About this Business Analytics course
This is a free Business Analytics course split into five main areas. You will be introduced to a general overview, history, and what’s to come of the business analytics world. You’ll also explore current problems and opportunities in the field. Then this online course ends with a “next steps” section for business analytics professionals.
- What Is Business Analytics?
- History of Business Analytics
- Future of Business Analytics
- Challenges & Opportunities in Business Analytics
- Next Steps for Business Analytics Professionals
This free online Business Analytics course directs you toward free videos, podcasts, and resources followed by questions to aid in processing this exciting field. Each module is created to encompass two to three hours of learning material.
Whenever you see this icon, it is time to stop and watch, listen, or read.
At the end of each section in this free business analytics course is an opportunity to invest further. These suggestions offer courses and certifications within a wide range of financial and time investments to further studies and enrichment in the field of business analytics.
1│What Is Business Analytics?
Business analytics uses data to create mathematical models to help organizations make decisions that bring value or are in their best interest. There is much data out there for organizations to use, though what data they choose and why they use it will vary from industry to industry.
Data is such an essential tool for organizations because the data itself is objective, grounded in fact. It’s this objectivity that can be most helpful for organizations in determining what decisions they need to make to create the most value for them.
What are some benefits you can think of in using business analytics for an organization? Jot down a couple and then read this brief article on business analytics from the Harvard Business School Online and see if they had the same benefits you thought of.
Types of Business Analytics
There are a few types of business analytics, all of which use data to accomplish a goal. Predictive analytics is focused on identifying probable outcomes. Prescriptive analytics recommends actions to an organization to help reach organizational goals. And descriptive analytics identifies an organization’s historical trends. Most business analytic professionals use either predictive or descriptive analytics.
Read this analytics article from Forbes that discusses these three types. What are your thoughts about the future of data analytics being focused on prescriptive analytics?
Watch this introduction to business analytics. It discusses how organizations can create objective expectations using business analytics to create value for their organizations (both internally and externally). Now, write down a list of all the different stakeholders at your organization. Then write down your organization’s goals. What kind of data would a business analytics professional need to consider to make stakeholders happy and help the organization meet its long-term goals?
What a Business Analytics Professional Does
Working in the field of business analytics, at its core, means collecting and analyzing data in a way that is useful to your organization’s decision-making process.
There are many avenues a business analytics professional can follow. Business analysts, data analysts, and market research analysts are just a few. What about data science? Think of data science as a section of business analytics that spends more time in more in-depth, more complex algorithms of the data than other areas of business analytics.
It’s also important to know that data analytics is basically the same as business analytics. But data analytics is focused on analyzing data and making predictions, while business analytics is also concerned with making decisions from that data and predictions.
Here’s a quick video that explains the difference between the roles of data analyst and business analyst. But for this free online business analytics course, we’re going to group anyone working with analytics under the large umbrella of business analytics.
Listen to this podcast episode from Tech Career Talk about what it’s like to be a lead business analyst. What about the role stood out to you? Did you gain any insights on what you might need to be in a role like this or where such a role might lead you in your career?
Kaggle is a website that hosts competitions so that data science professionals can learn and hone their skills. It can be intimidating when you first join, especially since some of their competitions have high monetary stakes. But check out this article on why you shouldn’t fear Kaggle, along with some ideas of how to get started. Kaggle is entirely free to use, and it includes a bunch of training. If you need a little more guidance, here’s a step-by-step beginner’s guide to Kaggle.
Take this course from Wharton through Coursera, which is a beginners course on data analytics. It covers how to use data to help organizations make decisions. At around three hours of work each week, it should take about six months to complete. If you’re not a total beginner, you can also take this advanced course on business analytics through Coursera from the University of Colorado-Boulder, which should take about five months at three hours each week to complete.
2│History of Business Analytics
System of Scientific Management
The early days of business analytics were focused on how to improve production through efficiency, higher quantities, and increased cost-effectiveness. The Industrial Revolution meant the creation of new processes of manufacturing, developing complex industries. In the late 1800s in the United States, Fredrick Winslow Taylor presented a formalized system of business analytics with this system of Scientific Management. This system used time studies to analyze preferred methods of operation to ensure that productivity was at its highest among workers. This system led directly to Henry Ford’s assembly lines, which revolutionized manufacturing.
Decision Support Systems + Relational Databases
In the 1970s, larger companies began using computers. This meant that all the information gathered by an organization was now more easily shared and updated with the use of computers instead of through handwritten ledgers.
Business analytics used Decision Support Systems during this period. DSSs collate data from various areas of the organization. They are a useful tool to sort and filter that data in a way that is useful for decision-makers to see a 30,000-foot perspective. The fact that they were now able to examine their data through different filters was also a game-changer.
