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Our Courses
Data and Statistics Foundation for Investment Professionals
Aimed at investment professionals or those with investment industry knowledge, this course offers an introduction to the basic data and statistical techniques that underpin data analysis and lays an essential foundation in the techniques that are used in big data and machine learning. It introduces the topics and gives practical examples of how they are used by investment professionals, including the importance of presenting the “data story" by using appropriate visualizations and report writing.
In this course you will learn how to:
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Course by
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Self Paced
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21 hours
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English
Using Custom Fields in Looker Explores
This is a Google Cloud Self-Paced Lab. In this lab, you will learn how to utilize custom fields in Looker Explores queries. Looker provides the ability for non-developer users to create and utilize ad hoc fields for richer data analysis.
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Course by
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Self Paced
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1 hour
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English
Data Modeling in Power BI
This course forms part of the Microsoft Power BI Analyst Professional Certificate. This Professional Certificate consists of a series of courses that offers a good starting point for a career in data analysis using Microsoft Power BI. In this course, you'll learn how to use Power BI to create and maintain relationships in a data model and form a model using multiple Schemas. You'll explore the basics of DAX, Power BI's expression language, and add calculations to your model to create elements and analysis in Power BI.
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Course by
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Self Paced
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English
Get Started with Python
This is the second of seven courses in the Google Advanced Data Analytics Certificate. The Python programming language is a powerful tool for data analysis. In this course, you’ll learn the basic concepts of Python programming and how data professionals use Python on the job. You'll explore concepts such as object-oriented programming, variables, data types, functions, conditional statements, loops, and data structures.
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Course by
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Self Paced
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31 hours
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English
The Total Data Quality Framework
By the end of this first course in the Total Data Quality specialization, learners will be able to: 1. Identify the essential differences between designed and gathered data and summarize the key dimensions of the Total Data Quality (TDQ) Framework; 2. Define the three measurement dimensions of the Total Data Quality framework, and describe potential threats to data quality along each of these dimensions for both gathered and designed data; 3.
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Course by
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Self Paced
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12 hours
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English
Marketing Analytics Capstone Project
This capstone project will give you an opportunity to apply what we have covered in the Foundations of Marketing Analytics specialization. By the end of this capstone project, you will have conducted exploratory data analysis, examined pairwise relationships among different variables, and developed and tested a predictive model to solve a marketing analytics problem. It is highly recommended that you complete all courses within the Foundations of Marketing Analytics specialization before starting the capstone course.
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Course by
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Self Paced
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11 hours
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English
Data Analysis and Representation, Selection and Iteration
This course is the second course in the specialization exploring both computational thinking and beginning C programming. Rather than trying to define computational thinking, we’ll just say it’s a problem-solving process that includes lots of different components. Most people have a better understanding of what beginning C programming means! This course assumes you have the prerequisite knowledge from the previous course in the specialization. You should make sure you have that knowledge, either by taking that previous course or from personal experience, before tackling this course.
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Course by
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Self Paced
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11 hours
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English
Data Science with NumPy, Sets, and Dictionaries
Become proficient in NumPy, a fundamental Python package crucial for careers in data science. This comprehensive course is tailored to novice programmers aspiring to become data scientists, software developers, data analysts, machine learning engineers, data engineers, or database administrators. Starting with foundational computer science concepts, such as object-oriented programming and data organization using sets and dictionaries, you'll progress to more intricate data structures like arrays, vectors, and matrices.
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Course by
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Self Paced
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31 hours
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English
Unsupervised Machine Learning
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data.
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Course by
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Self Paced
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23 hours
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English
Population Health: Responsible Data Analysis
In most areas of health, data is being used to make important decisions. As a health population manager, you will have the opportunity to use data to answer interesting questions. In this course, we will discuss data analysis from a responsible perspective, which will help you to extract useful information from data and enlarge your knowledge about specific aspects of interest of the population. First, you will learn how to obtain, safely gather, clean and explore data.
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Course by
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Self Paced
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20 hours
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English
Design Strategies for Maximizing Total Data Quality
By the end of this third course in the Total Data Quality Specialization, learners will be able to: 1. Learn about design tools and techniques for maximizing TDQ across all stages of the TDQ framework during a data collection or a data gathering process. 2. Identify aspects of the data generating or data gathering process that impact TDQ and be able to assess whether and how such aspects can be measured. 3. Understand TDQ maximization strategies that can be applied when gathering designed and found/organic data. 4.
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Course by
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Self Paced
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9 hours
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English
Specialized Models: Time Series and Survival Analysis
This course introduces you to additional topics in Machine Learning that complement essential tasks, including forecasting and analyzing censored data. You will learn how to find analyze data with a time component and censored data that needs outcome inference. You will learn a few techniques for Time Series Analysis and Survival Analysis.
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Course by
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Self Paced
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11 hours
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English
Calculus through Data & Modeling: Applying Differentiation
As rates of change, derivatives give us information about the shape of a graph. In this course, we will apply the derivative to find linear approximations for single-variable and multi-variable functions. This gives us a straightforward way to estimate functions that may be complicated or difficult to evaluate. We will also use the derivative to locate the maximum and minimum values of a function. These optimization techniques are important for all fields, including the natural sciences and data analysis.
