Upskilling 101 (Definition, Examples, Implications)

by Doc Kane
Updated: February 22, 2022

The five main upskilling areas that will help you up your digital quotient and competence include: cyber security, data analytics, artificial intelligence (AI), blockchain and, how about this one… how to onboard a robot. Now that sounds like fun.

Upskilling your digital literacy and digital competency with an eye toward the next five to twenty years will likely be one of the most forward-thinking and financially rewarding decisions you're likely to make. And you don't have to be twenty years old to get a head start. Personally, I think those of us out there who are forty and above actually have more to offer in this area, AND, more to gain. More on that later. But for now, let's take a look at a few digital skills you can literally learn in a year or so, that will put you far ahead in planning for the digital revolution and transformation that is underway now. Not when we'll be retired. And, who wants to retire anyway?

1. Cybersecurity

From passwords to hacking techniques. What is an XSS vulnerability? How do I protect my website from rogue scripts? Or, my account from hackers? These are the questions you'll be addressing when it comes to cybersecurity, and what cybersecurity experts do for a living?

Cyber Security is the act of protecting our computers (and other devices) from viruses, cyber attacks, identity theft, spyware. It's about protecting our information; whether it's credit card data or personal login credentials. Cybersecurity has become increasingly important as we use the internet for so much of our daily activity. Cybersecurity experts work hard to protect us from threats online, and keep our information safe from cybersecurity risks. They use a range of strategies to mitigate cybersecurity threats, including developing cybersecurity systems, identifying cybersecurity threats, and repairing cybersecurity vulnerabilities in existing systems.

Cybersecurity is an ever-changing field in which cybersecurity experts must always be updating their knowledge in cybersecurity best practices in order to stay current. There is a cybersecurity expert for every business and job today; from cybersecurity analysts, cybersecurity architects, cybersecurity auditors, cybersecurity engineers, cybersecurity incident responders, cybersecurity managers, cybersecurity researchers and even CEOs with a “Chief Security Officer” (CSO).

Security management has become a very important cybersecurity concern, especially with the recent spate of hacking attacks from groups such as Anonymous and Lulzsec. I think every cybersecurity expert plays a part in making cybersecurity one of the most important issues for business today.

Cybersecurity is the practice of protecting against invasion or attack over a network, particularly the internet. These threats can cause a lot of harm to a company's cybersecurity and lead to identity theft, stolen credit card numbers, loss of money and more. Often times cybersecurity experts have to use their cybersecurity expertise with cybersecurity software or other cybersecurity equipment.

Cybersecurity is important for every business that operates online. A company's cybersecurity can be compromised in many ways. For example, a cybersecurity expert could test the cybersecurity of their company by attempting to hack into its network. They may also do cybersecurity training with employees or update cybersecurity systems. Computer hardware and software is constantly changing which means cybersecurity experts must always stay up-to-date with cybersecurity news and updates in order to protect their company from cybersecurity risks.

Cybersecurity Skills you can upskill on now:

  • Machine Learning
  • Data Analysis
  • Networking and cybersecurity
  • Programming in Python or C++ [or another language?]
  • Web development [HTML, CSS, JavaScript] [can be used to create cybersecurity tools]
  • System Administration
  • Database management [can be used to store cybersecurity data]
  • Cryptography [NOT CRYPTOGRAPHY; cryptography is the practice of secure communication in the presence of third parties, whereas with cybersecurity is more broadly about building and maintaining secure systems (not necessarily over communications)].
  • Scripting languages [Python, Perl, Bash]
  • Reverse engineering [how cybersecurity experts dig through disassembled code to understand the piece's original function]
  • Networking basics [IP addresses, DNS servers]
  • Programming in Python or C++
  • Web Development [HTML, CSS, JavaScript]
  • System Administration
  • Database Management [can be used to store cybersecurity data]

Cloud Computing

Cloud computing skills are in high demand worldwide. Each cloud computing platform has a different set of tools and techniques to use for designing or deploying applications. There is no one best skill set that works across all platforms. What works well on an Amazon Web Services deployment will likely not work as well on a Google Cloud environment.  The best part is, these skills are easy to pick up online using learning platforms like edX.

According to research from the IEEE Computer Society , the most in-demand cloud computing skills are for AWS, Google Cloud, Azure, IBM Cloud, and Linux. However, this does not mean that developers only need to know one of these technologies in particular.

AWS is an acronym for Amazon Web Services . It provides services including S3 storage , EC2 compute power, ElastiCache, Route 53 DNS management, and more.