In this lecture, you’ll learn more about DSSs and the general reasons behind them. This is a three-part video, so here’s part 1, part 2, and part 3. Before you start, generate a list of decisions that you need to make as an individual on a daily, weekly, annual, and lifetime-basis. This is the same exercise the class does as the lecture kicks off.
With increased storage capacity and easier access to said stored data because of computers, there was a new focus on historical data. This data, such as pricing, growth, and market trends, became accepted information for organizations to use when making decisions.
In 1985, Microsoft Excel launched, which can be used as a DSS structure by allowing users to filter and sort data and display the data in a variety of specific ways through programmed formulas. Here is a tutorial for you to work through. In the first section, you will use Excel to create your own DSS program for a small business that wants to expand. In the second, you’ll learn how to use Excel’s Scenario Manager to make your DSS more efficient, and in the third, you’ll practice using the Scenario Manager to model a DSS for a new problem.
In 2005, Google introduced Google Analytics, a free way for users to analyze the myriad of data available in the world. Using Google Analytics, website owners can see metrics like new users, visitors’ demographics, total visits or click-throughs, bounce rates, and more. Google Analytics brought data accessibility to the masses.
There’s so much more in the history of business analytics beyond these few short paragraphs. Here’s one more article to read that adds a bit more to this layered history that impacts daily impacts our lives today.
Sign up for LinkedIn Learning to access a library of Google Analytics tutorials. Courses are asynchronous, and you can earn a certificate upon completing a course.
3│Future of Business Analytics
People analytics isn’t new. But it’s starting to gain traction in organizations. And it’s precisely what it sounds like; it’s using data to understand and improve the people side of the organization. In theory, intelligently used people analytics can help decrease turnover, make better hiring decisions, testing employee policies for effectiveness, or figuring out what your organization needs in terms of employees in the future.
This document outlines the benefits, challenges, and implementation strategies related to using people analytics. As you read, consider the ways people analytics might be implemented and used in your organization.
Business analytics tools will continue to improve in terms of their usability. This means that more users, not just those trained in analytics to create reports or extract data. As more and more organizations place an increasingly heavy value on data, the benefits of having a self-service analytics platform widely available to their employees are becoming even more critical.
Here’s a webinar that compares several of the top self-service analytics tools out there right now. These include Tableau, Microsoft Power BI, and more. As you watch, consider the information provided on each service. For your organization, which self-service platform would be most beneficial and why? What are some significant downfalls, if any, you notice on your chosen platform?
The Future-Shaping Pandemic
When COVID-19 hit, many organizations had to quickly adapt or evolve the ways they were using analytics and artificial intelligence in order to navigate the new realities the pandemic caused. Many organizations had adopted analytics and AI for some time. But with the arrival of COVID-19, there was a new urgency for using these tools well when it came to identifying possible disruptions in the supply chain, determining how effective a crisis intervention would be, forecasting demand for a product, or identifying high-risk workers.
KPMG has developed an entire analytics platform that uses over 2,000 datasets to help businesses identify critical indicators essential for them to consider when it comes to areas of growth, recovery, and risk related to the pandemic. Check out their sample data platform and consider other ways these types of datasets might be widely relevant.
This analytics piece from May 2020 from McKinsey discusses the necessity of accelerating the use of analytics to manage the reality of COVID-19. As you read, what are some of the ways that these changes in business analytics will have lasting repercussions on the future of analytics as well?
Watch two days’ worth of speakers from the Data Innovation Summit 2020. This summit covers topics on data and AI business like current policies, trends, challenges, and breakthroughs, as well as the future of data management. Day one and day two are full six- to eight-hour days, but use the contents in the description box to jump around to the topics that are the most interesting to you.
Earn a certificate in business analytics through Simplilearn and Perdue University. The course is entirely online, and you can complete it in six months. Students complete five to 10 hours of work each week. In this program, you’ll study topics like Agile Scrum methodologies, visualization tools, SQL databases, and building dashboards. It includes master classes taught by Perdue faculty and case studies from Harvard Business Publishing Education.
4│Challenges & Opportunities in Business Analytics
Artificial Intelligence & Scaling
Artificial Intelligence is becoming increasingly prominent across industries, including business analytics. We’ve talked already about the fact that organizations are increasingly pulling more and more data, both in terms of volume and complexity. Consequently, more and more organizations are using AI to help manage their analytics. AI, in this instance, can be used to read and understand particular types of text (such as suggestions) or classify visual images as a few examples. This article covers more on the use of AI in business analytics.
One of the challenges facing the use of AI, however, is the lack of implementation. It takes a significant amount of resources and time to make the organizational shift needed to bring AI to a place where it can deliver its true value. As a result, AI scaling isn’t happening. This piece by McKinsey discusses what it takes to scale AI in an organization. Where do you see business analytics professionals in this process?
