Last month, after the Belgian Olympic committee requested 500 vaccines from the government, the team doctor Johan Bellemans told Sporza, a national TV network, that Olympic athletes were at higher risk for infection because of their travel schedules, and said that they had been testing positive at a higher rate than the country’s general population. And, he said, “Obviously, we don’t want our athletes to be at a competitive disadvantage.”
All of this has presented a quandary for the International Olympic Committee and organizers of the Tokyo Games. When the athletes arrive in Japan in July, they will be entering a country that is nowhere close to herd immunity. Japan started vaccinating health care workers only in mid-February, and it does not plan to start inoculating older residents until mid-April. Taro Kono, the cabinet minister in charge of the vaccine rollout, recently said that the Games were “not on my schedule at all.”
Daily Word Challenge
Can you correctly use the word quandary in a sentence?
Based on the definition and example provided, write a sentence using today’s Word of the Day and share it as a comment on this article. It is most important that your sentence makes sense and demonstrates that you understand the word’s definition, but we also encourage you to be creative and have fun.
Then, read some of the other sentences students have submitted and use the “Recommend” button to vote for two original sentences that stand out to you.
If you want a better idea of how quandary can be used in a sentence, read these usage examples on Vocabulary.com.
If you enjoy this daily challenge, try using multiple Words of the Day in a 50-word story that you submit to our Monthly Vocabulary Challenge.
Data science is all about turning data into action, and we create more data now than ever before. As a result, more and more companies are looking for data science professionals to analyze their data. According to LinkedIn, the job market for data science specialists grew by 35% this year — both data scientists and data engineers appear high on their list of the top emerging jobs in the U.S.
The supply of data scientists can’t keep up with the demand. In other words, it’s the perfect time to enter the rapidly-growing job market — and we’ll show you how.
In this article, we’ll cover everything you need to know to figure out if a career in data science is right for you. Then we’ll show you how to get started with courses like our Data Scientist Career Path that teach you all the skills you’ll need to enter the job market. Read on for a full overview or use the table of contents below to skip to a specific section.
What is data science?
Data science combines probability, statistics, and machine learning with domain knowledge to generate insights from data. These insights range from predicting outcomes and trends to visualizing relationships and patterns.
Data science affects almost everything we come in contact with from curated playlists to shopping recommendations to disease detection. Data scientists are also at the forefront of creating self-driving cars, chatbots, and our data-driven world.
Every time you open an app or log onto a website, you’re contributing to the wealth of data that data scientists use to help businesses better serve their customers. In another post, we examine some of the other factors behind the demand for data science jobs.
Turning all this data into actionable insights is no easy task. It requires the collaborative effort of skilled professionals with varied knowledge and expertise. But what’s the difference between data analysts vs. data scientists? Data scientists vs. data engineers? Let’s find out.
Data Scientist
Ultimately, data scientists help organizations collect, organize, and interpret data to achieve their goals. These goals include market research, prediction, generating insights from data, building machine learning models, and more.
Last year, we interviewed Catherine Zhou to find out what a data scientist does. Catherine explains how the many different ways we can use data make it hard to concretely define a data scientist’s responsibilities. They usually vary between companies as every organization has its own goals.
The nebulous responsibilities of data scientists leave many feeling confused about their career trajectory. But, once you start your career as a data scientist, you’ll find something that you excel at — and that will become your specialty. Possible specializations include (but aren’t limited to):
Reporting
Machine Learning
Natural Language Processing
Computer Vision
Big Data
Artificial intelligence
Data Analyst
Not every company needs a data scientist’s advanced capabilities in machine learning or predictive analysis. For many, a skilled data analyst may better fit their needs.
After data has been collected and organized, data analysts go through it to identify any prevalent trends. Unlike data scientists, who often use advanced algorithms to build models and test hypotheses, data analysts are primarily responsible for finding patterns, irregularities, and issues and communicating the results to stakeholders.
If you want to get started working with data right away without going deep into machine learning, check out our Data Analyst Career Path.
Data Engineers
In 2018, we spoke with Ryan Tuck, a data engineer at Warby Parker. When asked about his duties as a data engineer, Ryan explained how he created and maintained “the plumbing required to support the data and reporting needs of the business.”
Michelle, one of our Senior Curriculum Developers, elaborates on Ryan’s plumber analogy. She explains that Data Engineers are essential to keeping a company’s data safe. They keep the pipelines clear and free from leaks so that a company’s data is clean, safe, and reliable.
Machine Learning Engineers
Machine learning combines pattern recognition and predictive analysis with computational statistics to teach computers how to identify patterns and predict outcomes. Essentially, by training a computer with data about different things, you can teach it to discern between them.
Machine learning lies at the heart of emerging technologies like facial recognition, gene therapy, and artificial meat. New applications are invented every day as companies vie to find the next hot innovation. The field has become so popular that machine learning engineers have the largest job market in 2021.
Other titles
The data science industry is still evolving, so you may find job postings whose responsibilities align with those listed above but under a different title. Some of these titles include business intelligence analysts (BI analysts), Data Storyteller, Systems Analysts, NLP Engineers, Data Architects, Deep Learning Specialists and more.
Data science languages
There are hundreds of different programming languages, and many have their own applications in data science. Still, some languages are more prominent than others. Below, we’ll walk you through three of the most popular programming languages used by data professionals.
Python
Python is favored among programmers across every discipline for its versatility and readability. Its wide range of powerful libraries and packages allow it to perform the modeling and calculating required for every application of data science.
R
R is a statistical programming language with data structures, variable types, and tools built specifically for data science, including analysis and visualization. R’s base installation can perform functions such as linear regressions and t-tests, and you can use it with RStudio to easily inspect its output.
