Reasons to Become a Certified Hadoop Data Scientist

The rate at which data science has been growing has made IT professionals take great interest in the concepts that fuel it for their own professional interests. Not only does it require a solid set of skills, it makes use of mathematics and computer science to tangibly impact how organizations and their growth. Observing recent trends, we see that the surge in data science jobs in international markets will be at an all-time high in the coming years, making it ideal for talented professionals to capitalize upon their potential future in data science across any vertical they desire.

There have been several platforms over the last decade that have risen to prominence for their infrastructure and customization capabilities and one of the most popular platforms in the world has made itself stand apart from the rest- Hadoop is a mainstay in the industry for prospective data science professionals. Known for its capacity to deal with large data sets, alongside being approachable as an analysis platform, it has risen to prominence as the go-to platform for both veterans and new comers for its streamlined interface and computing capabilities. Acquiring a Hadoop certification to strengthen your future in the data science industry is a sound decision, with regards to training particularly for trained IT professionals looking to specialize with their skills. Here are four reasons to get certified through a Hadoop program-

  • DATA EXPLORATION WITH FULL DATASETS- Data researchers love their workplace. In the case of using Hadoop, they generally require a workstation with heaps of memory to examine data and create models. In the realm of big data, computer memory is never enough, and now and then off by a long shot. A typical methodology is to utilize an instance of the large dataset; as large a set as can fit in memory. With Hadoop, you would now be able to run several data examination procedures on full datasets, without testing. Simply create a map-reduce job, PIG or HIVE content, deploy it specifically on Hadoop over the full dataset, and recover the outcomes appropriate to your workplace.
  • MINING LARGER DATASETS- As a rule, machine-learning analysis allows better business outcomes when they have more data to gain from, especially for clustering, for example, grouping, outlier identification and product recommenders. Understandably, large datasets were not accessible or excessively costly to procure and store, thus machine-learning professionals needed to create innovative approaches to enhance models with rather restricted datasets. With Hadoop as a stage that gives directly adaptable capacity and preparing power, you would now be able to store ALL of the data in RAW organization, and utilize the full dataset to manufacture better, increasingly exact models.
  • LARGE SCALE PRE-PROCESSING OF RAW DATA- Modern data researchers will tell you that 80% of data science work is normally with data acquisition, transformation, cleanup and feature extraction. This "pre-handling" step changes the crude data into a formatted consumable by the machine-learning tool, commonly in a type of an element grid or previous created program. Hadoop is a perfect platform for executing this kind of pre-handling productively and in a diverse way to effectively deal with massive datasets, utilizing map-decrease or instruments like PIG, HIVE, and scripting dialects like Python. For instance, if your application includes text processing, it is often required to represent it in a word-vector organize format TFIDF, which includes counting word frequencies over substantial corpus of reports, which is ideal for a map-reduce job. Essentially, if your application requires joining long tables with billions of lines to make feature vectors for every data object, HIVE or P IG are exceptionally helpful and productive for it.
  • DATA AGILITY- It is regularly referenced that Hadoop is "schema on read", rather than most customary RDBMS frameworks which require a strict mapping definition before any data can be injected into them. This allow professionals to remain sufficiently adaptive in their daily tasks. "Schema on read" makes "data agility": when another new data field is required, one isn't required to begin creating the lengthy process of a schema redesign and data migration functions. The positive effect swells through an organization, and rapidly, everyone must use Hadoop for their project, to accomplish a similar level of readiness, and increase the advantage for their business and product offerings.

The adaptive nature of a data scientist is what Hadoop relies upon and it gives professionals the tools needed to use the platform according to their own needs. While it is not the only platform many professionals will be required to work with, it has become a mainstay in the corporate world for its capabilities and business application potential. Undertaking a Hadoop Data Scientist Certification will help your career grow to its fullest as you gain experience through your technical skills. With the demand high for trained professionals, we must take advantage of the new opportunities that will be available to us in the near future.

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