Google Cloud is a cloud computing platform that allows users to leverage machine learning (which you can learn for free using CodeAcademy), data storage, analytics tools, and other resources for deploying applications. It is free to use on the lowest tiers of service. However, as you scale up with usage the price becomes more competitive than AWS or Azure.

Azure provides a cloud computing platform that allows users to utilize many of Microsoft's developer and operating systems tools for deploying applications. Users can manage their data storage, virtual machines, and containers using the Azure Portal .

IBM Cloud is an enterprise-grade public cloud provider that has been in business since 1998. It offers several services in the IBM Cloud including artificial intelligence, blockchain technologies, serverless compute, and more.

Linux is an open source operating system that was created in 1991 by Linus Torvalds with assistance from thousands of volunteers. Today, it is one of the most popular operating systems for internet servers. Linux has also been ported to mainframes, mobile devices, and supercomputers . It runs the majority of the world's servers, supercomputers , and embedded systems .***

Cloud computing skills you can upskill on now:

 

  • Knowledge of programming languages such as Python, C++, Java, etc.
  • Understanding different cloud models such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS)
  • Knowledge of cloud storage technologies such as Amazon S3, Google cloud Storage etc.
  • Understanding cloud deployment models such as cloud bursting, cloud federation, cloud manual deployments and cloud continuous deployments.
  • Knowledge of cloud screening and cloud infrastructure scanning methodologies such as cloud computing intelligence, cloud security intelligence etc. is required.
  • Cloud architecture knowledge with cloud design patterns and cloud development best practices is essential to get started in cloud computing effectively. In cloud computing there are mainly three types of cloud architecture styles. They are cloud optimized classic cloud architecture, cloud-native cloud architecture and cloud integration cloud architectures.
  • Knowledge of modern cloud computing tools such as Puppet, Chef, Ansible etc. is required to effectively use cloud services in the organization for its business needs.
  • Knowledge of cloud security frameworks such as cloud trust, cloud security alliance cloud controls matrix and cloud infrastructure security requirements is required.
  • Knowledge of cloud management platforms such as Splunk cloud, Amazon cloud watch etc.
  • Computer Programming Language(s)
  • Cloud Computing Models (Infrastructure as a Service, Platform as a Service and Software as a Service)
  • Cloud Storage Technologies (Amazon S3, Google cloud Storage etc.)
  • Cloud Deployment Models (Cloud Bursting, cloud federation cloud manual deployments and cloud continuous deployments)
  • Cloud Screening Methodologies (cloud computing intelligence, cloud security intelligence etc.)
  • Cloud Architecture styles (Cloud Optimized classic cloud architecture, cloud-native cloud architecture and cloud integration cloud architectures)
  • Modern cloud computing tools (Puppet, Chef, Ansible etc.)
  • Cloud Security Frameworks (Cloud Trust, cloud security alliance

Data analytics

Data analytics is a process of studying large data sets to identify patterns and trends. This information can be used to make better decisions, for example by companies like Amazon who use it to suggest products to customers based on their past purchases, or Google who use it to provide images that are relevant to your search term.

The data itself can come from any number of sources, for example, Facebook analytics use your interactions on the site to provide information about what's popular with other users. An example of such data is the number of times a particular video has been chosen as a favorite, and posted on other profiles.

This information is then used to create tables and charts that show how well different items are performing, helping companies decide what products to invest in and which ones should be dropped from their line-up.

Data analytics is a relatively new branch of information that is growing more popular as the amount of data on hand increases into the terabytes.

Whereas traditional data requires significant time and effort to produce, analytics can provide millions of pieces of data within seconds meaning that it is much more accessible and relevant for businesses who need up-to-date information to make quick decisions.

However, as more and more information becomes available, businesses start to struggle to keep up which leads to the need for people who can make sense of it all – hence the emergence of data scientists. And, believe it or not, if you're working on a project and need help from a data scientist, these folks are actually on Fiverr. No, they're not gonna set you back only five bucks, but you can indeed find skilled data scientists on Fiverr.

Data scientists work with companies to help them sort through this vast amount of information and provide them with only the most relevant and useful data.

This makes data analytics a very beneficial area of work for people who enjoy learning about trends and statistics, and enjoy making sense of this information to help improve their company or organization.

– Cloud computing skills – cloud computing is the delivery of hosted services over the internet. It can be as simple as giving your users access to virtual machines and networking on demand.

– Cognitive Computing – sometimes called machine intelligence, this branch of artificial intelligence technology enables technology systems to learn from their interactions with people and other technologies .