SQL
Programmers use SQL to query and edit the data stored in their databases. SQL is a staple in data science and data analysis, as data professionals use it to extract data from a database before analyzing it with Python or R. It’s also incredibly versatile, with its syntax for basic queries being similar to other relational databases like MySQL, PostgreSQL, and SQLite.
Other languages
In addition to the three listed above, there are other lesser-known programming languages used by data scientists. If you’re interested in data science for business or healthcare, you might want to learn SAS. If you’re more interested in math or science, Julia or MATLAB might be a better fit.
The languages you’ll use are largely determined by your goals as a data science professional. To help you find the best one for you, we’ve put together this list of data science languages and their many applications.
Data science skills
Knowing how to code is only half the battle. Data scientists also need to collect, organize, and manipulate data, use it to find solutions, and convey their solutions in an easily comprehensible way.
Critical thinking
Turning huge amounts of raw data into something useful can be difficult. Before getting started, you’ll need to know what problems you can solve using data and what types of data you’ll need to solve them.
Data manipulation and analysis
Once you have a question or problem in mind, the next step is collecting and organizing relevant data. Data scientists use tools like SQL or APIs to extract relevant data from larger datasets, then languages like Python or R to explore and visualize it.
This may sound straightforward, but cleaning and preparing data can be very time-consuming. You’ll need to keep an eye out for missing data, outliers, errors — anything that might throw off your results.
Communication
Many data scientists work closely with non-technical teams. To effectively communicate with managers, executives, and other stakeholders, you’ll need to know how to present your findings in terms that are easily understood.
These skills form the foundation of those you’ll need for your career in data science, but there are many more. Sophie elaborates on the concepts listed above and more in her list of skills you’ll need as a data scientist.
How to start your career in data science
As you can see, data professionals have an expansive list of knowledge and skills. The road towards a data science career is a long one — but we’ll show you how to get there.
Step 1: Build your knowledge
Now that you have a basic understanding of data science careers, the next step is getting you there. Codecademy’s Curriculum Developers have cultivated a wide range of courses to help you prepare for your career in data science. If you’re starting from scratch, our Data Scientist and Data Analyst Career Paths will teach you how to code, along with all the skills you’ll need to manipulate data.
If you’re already familiar with programming languages and want to learn how to use them for data science, we’ve got you covered. The Skill Paths linked below will show you how to:
After you gain the necessary knowledge and skills, it’s time to start creating projects and portfolios to help you stand out to prospective employers.
Step 2: Assemble your portfolio
Include data science projects in your portfolio to showcase your skills, which is a must if you don’t have any relevant work experience. If you don’t know how to build a portfolio, don’t worry. We’ve got you covered.
Both our Data Scientist and Data Analyst Career Paths include tutorials on portfolio building. You’ll also create Portfolio Projects using real-world data about medical insurance, GDP, endangered species, and more. Or, you could create projects independently.
Data analyst Albert Lee also gives us tips on building data science projects and portfolios. Before getting started, he recommends reviewing job postings by the companies you want to work for and speaking with data scientists.
Reviewing job postings will help give you a better understanding of the skills you’ll need to showcase in your portfolio. Data scientists can provide insight into the current state of the field.
If you don’t know any data scientists, reach out to one. There are multiple data science communities online filled with people who love discussing their work and contributing to others’ growth.
Immersing yourself in data science communities will help you develop your projects. Looking at other people’s projects can help you find new ideas and datasets. And, once you’ve completed your project, you can share it with data science professionals and receive feedback and suggestions for improvement.
Once you’ve completed your first data science project, start another. Continue to level up your skills by taking on more sophisticated projects. This will make your portfolio more impressive, and it’s also a great way to prepare for technical interviews.
Step 3: Begin your job search
After you’ve built your skills and assembled your portfolio, it’s time to start your job search. Our Skill and Career Paths also include interview practice and tips to help you launch your data science career. We wish you the best of luck on your journey!
2. What do you think is the purpose of this podcast? Who might be its audience?
3. What do you notice about Mr. Barbaro’s interviews? How does he both guide conversations and allow his subjects to share their thoughts freely? What makes his style effective or appealing?
4. What types of questions does Mr. Barbaro ask? Which questions did you find most effective? What kind of preparation do you imagine Mr. Barbaro does for each interviewee and episode?
5. How does “The Daily” use sound to tell a story? How do the audio elements enhance the experience for listeners? What is the difference between listening to and reading a news story on the same subject? What are the benefits and drawbacks of each?
6. What other “moves” did the podcast host or producers make that you admired? What skills or techniques might you want to use in your own interviews?
Going Further
Option 1: Conduct your own interview
Now, it’s your turn: Using “The Daily” as a mentor text, conduct an interview with someone whose views, experiences or life you would like to know more about. You can conduct your interview in person, on the phone or via video chat, but it should be at least 10 minutes long.
1. First, learn more about your subject and interviewee — do some preparation and research.
2. Write a set of at least 10 questions and order them. Remember, your interview questions don’t need to be long or complicated. Mr. Barbaro often asks simple, direct and open-ended questions, such as:
3. Conduct your interview using the techniques you learned from analyzing Mr. Barbaro’s interviews, such as active listening and asking follow-up questions. Keep in mind that while Mr. Barbaro is always well prepared for each interview, he is a great listener. Try not to mechanically go through your written set of questions, one after the other, regardless of the answer given by your subject; listen and ask follow-up questions.
To help, you might use these follow-up question starter phrases drawn from “The Daily”:
“So, tell me more about _________?”
