FinTech is easy to get wrapped up in. It’s an exciting field that’s taking power away from traditional, bloated banks and giving the industry a much needed facelift. It’s helping the underbanked (as any FinTech enthusiast will tell you a hundred times), and many popular FinTech technologies are becoming integrated into everyday life. But what’s with all the lingo? Despite the excitement, understanding exactly what’s happening in the field can be hard. There’s banking terms, tech lingo, and pure FinTech jargon. Here’s a primer to get you ready for the next time you encounter FinTech.

AML: Anti-Money Laundering (AML) refers to existing laws or procedures meant to reduce illegally obtained income.

API: Application Programming Interface (API) represents the functionalities of a certain program. These are important because they enable other programmers to use components of existing software, allowing for faster and more reliable software development—a major component of the FinTech movement!

Cryptocurrency: A digital currency using cryptography for regulation and security. It’s a decentralized system, meaning no central entity exists to oversee the processes. Instead, it uses a blockchain. There are several different kinds of cryptocurrency, including Bitcoin, Ethereum, and Ripple.

Bitcoin: The most popular cryptocurrency, generally deemed the first of its kind. The open source software comes with an elusive and mysterious history. No one is really sure who made it.

Blockchain: Where cryptocurrency transactions get recorded. It operates like a public ledger where information, once entered, can’t be altered. Blockchain technology also has several non-cryptocurrency applications including smart contracts and the recording of digital assets.

Collaborative Consumption: An economic model based on the sharing, swapping, and renting of services. The “Sharing Economy” or “Collaborative Economy” can be seen in platforms like Airbnb or Kickstarter and is growing in FinTech solutions via solutions like peer-to-peer lending.

Digital Native: A person raised in the age of digital technology. This demographic is vital to the growth of FinTech as they are more likely to expect their banking services to be technologically advanced and always online.

DRAAS: Disaster-Recovery-as-a-Service, the hosting of servers by a third party in case of a disaster. This means all that vital data can stay safe no matter what happens to us.

EMV: Represents the global standard for credit and debit cards. The title comes from its original developers, Europay, MasterCard, and Visa. Many cards already feature the EMV chip designed to fight card fraud.

Encryption: The process of encoding messages. Encryption is vital to FinTech, the blockchain, and anything else that needs to be secure. Data, like names and numbers, is turned into a code using algorithms (mathematical formulas). A key is required to turn that code back into useful data.

FinTech: Financial Technology, an industry known for championing software and technology in the financial sector. They’re also popular for generally challenging traditional banking and incumbent institutions.

FinServ: An abbreviation that appears largely on Twitter, referring to anything in the Financial Services industry.

KBA: Knowledge-Based Authentication aids is used for fraud prevention. Consumers probably know this as the “secret question” users must answer before being granted access.

KYC: Know Your Customer also revolves around authenticating users. Requirements of thorough identification checks and due diligence information seem to have grown more powerful in recent years to fight fraud by requiring users to prove their integrity.

Messaging Commerce: Where messaging apps meet point of sale. This trend is currently largest in Asia but will likely continue growing. This kind of commerce lets users make purchases with something as simple as messaging apps.

On-boarding: Includes all the steps to get a new customers integrated into a new program. Exactly what counts as on-boarding varies from company to company, but it refers to all the steps that get users up and running. Streamlined on-boarding processes are often considered one of FinTech’s advantages over traditional banks.

Payment Gateway: A service provider that authorizes credit card payments. They act as an intermediary between a payment portal, like a website, and a bank.

PCI Compliance: Payment Card Industry Compliance is a set of security standards designed to protect card information during and after financial transactions. All card brands are required to comply to these industry standards, and, though not always explicitly required, many FinTech companies are being pushed into PCI compliance in order to assure a certain security standard.

POS: Point-of-sale is that important step where customer payment information is taken at a physical location when making a purchase. Several popular FinTech startups have created apps and services to expedite this process and keep it safe.

P2P Lending: Peer-to-peer lending, or Social Lending, involves lenders loaning money directly to borrowers without the traditional processes and structures. Online platforms match lenders and borrowers where the services can usually be provided at a lower cost than traditional institutions.

Robo-Advisors: Automate investment advice. Though they sound like metal robots in ties, they are primarily rooted in algorithms. Robo-advice comes from online platforms and limits the need for human interaction when managing a portfolio.

SSO: Single Sign-On authentication saves users from the barrage of IDs and passwords by allowing one set of login credentials to sign in for multiple applications.

