As developers, it is easy to go by without dealing with large data amounts or data that requires complex logic around it. For that reason, it is easy to neglect the need to improve your skills around working with data. The problem becomes obvious when you try to advance your career. There, everything is about data and it is not all programming-related.
Why learn about data?
Whenever I conducted interviews in the past, most of the candidates struggled to create logic or handle particular data pieces. Whenever they figured out a solution, sometimes that solution proved to be inefficient if the data amount increases or has too many variations. If the challenge was about designing a system, the data flow, storage, and management were obviously the most complex.
As a programmer, you cant avoid data when programming. As a matter of fact, a program can even be used as data if you are meta-programming. For the most part, you are reading data from an API or Database, manipulating data to be displayed to the user, or making analysis to help you come up with a solution on how to store it, ship it, share it, or design a system around it.
Here a few reasons to deep dive into data:
- Ask better questions and make better decisions:
When you understand data it helps you ask the right question around decisions that are made inside a company and even drive solutions by providing better insight. It also helps you make better decisions and navigate the company better.
- Communicate in terms of value and insight:
Approaching stakeholders, managers, or a boss with a non-data-based idea can backfire quickly. When you know your way around data you can better communicate value and provide insights that boost your career as well as put you upfront in many decisions the team or company makes.
Nobody understands data more than the people dealing with it. A lot of managers, bosses, and even CTOs are relying on good developer data analysis to help drive decisions and features. That’s why a data scientist can drive a lot of value to the company. As developers, being that “translator” can give you great benefits.
When you know how to work and handle data, you don't need someone to set up databases and API for you to do your work. As a UI developer of any kind, mobile, frontend, etc, learning about these things helps you move on and be independent, and to do your work better.
- Become more technical:
With so much driven by data, being able to get technical with the system engineering, data scientists, CTO, stakeholders, etc can go a long way. If you are not an engineer, learning data-related things help you understand the conversation, better manage and create things of value that will solve the problems needed to be solved.
Data is Everywhere
Never before the talk around data was so relevant. Companies try their best to collect as much data as possible. Systems are being built to collect, analyze and help make sense of the data. Everything around us is relying on some piece of data to function, from the apps on our phones to our self-driving cars and planes.
It was never more important to ramp up on knowledge and skills around data. We have experienced an increased demand for data scientists mostly driven by advancements in AI, Machine learning, and cyber-security. You probably heard “Big Data” being thrown around and probably thought it does not affect non-data engineers. And, you are wrong!
You dont have to be a Data Engineer/Scientist
Think about this: once the system is improved to handle the bigger load of data and faster, we will need to create ways to read and handle the data. Backend and cloud developers feel the effect first and from there, these data need to be displayed somewhere, whether as videos, graphs, tables, etc. Then any developer that handles UI feels the need to handle this data by making smarter decisions around it and learning new ways to display them for the user.
Is not just programming
The data trend does not affect just developers. Now, more than ever, designers have to find a way to feed this data to users in easy-to-use and make sense of the data without users feeling overwhelmed. This most of the time requires designers to learn more about the data and even influence how systems are built to accommodate a certain feature.
Managers, marketers, salespeople, politics, entrepreneurs, CTO, CMO, all can benefit from data knowledge. Data helps make better decisions and identify problems to be solved — It helps new things to exist. Data is the universal language that can drive ideas and innovations, one that should be spoken fluently by all involved.
Data Technologies You Will Need
There are so many data technologies built around managing and handling data that this article would be enough to talk about all of them. Whenever “Big Data” is mention you may hear about the Apache Hadoop ecosystem (Common, MapReduce, HDFS, Yarn, etc), Python, SQL, R, etc
SQL databases are still super popularly used and any developer should learn SQL. MongoDB is probably one of the most recognizable No-SQL databases but Cassandra and HBase — both apache’s — are commonly well mentioned when the topic is big data storage.