“Mmm … so, you’re saying _________?
“What does that mean?” “What did it mean in the moment?”
“OK. So when you hear people say _________?”
“And how do we explain that?”
“And why does that matter?”
4. If possible, record your interview so that you can preserve the conversation and critique and evaluate your performance.
Have you ever wanted to create your own podcast? What would it be about? What fresh perspective could you provide on a topic? Who would you invite to be part of your show?
Before Tai Poole could kick off the third season of his podcast for the Canadian Broadcasting Corporation, “Tai Asks Why,” he needed to address a change in the show’s sound. Specifically, his voice.
“If you’ve been a longtime listener of my podcast, you may be thinking, ‘Hey, Tai sounds a little bit different this year,’” the 14-year-old host said in the season’s opening episode. “My voice is changing. Even I can hear it. As much as I may try to fight it,” Tai let out a wistful sigh — “I’m growing up.”
Mirroring the industry they joined at a very young age, Tai and other child podcasters have matured on mic. Some have dropped out as they got older, like the host of “Chloe’s Friendship Circle,” who ended her three-year career in 2018 at the tender age of 8. But podcasters like Tai have no plans to forsake the medium.
CBC Radio listeners were introduced to Tai as a 9-year-old “math whiz kid” in 2016. That’s when he appeared in three episodes of the show “Sleepover,” in which strangers were brought together for a night to learn about each other’s challenges and offer advice from their own perspectives. Tai was one of the few kids featured in it, and his precocious charm and insatiable curiosity stood out enough that the show’s producers tapped him to host “Tai Asks Why,” in which he asks family members, experts and scientists life’s big questions, like, “What happens after you die?” He was 11.
Tai explains how his show has evolved over time:
“I noticed that my questions I was asking seemed to have changed,” he said. “My attitude was different, too. I’m kind of like, what’s up with that? So I thought, let’s do an episode about how my questions are changing and how my teen brain is changing.”
The article also introduces readers to Nate Butkus, the 10-year-old host of “The Show About Science”:
Nate’s preternatural scientist’s mind has served him well for over 85 episodes, in which he’s interviewed nearly as many scientists on topics, including nanotechnology, 3-D printed organs and black holes. Each episode of his show is led by his own curiosity. He comes up with something that interests him — like cosmic microwave background or quantum mechanics — and then searches on Google for scientists in that field.
… Over the years, Nate has had to learn to ask questions from his audience’s point of view. “It’s something we’ve had to talk about as he’s gotten smarter and smarter,” his mother, Jenny Butkus, said. “Like, ‘Hey Nate, remember, just because you know the answer doesn’t mean everybody else does.’”
But that doesn’t mean Nate’s interviews are dumbed down. The surprise in his expert guests’ voices can often be heard after a smart question comes their way. The reaction was epitomized at the end of the Watergate episode of Nate’s spinoff series, “The Show About Politics & History.” Leon Neyfakh, the creator of the podcast “Slow Burn,” which took on that scandal, apologized: “Sorry I assumed you knew less than you do. You know everything!”
If you could make your own podcast, what would it be about? What topics would it explore? What would you want it to sound like?
Have you listened to the podcasts mentioned in the article, or any other podcasts with young hosts? What do you think that young podcasters add to the medium? Should all adults listen to at least one podcast created by a student?
How did Tai and Nate use their own curiosity to drive their podcasts? What questions do you have about the subjects that fascinate you? Who might be able to answer those questions?
In an article on “podcast voice,” Alexis Soloski writes about the lack of diversity in the podcast industry:
About two-thirds of podcast hosts are male. Perhaps 80 percent are white, ratios that become starker among the most downloaded podcasts, those that generate the most ad revenue. If you read through comment threads about the best voices in podcasting, white voice follows white voice. (MVPs? Phoebe Judge and Roman Mars.) This makes any conversation about voice also a conversation about power.
What is podcasting missing? Whose voices, and what stories, do you think are being left out or overlooked? What unique perspective could you provide as a podcast host? How could your podcast draw upon your own voice, experiences or identity?
Nate Butkus was 5 years old when his podcast debuted. If you had made a podcast when you were that age, what would it have sounded like? Imagine you were still producing it. How would its sound and content have changed?
One would expect that when health care expenditures increase, life expectancy would increase too. But, for 2000 to 2017, this relationship has not been consistent globally.
After looking closely at the graph above (or at this full-size image), answer these four questions:
What do you notice?
What do you wonder?
The questions are intended to build on one another, so try to answer them in order.
2. Next, join the conversation online by clicking on the comment button and posting in the box. (Teachers of students younger than 13 are welcome to post their students’ responses.)
3. Below the response box, there is an option for students to click on “Email me when my comment is published.” This sends the link to their response which they can share with their teacher.
4. After you have posted, read what others have said, then respond to someone else by posting a comment. Use the “Reply” button to address that student directly.
On Wednesday, April 14, teachers from our collaborator, the American Statistical Association, will facilitate this discussion from 9 a.m. to 2 p.m. Eastern time.
5. By Friday morning, April 16, we will reveal more information about the graph, including a free link to the article that included this graph, at the bottom of this post. We encourage you to post additional comments based on the article, possibly using statistical terms defined in the Stat Nuggets.
Reveal
We’ll post more information here on Thursday afternoon. Stay tuned!
• Learn more about the “Notice and Wonder” teaching strategy and how and why other teachers are using this feature from our on-demand webinar.
• Sign up for our free weekly Learning Network newsletter so you never miss a graph. Graphs are always released by the Friday before the Wednesday live-moderation to give teachers time to plan ahead.