Smart contracts: Computer programs that automatically execute a contract. These automated and often blockchain-based contracts could save time and reduce costs in common transactions.

SaaS: Software-as-a-Service is a common tool utilized by startups. A vendor is paid to hosts applications on a cloud for users to access online. As a result, many startups are faced deciding whether to position themselves as SaaS or FinTech.

Tokenization: Replaces sensitive data with unique symbols. These “tokens” allow users to retain essential information about their credit cards and transactions without compromising security. It also turns complex information into short, useful codes.

Underbanked: People who don’t have access to proper banking or services offered by retail banks. They might have a banking account, but rely largely on alternative methods. The ability to serve the underbanked is considered one of the most important facets of FinTech.

image credit: Susana Fernandez

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Who doesn’t want to learn about data scientist these days? The field is still hot, and the ample job listings for data scientists might make folks working in other fields instantly jealous. For young students, there are full degree programs and specialized courses to prepare them for the data-driven world but for those already in the field it’s not so simple. Going back to school is a huge and pricey ordeal. Thankfully, there are several online options. Whether you want to learn the basics for fun, sharpen your technical knowledge, or feel properly trained on specific platforms, there’s a course for you.

Choosing a course isn’t easy. It’s important to know exactly what goal the course should fulfill and what your limitations are.

Big Data University is the IBM-founded initiative based on the idea that education should be a right, not a privilege. The “data science for a social good” platform is designed to democratize access to useful data skills. Courses mostly range from two to ten hours and several are available in Japanese, Spanish, Portuguese and other languages. Courses are self-paced and mostly free. Big Data University offers a big data fundamentals course as well as several programming and database usage courses.

Code School. Coding is not the first thing that comes to mind when talking about data science, but it’s easily one of the most important pieces. Learning the right languages is absolutely paramount to succeeding in the field. Many beginner Data Science courses introduce programming languages but that might not be enough. Choose your weapon, presumably either Python or R, and get started with at least the basics. Note that, while many prefer the video-based learning style of Code School, there is also Code Academy which is work-based and completely free while Code Academy costs $29 per month.

Coursera is a popular MOOC (Massive Open Online Course) and home to the famed Data Science Specialization track, a nine-course program from Johns Hopkins University. While it is an introductory course, it’s not exactly beginner-friendly when it comes to statistics and algorithms. Coursera also hosts a Machine Learning course from Stanford professor Andrew Ng, one of the most regularly recommended courses online. These courses, however, do only start on specific dates. Luckily, Coursera has a huge breadth of other offerings, all of which vary wildly in duration, commitment-level and cost.

DataQuest and DataCamp are two often recommended and surprisingly comparable online programs designed to take users from zero to fully-prepped data scientists. The only glaringly obvious difference between the two programs is that DataQuest is often touted as Python-focused and DataCamp R-focused. DataQuest is also more comprehensive, appearing much like a typical university curriculum. Both platforms are similarly priced, DataQuest at $29/month and DataCamp at $25/month.

Educast, run by data storage company EMC, is a pricier option for those with specific needs. While there are some free courses, like one on data lakes, their focus is on paid options with video access starting at $600 and going up from there. Businesses looking to educate themselves or their employees may find specialized courses on Data for Business Transformation or Data Protection more than worth the cost.

EdX is a slightly different MOOC founded by Harvard and MIT. The nonprofit platform offers a lot of free courses from top universities. The Analytics Edge gets into the nitty gritty of analytics methods using R, and is a great free option for those looking to dive deeper than the typical “Intro to Data Science” courses. Other EdX courses look into topics that generic websites often gloss over like Marketing Analytics, visualizations, and education. Unfortunately, their courses do not run as regularly as on some other sites.

Explore Data Science was originally from Booz Allen, making it very special, being one of the few online programs attached to a hugely respectable consulting firm. The program is now run by Metis, a more classroom-based data science training company. This sort of notoriety also means the self-paced course isn’t necessarily cheap, at $99 for two months of access. Unlike free courses, however, this is a shiny and sleek program to get those with a basic proficiency in statistics, linear algebra, and programming into data science.

MapR may not be Cloudera or Hortonworks, but they’re still a player in the Hadoop world. More importantly, MapR Academy offer several short online courses for free as well as various certifications. Courses can be on demand or instructor-led. If, at the end of all your courses, you want to keep plugging through, you can check out their Certified Developer programs. They are also in the process of uploading a Big Data Essentials course.
Yes! Cloudera does offer some free video tutorials and webinars, like their Cloudera Essentials for Apache, but most courses cost several thousand dollars.

