Course
Statistical Thinking in Python (Part 1)
- IntermediateSkill Level
- 4.9+
- 576
Build the foundation you need to think statistically and to speak the language of your data.
Probability & Statistics
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182 Courses
Course
Market Basket Analysis in Python
- IntermediateSkill Level
- 4.9+
- 574
Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.
Machine Learning
Course
Introduction to Polars
- BasicSkill Level
- 4.8+
- 570
Learn how to efficiently transform, clean, and analyze data using Polars, a Python library for fast data manipulation.
Data Manipulation
Course
Building Dashboards with Dash and Plotly
- IntermediateSkill Level
- 4.8+
- 528
Learn how to build interactive and insight-rich dashboards with Dash and Plotly.
Data Visualization
Course
Introduction to Predictive Analytics in Python
- BasicSkill Level
- 4.8+
- 498
In this course youll learn to use and present logistic regression models for making predictions.
Machine Learning
Course
Quantitative Risk Management in Python
- AdvancedSkill Level
- 4.8+
- 495
Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.
Applied Finance
Course
Practicing Coding Interview Questions in Python
- AdvancedSkill Level
- 4.8+
- 477
Prepare for your next coding interviews in Python.
Software Development
Course
Financial Trading in Python
- IntermediateSkill Level
- 4.8+
- 474
Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!
Applied Finance
Course
AI Agents with Hugging Face smolagents
- AdvancedSkill Level
- 4.8+
- 466
Learn how to build intelligent agents that reason, act, and solve real-world tasks using Python.
Artificial Intelligence
Course
Importing and Managing Financial Data in Python
- IntermediateSkill Level
- 4.8+
- 447
In this course, youll learn how to import and manage financial data in Python using various tools and sources.
Applied Finance
Course
Ensemble Methods in Python
- AdvancedSkill Level
- 4.9+
- 445
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.
Machine Learning
Course
Foundations of Probability in Python
- IntermediateSkill Level
- 4.8+
- 433
Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.
Probability & Statistics
Course
Introduction to TensorFlow in Python
- IntermediateSkill Level
- 4.8+
- 426
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
Machine Learning
Course
Introduction to Portfolio Analysis in Python
- AdvancedSkill Level
- 4.9+
- 408
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 Data Visualization with Seaborn
- IntermediateSkill Level
- 4.9+
- 408
Use Seaborns sophisticated visualization tools to make beautiful, informative visualizations with ease.
Data Visualization
Course
Fraud Detection in Python
- IntermediateSkill Level
- 4.8+
- 406
Learn how to detect fraud using Python.
Machine Learning
Course
Dealing with Missing Data in Python
- IntermediateSkill Level
- 4.8+
- 405
Learn how to identify, analyze, remove and impute missing data in Python.
Data Manipulation
Course
Visualizing Time Series Data in Python
- IntermediateSkill Level
- 4.8+
- 404
Visualize seasonality, trends and other patterns in your time series data.
Data Visualization
Course
ARIMA Models in Python
- AdvancedSkill Level
- 4.9+
- 402
Learn about ARIMA models in Python and become an expert in time series analysis.
Machine Learning
Course
Graph RAG with LangChain and Neo4j
- AdvancedSkill Level
- 4.8+
- 377
Create more accurate and reliable RAG systems with Graph RAG and hybrid RAG.
Artificial Intelligence
Course
Spoken Language Processing in Python
- AdvancedSkill Level
- 4.8+
- 375
Learn how to load, transform, and transcribe speech from raw audio files in Python.
Data Manipulation
Course
Reinforcement Learning from Human Feedback (RLHF)
- AdvancedSkill Level
- 4.8+
- 365
Learn how to make GenAI models truly reflect human values while gaining hands-on experience with advanced LLMs.
Artificial Intelligence
Course
Visualizing Geospatial Data in Python
- IntermediateSkill Level
- 4.8+
- 358
Learn how to make attractive visualizations of geospatial data in Python using the geopandas package and folium maps.
Data Visualization
Course
Improving Your Data Visualizations in Python
- IntermediateSkill Level
- 4.7+
- 354
Learn to construct compelling and attractive visualizations that help communicate results efficiently and effectively.
Data Visualization
Course
Recurrent Neural Networks (RNNs) for Language Modeling with Keras
- AdvancedSkill Level
- 4.8+
- 350
Learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages.
Artificial Intelligence
Course
Monte Carlo Simulations in Python
- IntermediateSkill Level
- 4.8+
- 346
Learn to design and run your own Monte Carlo simulations using Python!
Probability & Statistics
Course
Anomaly Detection in Python
- IntermediateSkill Level
- 4.8+
- 338
Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.
Probability & Statistics
Course
Introduction to Network Analysis in Python
- IntermediateSkill Level
- 4.8+
- 338
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.
Probability & Statistics
Course
Bayesian Data Analysis in Python
- IntermediateSkill Level
- 4.8+
- 335
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!
Probability & Statistics
Course
Introduction to Linear Modeling in Python
- IntermediateSkill Level
- 4.7+
- 321
Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.
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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