Students 13 and older in the United States and the United Kingdom, and 16 and older elsewhere, are invited to comment. All comments are moderated by the Learning Network staff, but please keep in mind that once your comment is accepted, it will be made public.
2. What biographical events most shaped Savage’s life and her work? What aspects of her life story resonate most for you?
3. How did Savage fight back against the prejudice and racism she faced throughout her life, such as the rescinding of a scholarship to attend the Fontainebleau School of Fine Arts in Paris when the administration learned that she was Black?
4. Why did Savage open the Savage Studio of Arts and Crafts in Harlem in 1932? What impact did it have on generations of Black artists? How was her vision of community-driven education part of the African-American tradition, according to Bridget R. Cooks, an art historian and associate professor at University of California, Irvine?
5. Sadly, most of Savage’s work has been lost or destroyed. “Imagine the power of somebody looking at ‘The Harp’ in its sort of monumental size for the last 70 years,” Niama Safia Sandy, a curator and visiting assistant professor at the Pratt Institute, is quoted as saying in the article. She asks, “What could that have changed?” How would you answer Ms. Sandy’s question? What do you think viewers have lost from the absence of Savage’s work in public life? Do you agree with calls to recreate “The Harp” and display it at the National Museum of African American History and Culture in Washington?
6. While Savage viewed her own legacy with humility, putting the emphasis on the success of her students, Ms. de León writes that “her work, and her plight, still resonate.” She quotes Jeffreen Hayes, a curator and the executive director of Threewalls, an arts nonprofit group in Chicago:
“I don’t think about Augusta Savage as someone who only made objects,” Dr. Hayes said, but rather as someone who “has really left behind a blueprint of what it means to be an artist that centers humanity.”
Do you agree with Ms. de León and Dr. Hayes’s assessment of Savages’s legacy? How should we remember Savage today? What is your own assessment of Savage’s artistry? What qualities of “The Harp” and other works showcased in the article do you find most affecting or artistically significant? What lessons and inspiration can we learn from her life and work?
Going Further
Option 1: Analyze and interpret an artwork by Augusta Savage.
Write your own analysis and interpretation of Savage’s “The Harp” (or another work, such as “Realization” or “Gamin,” both discussed in the article) using vivid and detailed sensory language.
While the original work was destroyed, you can watch this video of “The Harp” from the 1939 World’s Fair and read interpretations of the sculpture here. Additionally, you might read James Weldon Johnson’s “Lift Every Voice and Sing,” or listen to a recording of the song, which was the inspiration for Savage’s piece.
As the pandemic passes its one-year mark, have you had days when you’ve felt like you’re “in quicksand”? Do you find yourself asking: “What’s the date?”, “What time is it?” or “What did I do yesterday?” Have you started to feel less engaged at school, with your family and friends, or in your daily life?
These are some of the feelings that New York Times readers described in a recent questionnaire about work-related challenges in Month 13 of the coronavirus pandemic.
In “We Have All Hit a Wall,” Sarah Lyall writes about how different people are responding to what she describes as “late-stage pandemic burnout”:
Like many of us, the writer Susan Orlean is having a hard time concentrating these days. “Good morning to everyone,” she tweeted recently, “but especially to the sentence I just rewrote for the tenth time.”
“I feel like I’m in quicksand,” she explained by phone from California, where she has been under quasi-house arrest for the last year. “I’m just so exhausted all the time. I’m doing so much less than I normally do — I’m not traveling, I’m not entertaining, I’m just sitting in front of my computer — but I am accomplishing way less. It’s like a whole new math. I have more time and fewer obligations, yet I’m getting so much less done.”
Call it a late-pandemic crisis of productivity, of will, of enthusiasm, of purpose. Call it a bout of existential work-related ennui provoked partly by the realization that sitting in the same chair in the same room staring at the same computer for 12 straight months (and counting!) has left many of us feeling like burned-out husks, dimwitted approximations of our once-productive selves.
What time is it? What day is it? What did we do in October? Why are we standing in front of the refrigerator staring at an old clove of garlic? Just recently I myself spent half an hour struggling to retrieve a word from the faulty memory system that has replaced my prepandemic brain. (“Institution.” That was the word.) Sometimes, when I try to write a simple email, I feel I’m just pushing disjointed words around, like peas on a plate, hoping they will eventually coalesce into sentences. Am I excited about my daily work in this month of April, 2021? I would have to say that I am not.
“Malaise, burnout, depression and stress — all of those are up considerably,” said Todd Katz, executive vice president and head of group benefits at MetLife. The company’s most recent Employee Benefit Trends Study, conducted in December and January, found that workers across the board felt markedly worse than they did last April.
The study was based in part on interviews with 2,651 employees. In total, 34 percent of respondents reported feeling burned out, up from 27 percent last April. Twenty-two percent said they were depressed, up from 17 percent last April, and 37 percent said they felt stressed, up from 34 percent.
“People are saying they’re less productive, less engaged, that they don’t feel as successful,” Mr. Katz said.
No kidding. In this very bad year, of course, there are gradations of loss: loss of homes, of health, of income; the deaths of family members and other loved ones; the absence of security. In the most recent Household Pulse Survey, by the Centers for Disease Control and Prevention, 37 percent of those surveyed reported feeling anxious or depressed (in 2019, the figure was 11 percent). In the scheme of things, people who have jobs are lucky. But that doesn’t mean work itself is easy, or fun.
Students 13 and older in the United States and the United Kingdom, and 16 and older elsewhere, are invited to comment. All comments are moderated by the Learning Network staff, but please keep in mind that once your comment is accepted, it will be made public.