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Course by
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Self Paced
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7 hours
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English
Python for Data Analysis: Pandas & NumPy
In this hands-on project, we will understand the fundamentals of data analysis in Python and we will leverage the power of two important python libraries known as Numpy and pandas. NumPy and Pandas are two of the most widely used python libraries in data science. They offer high-performance, easy to use structures and data analysis tools. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
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Course by
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Self Paced
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2 hours
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English
Introduction to Data Analysis using Microsoft Excel
In this project, you will learn the foundation of data analysis with Microsoft Excel using sales data from a sample company. You will learn how to use sorting and filtering tools to reorganize your data and access specific information about your data. You will also learn about the use of functions like IF and VLOOKUP functions to create new data and relate data from different tables. Finally, you will learn how to create PivotTables to summarize and look at comparisons within your data.
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Course by
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Self Paced
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2 hours
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English
Data Science Fundamentals for Data Analysts
In this course we're going to guide you through the fundamental building blocks of data science, one of the fastest-growing fields in the world!
With the help of our industry-leading data scientists, we’ve designed this course to build ready-to-apply data science skills in just 15 hours of learning. First, we’ll give you a quick introduction to data science - what it is and how it is used to solve real-world problems. For the rest of the course, we'll teach you the skills you need to apply foundational data science concepts and techniques to solve these real-world problems.
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Course by
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Self Paced
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19 hours
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English
Mastering Data Analysis with Pandas: Learning Path Part 5
In this structured series of hands-on guided projects, we will master the fundamentals of data analysis and manipulation with Pandas and Python. Pandas is a super powerful, fast, flexible and easy to use open-source data analysis and manipulation tool. This guided project is the fifth of a series of multiple guided projects (learning path) that is designed for anyone who wants to master data analysis with pandas. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
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Course by
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Self Paced
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2 hours
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English
Web of Data
This MOOC – a joint initiative between EIT Digital, Université de Nice Sophia-Antipolis / Université Côte d'Azur, and INRIA - introduces the Linked Data standards and principles that provide the foundation of the Semantic web. You will learn how to publish, obtain and use structured data directly from the Web.
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Course by
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Self Paced
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18 hours
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English
Mathematical Biostatistics Boot Camp 1
This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.
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Course by
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Self Paced
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13 hours
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English
Qualitative Data Analysis with MAXQDA Software
This course will introduce you to MAXQDA software for easier data analysis during the qualitative research process. You'll explore how to do memos, variables, segmentation, coding, and data reduction techniques all in this course!
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Course by
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Self Paced
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16 hours
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English
Teaching Impacts of Technology: Data Collection, Use, and Privacy
In this course you’ll focus on how constant data collection and big data analysis have impacted us, exploring the interplay between using your data and protecting it, as well as thinking about what it could do for you in the future. This will be done through a series of paired teaching sections, exploring a specific “Impact of Computing” in your typical day and the “Technologies and Computing Concepts” that enable that impact, all at a K12-appropriate level.
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Course by
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Self Paced
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13 hours
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English
Microsoft PL-300 Exam Preparation and Practice
This course forms part of the Microsoft Power BI Analyst Professional Certificate. This Professional Certificate consists of a series of courses that offer a good starting point for a career in data analysis using Microsoft Power BI. This course will help you prepare for the Microsoft PL-300 exam. In this course, you’ll refresh your knowledge of all the key areas assessed in the Microsoft-certified Exam PL-300: Microsoft Power BI Data Analyst. In addition, you will prepare for the certification exam by taking a mock exam with a similar format and content as in the Microsoft PL-300 exam.
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Course by
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Self Paced
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1 hour
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English
Take a Swing at Baseball Analytics: Explore Player Careers
Former Major League Baseball (MLB) player Matt Kata joins MathWorks to introduce you to data analysis using baseball statistics. By analyzing historic batting statistics, you will explore player careers and answer the question: When do great hitters peak in their career?
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Course by
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Self Paced
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4 hours
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English
Data Analysis and Visualization with Python
In this course, you will learn how to read and write data from and to a file. You will also examine how to manipulate and analyze the data using lists, tuples, dictionaries, sets, and the pandas and Matplot libraries. As a developer, it's important to understand how to deal with issues that could cause an application to crash. You will learn how to implement exceptions to handle these issues. You do not need a programming or computer science background to learn the material in this course. This course is open to anyone who is interested in learning how to code and write programs in Python.
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Course by
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Self Paced
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16 hours
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English
Machine Learning with PySpark: Customer Churn Analysis
This 90-minute guided-project, "Pyspark for Data Science: Customer Churn Prediction," is a comprehensive guided-project that teaches you how to use PySpark to build a machine learning model for predicting customer churn in a Telecommunications company. This guided-project covers a range of essential tasks, including data loading, exploratory data analysis, data preprocessing, feature preparation, model training, evaluation, and deployment, all using Pyspark.
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Course by
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Self Paced
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3 hours
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English