Read this to learn about the job saving technology that every business major should have…

Pasted below is a series of blogs that I wrote on how you can learn job saving technology…

I recently had a student interview with several companies for supply chain internships. Most of the companies were Fortune 500 types that placed a premium on business data analytics and the ability to use Excel in some very advanced ways. We actually require all of our SCM majors to take a Predictive Data Analytics class (think Excel on steroids). This student received multiple internship offers because he demonstrated an ability to make sense of very large amounts of data.

HOWEVER, there was one company that he interviewed with that did not place a premium on this and actually asked why do so many SCM majors get a minor in Business Data Analytics? Students: proceed with caution in these types of situations. Note, this one company was a very small business with a single factory of fewer than 100 employees. The internship was very specific to Operations Management.

OK, an internship in Operations Management, especially one that involves hands-on experience on the shop floor, can be incredibly valuable for a student majoring in supply chain management. It provides practical insights into how products are made, the operational bottlenecks that occur, and how supply chain strategies directly impact production and overall business efficiency. Here are specific skill sets that the internship might require or help the student develop:

Process Understanding & Improvement: Knowledge of production processes, including the ability to identify areas of improvement, is crucial. The student will benefit from understanding Lean principles and other process improvement strategies (like Six Sigma) to enhance efficiency and reduce waste.

Project Management: Handling tasks from inception to completion under time and resource constraints, managing priorities, and coordinating with different teams or departments are skills associated with project management.

Supply Chain Integration: Understanding how the supply chain integrates with operations and the effects of supply chain decisions on production, inventory, and delivery. This involves practical skills in managing and scheduling inventory, understanding lead times, and coordinating with suppliers and customers.

Communication and Collaboration: Effective communication with team members, supervisors, and possibly suppliers or clients is essential. This includes both verbal and written skills, as well as the ability to work in a team and potentially lead or manage others.

Problem-Solving (hugely important in Operations): The ability to quickly identify problems that may arise during production, and working to solve these issues efficiently and effectively, often under pressure.

Adaptability and Learning Agility: Small businesses often require their employees to wear multiple hats, especially during an internship. The student should be prepared to take on various roles, show eagerness to learn, and adapt to rapidly changing circumstances.

Technical Knowledge: Understanding the machinery, technologies, or technical aspects of the production floor, even at a basic level, can be incredibly helpful. This might also involve understanding quality control and assurance procedures.

Safety Standards and Compliance: Knowledge of health and safety standards in a factory setting is a must. This means not only following procedures themselves but also recognizing unsafe practices and working to correct them.

Time Management: Balancing multiple tasks, often with tight deadlines, is a common requirement in operational roles. Effective time management and the ability to prioritize tasks will be key.

HOWEVER!!!!!!!….An internship in Operations Management (whether they realize it or not), will still require…

Analytical Skills: Ability to analyze data to improve operations, including forecasting demand, planning resources, and addressing production issues. This will likely involve the use of various software tools, so familiarity with these (or the ability to quickly learn) will be beneficial.

If they are not making use of these skill sets, and you have them, it is an opportunity for you to be a rock star and create a culture change (if you decide to work someplace that still needs this kind of culture change).

More on not becoming a victim of technology…

Top Skills You Need To Become a Data Scientist (every job has data). https://lnkd.in/gk7pgtQZ. Do not be a victim of technology! You could make the case that every Business major should minor (or double major) in Data Analytics. These skill sets would perhaps include:

Employers place a premium on –
1. Advanced Excel (power query & pivot) & macros;
2. Data visualization (Tableau, Power BI & python w/ seaborn & matplotlib);
3. Data mining/RapidMiner, machine learning & data science;
4. Python & Jupyter notebook (data analytics & statistical libraries such as pandas, numpy);
5. Relational data models (Excel data model);
6. Graphic & statistical libraries (Seaborn, Matplotlib, Pandas, & Plotly).

Business analytics addresses an increasing demand in orgs of all types to understand data related to their operations. Investments in information systems throughout the enterprise over the last 10-15 years are generating tremendous amounts of data, & orgs will spend at least the next 10 years developing processes that generate insight from those data.

