Review: Business Analytics Specialization by Wharton
Anything analytics-related is a great starting role for launching a career in tech or the corporate world. Analytics is the backbone of so many company functions, and being able to demonstrate how you've made data-driven decisions is highly prioritized in hiring processes. What we're trying to say is, if you're new to tech and looking for a speedy way to break in, analytics is one of your best shots.
We love the idea of a sampler pack. We're always sampling different foods and activities-- certifications are no different! Apparently the Wharton School at UPenn does too, with the creation of their elite Business Analytics Specialization. The beginner-level specialization is hosted by Coursera, and consists of 4 courses and a capstone with around 4 weeks of work each. This nets a total of 20 weeks, or 5 months for program completion.
We call this specialization the sampler because it covers the basics of analytics from several fronts, including product/marketing analytics, operational analytics, people analytics, and business accounting. The program has great principles woven throughout, and demonstrates how versatile a true understanding of analytical principles can be.
The five courses to complete the specialization are:
- Customer Analytics
- Operations Analytics
- People Analytics
- Accounting Analytics
- Business Analytics Capstone Project
The material is taught by real industry professionals and covers the basics of how analytics can drive decisions in each of the fields. As newbies to HR and accounting, we especially loved the modules on spotting earnings fluff in public companies (course 4, week 3). But we found most of the course content extremely interesting.
The curriculum is meant to be consumed over a 20-week period, but it can also be completed at your own pace, which would probably be much quicker if you're dedicated.
Best for: Beginners
This specialization has some great material, but it is definitely aimed at an audience with very little analytics experience and does not dive deep into concepts or require any coding. If that's you, great! We definitely recommend weighing this course as a way to broaden your expertise of the role and its responsibilities.
If you have experience with analytics teams, even in another role like design or engineering, this specialization may be focused on a level and taught at a pace that's a little slow for you. It definitely samples lots of concepts, but is not deep enough with the concepts for you to be workforce ready upon completion.
If you're truly new to the field of analytics but mathematically inclined, we recommend taking this course as a sampler project and locking down what's most interesting. Do you like product/marketing analytics? Tie that to course one-- customer analytics. If you like operations or HR, check out those courses as well.
If you're here because you know you which industry/field you want to pursue, we recommend a more advanced or targeted specialization. Check out our other recommendations here, or at the end of this review.
We’ve recapped the learning objectives from each week to set your expectations for course material. The great part about this program is that you can jump to any course, and any section if it’s interesting to you. For example, if you’re an HR professional looking to up your people analytics skills, hop ahead to course 3 and start there. However, you only can get the certificate if you complete all 20 weeks of content.
To audit an individual week-- find the exact course (we've linked them individually here) and click "audit" to save it to your profile. Then open the desired week on the side panel that aligns with our recaps.
Course 1: Customer Analytics
Learning Objectives from Week 1: Introduction to Customer Analytics
- A basic introduction to the instructors, materials, and teaching approaches.
- Learn about how to connect with other students through the discussion boards and activities.
Learning Objectives from Week 2: Descriptive Analytics
- Learn the major methods of customer data collection and synthesize how data can inform business decisions
- Explore causation versus correlation in business data.
Learning Objectives from Week 3: Predictive Analytics
- Identify main tools used for customer behavior prediction, and the appropriate uses for each tool.
- Apply the RFM (recency, frequency, monetary) principle to predict customer activity.
- FWIW: RFM analysis is a marketing technique used to quantitively rank and group customers based on three attributes: when the customer last visited the place/site, how often they visit, and what they've purchased.
Learning Objectives from Week 4: Prescriptive Analytics
- Define a business problem and break it into objectives and goals.
- Practice optimization techniques to test into a successful solution to the identified business problem.
Learning Objectives from Week 5: Applications and Case Study
- Practice putting data to use in your own fictional company based on effective data-driven principles.
- Identify the different methods used by well-known firms to solve problems with data.
Course 2: Operations Analytics
Learning Objectives from Week 1: Introduction to Operations Analytics
- Basic course intro: get acquainted with the learning structure and meet professors.
- Get introduced to underlying analytics concepts, such as random variables, descriptive statistics, common forecasting tools, and measures for judging the quality of a forecast.
Learning Objectives from Week 2: Prescriptive Analytics, Low Uncertainty
- Practice building optimization models and applying them to specific business challenges.
- Learn how excel can help optimize a solution with built-in formulas.
Learning Objectives from Week 3: Prescriptive Analytics, Risk
- Practice building and reading simulation models to evaluate complex business decisions.
- Learn techniques for balancing risk and reward, and use a simulation to estimate outcomes.
