Course
Dealing with Missing Data in Python
- IntermediateSkill Level
- 4.4+
- 382
Learn how to identify, analyze, remove and impute missing data in Python.
Data Manipulation
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Course
Visualizing Geospatial Data in Python
- IntermediateSkill Level
- 4.6+
- 369
Learn how to make attractive visualizations of geospatial data in Python using the geopandas package and folium maps.
Data Visualization
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Machine Learning for Marketing Analytics in R
- IntermediateSkill Level
- 4.6+
- 154
In this course youll learn how to use data science for several common marketing tasks.
Machine Learning
Course
Data Transformation with Spark SQL in Databricks
- IntermediateSkill Level
- 4.6+
- 137
Build end-to-end data pipelines - from cleaning and aggregation to streaming and orchestration.
Data Engineering
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Case Study: Supply Chain Analytics in Tableau
- IntermediateSkill Level
- 4.8+
- 94
Dive into our Tableau case study on supply chain analytics. Tackle shipment, inventory management, and dashboard creation to drive business improvements.
Data Visualization
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HR Analytics: Predicting Employee Churn in R
- IntermediateSkill Level
- 4.8+
- 48
Predict employee turnover and design retention strategies.
Machine Learning
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Introduction to dbt
- IntermediateSkill Level
- 4.5+
- 3.5K
This course introduces dbt for data modeling, transformations, testing, and building documentation.
Data Engineering
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Dashboard Design Concepts
- BasicSkill Level
- 4.5+
- 2.2K
Learn the skills needed to create impactful dashboards. Understand dashboard design fundamentals, visual analytics components, and dashboard types.
Data Visualization
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NoSQL Concepts
- IntermediateSkill Level
- 4.6+
- 563
In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.
Data Engineering
Course
Hypothesis Testing in R
- IntermediateSkill Level
- 4.4+
- 1.7K
Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.
Probability & Statistics
Course
Intermediate Python for Finance
- IntermediateSkill Level
- 4.5+
- 1.7K
Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.
Applied Finance
Course
Introduction to Natural Language Processing in Python
- IntermediateSkill Level
- 4.6+
- 1.2K
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
Machine Learning
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Credit Risk Modeling in Python
- IntermediateSkill Level
- 4.6+
- 708
Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.
Applied Finance
Course
Improving Query Performance in SQL Server
- IntermediateSkill Level
- 4.6+
- 598
In this course, students will learn to write queries that are both efficient and easy to read and understand.
Software Development
Course
AI Agents with Hugging Face smolagents
- AdvancedSkill Level
- 4.7+
- 564
Learn how to build intelligent agents that reason, act, and solve real-world tasks using Python.
Artificial Intelligence
Course
Fraud Detection in Python
- IntermediateSkill Level
- 4.7+
- 506
Learn how to detect fraud using Python.
Machine Learning
Course
Practicing Machine Learning Interview Questions in Python
- AdvancedSkill Level
- 4.5+
- 312
Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.
Machine Learning
Course
Python for Spreadsheet Users
- BasicSkill Level
- 4.6+
- 200
Use your knowledge of common spreadsheet functions and techniques to explore Python!
Software Development
Course
Financial Forecasting in Python
- IntermediateSkill Level
- 4.7+
- 158
Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.
Applied Finance
Course
Text Mining with Bag-of-Words in R
- IntermediateSkill Level
- 4.5+
- 156
Learn the bag of words technique for text mining with R.
Machine Learning
Course
R For SAS Users
- BasicSkill Level
- 4.6+
- 138
Learn how to translate your SAS knowledge into R and analyze data using this free and powerful software language.
Software Development
Course
Financial Trading in R
- IntermediateSkill Level
- 4.6+
- 104
This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.
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.