Course
Introduction to Data Quality with Great Expectations
- IntermediateSkill Level
- 4.8+
- 316
Ensure high data quality in data science and data engineering workflows with Pythons Great Expectations library.
Data Engineering
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182 Courses
Course
Supply Chain Analytics in Python
- IntermediateSkill Level
- 4.8+
- 308
Leverage the power of Python and PuLP to optimize supply chains.
Exploratory Data Analysis
Course
Monitoring Machine Learning in Python
- AdvancedSkill Level
- 4.8+
- 302
This course covers everything you need to know to build a basic machine learning monitoring system in Python
Machine Learning
Course
Advanced Deep Learning with Keras
- IntermediateSkill Level
- 4.8+
- 300
Learn how to develop deep learning models with Keras.
Artificial Intelligence
Course
Building AI Agents with CrewAI
- IntermediateSkill Level
- 4.8+
- 295
Build AI teams that work together, automate workflows, and generate content with CrewAI.
Artificial Intelligence
Course
Practicing Statistics Interview Questions in Python
- AdvancedSkill Level
- 4.8+
- 285
Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.
Probability & Statistics
Course
Generalized Linear Models in Python
- AdvancedSkill Level
- 4.8+
- 281
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
Winning a Kaggle Competition in Python
- AdvancedSkill Level
- 4.8+
- 276
Learn how to approach and win competitions on Kaggle.
Machine Learning
Course
Designing Machine Learning Workflows in Python
- AdvancedSkill Level
- 4.8+
- 269
Learn to build pipelines that stand the test of time.
Machine Learning
Course
Customer Segmentation in Python
- IntermediateSkill Level
- 4.9+
- 262
Learn how to segment customers in Python.
Data Manipulation
Course
GARCH Models in Python
- IntermediateSkill Level
- 4.8+
- 260
Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.
Applied Finance
Course
Introduction to Amazon Bedrock
- IntermediateSkill Level
- 4.8+
- 258
Learn to use Amazon Bedrock to access foundation AI models and build with AI - without managing complex infrastructure.
Artificial Intelligence
Course
Marketing Analytics: Predicting Customer Churn in Python
- IntermediateSkill Level
- 4.9+
- 256
Learn how to use Python to analyze customer churn and build a model to predict it.
Exploratory Data Analysis
Course
Analyzing Financial Statements in Python
- IntermediateSkill Level
- 4.7+
- 250
Learn to analyze financial statements using Python. Compute ratios, assess financial health, handle missing values, and present your analysis.
Applied Finance
Course
Building Agentic Workflows with LlamaIndex
- AdvancedSkill Level
- 4.7+
- 245
Build AI agentic workflows that can plan, search, remember, and collaborate, using LlamaIndex.
Artificial Intelligence
Course
Multi-Modal Models with Hugging Face
- IntermediateSkill Level
- 4.8+
- 241
Combine text, images, audio, and video with the latest AI models from Hugging Face, and generate new images and videos!
Artificial Intelligence
Course
Practicing Machine Learning Interview Questions in Python
- AdvancedSkill Level
- 4.9+
- 228
Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.
Machine Learning
Course
Customer Analytics and A/B Testing in Python
- IntermediateSkill Level
- 4.8+
- 227
Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.
Probability & Statistics
Course
Case Study: Building Software in Python
- AdvancedSkill Level
- 4.8+
- 226
Build real-world applications with Python—practice using OOP and software engineering principles to write clean and maintainable code.
Software Development
Course
Statistical Thinking in Python (Part 2)
- IntermediateSkill Level
- 4.8+
- 222
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
Probability & Statistics
Course
Building Chatbots in Python
- IntermediateSkill Level
- 4.7+
- 217
Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.
Machine Learning
Course
Data Transformation with Polars
- IntermediateSkill Level
- 4.9+
- 210
Take Polars further with text manipulation, rolling statistics, DataFrame joins, and advanced analytics.
Data Manipulation
Course
Writing Efficient Code with pandas
- IntermediateSkill Level
- 4.8+
- 202
Learn efficient techniques in pandas to optimize your Python code.
Software Development
Course
Building Recommendation Engines in Python
- IntermediateSkill Level
- 4.8+
- 194
Learn to build recommendation engines in Python using machine learning techniques.
Machine Learning
Course
Machine Learning for Marketing in Python
- IntermediateSkill Level
- 4.8+
- 188
From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.
Machine Learning
Course
Analyzing IoT Data in Python
- IntermediateSkill Level
- 4.8+
- 175
Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.
Data Manipulation
Course
Python for R Users
- IntermediateSkill Level
- 4.7+
- 172
This course is for R users who want to get up to speed with Python!
Software Development
Course
Analyzing Social Media Data in Python
- IntermediateSkill Level
- 4.9+
- 168
In this course, youll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.
Data Manipulation
Course
Survival Analysis in Python
- AdvancedSkill Level
- 4.8+
- 155
Use survival analysis to work with time-to-event data and predict survival time.
Probability & Statistics
Course
Python for Spreadsheet Users
- BasicSkill Level
- 4.9+
- 145
Use your knowledge of common spreadsheet functions and techniques to explore Python!
Software Development
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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