In addition to data generated internally, many orgs are exploring the effects of external data, primarily present in social media, web search, manufacturing, & the SUPPLY CHAIN. The ability to manage data to support business projects are the key to success in many disciplines. Business analytics will provide a comprehensive skill set for SCM professionals & future grads to analyze, VISUALIZE (Tableau & Power BI!), & report data.

This has become a bare minimum:

CIS 2640 Predictive Data Analytics (Excel on steroids): I get this kind of feedback often…https://lnkd.in/dQABdsXc – You will be glad to hear that (Anaconda + Jupyter Notebook + Python + visualization libraries) is what we have been teaching in CIS 2650 since the course was created.

In talking w/ a colleague, we both agreed that many hiring managers do not have a full understanding of the AI skill sets associated w/ our graduating students. Our SCM students told us many times that their hiring managers valued only the traditional Excel capabilities (lookup functions, pivot tables, etc. – that is NOT AI), & they greatly overlooked the opportunities from other analytical solutions (skill sets that our students have). This makes it a bit difficult to sell the analytical techniques taught in classes that go BEYOND our CIS 2640 (Excel on steroids) – Applied Analytics Foundations.

For example, our data mining class is essentially a machine learning class for business, which is the core of AI. The course is designed to solve the problems that Excel falls short on. Hopefully we do a better job of training our students to “sell” the AI skills & managers (SCM Leaders) become more open to embracing the benefits (which might require a culture change).

“Consulting firm McKinsey projects a supply-chain shattering event will happen every 3.7 years, on avg. Since the pandemic started approximately 2.5 years ago, the clock is ticking…” *orgs utilizing AI spend less on processing & require 53% fewer transactional FTEs than companies w/o AI. *Contract Analytics: Gartner – by 2024, the current amount of manual effort for contract review will be reduced by 50%. *Estimated 80% of supplier-related data is unstructured or “dark data.” *Gartner: nearly 45% of orgs have either already implemented AI

From Dr. Rob Handfield: “These [semiautonomous] processes will draw on supplier rating systems, meeting notes, customer input, social media, legal filings, news feeds, employee and customer data, economic indicators, weather data, and multiple forms of data that can be mined to provide alerts that trigger procurement actions and supplier management activities.”

From Sime: AI will likely have the greatest impact on SCM through the 2020s. However, one thing often not mentioned is the attitude of the hiring managers. In talking w/ a colleague, we both agreed that many hiring managers do not have a full understanding of the AI skill sets associated w/ our graduating students. Our SCM students told us many times that their hiring managers valued only the traditional Excel capabilities (lookup functions, pivot tables, etc. – that is NOT AI), & they greatly overlooked the opportunities from other analytical solutions (skill sets that our students have). This makes it a bit difficult to sell the analytical techniques taught in classes that go BEYOND our CIS 2640 (Excel on steroids) – Applied Analytics Foundations course.

For example, our data mining class is essentially a machine learning class for business, which is the core of AI. The course is designed to solve the problems that Excel falls short on. Hopefully we do a better job of training our students to “sell” the AI skills & managers (SCM Leaders) become more open to embracing the benefits (which might require a culture change).

CIS 4640 – Business Data Mining
This course focuses on the theoretical understanding & practical applications of data mining as a decision support tool. Specifically, it covers several types of modeling techniques & tools such as prediction, classification, segmentation& association detection algorithms. Students are introduced to the state-of-the-art data mining applications software such as SAS Enterprise Miner or SPSS Clementine for their class assignments & term project.

Again:

Learn – 1. Advanced Excel (power query & pivot) & macros;
2. Data visualization (Tableau,Power BI & python w/ seaborn & matplotlib);
3. Data mining/RapidMiner, machine learning & data science;
4. Python & Jupyter notebook (data analytics & statistical libraries such as pandas, numpy);
5. Relational data models (Excel data model);
6. Graphic & statistical libraries (Seaborn,Matplotlib,Pandas, & Plotly).

