Python is a general-purpose programming language that is becoming ever more popular for data science. Companies worldwide are using Python to harvest insights from their data and gain a competitive edge. Unlike other Python tutorials, this course focuses on Python specifically for data science. In our Introduction to Python course, you’ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses. Start DataCamp’s online Python curriculum now.

Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.

Discover how to use Python for data science, learning about the ways to store and manipulate data in the environment to begin conducting your own analyses.

Introduction to Python

Begin your journey into Data Science! Even if you've never written a line of code in your life, you'll be able to follow this course and witness the power of Python to perform Data Science. You'll use data to solve the mystery of Bayes, the kidnapped Golden Retriever, and along the way you'll become familiar with basic Python syntax and popular Data Science modules like Matplotlib (for charts and graphs) and pandas (for tabular data).

Dive into data science using Python and learn how to effectively analyze and visualize your data. No coding experience or skills needed.

Dive into data science using Python and learn how to effectively analyze and visualize your data using Pandas and Matplotlib. No coding experience needed.

Introduction to Data Science in Python

What is data science, why is it so popular, and why did the Harvard Business Review hail it as the “sexiest job of the 21st century”? In this non-technical course, you’ll be introduced to everything you were ever too afraid to ask about this fast-growing and exciting field, without needing to write a single line of code. Through hands-on exercises, you’ll learn about the different data scientist roles, foundational topics like A/B testing, time series analysis, and machine learning, and how data scientists extract knowledge and insights from real-world data. So don’t be put off by the buzzwords. Start learning, gain skills in this hugely in-demand field, and discover why data science is for everyone!

An introduction to data science with no coding involved.

Discover what data science is, why it's so popular, and learn how to do it with hand-on exercises. There is no code required in this introductory course. 

Data Science for Everyone

Learning Python is crucial for any aspiring data science practitioner. Learn to visualize real data with Matplotlib's functions and get acquainted with data structures such as the dictionary and the pandas DataFrame. After covering key concepts such as boolean logic, control flow, and loops in Python, you'll be ready to blend together everything you've learned to solve a case study using hacker statistics.

Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.

In this intermediate Python course, learn to visualize real data with Matplotlib's functions and get to know new data structures such as the pandas DataFrame.

Intermediate Python

It's time to push forward and develop your Python chops even further. There are tons of fantastic functions in Python and its library ecosystem. However, as a data scientist, you'll constantly need to write your own functions to solve problems that are dictated by your data. You will learn the art of function writing in this first Python Data Science Toolbox course. You'll come out of this course being able to write your very own custom functions, complete with multiple parameters and multiple return values, along with default arguments and variable-length arguments. You'll gain insight into scoping in Python and be able to write lambda functions and handle errors in your function writing practice. And you'll wrap up each chapter by using your new skills to write functions that analyze Twitter DataFrames.

Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.

Python Data Science Toolbox (Part 1)

In this second Python Data Science Toolbox course, you'll continue to build your Python data science skills. First, you'll learn about iterators, objects you have already encountered in the context of for loops. You'll then learn about list comprehensions, which are extremely handy tools for all data scientists working in Python. You'll end the course by working through a case study in which you'll apply all the techniques you learned in both parts of this course.

Continue to build your modern Data Science skills by learning about iterators and list comprehensions.

Python Data Science Toolbox (Part 2)

This course shows you how to solve a variety of problems using the versatile Keras functional API. You will start with simple, multi-layer dense networks (also known as multi-layer perceptrons), and continue on to more complicated architectures. The course will cover how to build models with multiple inputs and a single output, as well as how to share weights between layers in a model. We will also cover advanced topics such as category embeddings and multiple-output networks. If you've ever wanted to train a network that does both classification and regression, then this course is for you!

Build multiple-input and multiple-output deep learning models using Keras.

Advanced Deep Learning with Keras

Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition, and artificial intelligence, including the famous AlphaGo. In this course, you'll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting-edge library for deep learning in Python.

Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.

Discover how you can use deep learning to run natural language processing, image recognition, and artificial intelligence with Python package, Keras 2.0.

Introduction to Deep Learning in Python

Every day we interact with the cloud—whether it’s using Google Drive, apps like Salesforce, or accessing our favorite websites. Cloud computing has become the norm for many companies, but what exactly is the cloud and why is everyone rushing to adopt it? Designed for absolute novices, this course breaks down what the cloud is and explains terminology such as scalability, latency, and high-availability. You'll learn about the many advantages including ease of remote collaboration, how there are no hardware limitations, and reliable disaster recovery. You'll also discover the range of tools provided by major cloud providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. By the end of this course, you'll be able to confidently explain how cloud tools can increase productivity and save money, as well as ask the right questions about how to optimize your use of cloud tools.