Grids. No not the one that went down for the state of Texas, that’s a whole different type of grid. I’m referring to every analyst/consulting site that uses “Grids” in whatever format – design wise to identify top systems is this or that category. And yes, I do the same thing (that’s changing for 2021 aka formally the T-Grid).
For the last few years, I’ve rated the top B2B/B2C learning systems, via a ranking list during my yearly best award systems. A post here and post there.
Never though as a grid.
I’m not sure why people love grids. I admit have the time when I look at a few from the larger name entities, I can’t figure out how that vendor got to that part of that grid, what in the heck does that mean, and why is that vendor who isn’t even a learning system, appearing in that section of the grid over there?
Grids are confusing. The methodology for some of these grids equally can be quite obtuse. It’s a factor of course, when someone says, “hey your grid results are not like ABZF analyst firm’s grid, and then blah blah.” It’s enough to make me want to grow my hair out (you thought tear hair out, sorry, length rules).
So long Grid, Hello Bracket
I went total bracket here. Think hoop bracket or soccer bracket or a bracket of your favorite colors that you want Crayola to make, that type of bracket.
I identified what I saw are the top 16 Learning Systems that play in the customer education market and used a methodology to confirm how/why they were a “group of 16”. Next, I broke them into four “grid regions” – take that Grids – and created actual locations as though they would appear at those places.
Opening Round
You will notice the “grey boxes” – this is where folks – like you – could have made your own selections and submit them to me, sort of “fantasy Customer Education System thing”. Anyway, each time a system moved (i.e. defeated their competitor) then the bracket would change.
The “grid” term means nothing here. Initially I had listed as “groups”, then thought of something clever, but went “nah” and just did “grid” – since I am tired of seeing them. I’m sure there is a word for this, I have no idea and I am too lazy to look it up on the net.
I was amazed at the number of learning systems that are seen as “customer education” systems on the net on various sites, who are not actually that strong in the customer education market (the original term is customer training). Then there were those who were right on the cusp of being in the 16, and thus, if there was sort of a “play in” angle, they would have been there.
How the Customer Education market works
In order to get a better idea when it came to “who’s in”, it would be wise to explain how the customer education market works. There are two types of learning systems here (discussed previously in the where the LMS market is heading, i.e. Group 1 and Group 3).
Customer Education – Heavy Focus – They will take employees, but these are the hardcore, G1C I noted in the previous blog. Customer education/partner training is their key target here. In the 16 above – Thought Industries, Intellum, Eurekos, and Learndot are those vendors.
The others are combo, that is to say, they play in employees and customers, thus they may be 50% employees and 50% customers – i.e. SkillJar or a higher percentage of employees compared to say 30% customers – but they still push on that idea that the system can handle both segments. The ones folks are most familiar within the bracket are SkillJar – they push heavy on customer education, so the split was a surprise, Docebo – they push the ‘talent’ angle and equally will spin customer education – noted as extended enterprise in some areas, Absorb, Talent LMS and Cornerstone Learning (which plays more on the employee side of the house, but they have plenty of customer education clients too). On the bracket, you will notice Cornerstone LS ( I see Cornerstone Learning + Develop as the combo here, so I just refer to it as LS, Cornerstone does not).
In the combo group – vendors such as Knowledge Anywhere and BlueVolt aren’t as well known, but KA has a very solid system for customer education, including e-commerce and multi-tenants. It is streamlined a bit, but that doesn’t eliminate it, just a different twist. BlueVolt plays big time in the trade services space, which is why, not everyone has heard of them, but they do quite a bit in customer education for that market. BTW, they were the first vendor to offer gamification tied to reward credits to redeem.
Agylia has a lot of features for the customer market, but messaging I believe has always been a challenge as it relates to getting a bigger share of this group. Fuse on the other hand, is starting to push more (they have plenty of customer education clients) into the customer space, and with a whole new set of features – some of which – okay a couple are quite rare – they are poised.
Brightspace by D2L is a definite wildcard here. They have every feature you would need to land the customer education market. They do solid in the association space (which is member education, and follows similar principles to the customer education angle) and continue to improve. Again, messaging issue here.
Eurekos is the new riser in the space. Once focused on edtech, now driving into the customer space (i.e. customer education, B2B/B2C). With a slew of new features and functionality coming out in the next few months, and an attitude of “oh, you want that? Sure, we can do it”, it is a system poised to break thru. In a way they remind me of Docebo (when no one heard of them and they were starting out) – sort of this different fresh look and vibe.
Open LMS – Is the full Moodle customized system here, and companies due use Open LMS for their customer education/partner training. Open can do pretty much anything you need/ask for, and it wouldn’t make sense to leave them out, just because they build off of open source and take it to a whole different level.
EdCast – this is more towards their XP version and not Spark. XP offers a bit more, and to turn EdCast into customer education aka extended enterprise you need to purchase a “Customer Academy” and then something else (on top of that). If you want to use EdCast as your payment processor – you can, they do charge a point of sales fee (just as authorize.net would as well). The minus for them is the whole custom domain scene, which IMO is very important.
They are not the only customer education system, that either charges an outrageous price for white label custom domains OR in the case of some of the others up there, doesn’t even offer it. The way to get around it of course is to do URL masking.
Methodology
Well, as you can see I gave sort of a high level for each system, but what I looked at, regardless if they were combo or not, were the following
UI/UX – When you purchase a system for your extended enterprise aka customer education, you have one shot to bring those folks not only in, but get them coming back – which is what you want. These are consumers, just like you and me. They look at websites. Ask yourself, how many times have you bought a product or even thought about it, when the website looked dated or just plain awful? When you go house or flat hunting, how many times do you buy the one with dirty floors, or scuff marks, or they left the food out? Exactly.