Grad Cert at WMU:
https://lnkd.in/gJetuSjB

You will be glad to hear that (Anaconda+Jupyter Notebook+Python+visualization libraries) is what we have been teaching in CIS 2650 since the course was created. We have no plan to change it unless industry tells us otherwise. Agreed?

Anaconda + Jupyter Notebook + Python + visualization libraries, Say what? Business Analytics, many of our Supply Chain majors are BA minors, why?

CIS courses in our BA minor:
https://lnkd.in/eqiqSMRb

CIS 1020 (Business Computing), CIS 2640 (Business Analysis & Reporting), CIS 2650 (Programming for Data Analytics), CIS 3640 (Business Analytics), & CIS 4640 (Business Data Mining).

First, CIS 2650 is not just a “Python” class, but instead a Python class for analytics (big difference). In a traditional Python class, people teach Python straight for the whole semester with tons of syntax, data structure, software development, etc. That type of class is mostly designed for CIS majors. What we do is different. It is Python in a popular analytics platform (more technically speaking, it is Python in Jupyter, which is an analytics platform that data science & business analytics programs do).

The reason for this design is the following:

·      We studied the top skills in analytics jobs & Python was among the top skills. https://lnkd.in/e6r8_KBy
https://lnkd.in/e7xyUYGs

·      The visualization libraries in Python can produce the kind of visualizations not available in Tableau & Power BI. Students will be able to differentiate themselves from other schools.

·      Tableau & Power BI recently added Python or Python+Jupyter because certain Python analytics & visualization are not available in Tableau and Power BI. Using them together makes it a powerful analytics solution (e.g., the ability to transform visualization into implementable actions).

Here are examples:
TabPy makes it possible to use Python scripts in Tableau calculated fields.: https://lnkd.in/eF67p-Jp

PyCaret makes it possible to use machine learning in Tableau: https://lnkd.in/ebA9f3EH

Python can create machine learning modules & create the visualization based on the predicated result.
https://lnkd.in/esFizKPA
https://lnkd.in/etTtk74H

·      All data scientists stress the importance of Python in analytics programs. Note, “data scientist” is one of the fastest growing career paths w/ escalating salaries because not enough people are good at this stuff. https://lnkd.in/e_22MKuC

·      Python are “R” are both number one & two in analytics, but Python is easier to learn compared to R.
·      Past experience in our SCM program shows that non-technical students are able to handle the content that we designed.
·      Python+Jupyter (or its variant) are used in Big Data (Hadoop, Microsoft Azure, Amazon AWS, etc.). It can also be used w/ SAP HANA, IoT, AI, Blockchain, & smart contracts to implement SCM visibility, & other SC related solutions. https://lnkd.in/e4rmEJqF
https://lnkd.in/e6Rmzpw

FYI (I am starting to get a little redundant)…

ASCM/APICS now has a Supply Chain Technology Certificate, $495, 20 hrs of online training (hmm). Topics covered: Blockchain, Adv Analytics & Automation, Internet of Things, Cybersecurity, Demand Planning Technologies, & Additive Mfg (3D Printing). This covers a LOT of ground in 20 hrs. https://lnkd.in/gbrJJa9v.

For example, see their Adv Analytics & Automation…Objectives:
1. Define adv analytics & automation.
2. Compare & contrast descriptive, diagnostic, predictive, prescriptive, & cognitive analytics.
3. Explain the process of data mining & challenges the process can present to a company.
4. Discuss data storage options & cloud computing security concerns.
5. Describe how AI & ML are used in SCM.
6. List some of the skills employees need to work w/ adv analytics & automation.

Our Supply Chain students minor in Data Analytics by learning:
1. Advanced Excel (power query & pivot) & macros;
2. Data visualization (Tableau, Power BI & python w/ seaborn & matplotlib);
3. Data mining/RapidMiner, machine learning & data science;
4. Python & Jupyter notebook (data analytics & statistical libraries such as pandas, numpy);
5. Relational data models (Excel data model);
6. Graphic & statistical libraries (Seaborn, Matplotlib, Pandas, & Plotly).
https://lnkd.in/guvjPb_V

These analytics skill sets require our SCM students to take an additional 4 classes (so > 400 hrs of class & study time). Also, we teach it face to face because they learn this stuff best via hand holding (not online as we learned during the covid era). Note, the lack of interactivity & support at Massive Open Online Courses (MOOCs) usually causes a super low retention rate (9% – 16%).