Learning Objectives from Week 4: Prescriptive Analytics, High Uncertainty
- Learn about decision trees, and how they work together with optimization and simulation to solve complex problems.
- Use optimization frameworks to practice solving real problems using data.
Course 3: People Analytics
Learning Objectives from Week 1: Introduction to People Analytics and Performance Evaluation
- Learn about the three professors teaching the course and the structure and scope of the course.
- Understand the concept of performance evaluation and its role in the workplace.
- Learn about the four key issues in measuring performance: regression to the mean, sample size, signal independence, and process vs. outcome and how to apply them in real-world scenarios, specifically in the NFL.
Learning Objectives from Week 2: Staffing
- Learn about how to use data to analyze the key components of the staffing cycle: hiring, internal mobility and career development, and attrition.
- Explore different analytic approaches to predict performance for hiring and optimize internal mobility, reduce turnover and predict attrition.
- Learn the critical skill of understanding causality to avoid using data incorrectly.
Learning Objectives from Week 3: Collaboration
- Learn the basic principles of using analytics to encourage collaboration between employees within an organization.
- Explore how data is used to describe, map, and evaluate collaboration networks, and how to intervene in collaboration networks to improve collaboration using examples from real-world companies.
- Learn how to use tools of organizational network analysis to improve collaboration patterns within an organization to make it more productive, effective, and successful.
Learning Objectives from Week 4: Talent Management and Future Directions
- Learn how data may be used in talent assessment and development to maximize employee ability.
- Understand how to move from performance evaluation to a deeper analysis of employee evaluation to improve the effectiveness and equitability of the promotion process at your firm.
- Practice the four major challenges of talent analytics: context, interdependence, self-fulfilling prophecies, and reverse causality, the challenges of working with algorithms, and some practical tips for incorporating data sensitively and fairly.
- Understand the current challenges and future directions of the field of people analytics to put employee data to work in a smarter way.
Course 4: Accounting Analytics
Learning Objectives from Week 1: Ratios and Forecasting
- Understand the concept of ratio analysis and forecasting, including how it involves financial statement numbers.
- Learn about financial statements and sources of financial data through optional review videos.
- Analyze a single company's strategy and business model, using the DuPont analysis, profitability and turnover ratios, and liquidity ratios.
- Use each of the ratios to forecast future financial statements and understand how to identify sources of potential trouble.
Learning Objectives from Week 2: Earnings Management
- Understand the concept of earnings management and its practices of trying to intentionally bias financial statements.
- Learn about means, motives and opportunity; how companies make earnings look better, their incentives for manipulating earnings, and how they get away with it.
- Be able to spot earnings management to get a more accurate picture of earnings and be able to catch some bad guys in finance reporting.
Learning Objectives from Week 3: Big Data and Prediction Models
- Learn about using big data approaches to detect earnings management, specifically using prediction models to try to predict how the financial statements would look if there were no manipulation by the manager.
- Learn about Discretionary Expenditure Models, which try to model the cash portion of earnings.
- Understand Fraud Prediction Models, which try to directly predict what types of companies are likely to commit frauds.
- Explore Benford's Law and its use in detecting potentially manipulated financial statements.
Learning Objectives from Week 4: Linking Non-Financial Metrics to Financial Performance
- Understand the importance of linking non-financial metrics to financial performance.
- Learn how to uncover which non-financial performance measures predict financial results by asking fundamental questions such as: which are the key drivers of financial success, how to rank or weight non-financial measures and what performance targets are desirable.
- Read examples of how real-world companies use accounting analytics to show how investments in non-financial dimensions pay off in the future and the common organizational issues that can arise when using these models.
- Understand how predictive analytics can be used to determine what to measure, how to weight different performance measures, make trade-offs between short and long-term objectives, and set performance targets for optimal financial performance.
Course 5: Business Analytics Capstone Project
Learning Objectives from the Capstone
- Apply what you've learned about making data-driven decisions to a real business challenge faced by global technology companies like Yahoo, Google, and Facebook.
- Devise a course of action to optimize your company data to provide insights and analyses using principles from each of the courses above.
Cost and Auditing
The program is only $79/month, and comes with a Linkedin Certificate on behalf of the Wharton Business School from the University of Pennsylvania. If you complete the curriculum on the proposed timeline, it should take about 5 months, though you could blitz through it on a break in far less. We noticed each week only had about 2 combined hours of video and reading materials and one assignment. This means each week is about 3-4 hours of work.While $79/month may seem steep, compared to a degree or bootcamp this college-credited micro-certification is a steal!
If you have a learning budget at your current company, or are dedicated to upskilling your career into something analytics related– we recommend completing the program and getting the shareable certificate (GET RECEIPTS!). This will help make your Linkedin and resume more searchable to recruiters who may be looking for specific keywords and programs. To audit the program and simply learn the material, this program is completely free.
