Course
Deep Reinforcement Learning in Python
- AdvancedSkill Level
- 4.8+
- 500
Learn and use powerful Deep Reinforcement Learning algorithms, including refinement and optimization techniques.
Artificial Intelligence
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Course
Bayesian Data Analysis in Python
- IntermediateSkill Level
- 4.8+
- 499
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!
Probability & Statistics
Course
Math for Finance Professionals
- BasicSkill Level
- 4.8+
- 494
Learn essential finance math skills with practical Excel exercises and real-world examples.
Applied Finance
Course
AI for Human Resources
- BasicSkill Level
- 4.8+
- 492
Collaborate with AI to make recruiting, people ops, and policy engagement faster and fairer.
Artificial Intelligence
Course
Case Study: Supply Chain Analytics in Power BI
- IntermediateSkill Level
- 4.8+
- 492
Learn how to use Power BI for supply chain analytics in this case study. Create a make vs. buy analysis tool, calculate costs, and analyze production volumes.
Data Visualization
Course
Improving Query Performance in PostgreSQL
- IntermediateSkill Level
- 4.8+
- 491
Learn how to structure your PostgreSQL queries to run in a fraction of the time.
Software Development
Course
Foundations of PySpark
- IntermediateSkill Level
- 4.7+
- 491
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Data Engineering
Course
Introduction to Linear Modeling in Python
- IntermediateSkill Level
- 4.7+
- 490
Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.
Probability & Statistics
Course
Fraud Detection in Python
- IntermediateSkill Level
- 4.8+
- 489
Learn how to detect fraud using Python.
Machine Learning
Course
Introduction to GCP
- BasicSkill Level
- 4.8+
- 487
Get to know the Google Cloud Platform (GCP) with this course on storage, data handling, and business modernization using GCP.
Cloud
Course
Supervised Learning in R: Regression
- IntermediateSkill Level
- 4.7+
- 485
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
Machine Learning
Course
Ensemble Methods in Python
- AdvancedSkill Level
- 4.9+
- 482
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.
Machine Learning
Course
Financial Analytics in Google Sheets
- BasicSkill Level
- 4.8+
- 480
Learn how to build a graphical dashboard with Google Sheets to track the performance of financial securities.
Applied Finance
Course
RNA-Seq with Bioconductor in R
- IntermediateSkill Level
- 4.7+
- 480
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
Probability & Statistics
Course
Data Manipulation with data.table in R
- BasicSkill Level
- 4.7+
- 480
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
Data Manipulation
Course
Introduction to AWS Boto in Python
- IntermediateSkill Level
- 4.8+
- 478
Learn about AWS Boto and harnessing cloud technology to optimize your data workflow.
Cloud
Course
Visualizing Time Series Data in Python
- IntermediateSkill Level
- 4.8+
- 475
Visualize seasonality, trends and other patterns in your time series data.
Data Visualization
Course
Streaming Concepts
- BasicSkill Level
- 4.8+
- 474
Learn about the difference between batching and streaming, scaling streaming systems, and real-world applications.
Data Engineering
Course
Data Processing in Shell
- IntermediateSkill Level
- 4.9+
- 471
Learn powerful command-line skills to download, process, and transform data, including machine learning pipeline.
Data Manipulation
Course
ARIMA Models in Python
- AdvancedSkill Level
- 4.9+
- 470
Learn about ARIMA models in Python and become an expert in time series analysis.
Machine Learning
Course
Visualization Best Practices in R
- BasicSkill Level
- 4.9+
- 469
Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.
Data Visualization
Course
Web Scraping in R
- IntermediateSkill Level
- 4.7+
- 462
Learn how to efficiently collect and download data from any website using R.
Data Preparation
Course
Introduction to Spark SQL in Python
- AdvancedSkill Level
- 4.8+
- 460
Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.
Data Manipulation
Course
Visualizations in Sigma
- BasicSkill Level
- 4.9+
- 458
Learn to build and customize Sigma charts to tell clear, compelling data stories—no coding required.
Data Visualization
Course
Introduction to Julia
- BasicSkill Level
- 4.9+
- 458
Julia is a new programming language designed to be the ideal language for scientific computing, machine learning, and data mining.
Software Development
Course
Feature Engineering with PySpark
- AdvancedSkill Level
- 4.8+
- 458
Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
Data Manipulation
Course
Monitoring Machine Learning Concepts
- IntermediateSkill Level
- 4.8+
- 457
Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.
Machine Learning
Course
Streaming Data with AWS Kinesis and Lambda
- AdvancedSkill Level
- 4.8+
- 456
Learn how to work with streaming data using serverless technologies on AWS.
Cloud
Course
Understanding Digital Transformation
- BasicSkill Level
- 4.8+
- 453
Dive into the world of digital transformation and equip yourself to be an agent of change in a rapidly evolving digital landscape.
Data Literacy
Course
Graph RAG with LangChain and Neo4j
- AdvancedSkill Level
- 4.8+
- 452
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.
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