I hope the ASCM certificate addresses that many hiring managers do not have a full understanding of the AI skill sets associated w/ a data analytics education. Our SCM students told us many times that their hiring managers valued only the traditional Excel capabilities (lookup functions, pivot tables, etc. – that is NOT AI), & they greatly overlooked the opportunities from other analytical solutions (skill sets that our students & many employees have). This makes it a bit difficult to sell the analytical techniques that go beyond super advanced Excel, which would be our CIS 2640 class (Excel on steroids) – Applied Analytics Foundations.

For example, our data mining class is essentially a machine learning class for business, which is the core of AI. The course is designed to solve the problems that Excel falls short on. Hopefully we do a better job of training our students to “sell” the AI skills & managers (SCM Leaders) become more open to embracing the benefits (which might require a culture change). I hope this ASCM cert helps close that gap.

Note, I do not see Python in the ASCM cert. Is Python a must-have skill in the supply chain? https://lnkd.in/dcEJuZJX. How much Python? https://lnkd.in/e6Rmzpw & https://lnkd.in/gKFZXX_h.

If you look at this 2020-2030 job growth chart closely, all things supply chain related scored VERY well (i.e., operations & logistics). Basically, we are going to see 20-30% growth in SCM jobs over the next decade. Also, the jobs in SCM pay VERY well. Lastly, the data science jobs scored off the charts in growth & pay. So, SCM + data science = jobs & $. Finally, automation is killing all things manual (negative job growth). Note, there are not enough young people working in supply chain – https://lnkd.in/gtUcJpX7.

Students getting ready for career fairs: I actually have employers tell me they will NOT consider a student if they do NOT have a strong professional LinkedIn presence. The 8 Biggest Mistakes People Make With Their LinkedIn Profile:
https://lnkd.in/gECB3ut9

For students going into SCM, the prospects are great, but do not be a victim of technology!

Top 12 Skills You Need To Become a Data Scientist (many business grads should have these skills). https://lnkd.in/gwrjksAa.

Employers place a premium on – 1. Advanced Excel (power query & pivot) & macros;
2. Data visualization (Tableau, Power BI & python w/ seaborn & matplotlib);
3. Data mining/RapidMiner, machine learning & data science;
4. Python & Jupyter notebook (data analytics & statistical libraries such as pandas, numpy);
5. Relational data models (Excel data model);
6. Graphic & statistical libraries (Seaborn, Matplotlib, Pandas, & Plotly).

Business analytics addresses an increasing demand in orgs of all types to understand data related to their operations. Investments in information systems throughout the enterprise over the last 10-15 years are generating tremendous amounts of data, & companies will spend at least the next 10 years developing processes that generate insight from those data (visualization is huge, think Tableau and Power BI).

In addition to data generated internally, many companies are exploring the effects of external data, primarily present in social media, web search, manufacturing, & the SUPPLY CHAIN. The ability to manage data to support business projects are the key to success in many disciplines. Business analytics will provide a comprehensive skill set for SCM professionals & future SCM graduates to analyze, VISUALIZE (Tableau & Power BI!), & report data.

This has become a bare minimum:

CIS 2640 (Our Predictive Data Analytics class, Excel on steroids): I get this kind of feedback all the time…https://lnkd.in/dQABdsXc – You will be glad to hear that (Anaconda + Jupyter Notebook + Python + visualization libraries) is what we have been teaching in CIS 2650 since the course was created.

Why was the WMU SCM program ranked 2nd in SCM technology Because our students minor in business data analytics – https://lnkd.in/er3xiWj.

https://lnkd.in/dcEJuZJX

Will AI really kill the Business Data Analytics tools we teach? Would you trust the developer who built your software with a 3rd party code generation tool? Maybe. We are not going to stop teaching Excel in very sophisticated ways. All of our supply chain majors are required to take: CIS 2640 Predictive Data Analytics (Excel on steroids): I get this kind of feedback often…https://lnkd.in/dQABdsXc.