Udacity is an MOOC offering all kinds of courses for free as well as some with a small price tag. Udacity includes content created by professors, researchers and big name companies and reaches across the wide breadth of data topics. Their Data Analyst Nanodegree, however, is something a little more special. For those who want to get into data science but can’t waste several years on a specific degree, the nine to twelve month program is focused on learning useful skills and building a portfolio.

Udemy is yet another large MOOP boasting over 40,000 courses, both free and reasonably priced. It has its fair share of courses for data enthusiasts, including this incredibly popular MySQL Introduction. The course is a thorough, whopping 18 hours and is only one of several Udemy options on SQL. Users can also find shorter courses or courses on other specific data topics like using Tableau, data scraping and finding viral content.
Even though this seems like a lot of options, there are still more out there.

The Edureka platform offers more than one course on data science. CalTech, Stanford, MIT and Harvard all have their own unique programs to choose from. The Indian platform Jigsaw Academy offers a host of paid courses. There’s no shortage of options for those looking to get into the field. Choose your language and goal and get going.

image credit: University of Essex

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Love virtual reality? Whether you want to learn from developers, read up on the latest trends, or check out new games and releases, there’s a blog for you. Here’s some of the best virtual reality blogs around in mostly alphabetical order.

Road to VR
THE VR. Blog

This is pretty often considered the cream of the VR blog crop. It’s a bit more of a fully developed website, but who’s complaining? They bill themselves as the world’s largest independent news publication dedicated to consumer VR. Their repertoire includes podcasts, videos, daily roundups, and articles. Topics cover both industry developments and fun viral tidbits. There’s even gameplay videos. Whatever your itch, Road to VR is the first place to try scratching it.

Augmentl 
Intellectual Editorials

Augmental makes their readers happy with their “No advertising. Non profit. No noise,” attitude. The site combines original content with a hand-picked news feed by industry insiders. They also try to focus only important and meaningful updates, but there’s still feature plenty of fun content to go around. Topics span from reviews to news to opinions and editorials, so there’s plenty to love. Augmentl is neither too scientific nor too casual but rather inhabits a nice journalistic space between the two.

DNA News 
Real-World Usages

DNA or the “Digital Out-of-Home Interactive Entertainment Association” is another solid and professional source for fans. For those working in the field or looking to integrate VR technology into their business, DNA news covers use cases and more specific topics. From amusement parks to coffee shops, virtual reality is making its way into the real world, and that’s what businesses want to read about. DNA posts articles on real world applications as well as business purchases and developments. This means readers can stay in the loop and get inspired at the same time. Overall, it’s a fun but distinctly realistic look at the how VR technology is slowly being integrated into consumers’ lives.

Doc-Ok 
A Developer’s Perspective

Here, readers can get developers’ perspectives on the changing VR landscape. Much more technical and in-depth than generalist blogs, this should satisfy those who love the science behind the tech. They describe today’s common tools. To a lesser extent, there are topical news updates often relating specifically to companies and developers. This is a good starting point for those who want to know what developers are thinking.

MTBS3D 
Personable and Long-Form

If you want respectable, MTBS3D (Meant To Be Seen 3D) is a great place to start. They aren’t just VR enthusiasts, but activists. The world’s first stereoscopic* 3D certification and advocacy group has six major goals, the first being to educate consumers and developers. Posts aren’t exactly constant, but there’s a lot of existent content already on the site. Long interviews with developers and experts don’t stop at news and releases but include personal stories and impressions. There’s even a MTBS Members Blog section. MTBS does an interesting job combining business level knowledge and thoughts with a personable community feeling.

*Stereography is about creating the illusion of depth in a 2D medium.

Super Reality 
Up Close and Personal

This smaller-sized blog has a much more personal feel than the larger sites. They tend to have deeper looks into specific topics and events, especially conferences. Reports on major expos and summits could greatly help those who simply can’t get out there themselves, and editorials offer some interesting opinions on new developments.

Upload VR 
Looks like Buzzfeed

If you take your VR with a heavy dose of popular names and viral articles, Upload VR is full of them. Big and trendy, the site talks hardware and news but also “experiences,” which look at games, both existent and upcoming in a more hands-on and in-depth way than just specs. Those looking for hard facts and developments may not find too much here, but Upload VR is good for an upbeat and accessible look at the industry.

