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)

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

This course will introduce the key elements of machine learning to the business leaders. We will focus on the key insights and base practices how to structure business questions as modeling projects with the machine learning teams. You will understand the different types of models, what kind of business questions they help answer, or what kind of opportunities they can uncover, also learn to identify situations where machine learning should NOT be applied, which is equally important. You will understand the difference between inference and prediction, predicting probability and amounts, and how using unsupervised learning can help build meaningful customer segmentation strategy.

Understand the fundamentals of Machine Learning and how it's applied in the business world.

Machine Learning for Business

Object-oriented programming (OOP) is a powerful programming paradigm that reduces the complexity of systems as is employs heavy re-usability of code. OOP leverages the concept of objects and classes. Objects may contain data (known as attributes, stored as instance or class variables) as well as procedures (defined as methods). Objects are generated from blueprints known as classes, which specify the attributes and methods to be generated at the time of object instantiation. This course introduces the internals of classes and the utilization of objects, as well as important object-oriented programming fundamentals such as inheritance, polymorphism, and composition.

Learn the fundamentals of object-oriented programming: classes, objects, methods, inheritance, polymorphism, and others!

Working with the Class System in Python

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

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)

Have you got your basic Python programming chops down for Data Science but are  yearning for more? Then this is the course for you. Herein, you'll consolidate and practice your knowledge of lists, dictionaries, tuples, sets, and date times. You'll see their relevance in working with lots of real data and how to leverage several of them in concert to solve multistep problems, including an extended case  study using Chicago metropolitan area transit data. You'll also learn how to use many of the objects in the Python Collections module, which will allow you to store and manipulate your data for a variety of Data Scientific purposes. After taking this course, you'll be ready to tackle many Data Science challenges Pythonically.

Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.

Data Types for Data Science in Python

Time series data is ubiquitous. Whether it be stock market fluctuations, sensor data recording climate change, or activity in the brain, any signal that changes over time can be described as a time series. Machine learning has emerged as a powerful method for leveraging complexity in data in order to generate predictions and insights into the problem one is trying to solve. This course is an intersection between these two worlds of machine learning and time series data, and covers feature engineering, spectograms, and other advanced techniques in order to classify heartbeat sounds and predict stock prices.

This course focuses on feature engineering and machine learning for time series data. 

Machine Learning for Time Series Data in Python

The real world is messy and your job is to make sense of it. Toy datasets like MTCars and Iris are the result of careful curation and cleaning, even so the data needs to be transformed for it to be useful for powerful machine learning algorithms to extract meaning, forecast, classify or cluster. This course will cover the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering. With size of datasets now becoming ever larger, let's use PySpark to cut this Big Data problem down to size!

Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.

Learn to use PySpark to cut Big Data problems by using data wrangling and feature engineering so you can extract meaning, forecast, classify or cluster.

Feature Engineering with PySpark


Churn is when a customer stops doing business or ends a relationship with a company. It’s a common problem across a variety of industries, from telecommunications to cable TV to SaaS, and a company that can predict churn can take proactive action to retain valuable customers and get ahead of the competition.  This course will provide you a roadmap to create your own customer churn models. You’ll learn how to explore and visualize your data, prepare it for modeling, make predictions using machine learning, and communicate important, actionable insights to stakeholders. By the end of the course, you’ll become comfortable using the pandas library for data analysis and the scikit-learn library for machine learning.

Learn how to use Python to analyze customer churn and build a model to predict it.

Marketing Analytics: Predicting Customer Churn 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

Data scientists can experience huge benefits by learning concepts from the field of software engineering, allowing them to more easily reutilize their code and share it with collaborators. In this course, you'll learn all about the important ideas of modularity, documentation, & automated testing, and you'll see how they can help you solve Data Science problems quicker and in a way that will make future you happy. You'll even get to use your acquired software engineering chops to write your very own Python package for performing text analytics.

Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.

Software Engineering for Data Scientists in Python

Spark is a powerful, general purpose tool for working with Big Data. Spark transparently handles the distribution of compute tasks across a cluster. This means that operations are fast, but it also allows you to focus on the analysis rather than worry about technical details. In this course you'll learn how to get data into Spark and then delve into the three fundamental Spark Machine Learning algorithms: Linear Regression, Logistic Regression/Classifiers, and creating pipelines. Along the way you'll analyse a large dataset of flight delays and spam text messages. With this background you'll be ready to harness the power of Spark and apply it on your own Machine Learning projects!

Learn how to make predictions with Apache Spark.

Learn how to make predictions from data with PySpark in Python, using decision trees, logistic regression, linear regression, ensembles, and pipelines.

Machine Learning with PySpark

Working with data is tricky - working with millions or even billions of rows is worse.
Did you receive some data processing code written on a laptop with fairly pristine data?
Chances are you’ve probably been put in charge of moving a basic data process from prototype to production.
You may have worked with real world datasets, with missing fields, bizarre formatting, and orders of magnitude more data. Even if this is all new to you, this course helps you learn what’s needed to prepare data processes using Python with Apache Spark.
You’ll learn terminology, methods, and some best practices to create a performant, maintainable, and understandable data processing platform.

Learn how to clean data with Apache Spark in Python.

Learn how to use PySpark to clean your data in Python with DataFrames and data pipelines. You'll also learn why clean data is so important for analysis

Cleaning Data with PySpark

Machine Learning models are based on numerical values to make predictions/classification, but how can computers deal with text data? With the huge increase of available text data, applications such as automatic document classification, text generation, and neural machine translation became possible. In this course, you will learn how to use Recurrent Neural Networks to classify text (binary and multiclass), generate phrases simulating the character Sheldon from The Big Bang Theory TV Show, and translate Portuguese sentences into English. Are you ready to start your journey into Language Models using Keras and Python? Dive in!

Use RNNs to classify text sentiment, generate sentences, and translate text between languages.

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