The world around us, now, lives in bits and bytes, numbers and indices, facts and figures. Data has been hailed to be the king of modern-day business analytics. There is literally so much data to process that there is a separate subject for the automation of data studies i.e. Data Science.  So much so, that there are a host of pioneering data science colleges in India and across the world.

This is not something new, as the world of data science has been evolving at an increasingly accelerated pace. Gone are the days where all your data used to be consolidated in a single database. The advances in Data Science have led to a whole host of technologies and programming methodologies that one needs to be abreast of, in order to even begin venturing into this field.

Fret not, in this piece, we’re going to delve into the step-by-step process of becoming a professional data scientist.

The Eight-Fold Path

A famous quote by a renowned data scientist goes as follows:

Think of data engineers like crop farmers, who ensure that their fields are well maintained and the soil and plants are healthy. They’re responsible for cultivating, harvesting, and preparing their crops for others. This includes removing damaged crops to ensure high quality and high yielding crops.

There are essentially 8 steps to rinse and repeat throughout your data science career to help you navigate these career options with relative ease. Take note, that data science is an ever-evolving field, so you will need to keep yourself up-to-date with all the latest trends and technology shifts and efficient algorithmic techniques.

Basically, becoming an efficient data science professional is an 8-step process:

  1. Become efficient at programming
  2. Learn automation and scripting
  3. Inculcate the study of databases
  4. Master Data Processing Techniques
  5. Schedule Workflows
  6. Study Cloud Computing
  7. Internalize Infrastructures
  8. Follow the latest trends
  1. Master Programming

The top engineering colleges in Gurgaon and across the nation have introduced basic to advanced level programming credit courses in order to help students attain mastery in coding. Data engineers lay at the crossroads of programming and data science and hence, it is pivotal to have a grasp on programming.

Languages you need to know: Python, Scala

  1. Internalize Automation

In order to progress as a data scientist, it is important that you understand the significance of automating your tasks. Many tasks involved in the process are highly repetitive and can be tedious to execute over and over again. For eg. you might need to clear up a database on an hourly basis. If you know of tasks that will be repeated constantly, the best bet is to automate it.

Tools you need to know:  Shell Scripting

  1. Master Databases

Your knowledge of databases is the main ingredient for crafting a perfect recipe for a competent data science career. Since you will be dealing with data structures that hold, databases are, without a doubt, integral to your understanding.

Technologies Needed:  SQL, MongoDB

  1. Learn Data Processing

Data processing is one of those tasks that come with experience – whether you are processing data from R or Python databases or gigabytes of data, it is pivotal that you understand what techniques to deploy for different kinds of data. Learning to process big data in batches will be a crucial skill to learn.

Technologies Needed: Apache Spark

  1. Manage Workflow Scheduling

Once you’re equipped with the knowledge and hands-on experience to build jobs that process data, scheduling them on a regular basis will become the need of every hour.  Use whichever tool is the best for your workflow management and proper execution of jobs you have built.

Technologies Needed: Apache Workflow

An observant reader might see a pattern emerging in these open-source tools. Indeed, a lot of them are maintained by the Apache Software Foundation. In fact, many of Apache’s projects are related to big data, so you might want to keep an eye out for them.

  1. Master Cloud Computing

As you will be dealing with huge databases and numerous data sets which need processing and job execution, you need to consider parallel processing .  Clouds computing has helped make the shift from large racks of discs of databases in earlier times. It will be a great addition in order to increase processing power.

Technologies Needed: Microsoft Azure, AWS

  1. Internalize Infrastructure

You might be surprised to see this here, but being a data engineer means you also need to know a thing or two about infrastructure. As infrastructure in itself is a complex web of technical prowess, we will not delve too deep into it, for the sake of simplicity. However, there are two tools you need to read upon –  Docker and Kubernetes.

  1. Follow Trends

It is not easy to navigate a career in Data Engineering. You have to, for most of the time,  be on your toes and at the very least, stay with the curve if you’re not innovating new techniques to tackle the major problems that arise in Data Engineering.

  • To diversify your ambit of knowledge, check out new software and services like Delta Lake, Koalas, Metaflow, and Rockset. You can also watch the conference talks on ApacheCon.
  • Keep an eye out for posts by Hacker News, Reddit, and the DataCamp Community.
  • Tune in to podcasts like The O’Reilly Data Show Podcast and the Data Engineering Podcast.
  • Or review this curated list of data engineering tools on GitHub.

That’s it! You’re at the end of the road. At this point, you’re practically a data engineer – but you must apply what you’ve learned. Building experience as a data engineer is the hardest part. Luckily, you don’t need to be an expert in all of these topics. You could specialize in one cloud platform, like Google Cloud Platform, to begin with. You could even start your first pet project using one of Google’s public BigQuery datasets. Do whatever you’re passionate about. Good luck!