Cognitive computing systems become more effective as they gain experience and “learn” from interactions with people and other technologies. These systems can detect complex patterns of information, learn the preferences of individuals, and make decisions based on available data. Cognitive computing enables technologies to each play a greater role in the way we work and live.

Data analytics skills you can upskill on now:

 

  • Python –  A multi-paradigm programming language that is designed to emphasize code readability. Many different techniques are used throughout the language, making it easier for developers to express their desired solution in fewer lines of code. Python is typically used with open source libraries and frameworks, including Django, NumPy, Pandas, SciPy, and Scikit-Learn.
  • SQL –  The most popular data analytics language for data professionals is SQL (short for Structured Query Language). It's a special-purpose programming language created completely to work with data stored in a relational database management system (RDBMS), although it does support extensions to handle data stored in standard data files.
  • Java –  A general-purpose programming language used to create diverse set of applications ranging from consumer apps to enterprise software. Java is most frequently used with the Java EE platform, which takes care of common infrastructure tasks so developers can focus on specific business logic for their application.
  • Hadoop –  An open-source data analytics framework that is designed to handle data sets of potentially unlimited size. Hadoop contains everything needed to process data, including the ability to write data to disk and read it back.
  • R – An open-source data analytics programming language for statistics. R provides data visualization, data manipulation, predictive analysis and data modeling. The language allows users to interact with data using a command-line interface or a graphical user interface.
  • C – A general-purpose programming language that supports low-level memory manipulation and features such as pointers. C is considered one of the easiest languages for developers to learn.
  • JavaScript – An object-oriented computer programming language that is most commonly used as part of web browsers. JavaScript uses an interpreted approach, making it faster to write and debug than other languages.
  • PHP – A general-purpose programming language for web development. PHP is designed for server-side processing and usually comes with a database interface and template engine.
  • C++ – A general-purpose programming language that supports low-level memory manipulation and features such as pointers. C++ enables developers to use object-oriented programming techniques, allowing data structures and algorithms written by other programmers to be reused without having to understand the original code base.
  • Perl – A data analytics language that supports high-level data management and text processing. Perl provides support for regular expressions, allowing data scientists to filter data based on patterns and other data extraction operations.
  • Java – A general-purpose programming language used to create diverse set of applications ranging from consumer apps to enterprise software. Java is most frequently used with the Java EE platform, which takes care of common infrastructure tasks so developers can focus on specific business logic for their application.
  • C# – A general-purpose and object-oriented programming language that allows data professionals to access data in the Microsoft .NET environment like Excel, SharePoint, and SQL Server.
  • CSS – A data analytics language used to style a website's data content. CSS is a markup language, meaning it uses special syntax to open and close data elements so browsers can interpret data correctly.
  • Scala – An object-oriented data analytics programming language that allows developers to access data in the Java Virtual Machine data analytics environment like Apache Spark.
  • Visual Basic – A data analytics language used to create data applications. Visual Basic is designed for data analysis and reporting, making it useful among data professionals who need to manipulate data to create reports for clients.
  • C – A general-purpose programming

 

Machine Learning

A branch of artificial intelligence technology, machine learning enables computers to learn from presented data and make decisions without being explicitly programmed .

Machine learning skills you can upskill on now:

 

  • 1. Python (or R)
  • Statistics (and basic algorithm knowledge, like linear regressions and classifications)
  • Machine learning library (like scikit-learn for Python, or caret for R)
  • With these three primary skills, machine learning is open to you! You can start tinkering with these tools today by following

Machine learning uses a set of algorithms that iteratively consume data, derive patterns from it, and use those patterns to predict outcomes. By applying these algorithms across many processes in a system, the system can make decisions autonomously, freeing up time for human analysts.

– Natural Language Processing (NLP) – the branch of AI technology that enables systems to detect patterns in textual information and derive contextual meaning .

Natural language processing is an AI technology that allows computers to understand written or spoken words, sentences, and entire conversations. For example, if a computer receives the sentence “I found this great website!”, it analyzes the words in that sentence and detects that the speaker probably means they like the website. Natural language processing also enables computers to detect patterns in written or spoken words. It can recognize patterns in dialogue interactions. It can even recognize patterns related to things people find funny, such as puns and sarcasm.

– Machine vision – a field of AI technology that enables systems to detect patterns in images .

Machine vision is an AI technology that allows computers to understand what's happening in digital images and videos. By detecting patterns within these visual data, machines can learn about the objects in the image, how they are related, and what actions are taking place. Machine vision can detect patterns in more than just images. It can also analyze sounds, enabling it to understand where objects are within a room based on the sound of their speakers.

