Business Intelligence (BI) Essentials (Coursera)

Offered by IBM,
Business Intelligence (BI) Essentials (Coursera)

This course provides a comprehensive introduction to business intelligence (BI), its key concepts, components, and the benefits and challenges of implementing BI solutions. It also discusses career opportunities and roles available in the BI arena and the skills and qualifications required.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

You will also gain insight into the data ecosystem, BI analytics landscape, data repositories, and the extract, transform, and load (ETL) process. Additionally, you will be introduced to the role of statistical analysis in mining and visualizing data to identify patterns and trends and how to weave a compelling story with data.
The course offers practical exposure with hands-on activities and a final project that enables you to apply your knowledge in real-world scenarios. This specialized program is tailored for individuals interested in pursuing a career as a BI analyst, and no prior data analytics experience or degree is required to take this course.
This course is part of the IBM Business Intelligence (BI) Analyst Professional Certificate.

What you'll learn

  • Explain the concept of business intelligence (BI), the key components and challenges involved, and the career options in this field.
  • Describe data analytics and its significance in BI, recognizing its role in extracting insights from data.
  • Evaluate different business intelligence tools and technologies used to analyze the business context and requirements of a BI project.
  • Develop actionable insights using appropriate tools and techniques for data gathering, wrangling, analyzing, mining, visualizing, and reporting.

Syllabus

Introduction to Business Intelligence (BI)
This module introduces you to the field of BI. You will gain insight into the key concepts of BI, understand its importance in modern business operations, and explore the benefits and challenges associated with implementing BI solutions through various examples. You will also gain insight into how BI, data analytics, data science, and data engineering are different. Additionally, you will learn about the career opportunities and roles in BI and the skills and qualifications to develop a successful career in this field. By the end of the module, you will have a fundamental foundation in BI and be able to apply your knowledge to understand its significance in real-world business scenarios.

The Data Ecosystem
In this module, you will learn about the different types of data structures, file formats, sources of data, and the languages data professionals use in their day-to-day tasks. You will gain insight into various types of data repositories, such as databases, data warehouses, data marts, data lakes, and data pipelines. In addition, you will learn about the extract, transform, and load (ETL) process, which is used to extract, transform, and load data into data repositories. Finally, the module also provides an overview of big data and big data processing tools such as Apache Hadoop, Hadoop Distributed File System (HDFS), Hive, and Spark.

BI Analytics Landscape
This module explores the ecosystem of business intelligence (BI) analysts and provides insights into the types of analytics, such as descriptive, diagnostic, predictive, and prescriptive analytics, and understanding their unique contributions to data analysis. You will also learn about the key BI components that make up its process and the relevance of key performance indicators (KPIs) and metrics used in evaluating business performance. Additionally, you will gain insight into different BI technologies and tools used, the differences between these technologies, and how to analyze the business context, processing requirements, and objectives of a BI project to gain a comprehensive understanding of its scope and potential impact. Finally, the module introduces you to the overall BI process and delves into the privacy and security issues and the necessary regulatory compliance.

Gathering and Wrangling Data
In this module, you will learn how to identify, gather, and import data from disparate sources. You will learn about the tasks involved in wrangling and cleaning data to prepare it for analysis. In addition, you will learn about different tools that can be used for gathering, importing, wrangling, and cleaning data, along with some of their characteristics, strengths, limitations, and applications.

Mining and Visualizing Data and Communicating Results
In this module, you will learn about the role of statistical analysis in mining and visualizing data. You will also be introduced to various statistical and analytical tools and techniques that can be used to gain a deeper understanding of your data. These tools help you analyze the patterns, trends, and correlations in data. Additionally, you will learn about various types of data visualizations to communicate and tell a compelling story and different tools that can be used for mining and visualizing data, along with some of their characteristics, strengths, limitations, and applications. Finally, the module delves into how you can effectively present the BI insights you have gained.

Applying BI Techniques and Final Project
In this module, you will identify and apply the right BI techniques and tools to various real-world business scenarios and develop a comprehensive BI project. You will also gain an opportunity to apply your acquired knowledge and skills in a hands-on assignment.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Foundations of strategic business analytics (Coursera) Coursera
ESSEC Business School

Foundations of strategic business analytics (Coursera)

Who is this course for? This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role. You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering.

Jun 8th 2026
4 Weeks
Data Visualization (Coursera) Coursera
Ball State University

Data Visualization (Coursera)

In the era of big data, acquiring the ability to analyze and visually represent “Big Data” in a compelling manner is crucial. Therefore, it is essential for data scientists to develop the skills in producing and critically interpreting digital maps, charts, and graphs. Data visualization is an increasingly important topic in our globalized and digital society. It involves graphically representing data or information, enabling decision-makers across various industries to comprehend complex concepts and processes that may otherwise be challenging to grasp.