The UI for customer segment should be great on both sides, front and back-end, but that isn’t always the case. Docebo has a very nice front-end for example, but the Admin side while fine, needs an update. Thought Industries has a wonderful front-end for learners, and the back-end is slick too.
Regardless, the system must be able to have different looks for each tenet aka portal aka child (parent-child, where you are the parent and then if you have say ClientX and they want to be on the system, they become the child OR your product that you sell to professionals wants a different look than the one for consumer grade). The more options the better – and it played an important role here for more so than a learning path – which they all do.
E-Commerce – Either it comes with it today, or it is coming soon – and they can integrate today with a vendor such as Course Merchant or similar. It doesn’t have to be course merchant, there are other e-commerce 3rd party that can do a deep integration (i.e. white label and it looks like the system). Having a Shopify app is a plus, but it is not a full deep integration in the way, course merchant is for example. The more e-commerce features you had, the better. Some offer Virtual Wallets. VAT appears.
API integrations with other e-commerce sites or m-commerce. Again, Shopify and some of their competitors. API integrations with sites for e-mail marketing, newsletters, CRMs (beyond just Salesforce), sales enablement platforms (if so desired, but not common), online accounting software such as Quickbooks, Bill.com, Waveapps (my personal fav). In other words, creating a customer education ecosystem. I mean it can be done for employees, so why not the customer side of the house?
Features that are essential for an extended enterprise (aka customer education/partner training). For example, is content curation a must here? Today, no. But if you are a combo, then yes you need it, and thus those heavy on the customer ed side, will need it too. Thus, it played a tiny role. One form of content curation is this ability where you can scan the web, and curate content that automatically appears in your learning system based on certain options you select – free that is. Only a small fraction of vendors actually build this, most go third party – and that vendor who does the best job is Anders Pink. One of the vendors in the grid tells their prospects that their AI scrubs the net and brings back all this content. No, it doesn’t. It’s Anders Pink.
A.I. – Either you have it now, or it is coming.
Metrics and Reporting showing items such as how much has been generated (prefer by content type, and content name – but most haven’t gotten there yet), other types of sales data, usage that goes beyond “views”, which is worthless, unless your learning system is a search engine. If you offer segmentation that was a huge plus. Data visualization is relevant here. Some folks want a BI integration which is fine, but to be successful in customer ed you do not have to have a BI tool. I should add that for customers you can give them your content for free or have them pay or do some combo aspect.
PaaS capabilities – Platform As A Service. It is fair to argue that an LMS is really a PaaS, but I’m not here to discuss this, I looked at it from the standpoint of deep integration options. Maybe a system offers online proctoring thru a deep integration compared to say SSO.
Combining it all, I then came up with a point system and assigned points based on the above and some intanglibles, so that the perfect score would be 100.
When I did the matchups, I matched the systems up using that scaling process, and the output of points dedicated to the final results.
Not A Factor
None factor – 3rd party marketplaces. Look I am a strong supporter of providing personal and professional development to your customers as a value-add and differentiator compared to a competitor. With COVID and thus the world as we recover from COVID-19, giving back and taking care of your customer is a nice win here. If they share this content with their family, partners, spouses, others in the home – what’s the downside to you? I see only the upside.
That said, the systems that play heavy on the customer ed side lack the amount of content versus a combo. So despite what I would love to see, it didn’t play a role.
Places
I picked places that I have been to or loved, and in one case who gets the short end of the stick in their own country.
El Paso is my hometown. I wear more UTEP gear than the other universities I attended and received degrees from. London is a fantastic place, St.Petersburg is a must- the hermitage is just WOW, Egypt is one of my favorite places to visit and see, Mexico City is just magical – and a must for anyone IMO. Perth – never been there, but I found a lot of people in Australia see it as being out in the “sticks” and a bunch of country bumpkins. I know folks who grew up and live in Australia on the “gold coast” – that is over there in Victoria and NSW and have never been to Perth. Then again, they have never been to Canberra or Alice Springs, so there’s that too.
Whos’ not in and why
SAP Litmos – Yes they are a combo system, but they are left out, because I feel as though they are in a ‘rut’. The administration side needs a revamp, and the metrics – data visualization wise seriously needs an investment. Metrics for customer education wasn’t really there and overall, while they have some nice features such as the video skills validation piece, the customer education side seems to be secondary (IMO). A shame really, because there are items that I like about the system, but updates are needed!
Raven360 – Customer education-focused system, more so than a combo, and definitely right on the edge, this would be the call in – Raven360 vs Open LMS. Super close. In a year from now, a top 16, maybe top 10 system – depending on what they do in the next six months.
Adobe Captivate Prime – Not a strong customer education platform IMO. Not a top 16, nor top 20 IMO.
SumTotal – They have clients using them for the extended enterprise, but this is a system strongly on the employee side of the house. Plus they pitch themselves as a TDP, although they are missing a few items, anyway, nope, not listed.
LearnUpon – Personally I like the system, but while they play in B2B/B2C and are a combo, they just are not a top 16. Top 20? Sure. Top 16? No.
The Final 16 Systems (Click on their name, to visit their web site)
Round Two
An early-round upset with Eurekos topping SkillJar. This was a result of a couple of key factors a) Feature-wise it was pretty close, although Eurekos’s what do you need and we will do attitude raised above. I also liked that the system is committed to the customer education market as the key far more so than SkillJar, who again, as of my analysis was a combo system. When a system’s focus is one on a specific segment, then you will start to see that in how they build out and develop it, I am seeing this more with Eurekos each and every time. SkillJar is a good system, lots to like – hence the #3, but in this match-up, Eurekos just came out slightly ahead.
