WPP AI Academy

In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI.

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AI is not only for engineers. If you want your organisation to become better at using AI, this is the course to tell everyone – especially your non-technical colleagues – to take.

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This course is intended to be an introduction to machine learning for non-technical business professionals. There is a lot of hype around machine learning and many people are concerned that in order to use machine learning in business, you need to have a technical background. For reasons that are covered in this course, that's not the case.

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This one-week course describes the process of analysing data and how to manage that process.

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Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximised at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand.

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One of the skills that characterises great business data analysts is the ability to communicate practical implications of quantitative analyses to any kind of audience member. Even the most sophisticated statistical analyses are not useful to a business if they do not lead to actionable advice, or if the answers to those business questions are not conveyed in a way that non-technical people can understand.

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In this course, you will learn best practices for how to use data analytics to make any company more competitive and more profitable. You will be able to recognise the most critical business metrics and distinguish them from mere data.

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In this course, you will learn best practices for how to use data analytics to make any company more competitive and more profitable. You will be able to recognise the most critical business metrics and distinguish them from mere data.

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By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organisations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists.

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Businesses run on data, and data offers little value without analytics. The ability to process data to make predictions about the behaviour of individuals or markets, to diagnose systems or situations, or to prescribe actions for people or processes drives business today.

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This course provides an analytical framework to help you evaluate key problems in a structured fashion and will equip you with tools to better manage the uncertainties that pervade and complicate business processes. The course aim to cover statistical ideas that apply to managers.

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The use of Excel is widespread in the industry. It is a very powerful data analysis tool and almost all big and small businesses use Excel in their day to day functioning.

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The ability to understand and apply Business Statistics is becoming increasingly important in the industry.

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Confidence intervals and Hypothesis tests are very important tools in the Business Statistics toolbox. A mastery over these topics will help enhance your business decision making and allow you to understand and measure the extent of ‘risk’ or ‘uncertainty’ in various business processes.

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Regression Analysis is perhaps the single most important Business Statistics tool used in the industry.

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The Business Statistics and Analysis Capstone is an opportunity to apply various skills developed across the four courses in the specialisation to a real life data.

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In this first course of the specialisation Excel Skills for Business, you will learn the essentials of Microsoft Excel.

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Spreadsheet software remains one of the most ubiquitous pieces of software used in workplaces across the world.

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Spreadsheet software remains one of the most ubiquitous pieces of software used in workplaces across the world.

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Spreadsheet software remains one of the most ubiquitous pieces of software used in workplaces across the world.

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In this course, you will learn best practices for how to use data analytics to make any company more competitive and more profitable.

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Important: The focus of this course is on math - specifically, data-analysis concepts and methods - not on Excel for its own sake.

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One of the skills that characterises great business data analysts is the ability to communicate practical implications of quantitative analyses to any kind of audience member.

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This course is an introduction to how to use relational databases in business analysis.

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In this final course you will complete a Capstone Project using data analysis to recommend a method for improving profits for your company.

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This course will expose you to the data analytics practices executed in the business world.

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This course will introduce you to some of the most widely used predictive modelling techniques and their core principles.

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In this course you will learn how to create models for decision making. We will start with cluster analysis, a technique for data reduction that is very useful in market segmentation.

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In this course you will learn how to communicate analytics results to stakeholders who do not understand the details of analytics but want evidence of analysis and data.

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The analytics process is a collection of interrelated activities that lead to better decisions and to a higher business performance. The capstone of this specialisation is designed with the goal of allowing you to experience this process. The capstone project will take you from data to analysis and models, and ultimately to presentation of insights.

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This is the first course in the four-course specialisation Python Data Products for Predictive Analytics, introducing the basics of reading and manipulating datasets in Python. In this course, you will learn what a data product is and go through several Python libraries to perform data retrieval, processing, and visualisation.

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This is the second course in the four-course specialisation Python Data Products for Predictive Analytics, building on the data processing covered in Course 1 and introducing the basics of designing predictive models in Python.