A non-coding introduction to the world of cloud computing.

Discover what the cloud is, how it works, and what its benefits are. You’ll also learn about terminology like scalability, latency, and high availability.

Cloud Computing for Everyone

In this course, you'll learn natural language processing (NLP) basics, such as how to identify and separate words, how to extract topics in a text, and how to build your own fake news classifier. You'll also learn how to use basic libraries such as NLTK, alongside libraries which utilize deep learning to solve common NLP problems. This course will give you the foundation to process and parse text as you move forward in your Python learning.

Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.

Learn fundamental natural language processing (NLP) techniques using Python and how to apply them to extract insights from real-world text data in Python.

Introduction to Natural Language Processing in Python

Images are everywhere! We live in a time where images contain lots of information, which is sometimes difficult to obtain. This is why image pre-processing has become a highly valuable skill, applicable in many use cases. In this course, you will learn to process, transform, and manipulate images at your will, even when they come in thousands. You will also learn to restore damaged images, perform noise reduction, smart-resize images, count the number of dots on a dice, apply facial detection, and much more, using scikit-image. After completing this course, you will be able to apply your knowledge to different domains such as machine learning and artificial intelligence, machine and robotic vision, space and medical image analysis, retailing, and many more. Take the step and dive into the wonderful world that is computer vision!

Learn to process, transform, and manipulate images at your will.

Learn how you can restore damaged images, perform noise reduction, smart-resize images, apply facial detection, and more, using scikit-image in Python.

Image Processing in Python

In this course you'll learn all about using linear classifiers, specifically logistic regression and support vector machines, with scikit-learn. Once you've learned how to apply these methods, you'll dive into the ideas behind them and find out what really makes them tick. At the end of this course you'll know how to train, test, and tune these linear classifiers in Python. You'll also have a conceptual foundation for understanding many other machine learning algorithms.

In this course you will learn the details of linear classifiers like logistic regression and SVM.

Linear Classifiers in Python

Deep learning is here to stay! It's the go-to technique to solve complex problems that arise with unstructured data and an incredible tool for innovation. Keras is one of the frameworks that make it easier to start developing deep learning models, and it's versatile enough to build industry-ready models in no time. In this course, you will learn regression and save the earth by predicting asteroid trajectories, apply binary classification to distinguish between real and fake dollar bills, use multiclass classification to decide who threw which dart at a dart board, learn to use neural networks to reconstruct noisy images and much more. Additionally, you will learn how to better control your models during training and how to tune them to boost their performance.

Learn to start developing deep learning models with Keras.

Learn how to develop your own deep learning models with Keras by learning regression, neural networks, visualizations, binary and multiclass classification

Introduction to Deep Learning with Keras

Neural networks have been at the forefront of Artificial Intelligence research during the last few years, and have provided solutions to many difficult problems like image classification, language translation or Alpha Go. PyTorch is one of the leading deep learning frameworks, being at the same time both powerful and easy to use. In this course you will use PyTorch to first learn about the basic concepts of neural networks, before building your first neural network to predict digits from MNIST dataset. You will then learn about convolutional neural networks, and use them to build much more powerful models which give more accurate results. You will evaluate the results and use different techniques to improve them. Following the course, you will be able to delve deeper into neural networks and start your career in this fascinating field.

Learn to create deep learning models with the PyTorch library.

Learn to create deep learning models with the PyTorch library by building neural networks, including convolutional, to predict digits from MNIST dataset.

Introduction to Deep Learning with PyTorch

Deep learning methods use data to train neural network algorithms to do a variety of machine learning tasks, such as classification of different classes of objects. Convolutional neural networks are deep learning algorithms that are particularly powerful for analysis of images. This course will teach you how to construct, train and evaluate convolutional neural networks. You will also learn how to improve their ability to learn from data, and how to interpret the results of the training.

Learn powerful techniques for image analysis in Python using deep learning and convolutional neural networks in Keras.

Learn to construct, train and evaluate convolutional neural networks and improve their ability to learn from data to conduct image analysis using Keras.

