Data science involves the use of complex algorithms to extract, organize, and synthesize big data for the purpose of answering difficult questions and driving intricate strategies. With the right data science skills in place, your organization can leverage numerous competitive advantages, including:

  • Better, faster decision-making capabilities
  • Real-time trend tracking
  • Greater operational visibility
  • Process improvement and increased efficiency
  • Improved risk management
  • Better customer experiences
  • The ability to identify and act on opportunities
  • Predictive and proactive business (versus reactive)

To harness the power of data science, you need data scientists. Data scientists have the skills needed to build those complex algorithms and communicate with machines (or a network of machines). They also know how to convert big data into actionable information that non-technical people can understand and utilize. 

Essential IT Skills For Data Science

Data scientists are a hot commodity in today’s job market, which makes them exceedingly difficult to hire externally. Fortunately, many organizations have a population of employees that make excellent candidates for data science skills training through corporate upskilling and reskilling programs. Begin your search by looking for existing skills within your workforce that are adjacent to the IT skills you need. For example, digital marketing professionals with social listening skills can advance their sentiment analysis skills and parlay them into natural language processing, machine learning, topic modeling, and more. Here are the most in-demand IT skills employees will need to develop to fill data science roles within your organization:

The Foundational Trio:



For: Creating/authoring website pages (text, images, links, etc.).

HyperText Markup Language (HTML) is a core web language that defines how web content should be structured, including text, headers, links, bullet points, and more. As such, it’s a must-know skill for basic web development. 


For: Styling HTML elements (e.g. text color, text placement, etc.).

Cascading Style Sheets (CSS) is a stylesheet language. Similar to (and used in conjunction with) HTML, CSS is also considered a core web language but, instead of defining web structure, CSS defines web style and determines how web elements will be rendered for appearance. 


For: Implementing dynamic web page elements (e.g., interactive maps, 2D/3D graphics, scrolling features, etc.).

JavaScript is a programming language that is more difficult to learn compared to HTML and CSS because it’s used to build functions that are more complex and interactive compared to structure and style elements. As such, it is recommended that learners first acquire a working knowledge of other basic programming skills, such as HTML and CSS, before attempting to learn JavaScript.

Other In-Demand IT Skills


Python Coding

For: Data analytics, artificial intelligence, and machine learning applications.

Python is an easy-to-learn “general purpose” coding language, which means it can be used for programming and software beyond web development. Thanks to its learnability, this coding language is quickly becoming the focus of many corporate training and employee development programs for upskilling and reskilling workforces. This highly versatile IT skill is also on track to become one of the most popular coding languages — right up there with JavaScript. In particular, Python coding is well suited for beginners in computer programming, web and mobile app developers, software engineers, and —  you guessed it —  data science learners.

SQL/Database Coding

For: Storing, managing, and utilizing databases.

Structured Query Language (SQL) is a database programming language that allows data scientists to communicate, work with, and manage the storage of massive quantities of data generated by web and mobile applications. IT professionals encounter SQL coding just about anywhere that involves significant amounts of data, including banking applications and payment processors, media streaming applications that store vast personalized music or video libraries, and social media applications that store millions of user profiles.

R Programming

For: Statistics, data visualization, data analytics, and machine learning.

R programming is a flexible coding language that bridges the gap between data analysis and software development. In fact, this programming language was specifically designed for data scientists to help solve virtually any problem they might encounter. As such, it also happens to be one of the most popular analytics tools used in business. 

C# and .NET

For: Desktop and web application development, machine learning, data mining.  

C# (pronounced “c sharp”) is another general-purpose programming language created by Microsoft to provide a simpler, more flexible alternative to Java and C++ programming languages. The “.NET” refers to the framework on which the C# language is built. C# is more commonly used for Microsoft applications and professional desktop applications but has also gained popularity in game development.  


How Businesses Use Data Science Skills

  1. Recommendation Systems

Recommendation systems can be deployed as a lead generator, conversion tool, or full-scale user experience. These systems collect data about a user to filter out options and curate a recommended list of products, information, or media. For example, matching new or existing customers with a specific solution or service. 

  1. Dynamic Pricing

Dynamic pricing models can be created by data scientists to set and manage rates based on real-time supply and demand, competitor pricing, and other factors that routinely impact business. For example, incorporating surge pricing on a ride-sharing application during peak commuter timeframes. 

  1. Fraud Detection

Financial applications, such as mobile banking apps and online payment processing, use complex data lookups and decision algorithms (created by data scientists) to validate loan applications, payments, and other web-based financial activities.

  1. Digital Marketing

Advertising agencies use data science and algorithms to target new and existing leads by serving up personalized ads based on user behavior, demographic information, buying history, and more. 

  1. Delivery Logistics 

Companies like FedEx and UPS leverage data science to improve operational efficiency. More specifically, their data scientists use complex data tracking and analytics to determine the best route, time, cost, and mode of transportation for delivering packages based on numerous external factors, including fuel and energy prices, traffic demand, and more.

Designing and implementing a corporate training and employee development program for upskilling and reskilling your workforce is one of the most affordable solutions to the data science skills gap. The key to minimizing risk is to work with an IT skills education expert that has the experience, resources, delivery model, and technical support to deliver tangible business outcomes.

Assess your current IT skill levels for data science learning with help from one of the most experienced curriculum development companies in the country. We make upskilling and reskilling for data science easy and affordable. Let’s Talk!