Scores
Thought Industries 94 Talent LMS 74
Learndot 85 Cornerstone Learning 84
Absorb LMS 88 Brightspace by D2L 79
Eurekos 89 SkillJar 86
Intellum 93 Open LMS 73
Fuse 83 Agylia 76
Docebo 90 Knowledge Anywhere 81
EdCast 92 Blue Volt 77
In each point round, there were .5, which was rounded up
Round Three
The big matchups featured EdCast against Docebo and Absorb against the upstart Eurekos.
Docebo is tough. Boy, a good system indeed. But, two major drawbacks that EdCast exploited in the match-up, first – administration side. Sluggish with almost an afterthought in terms of what is their aim here, when the learning side UI/UX does a far better job. Metrics – Reporting is a tad underwhelming and needs a revamp. If you go to the individual learner profile it has a nice fresh metrics piece. The system itself? Let’s just say “brick” when you play hoops comes to mind. EdCast XP on the other hand, far better analytics and data visualization (not including Domo – which is a metric monster but an add-on). Administration UI/UX is far better too. Both systems are combo systems, and neither at the 5,000 and up user base are inexpensive. Customer Education-wise they are neck and neck, but if you are willing to pay the extra for customer academy (frankly I think it should be free, but uh whatever), and the extra for the other parts then EdCast leads out. If you choose not to, and want to use EdCast as an extended enterprise you can, I mean they come with e-commerce (extra fee though) and they offer multi-tenants and will do a custom domain. Docebo comes with e-commerce (I haven’t seen their newest pricing – since the launch of Learning System, but prior to when they had various pricing packages, at lower ones it was a fee, at “Growth” I believe it was included). White label is doable. The breaker for me again was that admin side, and then when you compare functionality – NexGen – EdCast crushes it. That was more than enough for them to pass Docebo in the end. Oh, did I mention the administration side?
Absorb vs Eurekos
A heart-stopping event, went down to the wire. The strategy was key here, Absorb goes combo and as of late, definitely seems more on the employee side, which is absolutely fine. After all, they are a top-five learning system. Functionality they are close to one another, but Absorb continues to hold off adding ad-hoc (it comes later this year). Big runs included the addition of Pinpoint – sweet offering, Absorb Create – their lite authoring tool, and improved metrics. For a moment, the fans (watching virtually) were screaming “A-B-S-O-R-B”, and “Dominates”. Alas, as with any run, there tends to be a stop, and that is where Eurekos pulled close and past. They too come with an authoring tool, which you can do lite or if you have an instructional designer can do a tad more tapping into the product (that said, a built-in authoring tool is never a match for say a Storyline or dominKnow or Elucidat).
Ever scored a goal from the half way point of a soccer field, hockey field, or a basketball shot from half court, then you will now the thrill of excitement.
Anyway, this has nothing to do with the battle here, just two top systems, with the newcomer for customer ed, coming out ahead, by one, okay .5.
The other two matchups, featured #1s Thought Industries (coming in ranked #1 for customer education/partner training/B2B/B2C for 2021) and Intellum (now the second best customer education system on the market, and sadly doesn’t get enough recognition as such, but that’s for another day).
TI just out duked Learndot. Not that close. Fuse gave Intellum a run for the money, err system, but Intellum is really good. Really good. And thus, in the end, Fuse couldn’t keep up.
Score Results
Thought Industries 94 Learndot 85
Eurekos 89 Absorb 88
Intellum 93 Fuse 83
EdCast 92 Docebo 90
Round Four
Sometimes it happens. The upstart gets up against the powerhouse and the upstart wins out. Other times, the powerhouse is just too powerful, strong and seasoned and the upstart loses. That’s the case here. Thought Industries is an amazing system. The multi-tenant (you get like 20 to start with – called Panoramas) and what you are able to do with each one beyond just the skin/color and logo is outstanding. Metrics are good, feature-rich system with a video skills validation including digital coaching (great for customers BTW, folks often think just employees) and scenarios – another wonder win. Thought Industries rolled in as the #2 learning system for 2021, and there is a reason behind that – they are just an awesome system. But not perfect, then again, no system is.
In the night cap, it was Intellum vs EdCast. User size wise, Intellum has a minimum number of end-users they will consider, and then a number they prefer. EdCast XP user base wise for customers, is similar in numbers for ideal, but a bit lower in the minimum. Pricing edge goes Intellum. System feature wise related to the extended enterprise segment, Intellum wins out. This is a kick-rock of a system. Learner UI/UX is good. Admin side is good too. Metrics specific for sales data for example, wow – rock city. So much potential there, that they haven’t even hit upon in the system, but what exists today alone beats out EdCast. The system doesn’t require any add-ons that you have to buy to take it to the customer education level, a plus, and the system and Intellum themselves are 100% customer education focused system. EdCast is combo.
Nevertheless, EdCast is #1 learning system for 2021 for a reason, and thus this was a matchup that was made for the internet, television, the woodpecker who enjoys the tree in front of my office, and others.
Intellum though topped out ahead. Setting up the dream match up.
Score Results
Thought Industries 94 Eurekos 89
Intellum 93 EdCast 92
Championship
Intellum vs Thought Industries
Godzilla (wasn’t it once the nice one here?) vs. King Kong. Rocky vs. Rocky II (The Sequel).
You get the point. Two outstanding systems. And it could have gone either way. Overall scoring of all the weights and points that each system accumulated based on methodology, customer education focus (both are 100%), support (both are excellent), pricing (TI has a slight edge) and what you get with everything (neck and neck), it was super close.