 

Big Data

The term used for datasets that are too large or complex to be analyzed using traditional data processing applications .

Big data is used to refer to datasets that are too large or complex for traditional data processing applications. These types of datasets can be analyzed by applying AI technologies, including machine learning and natural language processing. Data scientists have the ability to detect all kinds of patterns in big data, which can enable them to understand customer behavior.

Blockchain

As companies look to transform their businesses with digitalization, you'll want to up your game with blockchain – knowing what it is, how it works (or doesn't), best practices. By understanding the  technology and its implications, you'll be able to communicate more effectively with various stakeholders in your company – from board members and executives to customers and suppliers.

– Blockchain seems challenging because it is a new technology, but once we dig deeper into the topic we find that the process of learning blockchain technologies is actually quite easy. The main reason for this is that it is a lot like reading a book. When we read a book, we do not need to understand every single sentence or word in order to grasp the meaning of the entire thing – we only need to be familiar with a small number of these phrases and terms. In much the same manner, blockchain technologies are only hard to comprehend at first because we do not understand the majority of terms and phrases associated with it. However, if we remove these complex components from our minds for now, what remains is a straightforward process that is not too difficult to understand.

Blockchain skills you can upskill on now:

 

  • Solidity

Solidity is a contract-oriented, high-level language for implementing smart contracts on various blockchain platforms. It was influenced by C++, Python and JavaScript and is designed to target the Ethereum Virtual Machine (EVM).

  • Go-ethereum

Go Ethereum or go-ethereum  is a client implementation of the Ethereum blockchain written in Go. It provides a multi-platform, command-line interface for running EVM blockchain nodes.

  • Fabric

Fabric is blockchain framework implementation authored by IBM Research, designed to simplify the creation of blockchain networks and enable interactions among network members with established confidentiality and data integrity.

  • Truffle

Truffle is a blockchain software development kit that enables programmers to deploy smart contracts on multiple blockchain platforms. It provides the bedrock for blockchain application development with features like contract compilation, linking, deployment and binary management.

  • Solidity-coverage-report

Solidity-coverage-report plugin collects data about which percentage of code has been executed or tested by tests and it facilitates unit testing blockchain projects.

  • Blockchain explorer

A blockchain explorer is a web application that runs on a blockchain node and serves as a useful tool for blockchain users to see the details of transactions, such as transaction amounts, sender addresses etc.

  • Ethereum-blockchain-explorer

The page lets blockchain explorers like Etherscan retrieve information about blockchain and the blocks within it.

  • Ethereum-blockchain-downloader

Ethereum blockchain downloader downloads blockchain data from blockchain.info to your computer so you can load it up in a blockchain explorer like Etherscan.

  • Blockchain API

A blockchain API is blockchain-as-a-service that enables blockchain developers to send blockchain transactions and access blockchain data using their own blockchain nodes, without having to run blockchain nodes themselves.

  • Oracle

Ethereum blockchain uses blockchain oracles for allowing interactions with other applications and systems such as web3 frontends written in javascript.

  • Solidity IDE

Solidity is a blockchain programming language and an integrated development environment (IDE) for writing blockchain-based decentralized applications. It is designed to target the Ethereum Virtual Machine and includes features such as syntax highlighting, autocompletion, real-time inline errors and extended documentation.

  • HTML5 blockchain games

Many blockchain games currently use HTML5, a markup language for structuring and presenting content on the World Wide Web, originally proposed by Opera Software and developed by the World Wide Web Consortium (W3C) in 2007.

  • JavaScript blockchain frameworks

JavaScript blockchain frameworks help blockchain developers to build blockchain applications using JavaScript programming language.

  • Consensus mechanism

 

Onboarding a Robot

Up your game in robotics and machine learning with off the shelf robots like Baxter or Pepper, home automation systems like Alexa, smart TVs like X1… choose one of these to upskill on.

Onboarding robots will be robot trainers who teach robots how to behave, think and react. Robots will need training before they can interact with the world around them such as houses or offices, so it is important that they don't damage anything when moving around. Onboarding means getting a robot accustomed to its new home and teaching it the routes and places not to go.

 

 

Keap, SQUARE, SP



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About Me

Howdy, all, I'm Doc. I live in the beautiful port city of Kobe, JAPAN with my wife Reiko. Together we co-founded the Japanese literature translation firm, Maplopo. Nihon Hustle grew out of my desire to help others interested in working with, or starting, a business Japan—or anywhere else in the world!

We all wear different hats and my job is to help you find the one that fits you best. Thanks for reading, and go get 'em!