Virtual Reality Reviewer 
All the Reviews

Love VR games? Trailers? Reviews? Software? Hardware? It’s all here. The focus is, as the name implies, reviews. Whether it’s a game, demo, or tool, they’ve broken it down into pros, cons, and easy-to-skim star ratings. And they’re not afraid to be tough, either. They also have a 20 Questions series where they ask industry developers and personalities 20 questions. There’s not too much personality or color across the site, but there’s a whole lot of information.

VR Bites
News, Reviews, and Hardware

Bites is a major buffet of news, reviews, and gampeplay. Their stories tend to be a bit different from the usual daily updates that go through all the other VR new sources. They also realize that cameras, controllers, and headsets are important to both industry insiders and outsiders. The VR sphere isn’t just cool software and machine learning technology but also solid hardware. To this end, VR Bites goes into incredible detail on their hardware reviews which should prove valuable for gamers and developers alike.

VR Focus
Tip, Tricks, and Brand-Love

This place is rife with reviews, insights, and up-to-date news developments. There’s also occasional “tips and tricks” including game cheats and real world money savers. Their previews and focus on games and technology for all the major systems are particularly noteworthy. Focus also divides content up by major developers, most importantly Rift, Vive, Playstation, Cardboard, and Samsung. This isn’t done just to help fanboys and girls, but to hook readers up with the right information. Google fans will find platform-specific comparison guides and development kits; Vive fans will find relevant gameplay and news; everyone wins!

Don’t forget, developers also often have blogs of their own! Check Unity, Occulus, Unreal, Nvidia, and other industry favorites for more options.

image credit: re:publica

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Some of the most inspiring data can be found in an unexpected place… TED Talks. Here are the best talks on everything data science.

Big data and data science appear on both sides of a coin: they are driving forces behind both business and science, yet they are also a kind of modern art. Data narratives combine technology and seemingly futuristic machine learning to create something unique, and even beautiful. It’s no surprise how many TED Talks exist on the topic.

Several of the most popular TED Talks on data are highly specific, but we wanted a list that combined use cases and inspirational stories, as well as tools for business and math lovers.

Happy watching.

The Best Stats You’ve Ever Seen

Hans Rosling (10,341,000 views)
Hans Rosling just had to be first on this list. He has multiple TED Talks, all of which have become hits. Viewing statistics can’t be done in a vacuum. People always bring their world view and notions along. In order to conduct proper analysis, people need to begin thinking differently. Rosling makes a great argument for liberating data and, as the title implies, shares some mesmerizing statistics. Not to mention, the statistician/academic/professional public speaker is absolutely captivating. There’s no arguing with over 10 million views.

The Beauty of Data Visualization

David McCandless (2,264,000 views)
McCandless does not like how data and numbers can be rendered meaningless. In response, he has turned otherwise hard-to-grasp information into gorgeous diagrams. These allow viewers to readily understand the information, and invite them to make patterns and connections. In this talk, he discusses human fears, the military, and even CO2—all of which is does hilariously and perfectly spot-on. This talk will help viewers better appreciate data and the role of proper presentation. He makes it clear that knowledge is never just about numbers.

The Wonderful and Terrifying Implications of Computers That Can Learn

Jeremy Howard (1,580,000 views)
This is THE talk about machine learning. Howard discusses a broad range of machine learning applications—not just Google, but even language translation, and medicine. For those needing to better understand the importance and evolution of machine learning, this is the go-to TedTalk.

How Data Will Transform Business

Philip Evans (1,174,000 views)
This isn’t a trendy “how to employ data to make money.” Rather, it’s a look at fundamental ideas behind business and technology. Big data means big scaling and adapting. Business, Evans argues, has always been based around assumptions on technology. As shifts in technology become bigger and bolder, our very understanding of business strategy may shift as well.

“What do we do with all this Big Data?”

Susan Etlinger (1,044,000 views)
Data doesn’t mean anything on its own. This talk is about the line where science and arts intersect in data analysis. It’s said that liberal arts students have skills many of their peers do not: namely, critical thinking. And that’s what we really need to understand data. In this particularly inspirational talk, Etlinger manages to perfectly walk the line between art and science in the big data discussion.

Big Data is Better Data

Kenneth Cukier (1,021,000 views)
What’s America’s favorite pie? Yes, Cukier uses data and analysis to find the unexpected answer. More importantly, he explains that big data isn’t just a large amount of data. It’s a different kind of data. Real insight is in the details that only exist in big datasets. He also delves into how big data is going to shape our future. This inspiring talk is a mixture of history, science and the future from the eyes of data lovers.