However, our analytics faculty are on top of this AI wave. They have already built an AI course in our business college (CIS 5550) to cover all kinds of AI for business applications. The course is designed for non-technical students (a perfect fit for SCM types). After this course, AI embedded modules in existing courses will be added.

So, Business Analytics + AI = more SCM success.

Per discussions w/ our CIS faculty, we all agree there is nothing wrong w/ using these code generators as long as you understand the generated code & know how to fix it when needed. Rarely will the generated code be enough to suit your needs. The more realistic scenario is to use the tool to generate the base code & modify it manually for the intended problems.

Many companies PROBABLY do not want their employees to use AI for any calculations like this. So, in all of the above scenarios, skills in our Adv Excel class (and all of our Business Data Analytics classes) are still relevant.

Skills such as:
1. Advanced Excel (power query & pivot) & macros;
2. Data visualization (Tableau,Power BI & python w/ seaborn & matplotlib);
3. Data mining/RapidMiner, machine learning & data science;
4. Python & Jupyter notebook (data analytics & statistical libraries such as pandas, numpy);
5. Relational data models (Excel data model);
6. Graphic & statistical libraries (Seaborn,Matplotlib,Pandas, & Plotly).

Business Analytics, many of our SCM majors are BA minors. CIS courses in our BA minor: https://lnkd.in/eqiqSMRb.

Also, WMU offers new course on the intersection of AI and writing: https://lnkd.in/g476QVTS. As AI continues to grow in importance, more companies and potentially universities will likely establish executive roles such as VP of AI to oversee AI strategies…In the meantime, WMU is bringing together AI and the art of writing:

“One of the key aspects of AI is the interaction between human intelligence & the platform—humans assign writing tasks to the technology & the technology generates text. This course is really a new take, with evolving technology, on a very old interaction: speakers and listeners, writers & readers,” says Gogan.

How will AI impact your future?
https://lnkd.in/g4cci-tA
AI @ WMU: A Deep Dive into the Resources:
https://lnkd.in/g2EZvtPz

If you’re ready to explore how writing can be used to understand the boundaries between human & AI and learn how you can leverage these emerging technologies to your benefit, this course is for you. https://lnkd.in/g4cci-tA.

Also, Deloitte 2023 Global Chief Procurement Officer Survey…Full report at: https://lnkd.in/gUiVHTtu. Nice summary at: https://lnkd.in/g9P2UAJ5. From me: VERY consistent w/ hiring managers from our SCM program. On Rising Inflation & Technology…

Inflation: 70% of new supplier agreements contain inflation driven economic adjustment clauses with the use of indices being the most common. https://lnkd.in/g_ByzrNU. It might be time for price risk sharing in buyer-supplier relationships. https://lnkd.in/gwPfkdkK. I have asked a lot of SCM managers how they “prepare” to negotiate price increase requests from their suppliers. In particular, I was curious about how and where they get their data from (i.e., CME, COMEX, etc.). Many said their suppliers provide that information. I am not convinced that using data from your suppliers is a form of “Preparation” for negotiation. More: https://lnkd.in/dnsxF6Ga
https://lnkd.in/gNrUtNU5

Technology: AI will have a great impact on SCM. However, one thing often not mentioned is the attitude of hiring managers, as many do not have a full understanding of the AI & automation skill sets of graduating students. Our SCM students told us many times that their hiring managers valued only the traditional Excel capabilities (lookup functions, pivot tables, etc. – that is NOT AI), & they greatly overlooked the opportunities from other analytical solutions (skill sets that our students have). This makes it a bit difficult to sell the analytical techniques taught in classes that go BEYOND our CIS 2640 (Excel on steroids) – Applied Analytics Foundations. For example, our data mining class is essentially a machine learning class for business, which is the core of AI. The course is designed to solve the problems that Excel falls short on. Hopefully we do a better job of training our students to “sell” the AI skills & managers (SCM Leaders) become more open to embracing the benefits (which might require a culture change). More: https://lnkd.in/gxRayD3j.

