Course
Feature Engineering for NLP in Python
- AdvancedSkill Level
- 4.9+
- 598
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
Machine Learning
Data, AI, and Cloud Courses
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Course
Introduction to Polars
- BasicSkill Level
- 4.8+
- 598
Learn how to efficiently transform, clean, and analyze data using Polars, a Python library for fast data manipulation.
Data Manipulation
Course
Improving Query Performance in SQL Server
- IntermediateSkill Level
- 4.8+
- 583
In this course, students will learn to write queries that are both efficient and easy to read and understand.
Software Development
Course
Machine Learning with Tree-Based Models in R
- BasicSkill Level
- 4.9+
- 580
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Machine Learning
Course
Demystifying Decision Science
- BasicSkill Level
- 4.8+
- 580
Solidify your decision science skills by designing data-informed frameworks and implementing efficient solutions.
Data Literacy
Course
Introduction to Predictive Analytics in Python
- BasicSkill Level
- 4.8+
- 576
In this course youll learn to use and present logistic regression models for making predictions.
Machine Learning
Course
AI Agents with Hugging Face smolagents
- AdvancedSkill Level
- 4.8+
- 576
Learn how to build intelligent agents that reason, act, and solve real-world tasks using Python.
Artificial Intelligence
Course
Case Study: Analyzing Job Market Data in Power BI
- BasicSkill Level
- 4.8+
- 571
Help a fictional company in this interactive Power BI case study. You’ll use Power Query, DAX, and dashboards to identify the most in-demand data jobs!
Data Manipulation
Course
String Manipulation with stringr in R
- IntermediateSkill Level
- 4.8+
- 571
Learn how to pull character strings apart, put them back together and use the stringr package.
Software Development
Course
Market Basket Analysis in Python
- IntermediateSkill Level
- 4.9+
- 570
Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.
Machine Learning
Course
Data Ingestion and Semantic Models with Microsoft Fabric
- BasicSkill Level
- 4.8+
- 570
Learn to bring data into Microsoft Fabric, covering Pipelines, Dataflows, Shortcuts, Semantic Models, security, and model refresh.
Other
Course
Building Dashboards with Dash and Plotly
- IntermediateSkill Level
- 4.8+
- 556
Learn how to build interactive and insight-rich dashboards with Dash and Plotly.
Data Visualization
Course
Introduction to Bioconductor in R
- IntermediateSkill Level
- 4.8+
- 552
Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!
Probability & Statistics
Course
Developing Machine Learning Models for Production
- IntermediateSkill Level
- 4.8+
- 551
Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.
Machine Learning
Course
NoSQL Concepts
- IntermediateSkill Level
- 4.8+
- 548
In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.
Data Engineering
Course
Unsupervised Learning in R
- IntermediateSkill Level
- 4.8+
- 545
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Machine Learning
Course
Practicing Coding Interview Questions in Python
- AdvancedSkill Level
- 4.8+
- 539
Prepare for your next coding interviews in Python.
Software Development
Course
Quantitative Risk Management in Python
- AdvancedSkill Level
- 4.8+
- 536
Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.
Applied Finance
Course
Case Study: Analyzing Healthcare Data in Power BI
- IntermediateSkill Level
- 4.9+
- 535
Practice Power BI with our healthcare case study. Analyze data, uncover efficiency insights, and build a dashboard.
Data Visualization
Course
Introduction to TensorFlow in Python
- IntermediateSkill Level
- 4.8+
- 535
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
Machine Learning
Course
Writing Functions and Stored Procedures in SQL Server
- IntermediateSkill Level
- 4.9+
- 531
Master SQL Server programming by learning to create, update, and execute functions and stored procedures.
Software Development
Course
Machine Learning with caret in R
- IntermediateSkill Level
- 4.9+
- 529
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
Machine Learning
Artificial Intelligence
Course
Cleaning Data in PostgreSQL Databases
- IntermediateSkill Level
- 4.8+
- 524
Learn to tame your raw, messy data stored in a PostgreSQL database to extract accurate insights.
Data Preparation
Course
Importing and Managing Financial Data in Python
- IntermediateSkill Level
- 4.8+
- 519
In this course, youll learn how to import and manage financial data in Python using various tools and sources.
Applied Finance
Course
Statistical Techniques in Tableau
- IntermediateSkill Level
- 4.8+
- 515
Take your reporting skills to the next level with Tableau’s built-in statistical functions.
Probability & Statistics
Course
Generalized Linear Models in R
- IntermediateSkill Level
- 4.8+
- 515
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
Probability & Statistics
Course
Case Study: Net Revenue Management in Excel
- IntermediateSkill Level
- 4.8+
- 513
You will use Net Revenue Management techniques in Excel for a Fast Moving Consumer Goods company.
Applied Finance
Course
Power BI for End Users
- BasicSkill Level
- 4.8+
- 510
Explore Power BI Service, master the interface, make informed decisions, and maximize the power of your reports.
Reporting
Course
Foundations of Inference in R
- IntermediateSkill Level
- 4.7+
- 505
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
Probability & Statistics
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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