Data Specialists and Other Talent Shortages
There is a lack of data professionals, such as data analysts and scientists at the moment. While this section will focus on data scientists, know that these general challenges are true across the analytic profession board. This was a shortage on the horizon for some time. Still, with the increased focus on big data, smaller organizations jumping into harnessing the power of analytics, the rapid growth of the cybersecurity industry, and the way the Internet of Things is continuing to expand, that shortage has only become more pronounced.
There are several solutions to this shortage. We already discussed the expanded influence of self-service analytics and the movement toward “citizen data scientists” and upskilling their employees on analytics. Nonetheless, the shortage, especially when it comes to highly-trained professionals, remains. In this article from CIO, some solutions to the deficit are suggested, some of which we have already discussed. Before you read, draw up a quick list of other possible routes to take to make up for an organization’s lack of data scientists or different business analytic-type roles.
If you’re thinking of taking on the data scientist side of business analytics, here’s a podcast episode about what that might look like. The episode is an interview with Andrew Therriault, a data scientist and educator. In this episode, Therriault discusses how he learned data science in the first place, his career path, and advice for those considering going into data science.
Consider going through a professional bootcamp for business analytics. In response to the professionals’ shortage we just discussed, many bootcamps have cropped up. As we know, one of the issues is the lack of business analytics professionals with in-depth knowledge and years of experience in the field. But in order to get there, you’ve got to start somewhere. Here’s a list compiled by CIO of the 15 best data science bootcamps. Many of these bootcamps even go so far as to guarantee employment after you graduate. There are so many out there. Do a little research, to find the one that is the best fit for you.
Once this free business analytics course is completed, take the Business Analytics Training from Educba. This training certification is earned over the course of 14 courses with over 88 hours of video tutorials. Throughout the training, you’ll complete eight projects while learning about programming languages like R and SAS and various analytics types such as marketing analytics and customer analytics. You do need basic knowledge of Microsoft Excel, marketing, and the fundamentals of data mining techniques. If this isn’t the right fit for you, Educba offers various other business analytics courses and certificates.
5│Next Steps for Business Analytics Professionals
Find Your Niche
The world of business analytics is massive. Because virtually every industry is now using data, there is a need for professionals with a business analytics background. And, as we’ve seen, there’s also scarcity in this department. If business analytics is something you’re considering, be sure to expand your idea of what types of roles are out there. To help you clarify your business analytics niche, spend some time writing down the industries you’re interested in or have experience in. Also, note some industries you’re excited or passionate about.
Here’s a list of some business analytics roles in sectors you might not have previously thought of. Did any surprise you or pique your interest?
Be a Good Communicator
Strong communication skills are of utmost importance for business analytics professionals because they are the ones who translate the data for the decision-makers. All the data in the world doesn’t do anyone any good if they don’t clearly understand what the data is telling them. It can be a challenge to present your findings to decision-makers, so it’s essential to choose the right platforms and visualizations to help you, as well as the know-how to craft data-driven stories that provide context and insight.
One well-known data visualization tool is Tableau. In this video, you’ll get a very basic walkthrough of how to build a dashboard.
Data visualizations help simplify complex data to help decision-makers make the wises choices. But a business analytics professional also plays a role in helping sway decision-makers because they understand the data the best. So persuasion is an important skill to hone.
Before watching this video on how to capitalize on your communication as a business analytics professional, think about what makes a good story. Consider aspects like story arc, characters, and context. What did you learn from this overview about how to tell a story with your data that is compelling and memorable?
Hone Your Hard Skills
The most used languages for business analytics professionals are R, SAS, and Python. The three of these languages make it easy to do data transformation. R and Python or open-source, while SAS is proprietary software. They also have vast libraries to help you out with guidance on creating models.
Spend some time exploring scikit-learn. This is a free library of resources for learning Python. It offers information on things like regression, classification, clustering, random forests, k-means, and DBSCAN. It’s also mean to interoperate with NumPy and SciPy, which are other Python libraries. It’s a huge source of information on all things Python.
Here is an overview of these three languages. As you watch, what do you note about the similarities and differences? Based on the job trends discussed, do you feel one might be more beneficial over the others for your career goals?
Machine Learning is another hard skill you might want to pick up. Machine learning helps analyze data, find trends that might have been missed, and transform the data into information to help with decision-making. As you read this article on how to go about machine learning, how do you see it working with other areas we’ve discussed, like programming languages and visualizations?
Since R and Python are both open-source, they’re a great place to start learning programming languages. And they are two of the most widely used languages out there, so the benefit of their value is significant.
Take this introductory IBM course from edX on Machine Learning with Python. This is a self-paced, five-week course, and you can expect to spend four to six hours per week on coursework. While it’s free, you can choose to pay for a certification. You will want a basic understanding of Python before you begin, though.
This is the end of our free online Business Analytics course. We hope it gives you a good background on the topic!