Why Every Hadoop Professional Needs Data Science Skills

How embracing Data Science can help you in the Hadoop environment

Hadoop is a cluster computing technology that makes use of techniques like data engineering, software engineering for distributed computing, warehousing methodologies, large-scale analytics, and distributed systems administration. It combines distributed computing techniques with distributed storage, and is by far the most efficient framework for performing high end analytics.

Data Science used SAS and R programming to perform statistical analysis. By clubbing SAS and R with Hadoop, you will be able to analyze large datasets with a variety of tools. You will also learn your way around higher-level data analytics tools like Hive and Spark.

This mix of Data Science and Hadoop skills will set you apart, and make you eligible for very lucrative jobs.

Perks of having expertise in both Data Science and Hadoop

If you know how to use Data Science techniques within Hadoop, you will understand how the various parts of Hadoop combine to form an entire data pipeline – managed my teams of data researchers, programmers, engineers, and business people. You will also be able to:

• Understand Hadoop architecture and set up a pseudo-distributed development environment
• Develop distributed computations with MapReduce and the Hadoop Distributed File System (HDFS)
• Work with Hadoop via the command-line interface
• Use the Hadoop Streaming utility to execute MapReduce projects in Python
• Explore data warehousing, higher-order data flows, and other projects in the Hadoop ecosystem
• Use Hive to query and analyze relational data within Hadoop
• Use filtering, summarization, and aggregation to move Big Data towards last-mile computation
• Understand how analytical workflows including feature analysis, iterative machine learning, and data modeling work in a Big Data context

Every company needs data scientists to comb through their data and find better ways to regulate production, forecast buying and selling behaviour, and resolve bottlenecks.

To be a good data scientist, you need to have a working knowledge of MapReduce, distributed systems, and distributed file systems. You should also know how to analyse backdata to understand market trends, demographic behaviour, and seasonal fluctuations. If you can use data analytics to spot patterns and derive insights form large volumes of data, companies will be thrilled to hire you.

How Data Science fits in like a puzzle piece with Big data

The Hadoop ecosystem is changing. Data scientists used to be lone wolves who performed a major analysis once a month; now the field is both more collaborative and iterative. Small and big insights are always being drawn from databases, and these insights have helped companies increase profits, reduce costs, retain customers and identify new opportunities. Data science methods are being used to solve problems in a variety of industries, and there are new job openings for specialists every day.

With extensive knowledge in both these fields, you will be able to:

• Identify potential business use cases where Data Science can provide impactful results
• Obtain, clean, and combine disparate data sources to create a coherent picture for analysis
• Use statistical methods to explore data and provide critical insight for the business
• Leverage Hadoop streaming and Apache Spark for Data Science pipelines
• Choose the best machine learning technique to use for a particular Data Science project
• Implement and manage recommenders using Spark’s MLlib
• Recognize the pitfalls of deploying new analytics projects to production-level scale 

Apart from building a strong skillset and being at the front of the line for interesting job roles, Hadoop professionals with Data Science skills make more money.

According to Glassdoor, the average salary for a Data Scientist is $113,436 per year. A Big Data specialist, according to Glassdoor, earns $62,066 per year.

By combining these skills, you will out-earn both data scientists and Big Data professionals and have a deeper understanding of the entire field of Data Analytics.