Xplenty has become a household name for data analysis for being code-free, powerful, security-focused, and provide great support and user experience. Analytics is another tool for powerful data analysis and dashboard. Skytree is another tool and probably the best data analytics tool along with Apache Spark. You have probably heard of Elastic Search which is JSON-based and is a powerful search and analysis engine.
It all depends on the type of data you are mining. For example, MonkeyLearn is an ML platform specializing in text mining and can do it in real-time and it is ready to use. Others are open-source and aspire to make things easier for everyone and like RapidMiner, H2O, Orange, Apache Mahot, Knime, etc. You can also find more advanced and powerful tools like Oracle Data Mining and IBM SPSS Modeler that promise even faster, powerful, and complex data analysis.
Data Skills Required
As a developer, you probably experience and deal with data in many shapes and forms. The way you consume data varies a lot and you probably spend a lot of time with Databases, API, and files. No matter what “dealing with data” looks like, there are some important skills you should have.
Data Structures & Algorithms
Data structures and algorithms are probably top skills related to data. Knowing how to organize and work with data starts here. It is the heart of programming and skill all employers are looking for in their top candidates. It further highlights the importance of using the right data structure and logic to make data flow easily and efficiently.
Data modeling is about creating a conceptional model of how data items relate to each other. This is an important skill when building and handling databases as well as how your software can organize and present the data. It aids engineers in modeling business requirements and translating business requirements into relations.
At some point, you will need to optimize your data, the data flow, or the algorithm that handles the data. Strong algorithm and problem-solving skills are very important here. It requires good data modeling and system engineering basis depending on the type of optimization you are doing.
Data Storing and Querying
I always recommend SQL and No-SQL database knowledge. They all come with their challenges and each force you to approach your data differently. One cannot just store and query data from a database. The database can fail you when badly structure and force you to make complex queries to read the data which will affect your system and software. Whatever the database you pick, make sure it solves your more crucial need and you can even use multiple at once.
Engineering is an extremely important skill to have. Applying engineering techniques to your software to make it well organized, well structured, and easier to maintain will go a long way. Even when it is not about your code, providing your team with your engineering skills can help you shape the software in a way everyone is proud to work with.
Machine & Deep Learning
Machine learning (ML) helps in developing a recommendation, personalization, and classification systems that are all around us. Machine learning is a new way to program altogether and it takes care of complex connections that normal programming would require months to accomplish and an eternity to maintain. Every developer should try to get into this field by learning machine learning algorithms as the future of programming is leading in that direction.
Real-time data is presented as it is acquired and proves to be good for navigation and tracking. Weather, navigation, and tracking apps all have been using this type of data for years. In a time of coronavirus, real-time data proved to be helpful in tracking the virus, and with the power of AI, more things are becoming real-time. In the robotics industry, this data is collected, analyzed by AI, and presented immediately. Self-driving cars also deal with this type of data and it is an exciting field to get involved with.
The R language is notoriously known in the statistics field since it is used for statistical computing. R is also popular in data mining for its statistical nature and statistics, in general, is an important mathematical and programming field. I pretty much powers data analysis and it is a field of study data scientists and engineers are required to be familiar with.
There are so many fields that function on loads of data like AR/VR, gaming, Blockchain, Natural Language Processing, etc. These fields are demanding strong data skill which surpasses the numbers of engineers in the market. We need more data-driven developers and engineers with reliable algorithm and problem-solving skills. The demand is high.
If you are a programming or data engineer put more effort into learning data structure and algorithm to strengthen your algorithm and programming skills in dealing with data. UI/Frontend engineers should really invest in data visualization tools like D3.js as the need to show the data to users will come more often than you may expect. Anyone adventuring into robotics, and data analysis should get involved in statistics, AI, ML, and data analysis in general.
Nonprogrammers like UX Designers, Managers, C-level folks, entrepreneurs, all should really learn the data lingo and get more involved with tools that let you visualize, analysis and make decisions in your day-to-day. The future of a lot of businesses really depends on how effectively they collect and handle data. The future of programming is around data no matter at which level or field you do it.
Let me know what you think, please share more insight and tools you know of in the comments, let's talk about this.