Course
Generalized Linear Models in R
- IntermediateSkill Level
- 4.6+
- 513
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
Probability & Statistics
Data, AI, and Cloud Courses
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Course
Improving Query Performance in PostgreSQL
- IntermediateSkill Level
- 4.6+
- 512
Learn how to structure your PostgreSQL queries to run in a fraction of the time.
Software Development
Course
Introduction to Google Workspace with Gemini
- BasicSkill Level
- 4.5+
- 512
You learn about the key features of Gemini and how they can be used to improve productivity and efficiency in Google Workspace.
Artificial Intelligence
Course
Data Processing in Shell
- IntermediateSkill Level
- 4.6+
- 508
Learn powerful command-line skills to download, process, and transform data, including machine learning pipeline.
Data Manipulation
Course
Conquering Data Bias
- BasicSkill Level
- 4.7+
- 504
Unlock your datas potential by learning to detect and mitigate bias for precise analysis and reliable models.
Data Management
Course
Supervised Learning in R: Regression
- IntermediateSkill Level
- 4.5+
- 504
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
Machine Learning
Course
Fraud Detection in Python
- IntermediateSkill Level
- 4.7+
- 501
Learn how to detect fraud using Python.
Machine Learning
Course
Foundations of PySpark
- IntermediateSkill Level
- 4.6+
- 501
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Data Engineering
Course
Foundations of Inference in R
- IntermediateSkill Level
- 4.5+
- 496
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
Probability & Statistics
Course
Introduction to AWS Boto in Python
- IntermediateSkill Level
- 4.7+
- 492
Learn about AWS Boto and harnessing cloud technology to optimize your data workflow.
Cloud
Course
Power BI for End Users
- BasicSkill Level
- 4.2+
- 491
Explore Power BI Service, master the interface, make informed decisions, and maximize the power of your reports.
Reporting
Course
ARIMA Models in Python
- AdvancedSkill Level
- 4.7+
- 489
Learn about ARIMA models in Python and become an expert in time series analysis.
Machine Learning
Course
Visualization Best Practices in R
- BasicSkill Level
- 4.7+
- 483
Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.
Data Visualization
Course
Ensemble Methods in Python
- AdvancedSkill Level
- 4.7+
- 481
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.
Machine Learning
Course
Statistical Techniques in Tableau
- IntermediateSkill Level
- 4.7+
- 481
Take your reporting skills to the next level with Tableau’s built-in statistical functions.
Probability & Statistics
Course
Streaming Concepts
- BasicSkill Level
- 4.7+
- 476
Learn about the difference between batching and streaming, scaling streaming systems, and real-world applications.
Data Engineering
Course
RNA-Seq with Bioconductor in R
- IntermediateSkill Level
- 4.5+
- 476
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
Probability & Statistics
Course
Machine Learning with caret in R
- IntermediateSkill Level
- 4.8+
- 474
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
Machine Learning
Course
Data Manipulation with data.table in R
- BasicSkill Level
- 4.3+
- 471
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
Data Manipulation
Course
Introduction to Julia
- BasicSkill Level
- 4.8+
- 465
Julia is a new programming language designed to be the ideal language for scientific computing, machine learning, and data mining.
Software Development
Course
Visualizing Time Series Data in Python
- IntermediateSkill Level
- 4.6+
- 461
Visualize seasonality, trends and other patterns in your time series data.
Data Visualization
Course
Web Scraping in R
- IntermediateSkill Level
- 4.4+
- 457
Learn how to efficiently collect and download data from any website using R.
Data Preparation
Course
Deep Reinforcement Learning in Python
- AdvancedSkill Level
- 4.4+
- 456
Learn and use powerful Deep Reinforcement Learning algorithms, including refinement and optimization techniques.
Artificial Intelligence
Course
Time Series Analysis in SQL Server
- IntermediateSkill Level
- 4.5+
- 455
Explore ways to work with date and time data in SQL Server for time series analysis
Data Manipulation
Course
Introduction to Spark SQL in Python
- AdvancedSkill Level
- 4.4+
- 455
Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.
Data Manipulation
Course
Feature Engineering with PySpark
- AdvancedSkill Level
- 4.6+
- 446
Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
Data Manipulation
Course
AI-Assisted Travel Planning
- BasicSkill Level
- 4.5+
- 442
Master travel planning with WanderBot: craft prompts, build confidence, and streamline your next adventure.
Artificial Intelligence
Course
Introduction to Portfolio Analysis in Python
- AdvancedSkill Level
- 4.6+
- 441
Learn how to calculate meaningful measures of risk and performance, and how to compile an optimal portfolio for the desired risk and return trade-off.
Applied Finance
Course
Intermediate Importing Data in R
- IntermediateSkill Level
- 4.6+
- 441
Parse data in any format. Whether its flat files, statistical software, databases, or data right from the web.
Data Preparation
Course
Graph RAG with LangChain and Neo4j
- AdvancedSkill Level
- 4.6+
- 441
Create more accurate and reliable RAG systems with Graph RAG and hybrid RAG.
Artificial Intelligence
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.