6+
- 2.4K
Improve data literacy skills by analyzing remote working policies.
Data Literacy
Course
Market Basket Analysis in Python
- IntermediateSkill Level
- 4.6+
- 604
Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.
Machine Learning
Course
Case Study: Analyzing Sales Data in Alteryx
- BasicSkill Level
- 4.8+
- 349
Explore Alteryx Designer in a retail data case study to boost sales analysis and strategic decision-making.
Data Preparation
Course
Case Study: Financial Analysis in KNIME
- IntermediateSkill Level
- 4.1+
- 221
Apply financial analysis in KNIME with real-world data, enhancing data preparation and workflow skills.
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.