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181 Courses

Course

Feature Engineering for Machine Learning in Python

  • IntermediateSkill Level
  • 4.8+
  • 1,023 reviews

Create new features to improve the performance of your Machine Learning models.

Machine Learning

4 hours

Course

End-to-End Machine Learning

  • IntermediateSkill Level
  • 4.7+
  • 345 reviews

Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.

Machine Learning

4 hours

Course

Time Series Analysis in Python

  • IntermediateSkill Level
  • 4.8+
  • 135 reviews

In this four-hour course, you’ll learn the basics of analyzing time series data in Python.

Probability & Statistics

4 hours

Course

Model Validation in Python

  • IntermediateSkill Level
  • 4.8+
  • 882 reviews

Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.

Machine Learning

4 hours

Course

Developing Python Packages

  • IntermediateSkill Level
  • 4.7+
  • 937 reviews

Learn to create your own Python packages to make your code easier to use and share with others.

Software Development

4 hours

Course

Regular Expressions in Python

  • BasicSkill Level
  • 4.7+
  • 196 reviews

Learn about string manipulation and become a master at using regular expressions.

Software Development

4 hours

Course

Financial Trading in Python

  • IntermediateSkill Level
  • 4.8+
  • 286 reviews

Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!

Applied Finance

4 hours

Course

Credit Risk Modeling in Python

  • IntermediateSkill Level
  • 4.7+
  • 285 reviews

Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.

Applied Finance

4 hours

Course

Analyzing Marketing Campaigns with pandas

  • BasicSkill Level
  • 4.7+
  • 419 reviews

Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!

Exploratory Data Analysis

4 hours

Course

Cluster Analysis in Python

  • IntermediateSkill Level
  • 4.8+
  • 981 reviews

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

4 hours

Course

Hyperparameter Tuning in Python

  • IntermediateSkill Level
  • 4.8+
  • 814 reviews

Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.

Machine Learning

4 hours

Course

Natural Language Processing with spaCy

  • IntermediateSkill Level
  • 4.7+
  • 596 reviews

Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.

Machine Learning

4 hours

Course

A/B Testing in Python

  • IntermediateSkill Level
  • 4.7+
  • 362 reviews

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

4 hours

Course

Sentiment Analysis in Python

  • IntermediateSkill Level
  • 4.8+
  • 441 reviews

Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.

Machine Learning

4 hours

Course

Introduction to Databases in Python

  • IntermediateSkill Level
  • 4.8+
  • 281 reviews

In this course, youll learn the basics of relational databases and how to interact with them.

Data Manipulation

4 hours

Course

Feature Engineering for NLP in Python

  • AdvancedSkill Level
  • 4.8+
  • 145 reviews

Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

Machine Learning

4 hours

Course

Dimensionality Reduction in Python

  • IntermediateSkill Level
  • 4.8+
  • 872 reviews

Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.

Machine Learning

4 hours

Course

Introduction to Portfolio Risk Management in Python

  • IntermediateSkill Level
  • 4.8+
  • 322 reviews

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

4 hours

Course

Reshaping Data with pandas

  • IntermediateSkill Level
  • 4.7+
  • 800 reviews

Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.

Data Manipulation

4 hours

Course

AI Agents with Hugging Face smolagents

  • AdvancedSkill Level
  • 4.8+
  • 248 reviews

Learn how to build intelligent agents that reason, act, and solve real-world tasks using Python.

Artificial Intelligence

3 hours

Course

Statistical Thinking in Python (Part 1)

  • IntermediateSkill Level
  • 4.8+
  • 110 reviews

Build the foundation you need to think statistically and to speak the language of your data.

Probability & Statistics

3 hours

Course

Biomedical Image Analysis in Python

  • IntermediateSkill Level
  • 4.8+
  • 236 reviews

Learn the fundamentals of exploring, manipulating, and measuring biomedical image data.

Data Manipulation

4 hours

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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