I chuckled (kind of) at #2 on the list: “Spreadsheets Do Not Yield The Best Decisions. During the pandemic, 94% of supply chain decisions were made based on spreadsheet analysis. The issue? A spreadsheet cannot adequately model supply chain complexity.”

10 Supply Chain Trends to Watch for in 2022 (ASCM)
#1. Advanced analytics

Adv analytics & automation will continue to accelerate, helping organizations mitigate disruption via digital, agile supply chain management. The implementation of predictive and prescriptive analytics — as well as advances in big data, algorithms & robotics — will have broad-reaching effects. Specifically, the organizations that harness the power of these solutions will benefit from greater visibility, data-driven decision-making, execution efficiency, predictability and profitability.
https://lnkd.in/gUSGGMmW

This class is req’d of every WMU supply chain major: CIS 2640 – Applied Analytics Foundations…This course is designed to train undergraduate students with skills to apply scientific and analytical methods to plan for analytics projects, design data collection methods, clean and analyze data, and report findings. The students will also be trained to interpret the findings from data analysis in a meaningful way. Emphasis will be placed on uncovering insights through visualization, basic analytics techniques, data manipulation, and other methods for intellectual inquiry.

That is why the WMU SCM program teaches Big Data w/ skill sets such as:
1. Advanced Excel (power query & pivot) & macros;
2. Data visualization (Tableau, Power BI & python w/ seaborn & matplotlib);
3. Data mining/RapidMiner, machine learning & data science;
4. Python & Jupyter notebook (data analytics & statistical libraries such as pandas, numpy);
5. Relational data models (Excel data model);
6. Graphic & statistical libraries (Seaborn, Matplotlib, Pandas, & Plotly).

Predictive Data Analytics (Excel on steroids): I get this kind of feedback all the time, see: https://lnkd.in/dQABdsXc – You will be glad to hear that (Anaconda + Jupyter Notebook + Python + visualization libraries) is what we have been teaching in CIS 2650 since the course was created. We have no plan to change it unless industry tells us otherwise. Agreed?

BA Minor

At WMU, students develop the capacity to manage data & conduct business projects through our business analytics minor, finding success in many disciplines. Courses cover:

Business intelligence
Databases
Data mining
Predictive analytics
Project management
Spreadsheets

Students analyze, visualize & report data, as well as explore processes to manage business projects. The minor can benefit all departments. Competencies in the knowledge domains of business analytics open the doors of opportunity.

#dataanalytics #supplychain #careeradvice

Common theme: BIG DATA is not going away, BUT one must understand why/when certain data formats create more value. Many of our supply chain students get a minor in Business Analytics & we actually teach a variant of Wide/Small data called Association Analysis (our Business Data Mining class). Notice #5: A Shift From Big Data To Small & Wide Data- 8 Trends Predicted To Define Data Analytics: https://lnkd.in/gi-iZ-Gb. From the WEF: https://lnkd.in/gMdsnjWT.

Wide data is data collected from different sources about individual objects (e.g., products, items, customers, etc.). Let’s say you would like to collect info about customers. Traditionally, you would build a table of customers w/ a fixed # of columns to capture mostly about demographic data & contact info. Today, it is usually not enough to just do that. People want to capture other things about customers, such as how many times they have called customer service, what they have said about the product in product reviews, etc. As you can see, a few things emerge from this need. That’s what characterizes wide data.

1. Traditional data tables w/ a fixed # of columns won’t work too well, because not all customers have the same # of service calls, have posted the same # of product reviews, etc.
2. Data comes from different sources (demographic, service logs, product reviews on web pages, etc.)
3. Not all data values are structured data (e.g., product reviews).

Small data is easily conceived as the opposite of big data, but there is more to it. Small data concerns about a “deeper” view of individual objects. Let’s say you want to know more about why product X is not selling well. Traditionally, people look at factors, such as seasons, locations, competition, environment, etc. That’s what all competitors do. However, if you could look at when/why product X drops out of a potential customer’s short list, that would give the manufacturer a lot of opportunities to exercise influence in the pre-purchase stage. To put it differently, if a supplier knows about who the competing products are in the same short list, there is a lot of last-minute influence that they can do (e.g., competitive priming, differential pricing, instant coupon, differentiation). The reason small data works well in some cases as opposed to big data is that certain data points of interest show unique characteristics different from the rest of the data. Therefore, generalizations derived from big data may not work too well in this case.