Image Processing with Keras in Python

So what is all this AI fuss about? Machine Learning, Deep Learning, Predictive Analytics -- what is the reality behind the hype? How do machines actually learn and what are their limits? How can we use Machine Learning to recognize written digits, predict customer churn and find structure in Elon Musk's tweets? All this -- and much more -- is the topic of this course, which will introduce you to the world of AI in a gentle, but firm and very practical manner.

Learn the fundamentals of AI. No programming experience required!

Learn to work with labeled data, train regression models, divide data into clusters, detect anomalies, select the right model, and create neural networks.

AI Fundamentals

If you're working with a lot of text, you'll eventually want to know more about it. For example, what's it about? What do the words mean in context? Who is doing what to whom? What companies and products are mentioned? Which texts are similar to each other? In this course, you'll learn how to use spaCy, a fast-growing industry standard library for NLP in Python, to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

Advanced NLP with spaCy

Messaging and voice-controlled devices are the next big platforms, and conversational computing has a big role to play in creating engaging augmented and virtual reality experiences. This course will get you started on the path toward building such applications. There are a number of unique challenges to building these kinds of programs, like how do I turn human language into instructions for machines? In this course, you'll tackle this first with rule-based systems and then with machine learning. Some chat systems are designed to be useful, while others are just good fun. You will build one of each and put everything together to make a helpful, friendly chatbot. Once you complete the course, you’ll also learn how to  <a href="https://www.datacamp.com/community/tutorials/facebook-chatbot-python-deploy" target="_blank">connect your chatbot to Facebook Messenger!</a>

Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.

Building Chatbots in Python

In this introduction to R course, you'll master the basics of this widely used open source language&mdash;including vectors, factors, lists, and data frames. With the coding skills you'll gain in this course, you'll be ready to undertake your own data analysis in R. There are millions of R users worldwide, cementing it as a leading programming language in statistics and data science. More and more organizations are using R, so begin your coding journey in one of DataCamp's most popular courses today!

Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.

DataCamp's Into to R training course teaches you how to use R programming for data science at your own pace with video tutorials & interactive challenges.

Introduction to R Online

Introduction to R

After all of the hard work of acquiring data and getting them into a form you can work with, you ultimately want to make clear, succinct conclusions from them. This crucial last step of a data analysis pipeline hinges on the principles of statistical inference. In this course, you will start building the foundation you need to think statistically, speak the language of your data, and understand what your data is telling you. The foundations of statistical thinking took decades to build, but can be grasped much faster today with the help of computers. With the power of Python-based tools, you will rapidly get up-to-speed and begin thinking statistically by the end of this course.

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

Statistical Thinking in Python (Part 1)

Do you know the basics of supervised learning and want to use state-of-the-art models on real-world datasets? Gradient boosting is currently one of the most popular techniques for efficient modeling of tabular datasets of all sizes. XGboost is a very fast, scalable implementation of gradient boosting, with models using XGBoost regularly winning online data science competitions and being used at scale across different industries. In this course, you'll learn how to use this powerful library alongside pandas and scikit-learn to build and tune supervised learning models. You'll work with real-world datasets to solve classification and regression problems.

Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

Explore the fundamentals of gradient boosting, with a focus on Regression with XGBoost, using XGBoost in pipelines and how to fine-tune your XGBoost model.

Extreme Gradient Boosting with XGBoost

Version control is one of the power tools of programming. It allows you to keep track of what you did when, undo any changes you decide you don't want, and collaborate at scale with other people. This course will introduce you to Git, a modern version control tool that is very popular with data scientists and software developers, and show you how to use it to get more done in less time and with less pain.

This course is an introduction to version control with Git for data scientists. 

Discover Git, a popular and modern version control tool for data scientists, and how you can use it work more efficiently, quickly, and with fewer errors.

Introduction to Git

The field of biomedical imaging has exploded in recent years - but for the uninitiated, even loading data can be a challenge! In this introductory course, you'll learn the fundamentals of image analysis using NumPy, SciPy, and Matplotlib. You'll navigate through a whole-body CT scan, segment a cardiac MRI time series, and determine whether Alzheimer’s disease changes brain structure. Even if you have never worked with images before, you will finish the course with a solid toolkit for entering this dynamic field.