The Best of the Best in Customer Education is Thought Industries.
For those who want more of the traditional scoring ranking here you go
Thought Industries 94
Intellum 93
EdCast 92
Docebo 90
Eurekos 89
Absorb 88
Skilljar 86
Learndot 85
Cornerstone Learning 84
Fuse 83
Knowledge Anywhere 81
Brightspace by D2L 79
Blue Volt 77
Agylia 76
Talent LMS 74
Open LMS 73
Congratulations to the winning coach – Barry Kelly of Thought Industries.
This is Craig and Spirit (Bark Bark)
Signing Off.
With a Grid to be seen,
Nevermore.
Bonus – I will be presenting a webinar on how to turn your customer education into a Profit Center – Register HERE – Session Date is April 15th at 12 p.m. ET/ 9 a.m. PT.
Bonus Two – I am starting a list of “customer education” sites that consumers can go to. These are sites for customers, so if you have a “customer education” site and want it posted, visit this page and contact me.
You may have heard the phrases “data analytics” and “data science” mentioned before. If you are new to the world of data, you might be wondering what these terms mean — and if you’re interested in a career in data, which is the right path for you?
Though there is a lot of overlap between the two areas (and disagreement about the exact definitions), the main difference is how much they rely on machine learning. In general, data analytics covers everything from collecting data to spotting trends to communicating insights. Data science is a broader field that includes data analytics, and often involves making predictions with tools like machine learning or conducting experiments with data.
Companies collect a great deal of data. Almost all of them can benefit from data analytics to help make sense of it. But not as many require building algorithms that predict the future or apply patterns to new information.
Interested in working with data, but not sure where to start? In this article, we’ll explore data analytics and data science in more detail, to help you decide between our Data Analyst Career Path and our Data Scientist Career Path.
What is data analytics?
Data analytics is all about helping organizations make decisions based on data. Page visits can inform marketing strategies, housing costs can affect policy changes, and patient outcomes can impact a hospital’s operations. Data analytics helps us find patterns and tell stories from the large quantity of data organizations have.
To do that, data analysts take a business question and translate it into a data question. Part of their job is collecting and reformatting data, analyzing it with statistics and probability, and sharing actionable insights in the form of visuals and reports.
“Every company is collecting some data. And a lot of companies need to leverage their data to make good data-driven decisions. There’s a huge opportunity for data analysts to really put that data to work.” says Michelle, a curriculum developer here at Codecademy.
What languages and tools are used for data analytics? Generally, data analysts use SQL and Python or R. SQL interacts with data housed inside databases, and Python and R analyze and graph the data to show trends and patterns.
Data analysts also use dashboard tools like Tableau or Looker, which are business intelligence tools for creating interactive data visualizations.
Getting started with data analytics
Interested in learning more about data analytics? You can dive into our Data Analyst Career Path to learn everything you need to become a Data Analyst.
If you want to learn a specific data analyst skill, check out the following Skill Paths:
Even if your ultimate goal is to become a data scientist, gaining a solid foundation in data analytics is a good first step to take.
What is data science?
Data science is a broad field that includes data analytics. It also covers making predictions with machine learning, working with big data, and developing artificial intelligence.
Data scientists create algorithms to automate data processes, recognize patterns in new information, and make recommendations based on past behavior. They work on things like forecasting the financial future, creating customer-facing chatbots, detecting tumors in X-ray images, and making suggestions of things you might like.
“Data science tends to be more specialized than data analytics, because not every company needs to make predictive data decisions, and not every company needs to leverage big data,” Michelle tells us.
To learn more about data science, watch the following video with Sophie from our Curriculum Team, or check out our article on What is data science?
Data scientists also commonly use SQL and Python or R. Python’s popularity among data scientists has been growing as more libraries are created that focus on working with data. But Python isn’t the only language, and depending on what industry you go into, you might need to pick up other data science languages.
Getting started with data science
Interested in learning more about data science? Check out our Data Scientist Career Path, which provides everything you need to know to become an entry-level Data Scientist, from the ground up.
If you finish the Career Path and find that you want additional specialization in machine learning, we suggest taking the Build Deep Learning Models with TensorFlow Skill Path. If you want to learn more about working with text data and natural language processing, we recommend checking out the Apply Natural Language Processing with Python Skill Path.
Learn More
Still not sure where to start? If you want to work in data, but you’re not sure in what capacity, we suggest starting with the Data Analyst Career Path. From there, you can switch over to the Data Science Career Path at any time. If you decide to make the switch, all of the data analytics coursework that you’ve completed will transfer over too.
“Figure out if it’s right for you. If you’re really excited about working with data, and you want to learn machine learning, then you can jump into the Data Scientist Career Path or take the Build Machine Learning Models with Python course and get that experience in addition,” says Mariel, a curriculum developer here at Codecademy.
“In general, if you want the most direct path to a job working in data, then go with the Data Analyst Career Path. It also opens up the opportunity to get into data science later on,” Michelle tells us.
If you’re interested in learning data analytics, our Data Analyst Career Path will set you up with the tools you need to become a Data Analyst. If you’re interested in learning data science, the Data Scientist Career Path will guide you through what you’ll need to know to become a Data Scientist.
Of course we love reading and think that blogs are great. But, we also love podcasts! Podcasts are a great way to digest information on the go. It can also be nice to take a break from screens, step outside and get moving while listening to a podcast. We have our own, The Learning Scientists Podcast, and there are tons of other wonderful education podcasts out there. Here, we feature 5 resources about podcasts that we think teachers might particularly enjoy. There are tons of podcasts to choose from, so some of these are digests. (How meta!)