We recommend auditing the one of the courses-- they're semi-independent--to decide if this program and the professors match your learning style. To audit, simply click "Enroll for Free" and click "Audit" on the bottom of the second step. Voila, you're in!
This is a older program that launched in January of 2016, so the program has several thousand reviews per course. Usually anything older than 2 years we flag, but the concepts are more high level and don't involve code, we didn't find too many dated hiccups. However, the courses within the specialization are very highly reviewed– averaging around 4.5 per course! The most reviewed class by far was the first course, customer analytics.
It's important to note, many of the reviews specifically called out how the courses focused more on high-level concepts. While we love this for a flashy certification, it may not be the best if you're looking to really learn the material in-depth. If that's the case, we'll recommend some supplemental activities to really bolster your analytics background.
Some of our favorite positive review points:
- Amazing course for even beginners in the field of customer analytics. Highly recommend to do this course for enhancing the analytical skills. Examples and case studies explains the concept very well. - Shruti S.
- An excellent course to understand the value of data, how companies are collecting and harnessing their data an efficiently applying AI/ML concepts to provide better services and products to their customers. - Hemalsha H.
Aggregations of negative review points:
- Some of the concepts were high level-- for example, in course 1 the professor talks about completing a churn analysis, but does not go into details on how to do this. (can be solved with a google search, though)
- Some of the videos were on the longer side.
- Most of each course material can be completed in 1-2 weeks instead of the recommended 4.
... and our favorite overall review:
Well, it was a superlative course, to say the least. As a person having expertise in finance and quantitative analysis, this course leveraged my skills , while adding immensely to them.
I knew Excel as a beginner, but the course exposed me to its immense potential. Predictive statistics, decisions under low uncertainty using Solvex and Data Analysis, exposure to Decision Trees and their interpretation from a risk-reward angle- all these were laid out in a very interesting manner. This enabled practical and inquisitive learning, making you hungrier for more courses as well planned as this one.
-Arvind K, on Course 3: Operations Analytics
We mentioned this a few times, but this course from Penn is great as a showable certification, but may not be the best for learning the down & dirty of analytics (which happens to be my specialty). Here are some of our favorite classes focused specifically on learning the material.
For upskilling your visualizations: Mastering Data Visualizations: Theory & Foundations
Data visualization is one of the top words that came back in our AI sorted analytics job descriptions, and for good measure. Showing off your results is one of the most important aspects of analytics to help your organization understand your findings and insights. On the flip-side, visualizations are easy to mess up, and this class takes care to demonstrate the most common graphing mistakes.
For A/B testing detectives: Complete Conversion Rate Optimization Course
You probably know by now, but conversion rate optimization is where we got our start in the tech world. So we'll always recommend it.
This was the best balance of affordability and material depth that we could find in an optimization course. Learn how to set up, track and analyze a/b tests with comprehensive modules and videos. This knowledge is crucial for both product and marketing analytics, and we loved how forward it was. We're not alone-- the class averages a 4.7 star rating with more than seven-thousand students at time of writing.
Similar Analytics Specializations
For the analytically-inclined coder: Duke University's Data Analytics Specialization
Duke University sponsors a similar data analytics certificate program through Coursera with a focus on R. Often jobs are looking for analysts with either SQL, R or Python familiarity under their belt. While R is probably the least commonly used, we loved the structure of this course and the incredibly practical focus on web statistics and probability testing. It's free to audit, but if you want the certificate to show off (recommended), it's $49 a month to complete at your own pace. Read our full writeup here.
For machine learning analytics: Stanford's Machine Learning Specialization
Stanford University does a baller specialization in machine learning through Coursera. This tried-and-true program has been around since 2012, and is taught by one of the most revered AI professionals in the industry. Read our full writeup here. It's free to audit, but if you want the certificate (recommended), it's $49 a month to complete at your own pace.
Product management, with a focus on AI: Duke University's AI Product Management
Both Duke and UVA are incredibly prestigious organizations working to increase public knowledge of product management. This is a mid-level course-- so have some basic knowledge under your belt first-- focused on product management in the realm of artificial intelligence. Read our full writeup here. It's also free to audit, but if you want the certificate (recommended), it's $79 a month to complete at your own pace.
University of Pennsylvania Business Analytics Specialization from Wharton is a great way for beginners to learn the basic areas of analytics. If you're looking for a way to dabble between different analytical worlds to see what's best for you, this could be a great option!
Here at Bridged we are huge fans of stacking micro-certifications to achieve desired career results. This program could be one notch in your arsenal to really kick your technical expertise into gear!