How We Found the Worst Place to Park in New York City—Using Big Data

Ben Wellington (901,000 views)
The importance of this talk isn’t just public transportation and the way data can change it. The real point is ordinary folks having access to data. Realizing that government agencies have a multitude of data and, oftentimes, little idea what to do with, the real challenge is finding the manpower and approach to put it to use. Opening up data may be the best way to bring on an exciting new world.

The Birth of a Word

Deb Roy (2,209,000 views)
Data-rich research. Human learning. Babies. This Talk follows a certain research project performed by Deb Roy, an MIT professor and Chief Media Scientist at Twitter. Roy captured some 90,000 hours of home video in hopes of understanding how his son learned language. He not only captured the first time his child used the word “water,” but he mapped how, when, and where the child began to internalize new words. Full of mind-blowing visualizations, this talk is bizarrely amazing.

The Weight of Data

Jer Thorp (203,000 views)
Thorp is an artist whose work has been featured in The New York Times, The Guardian, Scientific American, The New Yorker, and CBC. He turns data into amazing visuals that are both informational and incredible. Quirky and entertaining, Thorp plots people waking up and saying “good morning” all around the world, as well as visualizing how content travels on the internet.

Big Data in the Service of Humanity

Jake Porway (21,000 views)
Here’s a small TEDx video to get you looking forward to a happier data-rich world (“TEDx are independently organized talks inspired by TED Talks). Porway wants to inspire people to get into data and understand its full potential. Yes, it can be used to make money, and even to make better products, but it isn’t easy to maneuver. How can the little guy (or “littler” guys) utilize data to make the world better?

Big Data Dystopia

Kenneth Cukier (8,000 views)
For every data enthusiast there is a data doubter, and it’s important to hear both sides of the argument. The outlook of this TEDx Talk is very serious and a tad bleak, but Cukier, data editor for The Economist, voices many fears much the populace has. He also discusses how to balance data usage and skepticism by changing the way we think about data.

image credit: TED Conference

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Data Scientist are in high demand, ranked the #1 profession in America on Glassdoor. And according to Forbes, an additional 1,700 job openings paying an average salary of $116,000 US dollars are available, contributing to an exponential expansion of the field.

So are you interested in Data Science but not sure where to start? Here’s a breakdown of a few steps and resources to help you gain a foundation to pursue your studies or undergo a career change into the trending and lucrative field of Data Science.

1. Start by brushing up on your Math

Calc I-II
Professor Leonard’s videos – he is the best resource for Calc I and II in my opinion
Paul’s Notes  – great for practice sets and for clear notes
PatrickJMT’s YouTube account

Linear Algebra (books)
Linear Algebra from UC Davis
Linear Algebra from Saint Michael’s College
Linear Algebra Done Wrong

Statistics Foundation
Intro to Statistics 
Stanford’s online Statistical Learning with R

Probability, preferably using R (books)
Introduction to Probability and Statistics Using R
Introductory Statistics with R, 2nd edition

Bonus: Econometrics
Mark Thoma’s YouTube channel
idre UCLA Resources for R

2. Knowing a bit of programming is an advantage for Data Scientist and Python Language is the best bet. For an intro to Python:

Automate the Boring Stuff course – make sure to find a coupon for the class, the book is free online
Python for Everybody Specialization
An Introduction to Interactive Programming in Python – Part I & Part II
Introduction to Computer Science and Programming Using Python

3. Data Analysis, nuff said.

Data Science Class at Harvard (CS 109/ Stat 221)
Introduction to Computational Thinking and Data Science – followup to the Intro to Computer Science & Programming using Python
Data Analysis and Statistical Inference
Code Academy for Data Scientists

4. Machine Learning will help with your data analysis and access hidden insights.

Andrew Ng’s class – widely recommended by many people
Neural Networks for Machine Learning

5. SQL will make your life easier and help you access the data you need.

SAMS Teach Yourself SQL in 10 Minutes
Khan Academy course for introductory SQL
W3Schools

6. Last but not least, a few additional resources that are always a good go to.

Join meet-up groups, there is likely to be no shortage of good ones.
Read the book Data Science from Scratch
Finding interesting datasets – data.gov; Reddit r/datasets; R10 – Yahoo News Feed dataset, version 1.0 (1.5TB) & UCI Machine Learning Repository
Check out a list of 100 free data science books

Are there any additional resources that you use or have used? Please share with you fellow Data Scientists.

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