Become a Data Engineer – The Fastest Growing Tech Career In The World; Join The Industry-Supported Praxis Post-Graduate Program in Data Engineering
Why should you pursue the Praxis Post-Graduate Program in Data Engineering?
The demand for Big Data professionals – data scientists, data engineers, ML engineers – has seen explosive growth in recent times – and this has further accelerated due to the rapid COVID-driven digitalisation. The availability of trained resources, however, has not been able to keep pace with the demand. This presents a great opportunity for professionals to learn these skills and build exciting and rewarding careers.
Praxis Business School is a well-known name in the big data professional space – we have pioneered the formal teaching of business analytics/ data science in the country and our post graduate program in data science is ranked 2 in the country by AIM this year (and has been consistently ranked in the top 3 for the past 5 years).
According to the DICE tech job report of 2020, Data Engineering (DE) is the fastest-growing tech job with a nearly 50% year-on-year growth. Driven by this explosion in the demand for data engineers, and several conversations we have had with their data science program recruiters, we have decided to launch a 6-month, full-time, in-class post graduate program in Data Engineering at our Bangalore campus starting February-end this year.
What do Data Engineers do?
Data engineers set up and maintain the data infrastructures that support business information systems and applications. They design, build and install scalable data pipelines – this includes collection, processing, storing and transforming of data into formats that can be processed and analysed. According to Jesse Anderson, managing director of the Big Data Institute, data engineers are professionals who have specialised their skills in creating software solutions around big data.
To create data pipelines at big data scale, data engineers are required to bring together in-depth understanding of up to around 30 technologies and frameworks and choose the right combination of these technologies and tools to serve specific business requirements. They have to make sure that the data is clean, reliable and ready for use.
How are Data Engineers different from Data Scientists?
Data Scientists combine domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. They also present the results of their analyses using data visualisation techniques.
Data engineers, on the other hand, execute the data strategy of the organisation, which means creating the infrastructure and processes for identifying, storing, provisioning, processing and governing the data assets. Data engineers build the data platforms that enable data scientists to analyse data and train machine learning models.
What skills do Data Engineers need?
A data engineer should have advanced programming and systems creation skills. In addition to programming proficiency in Python, Java/ Scala and SQL, a data engineer needs to acquire the skills in the following core areas: cloud, distributed systems, databases, data warehousing, data processing, real-time data ecosystem, data orchestration, data science and ML (basics), full-stack development – and the associated technologies.
The big data ecosystem is evolving at a staggering speed, driven and supported by new technologies. Data engineers have to be adept at quickly understanding and using new technologies – this requires them to be good learners and quick adopters. They also need to be good at collaborating and communicating, as their work requires them to interact with different stakeholders and work closely with them.
The 6-month full-time Praxis Post Graduate Program in Data Engineering
The Data Engineering programme at Praxis is designed to create professionals who become
day-one productive for the organisation. The program equips professionals with the know-how of existing tools and technologies for Data Management & Data Modelling and introduces them to the paradigms of Distributed Systems and Cloud Computing. The participants get to work on a Capstone Project that requires them to migrate data to Big-Data platforms and manage the system on the Cloud.
The program is divided into three trimesters of two-months each covering
- working with traditional data
- engineering platforms for big data and
- running enterprise business on cloud
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