The age of data is here! And it is here to stay! Data Engineers are equipped with a vast array for data manipulation skills, ranging from data management to machine learning. Data Engineers are literally magician who can extract valuable information out of raw data. Data engineers are responsible for the conversion of data into informative insights by using advanced analytical models. In other words, been a Data Engineer in today’s world is one of the most popular and in demand job role.

On an average Data Engineers in the U.S earn $1,022k on an average/annually. We have put together this guide to help launch your lucrative and high rewarding career in Data Science, but before diving deeper in the data science training it is important to know the difference between Data Analysis and Data Science.

This is pretty obviously that both Data Analyst and Data Scientist have job roles involving data manipulation BUT there is a difference between the two fields, let’s discuss it in detail.

Data Analyst uses data to answer various questions by using analytics tools and platforms. These questions can be “why is a marketing campaign more successful in the U.S and not in the U.K?” to answer these questions data analyst resort to techniques like Data mining and Statistical Analysis. In fact Data Analyst upgrade their skillset by attending data science trainings and become Data Scientist.  

Data Scientists are capable of crafting data manipulation algorithms and custom analysis on given data sets according to the requirements. Unlike Data Analysts, a Data Scientist is fully able to code algorithms and implement predictive analysis model rather than relying on tools.

Skill Requirements to Become a Data Engineer

A data engineer needs to have their tools and by tools here I mean skills. There are a large range of data science training skills being used in the market, some popular are mentioned below:

• Statistical Analysis:

When considering a data science training, one must always make sure that the program roadmap contains statistical analysis in it. As a data engineer a big part of your job will be to extrapolate large amount of data sets and provide action items based on it. Statistical analysis is the key which unlocks this door, tools like SAS, Hive and Pig are important to learn here.

• Programming languages

Having good command on programming languages is an imperative skill for a Data Engineer to have. Drawing information from raw data often requires to make complex programs by using data driven algorithms. Python and R programming is the industry standard these days, because of their capacity for powerful date rendering, these languages have become the best practices in data science training.

• Machine Learning

Machine learning is the most popular gig out there when it comes to data science trainings. If you’re associated with the tech industry you must have heard about Machine Learning from one way or another. Machine Learning essentially enables data engineers to programs machines with the ability to make decisions without programming them specifically. When undergoing data science training, make sure you get exposure with Linear Regression, Clustering.

• Data Management

It goes without saying that a Data Engineer’s job revolves around Data (a lot!). When undergoing your data science training be sure to learn Data extraction, manipulation and loading. This basically means that you need to know how to extract data and transform it in the specific formation required. To handle this data manipulation frameworks like Spark and Hadoop are considered best practice.

• Data Intuition:

Data Intuition is probably the only non-technical skill that sets a Data Engineer apart from a Data Analyst. It basically involves the search for patterns in dataset where there seem to be none. This is like finding a needle in a stack of needles, which is usually the case for untapped data potential in most datasets. Data intuition is a skill achieved by constant exposure and work on datasets, when you work enough with data you learn its language and patterns and start seeing patterns that aren’t process able by the common minds.  

• Communication Skills

 Communication skills are important in every career aspect and role BUT it often goes overlooked that effective communication skills separate a good data engineer from a great data engineer. This is because while you can understand the data patterns yourself you should be able to translate it to others as well, after all where would all the information go? Data Engineers are storytellers, they tell stories by means of data. Being able to communicate effectively to stakeholders helps in making smart and result orientated decisions that eventually lead to business growth. 

How to land a job as a Data Engineer:

After spending weeks and months on your data science training, from learning to code to using analytics tools, you have come a long way. You did it! But journey has just began here, after completing your data science training and earning the certification it is time to put all of this to use. For that you need to land a job, here are 6 main keys steps that will not only get to interview but if followed correctly will unlock your lucrative career as a Data Engineer as well.

1. Identify your strengths, weaknesses and decide on a niche. Without direction all efforts sums to zero. When looking for a job identify a role that suits your skillset and target potential organizations accordingly.
2. Project yourself to outside world as a brand, speak that the employers want to hear. Showcase qualifications and skills relevant to the job role. Don’t bombard people with too much information, you will end up diluting your message.
3. Create an online portfolio, employers always do a little research on the applicant before calling them up, and these days having a digital presence is necessary. Make sure to have a solid and updated LinkedIn profile.

Our data science training program, offers a fully covered career counseling plan where our team will work with you ensuring that you execute the above mentioned points smoothly.

And there you have it! I hope this inspires you a start your data engineer journey and launch your highly rewarding career in data science.