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

Biomedical Image Analysis in Python

There's been a lot of buzz about Big Data over the past few years, and it's finally become mainstream for many  companies. But what is this Big Data? This course covers the fundamentals of Big Data via PySpark. Spark is a "lightning fast cluster computing" framework for Big Data. It provides a general data processing platform engine and lets you run programs up to 100x faster in memory, or 10x faster on disk,  than Hadoop. You’ll use PySpark, a Python package for Spark programming and its powerful, higher-level libraries such as SparkSQL, MLlib (for machine learning), etc. You will explore the works of William Shakespeare, analyze Fifa 2018 data and perform clustering on genomic datasets. At the end of this course, you will have gained an in-depth understanding of PySpark and its application to general Big Data analysis.

Learn the fundamentals of working with big data with PySpark.

Learn to conduct Big Data analysis using the Python package for Spark programming, PySpark, as well as higher level libraries like SparkSQL and MLlib.

Big Data Fundamentals with PySpark

Deploying machine learning models in production seems easy with modern tools, but often ends in disappointment as the model performs worse in production than in development. This course will give you four superpowers that will make you stand out from the data science crowd and build pipelines that stand the test of time: how to exhaustively tune every aspect of your model in development; how to make the best possible use of available domain expertise; how to monitor your model in performance and deal with any performance deterioration; and finally how to deal with poorly or scarcely labelled data. Digging deep into the cutting edge of sklearn, and dealing with real-life datasets from hot areas like personalized healthcare and cybersecurity, this course reveals a view of machine learning from the frontline.

Learn to build pipelines that stand the test of time.

Learn about supervised and unsupervised workflows, as well as model lifecycle management to build pipelines in Python that will stand the test of time.

Designing Machine Learning Workflows in Python

Have you ever wondered how data professionals use SQL to solve real-world business problems, like generating rankings, calculating moving averages and running totals, deduplicating data, or performing time intelligence? If you already know how to select, filter, order, join and group data with SQL, this course is your next step. By the end, you will be writing queries like a pro! You will learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon! Using flights data, you will discover how simple it is to use window functions, and how flexible and efficient they are.

Learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon! 

PostgreSQL Summary Stats and Window Functions

Not long ago, cutting-edge computer vision algorithms couldn’t differentiate between images of cats and dogs. Today, a skilled data scientist equipped with nothing more than a laptop can classify tens of thousands of objects with greater accuracy than the human eye. In this course, you will use TensorFlow 2.6 to develop, train, and make predictions with the models that have powered major advances in recommendation systems, image classification, and FinTech. You will learn both high-level APIs, which will enable you to design and train deep learning models in 15 lines of code, and low-level APIs, which will allow you to move beyond off-the-shelf routines. You will also learn to accurately predict housing prices, credit card borrower defaults, and images of sign language gestures.

Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.

Learn how to use TensorFlow 2.6 and high-level APIs, which enable you to design and train deep learning models and low-level APIs in 15 lines of code.

Introduction to TensorFlow in Python

There’s a lot more to Microsoft Excel than SUM and COUNT. Companies and institutions around the world use Excel’s powerful functions to efficiently transform mountains of raw data into clear insights, and now you can too.

<br /> <br />

In this course, you’ll develop employable analyst skills as you learn how to use time-saving keyboard shortcuts, convert and clean data types including text, times and dates, and build impressive logic functions and conditional aggregations. Through hands-on practice, you’ll learn over 35 new Excel functions, including CONCATENATE, VLOOKUP, and AVERAGEIF(S), and work with real-world Kickstarter data as you use your new-found Excel skills to analyze what makes a successful project.

Discover time-saving shortcuts in Excel to help you convert and clean your data. You will learn 35+ new Excel functions including CONCAT, VLOOKUP, and more.

Data Analysis in Excel

We’ve come to expect personalized experiences online—whether it’s Netflix recommending a show or an online retailer suggesting items you might also like to purchase. But how are these suggestions generated? In this course, you’ll learn everything you need to know to create your own recommendation engine. Through hands-on exercises, you’ll get to grips with the two most common systems, collaborative filtering and content-based filtering. Next, you’ll learn how to measure similarities like the Jaccard distance and cosine similarity, and how to evaluate the quality of recommendations on test data using the root mean square error (RMSE). By the end of this course, you’ll have built your very own movie recommendation engine and be able to apply your Python skills to create these systems for any industry.

Learn to build recommendation engines in Python using machine learning techniques.

Data Science Courses

Data Science Tracks

Our Learning Experience

@tridat123123

Workspace Publications (0)

Completed Tracks (0)

Completed courses (18)

Completed projects (9)