Jun 9th 2026
5-12 Weeks
Social Network Analysis (Coursera) Coursera
University of California, Davis

Social Network Analysis (Coursera)

This course is designed to quite literally ‘make a science’ out of something at the heart of society: social networks. Humans are natural network scientists, as we compute new network configurations all the time, almost unaware, when thinking about friends and family (which are particular forms of social networks), about colleagues and organizational relations (other, overlapping network structures), and about how to navigate delicate or opportunistic network configurations to save guard or advance in our social standing (with society being one big social network itself).

Jun 8th 2026
5-12 Weeks
Marketing Analytics (Coursera) Coursera
University of Virginia

Marketing Analytics (Coursera)

Organizations large and small are inundated with data about consumer choices. But that wealth of information does not always translate into better decisions. Knowing how to interpret data is the challenge -- and marketers in particular are increasingly expected to use analytics to inform and justify their decisions. Marketing analytics enables marketers to measure, manage and analyze marketing performance to maximize its effectiveness and optimize return on investment (ROI). Beyond the obvious sales and lead generation applications, marketing analytics can offer profound insights into customer preferences and trends, which can be further utilized for future marketing and business decisions.

Jun 8th 2026
5-12 Weeks
Reproducible Research (Coursera) Coursera
Johns Hopkins University

Reproducible Research (Coursera)

This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations.

Jun 8th 2026
4 Weeks
Text Retrieval and Search Engines (Coursera) Coursera
University of Illinois at Urbana-Champaign

Text Retrieval and Search Engines (Coursera)

Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. Text data are unique in that they are usually generated directly by humans rather than a computer system or sensors, and are thus especially valuable for discovering knowledge about people’s opinions and preferences, in addition to many other kinds of knowledge that we encode in text.

Jun 8th 2026
5-12 Weeks
Big Data Modeling and Management Systems (Coursera) Coursera
University of California, San Diego

Big Data Modeling and Management Systems (Coursera)

Once you’ve identified a big data issue to analyze, how do you collect, store and organize your data using Big Data solutions? In this course, you will experience various data genres and management tools appropriate for each. You will be able to describe the reasons behind the evolving plethora of new big data platforms from the perspective of big data management systems and analytical tools.

Jun 8th 2026
5-12 Weeks
Introduction to Spreadsheets and Models (Coursera) Coursera
University of Pennsylvania

Introduction to Spreadsheets and Models (Coursera)

The simple spreadsheet is one of the most powerful data analysis tools that exists, and it’s available to almost anyone. Major corporations and small businesses alike use spreadsheet models to determine where key measures of their success are now, and where they are likely to be in the future. But in order to get the most out of a spreadsheet, you have know how to use it. This course is designed to give you an introduction to basic spreadsheet tools and formulas so that you can begin harness the power of spreadsheets to map the data you have now and to predict the data you may have in the future.

Jun 8th 2026
4 Weeks
Relational Database Support for Data Warehouses (Coursera) Coursera
University of Colorado System

Relational Database Support for Data Warehouses (Coursera)

Relational Database Support for Data Warehouses is the third course in the Data Warehousing for Business Intelligence specialization. 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. Because of the importance and difficulty of managing implementations of data warehouses, we'll also delve into storage architectures, scalable parallel processing, data governance, and big data impacts. In the assignments in this course, you can use either Oracle or PostgreSQL.

Jun 8th 2026
5-12 Weeks
Data Manipulation at Scale: Systems and Algorithms (Coursera) Coursera
University of Washington

Data Manipulation at Scale: Systems and Algorithms (Coursera)

Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales.

Jun 8th 2026
4 Weeks
Introduction to Data Science in Python (Coursera) Coursera
University of Michigan

Introduction to Data Science in Python (Coursera)

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

Jun 8th 2026
4 Weeks
Market Research and Consumer Behavior (Coursera) Coursera
IE Business School

Market Research and Consumer Behavior (Coursera)

Your marketing quest begins here! The first course in this specialization lays the neccessary groundwork for an overall successful marketing strategy. It is separated into two sections: Market Research and Consumer Behavior. Gain the tools and techniques to translate a decision problem into a research question in the Market Research module. Learn how to design a research plan, analyze the data gathered and accurately interpret and communicate survey reports, translating the results into practical recommendations.

Jun 8th 2026
4 Weeks