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This course will help us to evaluate and compare the models we have developed in previous courses. So far we have developed techniques for regression and classification, but how low should the error of a classifier be (for example) before we decide that the classifier is "good enough"?

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The focus and themes of the Introduction to Calculus course address the most important foundations for applications of mathematics in science, engineering and commerce.

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In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices.

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This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time.

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This course aims to help you to ask better statistical questions when performing empirical research.

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In this course, we will meet some data science practitioners and we will get an overview of what data science is today.

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In this course, you'll learn about Jupyter Notebooks, RStudio IDE, Apache Zeppelin and Data Science Experience. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations.

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This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand.

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This introduction to Python will kickstart your learning of Python for data science, as well as programming in general. This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours.

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The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment.

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This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualisations, predict future trends from data, and more.

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The main goal of this Data Visualisation with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people.

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This course dives into the basics of machine learning using an approachable, and well-known programming language, Python.

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This capstone project course will give you a taste of what data scientists go through in real life when working with data.

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In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialisation - the course Inferential Statistics.

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The goal of the course is to introduce students to Python Version 3.x programming using hands on instruction. It will show how to install Python and use the Spyder IDE (Integrated Development Environment) for writing and debugging programs.

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In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyse sentiment from user reviews, retrieve documents of interest, recommend products, and search for images.

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In this course, you will explore regularised linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyse the impact of aspects of your data – such as outliers – on your selected models and predictions.

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In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting.

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In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximisation (EM) to learn the document clusterings, and see how to scale the methods using MapReduce.

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This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project.

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This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyse business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used.

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This course is all about data and how it is critical to the success of your applied machine learning model.

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This course synthesises everything your have learned in the applied machine learning specialisation. You will now walk through a complete machine learning project to prepare a machine learning maintenance roadmap.

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The course is hands on; most units introduce short programs that serve as illustrations of important concepts and invite you to play with them, modifying and improving them. The course is complemented by a series programming projects as homework assignments.

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In this course you will learn how to apply the functional programming style in the design of larger applications. You'll get to know important new functional programming concepts, from lazy evaluation to structuring your libraries using monads.

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In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelise familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library.

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In this course, we'll see how the data parallel paradigm can be extended to the distributed case, using Spark throughout. We'll cover Spark's programming model in detail, being careful to understand how and when it differs from familiar programming models, like shared-memory parallel collections or sequential Scala collections.

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In the final capstone project you will apply the skills you learned by building a large data-intensive application using real-world data.

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This course introduces the basics of Python 3, including conditional execution and iteration as control structures, and strings and lists as data structures.

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This course introduces the dictionary data structure and user-defined functions. You’ll learn about local and global variables, optional and keyword parameter-passing, named functions and lambda expressions.

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This course teaches you to fetch and process data from services on the Internet. It covers Python list comprehensions and provides opportunities to practice extracting from and processing deeply nested data.

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This course introduces classes, instances, and inheritance. You will learn how to use classes to represent data in concise and natural ways.

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This course will walk you through a hands-on project suitable for a portfolio. You will be introduced to third-party APIs and will be shown how to manipulate images using the Python imaging library (pillow), how to apply optical character recognition to images to recognise text (tesseract and py-tesseract), and how to identify faces in images using the popular opencv library.

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This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems.

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In this course, you will experience various data genres and management tools appropriate for each.

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This course is for those new to data science. Completion of Intro to Big Data is recommended.

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This course provides an overview of machine learning techniques to explore, analyse, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.

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This course gives you a broad overview of the field of graph analytics so you can learn new ways to model, store, retrieve and analyse graph-structured data.

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In this culminating project, you will build a big data ecosystem using tools and methods form the earlier courses in this specialisation. You will analyse a data set simulating big data generated from a large number of users who are playing our imaginary game "Catch the Pink Flamingo".

