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Frequently Asked Questions

Big Data refers to collecting complex and massive volumes of data with structured and unstructured forms of data that not a single traditional data management device or mechanism can store or process efficiently. It inundated an organization, companies, or businesses on a daily basis.

However, there are some basic tenets to describe Big Data in simpler ways.

  • Big data leads to a huge volume of data that is exponentially growing with time and technology.
  • Big Data is so large that it can not process and analyze data with traditional data processing techniques.
  • It includes data storage, data visualization, data mining, data analysis, and data sharing.
  • It is comprehensive, including semi-structured, structured, and unstructured data, a data framework with different techniques and tools used to process and analyze the data.

Since Data Scientist or Data Analytics is a modern and advanced career option in the information technology and management industry, the best certification and diploma are available at the post-graduation level, but the eligibility criteria that data scientist or data analytics required to fulfill are

  • Complete graduation with 60% marks
  • Students with a degree in Computer science, Maths, Statistics, Economics, etc
  • Some institutes and college prefer experienced candidates (at least two years)

Finding an accurate answer to this question is not possible. It depends on various factors such as an individual’s learning capability and speed, Big Data certification course duration, self-paced learning, dedication to learning, the sources and topics you are covering while learning Big Data. Big data includes so many factors when we talk about in-depth learning, and those are 

  • Introduction to Big data and Hadoop 
  • YARN and HDFS
  • Sqoop and MapReduce
  • Basics of Impala and Hive
  • Usage of Impala and Hive
  • Types of Data Formats
  • Advanced data file practicing and Hive concept.
  • HBase and Apache flume 
  • Pig
  • Basics understanding of Apache Spark
  • RDDS in Spark
  • Implementation of Spark Software
  • Processing of Spark Parallel
  • Spark Algorithms, RDD Optimization techniques, and Spark SQL

It is undeniable that many learners are enthusiastic about learning big data, but most of them are looking at the big data industry’s high salary and good prospects. Therefore, the starting learning point may be too eager and utilitarian to achieve success, but it doesn’t mean an individual can not learn this. Here are the five skills for making your big data learning period easy.

  • Skills for Programming and Data Mining
  • Data Warehousing
  • Computational frameworks
  • Quantitative Aptitude and Statistics
  • Business and Big data tool Knowledge 
  • Data Visualization Skills
  • Problem Solving Skills
  • Familiarity with Hybrid Clouds, Technologies, and Public Cloud

  • Sciencesoft
  • IBM
  • Teradata
  • Oracle
  • ScienceSoft
  • Xplenty
  • Amazon
  • Microsoft
  • HP Enterprise
  • SAP
  • Google
  • VMware
  • Cogito
  • EMC
  • Splunk
  • Alteryx

Yes, Learning Big Data is totally worthy in this competitive world. Let’s find out some of the advantages of doing a Big Data Certification Course from certified trainers and platforms.

  • Rising demand for Big Data analytics professionals
  • Huge career opportunities and reaching the high skill gap
  • Various choices in job titles 
  • Rapid growth in career
  • The Big Data technique is used everywhere.
  • Huge salary compensation
  • Big Data analytics and resources are high in demand.
  • The adoption of advanced Big Data analytics is increasing.
  • Rise of  semistructured, structured, and unstructured data analytics

Big data is a rising technology, which provides a vast range of fields in the industry’s various factors with the big job opportunities. Here is the list that includes a high-demanded career option that an individual can choose after completing a Big Data certification course or any degree in big data.

  • Data Scientist
  • Big Data Engineer
  • Database Administrator
  • Hadoop Architect
  • Business Analyst
  • Statisticians and Mathematicians
  • Data Visualization Experts
  • Machine Learning Specialists
  • Hadoop Developer
  • Business Managers
  • Software Testers
  • Business Intelligence Specialist
  • Analytics Engineer
  • IT Security Analysts
  • Information Architect

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