Foundations for Data & Analytics

Complete 12 or more courses you selected and earn a certificate in “Foundations for Data & Analytics” .

Beginner R1

R is a programming language that is used to store, analyze and visualize large amounts of data. R is used to create: 

• statistical models that will reveal patterns 
• mathematical models that lead to future projections 
• visualizations to present a large amount of data in an easy-to-read format

Beginner R1 will expose students to the underlying concepts of “R”. Key topics include the RStudio IDE, R fundamentals, installing and using packages, working with vectors and data frames, and running basic models. After completing this module, participants will learn to work with R environment, conduct basic of programming in R, and use the Tidyverse for data manipulation.

Beginner R2

This course will continue participants exposure to R focusing on visualizing, summarizing, tidying data. After completing this module, participants will be familiar with generating graphs with ggplot2 and aggregating, joining, and reshaping data using dplyr and tidyr.

This course will expose students to the underlying concepts of Python – used for a Machine learning and data analytics applications. Key topics include Python IDE (Jupyter Notebook), Python programming  basics, lists, functions and python libraries. After completing this module, participants will learn basic programming in Python, work with key Python data structures, learn to code functions in Python, and work with a Python library used for  working with arrays- NumPy.

This course will continue participants’ exposure to Python. Key topics of this module will include additional Python libraries, Python logical statements, control flow, and an introduction to Machine learning in Python. After completing this module, participants will have learned which are fundamental Python libraries, how to code control flow and set up logical statements and use a machine learning environment.

The basis of most data analytics is mathematical models. Beginner Math, Probability, and Statistics 1-2 modules will introduce you to basic mathematics that underlies the math concepts, probability functions, and statistical analysis that allow data analytics to work. It will teach you how people use, and misuse, statistics, how to create useful graphs with data, and introduce a simple model of data prediction. 

Beginner Math, Probability, and Statistics 1 will expose students to the underlying concepts of probability, statistics, and graphing necessary for simple data reports and data visualizations. It also serves as background introduction for future MSBA classes.

This course presents elementary topics in data analysis through regression.

Business Fundamentals for Analytics

For those without a business background, this module will provide you with a basic understanding of business concepts. It introduces you to how data analytics is used by marketing, management, and finance professionals. 

This course provides students a grounding in the basic terminology and theories of business, including accounting, finance, marketing, and operations management.

Beginner Data, Big Data Management & Cloud Computing

This course will give you an introductory level overview of data infrastructure including relational and non-relational databases. This module also focuses on how big data is different than traditional data workloads and how cloud solutions address the challenges of Big Data. Additionally, the module includes a hands-on activity that will walk students through the process of deploying a cloud database and accessing and importing data.

Data modeling is a tool to conceptually describe the data and relationships between data. We will learn how to create simple diagrams to describe and link complex data, showing the types of data used and how it can be organized.  

This course will cover conceptual, logical and physical database modeling, entity relationship diagrams, relational database modeling, and dimensional database modeling.

Beginner SQL 1

SQL, or structured query language, is a programming language used to query, organize, and interact with the data stored in databases. SQL is widely used in all major relational database management systems (RDBMS) software tools and is utilized across industries. In this course, you will learn how to use SQL for data input, manipulation, and retrieval.

This course will cover structured query language (SQL).

This beginner class will teach you how to create formulas, reference cells, create charts and manipulate data. Additionally, you will be introduced to a powerful mathematical modeling method that allows you to identify optimal choices with the click of a button. 

Beginner Data Visualization

This course will cover the basics of the art and science of data visualization and how to create insightful dashboards using Power BI via a case study. After completing this module, participants should be able to identify and create most appropriate diagrams for a given dataset and business purpose, which contributes to better data exploration, more informed decision making and more compelling communications.

Beginner Predictive Analytics – Data Mining & Machine Learning 1

Machine learning is the use of computer algorithms, or rules, to automatically detect patterns. Data mining takes machine learning a step further and uses those patterns to predict future behavior within the available data. These can be used for data you have previous conceptual knowledge about, or data that is completely new, to discover new patterns.

This course will expose students to the underlying concepts of machine learning pipelines. Key topics include predictive modeling, the split/train/score/evaluate framework, and basic data cleaning and preparation. After completing this module, participants will be able to generate their own machine learning web service which can be called from a Microsoft Excel spreadsheet.

Now you have the data, let’s transform it into actionable insights to inform business decisions. Learn how to define goals and objectives, and to analyze data ethically and in a socially responsible way.

This course will introduce, and expose students to the fundamentals of business intelligence, business analytics and data science in the era of big data and cloud computing for digital transformation.

In this course, you will learn the dynamic project management methodology known as Agile. Agile Project Management is a unique, dynamic method of leading a project that allows the flexibility required to tackle any IT project in which end-user demands are constantly evolving. Topics include team member roles and responsibilities, time, cost, and project schedule management, and methods for successfully handling changes throughout the project. 

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