A Data Science Engineer is someone who builds AI tools to automate processes that make value out of data. They play a valuable role in modern businesses that have to deal with masses of unstructured, semi-structured, or structured data. The current demand for this service has made such professionals highly sought-after. However, becoming a data science engineer is not as easy as it may seem. This is where data science certification can help you become a data science engineer.
Data science engineers are derived from specialists in software engineering and backend development. That’s because employers need them to write large SQL queries, and handle data using tools like Informatica ETL, Talend, and Pentaho ETL.
That’s not all.
Companies often require these professionals to have an advanced capacity in SQL, Java / Scala, Python, and expert use of cloud platforms (especially Amazon Web Services). Companies that generate massive data from multiple sources need the data science engineer to organize the collection, processing, and storage of information. Hence, those tools are valuable in data modeling, data warehousing, and related tasks.
Data engineers recommend and implement ways of improving data reliability, quality, and efficiency.
They achieve their role using a variety of computer languages and tools, which interlink different systems and pull new data from other systems.
Some of the typical responsibilities you’ll have in this profession include:
- Using machine learning
techniques to select features, and build and optimize classifiersrn- Deploying state-of-the-art methods to data minern- Incorporating third-party information sources to extend company datarn- Building analytic systems by including information which enhances data collection proceduresrn- Cleansing, processing, and verifying the integrity of data used in the analysisrn- Carrying out ad-hoc analysis and presenting the resultsrn- Constantly tracking performance with automated anomaly detection systems
Many companies are already hiring data science engineers, including:
- Cognizant Technology Solutions (CTS)
- Tata Consultancy Services (TCS)
- Capgeminirn- Wipro Technologiesrn- Hewlett-Packard (HP)
- HCL Technologiesrn- Mahindra Satyamrn- LatentView Analyticsrn- Mu Sigmarn- Deloittern- Wells Fargorn- PayPalrn- JPMorgan Chasern- Dellrn- Fractal Analyticsrn- Opera Solutionsrn- Nielsenrn- Equifaxrn- Cisco Systemsrn- Infosysrn- KPMGrn- Adobern- Targetrn- American Expressrn- CSCrn- CIBCrn- HSBCrn- Revolution Analyticsrn- MarketSharern...
Who can Become a Data Science Engineer?
To become a data science engineer, you need particular critical skills beyond the educational qualifications. These skills are essential in executing your duties and producing excellent performance.
Here’s an overview of several critical skills:
- A curious nature – you need a constant pursuit of learning. Since you’ll be dealing with so many areas and data points, you need an inherent curiosity driving your need to find answers.
- Excellent organization – without excellent organization skills, you’ll be overwhelmed by the millions of potential data points you have to manage. Good organization is necessary if you want to reach the right conclusions.
- Persistence/ stubbornness – this profession can be filled with numerous frustrations. Trying to find answers to challenging problems may seem impossible, and anyone without much determination will give up easily.
- Creativity, focus, and attention to detail – the combination of these traits ensure that you’re always trying new things, delivering results, and never miss out on any valuable aspect regardless of how unimportant it may seem.
- Exceptional communication skills – you’ll be collaborating with other team members to fulfill major projects. The success of those projects depends on how well you communicate.rnApart from those soft skills, you need hard skills that you’ll use daily at work.