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In this course, you will create relational databases, write SQL statements to extract information to satisfy business reporting requests, create entity relationship diagrams (ERDs) to design databases, and analyse table designs for excessive redundancy.

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This is the second course in the Data Warehousing for Business Intelligence specialisation. Ideally, the courses should be taken in sequence. In this course, you will learn exciting concepts and skills for designing data warehouses and creating data integration workflows.

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In this course, you'll use analytical elements of SQL for answering business intelligence questions. You'll learn features of relational database management systems for managing summary data commonly used in business intelligence reporting.

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This is the fourth course in the Data Warehouse for Business Intelligence specialisation. Ideally, the courses should be taken in sequence. In this course, you will gain the knowledge and skills for using data warehouses for business intelligence purposes and for working as a business intelligence developer.

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The capstone course, Design and Build a Data Warehouse for Business Intelligence Implementation, features a real-world case study that integrates your learning across all courses in the specialisation. In response to business requirements presented in a case study, you’ll design and build a small data warehouse, create data integration workflows to refresh the warehouse, write SQL statements to support analytical and summary query requirements, and use the MicroStrategy business intelligence platform to create dashboards and visualisations.

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The main goal of this Data Visualisation with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people.

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In this course, you will learn best practices for how to use data analytics to make any company more competitive and more profitable. You will be able to recognise the most critical business metrics and distinguish them from mere data.

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This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.

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In this course you will learn how to become a master at communicating business-relevant implications of data analyses.

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Being a product person today is a new game, and product managers are at the center of it. Today, particularly if your product is mostly digital, you might update it several times a day. Massive troves of data are available for making decisions and, at the same time, deep insights into customer motivation and experience are more important than ever.

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In this course, you will learn how to plan efficient experiments - testing with many variables. Our goal is to find the best results using only a few experiments. A key part of the course is how to optimise a system.

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This course is intended to be an introduction to machine learning for non-technical business professionals.

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In this course, you'll learn about Jupyter Notebooks, RStudio IDE, Apache Zeppelin and Data Science Experience. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations.

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This course is designed to give you a primer in the fundamentals of SQL and working with data so that you can begin analysing it for data science purposes.

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In this course, we will meet some data science practitioners and we will get an overview of what data science is today.

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This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualisations, predict future trends from data, and more.

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This course provides an analytical framework to help you evaluate key problems in a structured fashion and will equip you with tools to better manage the uncertainties that pervade and complicate business processes. The course aim to cover statistical ideas that apply to managers.

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In this course, you will learn best practices for how to use data analytics to make any company more competitive and more profitable. You will be able to recognise the most critical business metrics and distinguish them from mere data.

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To begin, we recommend taking a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments you’ll need to complete to pass the course.

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Learn in-depth concepts, methods, and applications of pattern discovery in data mining.

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Do you wish to know how to analyze and solve business and economic questions with data analysis tools? Then Econometrics by Erasmus University Rotterdam is the right course for you, as you learn how to translate data into models to make forecasts and to support decision making.

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In this second course of our Excel specialisation Excel Skills for Business you will build on the strong foundations of the Essentials course.

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In this first course of this specialisation, you will discover what data visualisation is, and how we can use it to better see and understand data.

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This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists.

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In this course, you will learn best practices for how to use data analytics to make any company more competitive and more profitable.

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In this first course of the specialisation Excel Skills for Business, you will learn the essentials of Microsoft Excel. Within six weeks, you will be able to expertly navigate the Excel user interface, perform basic calculations with formulas and functions, professionally format spreadsheets, and create visualisations of data through charts and graphs.

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Spreadsheet software remains one of the most ubiquitous pieces of software used in workplaces across the world. Learning to confidently operate this software means adding a highly valuable asset to your employability portfolio.

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In this course, we will meet some data science practitioners and we will get an overview of what data science is today.

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Understanding statistics is essential to understand research in the social and behavioural sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them.

