Course
Explainable AI in Python
- IntermediateSkill Level
- 4.8+
- 1K
Gain the essential skills using Scikit-learn, SHAP, and LIME to test and build transparent, trustworthy, and accountable AI systems.
Artificial Intelligence
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182 Courses
Course
Introduction to Natural Language Processing in Python
- IntermediateSkill Level
- 4.7+
- 1K
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
Machine Learning
Course
Time Series Analysis in Python
- IntermediateSkill Level
- 4.9+
- 1K
In this four-hour course, you’ll learn the basics of analyzing time series data in Python.
Probability & Statistics
Course
Cluster Analysis in Python
- IntermediateSkill Level
- 4.8+
- 1K
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
Machine Learning
Course
Experimental Design in Python
- IntermediateSkill Level
- 4.8+
- 957
Implement experimental design setups and perform robust statistical analyses to make precise and valid conclusions!
Probability & Statistics
Course
Reshaping Data with pandas
- IntermediateSkill Level
- 4.8+
- 938
Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.
Data Manipulation
Course
Feature Engineering for Machine Learning in Python
- IntermediateSkill Level
- 4.8+
- 926
Create new features to improve the performance of your Machine Learning models.
Machine Learning
Course
Analyzing Marketing Campaigns with pandas
- BasicSkill Level
- 4.8+
- 905
Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!
Exploratory Data Analysis
Course
Developing Python Packages
- IntermediateSkill Level
- 4.8+
- 887
Learn to create your own Python packages to make your code easier to use and share with others.
Software Development
Course
Model Validation in Python
- IntermediateSkill Level
- 4.9+
- 873
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
Machine Learning
Course
Image Modeling with Keras
- AdvancedSkill Level
- 4.9+
- 774
Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.
Artificial Intelligence
Course
Intermediate Object-Oriented Programming in Python
- AdvancedSkill Level
- 4.8+
- 765
Build your OOP skills with descriptors, multilevel inheritance, and abstract base classes!
Software Development
Course
Machine Learning for Time Series Data in Python
- AdvancedSkill Level
- 4.8+
- 758
This course focuses on feature engineering and machine learning for time series data.
Machine Learning
Course
End-to-End Machine Learning
- IntermediateSkill Level
- 4.8+
- 752
Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.
Machine Learning
Course
Introduction to MongoDB in Python
- IntermediateSkill Level
- 4.8+
- 729
Learn to manipulate and analyze flexibly structured data with MongoDB.
Data Engineering
Course
Hyperparameter Tuning in Python
- IntermediateSkill Level
- 4.9+
- 722
Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.
Machine Learning
Course
Introduction to Databases in Python
- IntermediateSkill Level
- 4.8+
- 694
In this course, youll learn the basics of relational databases and how to interact with them.
Data Manipulation
Course
Sentiment Analysis in Python
- IntermediateSkill Level
- 4.8+
- 644
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.
Machine Learning
Course
Credit Risk Modeling in Python
- IntermediateSkill Level
- 4.8+
- 642
Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.
Applied Finance
Course
Biomedical Image Analysis in Python
- IntermediateSkill Level
- 4.8+
- 636
Learn the fundamentals of exploring, manipulating, and measuring biomedical image data.
Data Manipulation
Course
Dimensionality Reduction in Python
- IntermediateSkill Level
- 4.8+
- 619
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
Machine Learning
Course
A/B Testing in Python
- IntermediateSkill Level
- 4.8+
- 617
Learn the practical uses of A/B testing in Python to run and analyze experiments. Master p-values, sanity checks, and analysis to guide business decisions.
Probability & Statistics
Course
Machine Learning for Finance in Python
- IntermediateSkill Level
- 4.8+
- 607
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
Machine Learning
Course
Natural Language Processing with spaCy
- IntermediateSkill Level
- 4.8+
- 594
Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.
Machine Learning
Course
Introduction to Portfolio Risk Management in Python
- IntermediateSkill Level
- 4.8+
- 590
Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.
Applied Finance
Course
Introduction to Optimization in Python
- IntermediateSkill Level
- 4.8+
- 589
Learn to solve real-world optimization problems using Pythons SciPy and PuLP, covering everything from basic to constrained and complex optimization.
Software Development
Course
Feature Engineering for NLP in Python
- AdvancedSkill Level
- 4.8+
- 588
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
Machine Learning
Course
Working with Geospatial Data in Python
- IntermediateSkill Level
- 4.8+
- 585
This course will show you how to integrate spatial data into your Python Data Science workflow.
Data Manipulation
Course
Intermediate Regression with statsmodels in Python
- IntermediateSkill Level
- 4.8+
- 581
Learn to perform linear and logistic regression with multiple explanatory variables.
Probability & Statistics
Course
Introduction to Financial Concepts in Python
- BasicSkill Level
- 4.9+
- 579
Using Python and NumPy, learn the most fundamental financial concepts.
Applied Finance
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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