Our Analytics MAJOR covers more. In the major, we talk about unstructured data, streaming data, AI powered extraction of insights, cloud-based analytics, etc. These are the things that enable the analysis of not just wide & small data, but also big data. There is also streaming. That is data that keeps on growing. Analytical techniques based only on static data can get dated quickly. We cover a simple technique to work w/ that in the minor, & we have a full treatment in the major.

Should we teach Tableau or Power BI or both? There are > 100K unfilled SCM Data Science jobs on LI. In our CIS 3640 class we focus on three major areas: (1) Advanced excel functions, Power Query, Power Pivot , & Data Models, (2) Excel Macros, & (3) Visualization. https://lnkd.in/gXYSQKc7

We do our best to go in-depth in all these 3 areas. Based on the feedback received from students, they are interested in learning about all 3 areas & found them helpful during their job search. However, it is not possible to talk about both Tableau & Power BI visualization because of the time limitation. Therefore, we decided to cover Power BI in 1 section & Tableau & Tableau data prep in the other section. As you know, ETL is one of the most important skills they need for visualization. Therefore, we always teach Power Pivot a & Query, which are foundations for Power BI in both sections. There are 8 assignments in the class. At least 2 are about Power Query & Power Pivot & two about Visualization in Tableau or Power BI.

I personally would encourage students to have an open mind on tool usage. The whole reason of teaching both Tableau & Power BI in our curriculum is backed by industry evidence, rather than by 1 or 2 individual companies. Both visualization tools have been in the best quadrant (i.e., leaders-visionaries quadrant) of Gartner’s “magic quadrant” survey for analytics & business intelligence platforms for years. So, it is not true that Tableau is not used at many companies. Kellogg’s, for example, is basically a Tableau shop.

The bottom line is that it really doesn’t matter which tool students learned (as long as there is industry evidence to support the tool selection). The two tools share similarities. Usually one should be able to transition from one tool to another w/ minimal learning.

I think one way going forward is probably this: If students already know what tools they are going to be using at the internship or job, they might want to talk to us first before taking CIS 3640. This will help them pick the desired course section. Otherwise, it is hard for us to predict what analytics & visualization tools each student will be using and which company they will end up working.

What if you are a student that wishes to learn both Power BI & Tableau, but your instructor only covers 1? Unfortunately covering both in 1 single class will not give either one a fair coverage. In the end, students won’t have good skills in visualization. This is the reason we offer 2 sections, one for each tool. The good news is that Power BI is a free download. Here is what I recommend students do:

1.    Download Power BI
2.    Go through the video tutorials of Power BI online (e.g., https://lnkd.in/gEYERYay)
3.    Work on class assignments in both Tableau & Power BI. Many things are available in both tools. That makes it possible to replicate certain parts of class assignments in Power BI.

#powerbi #tableau #bigdata

#bigdata

https://www.youtube.com/results?search_query=sime+curkovic+and+4.0

Tips/advice from this college professor (aka, educated idiot):
1. Read. People that read a lot make more $ (2.3 times more?!)
https://lnkd.in/ggBDzJrp
2. Learn the job saving technology.
https://lnkd.in/eu7ANq6 & https://lnkd.in/gE3wp6JU

3. Learn to interview well.
https://lnkd.in/ePzz3NG and https://lnkd.in/eZgTxWc
How to prep for virtual career fairs: https://lnkd.in/evKd-VzQ
Being job ready…
https://lnkd.in/ed-ZfanD
4. Learn to negotiate ($).
https://lnkd.in/gMJYNJkh and https://lnkd.in/guTUcvdu
5. Learn to network & use LinkedIn. https://lnkd.in/gPZPQtqR & https://lnkd.in/dAwyTUy & https://lnkd.in/gFa3iCsg.