Course
Feature Engineering with PySpark
- AdvancedSkill Level
- 4.8+
- 439
Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
Data Manipulation
Data, AI, and Cloud Courses
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Course
Introduction to Spark SQL in Python
- AdvancedSkill Level
- 4.8+
- 436
Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.
Data Manipulation
Course
ARIMA Models in Python
- AdvancedSkill Level
- 4.9+
- 434
Learn about ARIMA models in Python and become an expert in time series analysis.
Machine Learning
Course
Introduction to Portfolio Analysis in Python
- AdvancedSkill Level
- 4.9+
- 434
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
Graph RAG with LangChain and Neo4j
- AdvancedSkill Level
- 4.8+
- 415
Create more accurate and reliable RAG systems with Graph RAG and hybrid RAG.
Artificial Intelligence
Course
Reinforcement Learning from Human Feedback (RLHF)
- AdvancedSkill Level
- 4.8+
- 414
Learn how to make GenAI models truly reflect human values while gaining hands-on experience with advanced LLMs.
Artificial Intelligence
Course
Streaming Data with AWS Kinesis and Lambda
- AdvancedSkill Level
- 4.8+
- 400
Learn how to work with streaming data using serverless technologies on AWS.
Cloud
Course
Spoken Language Processing in Python
- AdvancedSkill Level
- 4.8+
- 389
Learn how to load, transform, and transcribe speech from raw audio files in Python.
Data Manipulation
Course
Recurrent Neural Networks (RNNs) for Language Modeling with Keras
- AdvancedSkill Level
- 4.8+
- 371
Learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages.
Artificial Intelligence
Course
Generalized Linear Models in Python
- AdvancedSkill Level
- 4.8+
- 362
Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.
Probability & Statistics
Course
Factor Analysis in R
- AdvancedSkill Level
- 4.8+
- 355
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.
Probability & Statistics
Course
Monitoring Machine Learning in Python
- AdvancedSkill Level
- 4.8+
- 352
This course covers everything you need to know to build a basic machine learning monitoring system in Python
Machine Learning
Course
Databricks with the Python SDK
- AdvancedSkill Level
- 4.8+
- 335
Master Databricks with Python: learn to authenticate, manage clusters, automate jobs, and query AI models programmatically.
Artificial Intelligence
Course
Winning a Kaggle Competition in Python
- AdvancedSkill Level
- 4.8+
- 326
Learn how to approach and win competitions on Kaggle.
Machine Learning
Course
Optimizing Code in Java
- AdvancedSkill Level
- 4.8+
- 324
Learn key techniques to optimize Java performance, from algorithm efficiency to JVM tuning and multithreading.
Software Development
Course
Object-Oriented Programming with S3 and R6 in R
- AdvancedSkill Level
- 4.8+
- 318
Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.
Software Development
Course
Inference for Numerical Data in R
- AdvancedSkill Level
- 4.9+
- 314
In this course youll learn techniques for performing statistical inference on numerical data.
Probability & Statistics
Course
Practicing Statistics Interview Questions in Python
- AdvancedSkill Level
- 4.8+
- 302
Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.
Probability & Statistics
Course
Designing Machine Learning Workflows in Python
- AdvancedSkill Level
- 4.8+
- 298
Learn to build pipelines that stand the test of time.
Machine Learning
Course
Inference for Categorical Data in R
- AdvancedSkill Level
- 4.8+
- 294
In this course youll learn how to leverage statistical techniques for working with categorical data.
Probability & Statistics
Course
Building Agentic Workflows with LlamaIndex
- AdvancedSkill Level
- 4.7+
- 287
Build AI agentic workflows that can plan, search, remember, and collaborate, using LlamaIndex.
Artificial Intelligence
Course
Case Study: Building Software in Python
- AdvancedSkill Level
- 4.8+
- 283
Build real-world applications with Python—practice using OOP and software engineering principles to write clean and maintainable code.
Software Development
Course
Inference for Linear Regression in R
- AdvancedSkill Level
- 4.8+
- 275
In this course youll learn how to perform inference using linear models.
Probability & Statistics
Course
Practicing Machine Learning Interview Questions in Python
- AdvancedSkill Level
- 4.9+
- 272
Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.
Machine Learning
Course
Advanced AI-Assisted Coding for Developers
- AdvancedSkill Level
- 4.9+
- 269
Learn to use AI as a senior engineering partner for code analysis, performance optimization, security, and software architecture decisions.
Artificial Intelligence
Course
Case Study: Data Analysis in Databricks
- AdvancedSkill Level
- 4.6+
- 269
Learn to analyze Airbnb data using SQL in Databricks, create dashboards, and derive actionable insights.
Importing & Cleaning Data
Course
Building Recommendation Engines with PySpark
- AdvancedSkill Level
- 4.8+
- 267
Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.
Machine Learning
Course
Hierarchical and Recursive Queries in SQL Server
- AdvancedSkill Level
- 4.8+
- 265
Learn how to write recursive queries and query hierarchical data structures.
Software Development
Course
Introduction to Testing in Java
- AdvancedSkill Level
- 4.9+
- 260
Learn how to write effective tests in Java using JUnit and Mockito to build robust, reliable applications with confidence.
Software Development
Course
Advanced Probability: Uncertainty in Data
- AdvancedSkill Level
- 4.8+
- 246
Develop a better intuition for advanced probability, risk assessment, and simulation techniques to make data-driven business decisions with confidence.
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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