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This introduction to Python will kickstart your learning of Python for data science, as well as programming in general. This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours.

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The Code Free Data Science class is designed for learners seeking to gain or expand their knowledge in the area of Data Science. Participants will receive the basic training in effective predictive analytic approaches accompanying the growing discipline of Data Science without any programming requirements.

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Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximised at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand.

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The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment.

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In this course you will learn how to quickly and easily get started with Artificial Intelligence using IBM Watson. You will understand how Watson works, become familiar with its use cases and real life client examples, and be introduced to several of Watson AI services from IBM that enable anyone to easily apply AI and build smart apps.

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This course will teach you how to get started with AWS Machine Learning. Key topics include: Machine Learning on AWS, Computer Vision on AWS, and Natural Language Processing (NLP) on AWS. Each topic consists of several modules deep-diving into variety of ML concepts, AWS services as well as insights from experts to put the concepts into practice.

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This two-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities.

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This course is intended to be an introduction to machine learning for non-technical business professionals. There is a lot of hype around machine learning and many people are concerned that in order to use machine learning in business, you need to have a technical background. For reasons that are covered in this course, that's not the case. In actuality, your knowledge of your business is far more important than your ability to build an ML model from scratch.

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What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you'll learn about Jupyter Notebooks, RStudio IDE, Apache Zeppelin and Data Science Experience. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations.

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This course is intended for folks who are new to programming and new to Unix-like operating systems like macOS and Linux distributions like Ubuntu. Most of the technologies discussed in this course will be accessed via a command line interface. Command line interfaces can seem alien at first, so this course attempts to draw parallels between using the command line and actions that you would normally take while using your mouse and keyboard. You’ll also learn how to write little pieces of software in a programming language called Bash, which allows you to connect together the tools we’ll discuss.

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In this course, we will meet some data science practitioners and we will get an overview of what data science is today.

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The main goal of this Data Visualisation with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people.

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This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials.

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AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone – especially your non-technical colleagues – to take.

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This is a focused course designed to rapidly get you up to speed on the process of building and managing a data science team. Our goal was to make this as convenient as possible for you without sacrificing any essential content.

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This course will focus on understanding key analytics concepts and the breadth of analytic possibilities. Together, the class will explore dozens of real-world analytics problems and solutions across most major industries and business functions.

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Through this interactive training, you’ll learn about core cloud business drivers – specifically Google’s cloud – and gain the knowledge/skills to determine if business transformation is right for you and your team, and build short and long-term projects using the “superpowers” of cloud accordingly.

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This is a focused course designed to rapidly get you up to speed on doing data science in real life. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward.

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This course is an introduction to how to use relational databases in business analysis.

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This course starts with the basics and assumes you do not have any knowledge or skills in SQL. It will build on that foundation and gradually have you write both simple and complex queries to help you select data from tables. You'll start to work with different types of data like strings and numbers and discuss methods to filter and pare down your results.

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In this course, you will learn best practices for how to use data analytics to make any company more competitive and more profitable. You will be able to recognize the most critical business metrics and distinguish them from mere data.

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In this third course of the specialisation, we’ll drill deeper into the tools Tableau offers in the areas of charting, dates, table calculations and mapping. We’ll explore the best choices for charts, based on the type of data you are using. We’ll look at specific types of charts including scatter plots, Gantt charts, histograms, bullet charts and several others, and we’ll address charting guidelines. We’ll define discrete and continuous dates, and examine when to use each one to explain your data. You’ll learn how to create custom and quick table calculations and how to create parameters. We’ll also introduce mapping and explore how Tableau can use different types of geographic data, how to connect to multiple data sources and how to create custom maps.

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Leveraging the visualisations you created in the previous course, Visual Analytics with Tableau, you will create dashboards that help you identify the story within your data, and you will discover how to use Storypoints to create a powerful story to leave a lasting impression with your audience.

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Download the full list of upskilling courses for data science teams