Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making.
The courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include:
Data strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
Course 1: Introduction to Business Analytics with R - Offered by University of Illinois at Urbana-Champaign. Nearly every aspect of business is affected by data analytics. For businesses to ... Enroll for free.
Course 2: Introduction to Business Analytics: Communicating with Data - Offered by University of Illinois at Urbana-Champaign. This course introduces students to the science of business analytics while casting a ... Enroll for free.
Course 3: Tools for Exploratory Data Analysis in Business - Offered by University of Illinois at Urbana-Champaign. This course introduces several tools for processing business data to obtain ... Enroll for free.
Course 4: Machine Learning Algorithms with R in Business Analytics - Offered by University of Illinois at Urbana-Champaign. One of the most exciting aspects of business analytics is finding patterns in the ... Enroll for free.
Course 5: Applying Data Analytics in Marketing - Offered by University of Illinois at Urbana-Champaign. This course introduces students to marketing analytics through a wide range of ... Enroll for free.
Course 6: Applying Data Analytics in Accounting - Offered by University of Illinois at Urbana-Champaign. This course explores business analytic applications in accounting. First, it presents ... Enroll for free.
This course introduces students to the science of business analytics while casting a keen eye toward the artful use of numbers found in the digital space. The goal is to provide businesses and managers with the foundation needed to apply data analytics to real-world challenges they confront daily in their professional lives. Students will learn to identify the ideal analytic tool for their specific needs; understand valid and reliable ways to collect, analyze, and visualize data; and utilize data in decision making for their agencies, organizations or clients.
This course introduces students to marketing analytics through a wide range of analytical tools and approaches. We will discuss causal analysis, survey analysis using regression, textual analysis (sentiment analysis), and network analysis. This course aims to provide the foundation required to make better marketing decisions by analyzing multiple types of data related to customer satisfaction.
Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings.
In this course you will use a data analytic language, R, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Cleaning, transforming, aggregating, and reshaping data is a critical, but inconspicuous step in the business analytic workflow.
As you learn how to use R to prepare data for analysis you will gain experience using RStudio, a powerful integrated development environment (IDE), that has many built-in features that simplify coding with R.
As you learn about the business analytic workflow you will also consider the interplay between business principles and data analytics. Specifically, you will explore how delegation, control, and feasibility influence the way in which data is processed. You will also be introduced to examples of business problems that can be solved with data automation and analytics, and methods for communicating data analytic results that do not require copying and pasting from one platform to another.
This course introduces several tools for processing business data to obtain actionable insight. The most important tool is the mind of the data analyst. Accordingly, in this course, you will explore what it means to have an analytic mindset. You will also practice identifying business problems that can be answered using data analytics. You will then be introduced to various software platforms to extract, transform, and load (ETL) data into tools for conducting exploratory data analytics (EDA). Specifically, you will practice using PowerBI, Alteryx, and RStudio to conduct the ETL and EDA processes.
The learning outcomes for this course include:
1. Development of an analytic mindset for approaching business problems.
2. The ability to appraise the value of datasets for addressing business problems using summary statistics and data visualizations.
3. The ability to competently operate business analytic software applications for exploratory data analysis.
One of the most exciting aspects of business analytics is finding patterns in the data using machine learning algorithms. In this course you will gain a conceptual foundation for why machine learning algorithms are so important and how the resulting models from those algorithms are used to find actionable insight related to business problems. Some algorithms are used for predicting numeric outcomes, while others are used for predicting the classification of an outcome. Other algorithms are used for creating meaningful groups from a rich set of data. Upon completion of this course, you will be able to describe when each algorithm should be used. You will also be given the opportunity to use R and RStudio to run these algorithms and communicate the results using R notebooks.
This course explores business analytic applications in accounting. First, it presents a survey of technology topics in accounting, including process mining, blockchain and applications in audit, tax, and assurance. Next, the course explores visualization and basic analytics in audit and control testing using R and Alteryx. Next, the course examines the uses of text analysis in accounting and conducts text analysis using R and RStudio. Finally, the course examines robot process automation in general using UiPath and its applications in accounting.
Ashish Khandelwal, Jessen Hobson, Joseph T. Yun, Kevin Hartman, Ronald Guymon and Unnati Narang