Fast Track Your Career in Big Data Analytics

Big Data Analytics

The field of Big Data offers lucrative career opportunities. Being a tremendously growing field, it offers opportunities to grow fast as well. To put the matter into perspective, the Big Data industry is expected to be worth $77 billion by 2023. The industry is growing by leaps and bounds. 

 Big Data is equally lucrative for entry-level and experience professionals. Glassdoor suggests the average salary of Big Data Analyst in the U.S is $102097. However, analyst isn’t the only role in Big Data. A few other roles like Data Engineer, Data Architect, and Data Scientists which are equally rewarding and demanding in Big Data. While data engineering and data architects have software engineering bent, data scientist and analyst roles are predominantly analytics role. 

 Naturally, the first step in starting a big data career is to decide a role that fits your skills and experience best. Each role differs from each other in terms of skills and experience required to excel. 

 In the parts below, we will discuss how to start a career in Big Data analytics. Of which, analyst and scientist are major roles. 

 Skills required for big data analytics

 Analytics is a multi-disciplinary field. You need to master a gamut of skills including programming and statistics to advance in analytics. Statistics, Python and SQL are core skills to excel in analytics, but a few other skills are necessary to perform efficiently in analytics role. Not to mention, these skills will add immense value to your candidature. Let’s discuss these skills in detail. 

 1. Statistics –Statistics is the foundation of types of analytics – descriptive, descriptive, diagnostic, and predictive. Various concepts of statistics like poison distribution, regression, correlation ship form the foundation of analysis. Similarly, concepts of probability like probability distribution and discrete mathematics is needed to do well. 

 2. Programming Python and R are common languages used for data analytics. SPSS, MATLAB, and SAS are other languages used for analytics. However, these are used for limited to academic purpose and less used in the industry. Learning SQL will help you to collect data from various databases. A few no-relational databases like Mongo DB, Cassandra, etc. are now used widely in the industry which are often required to be used for analysis. 

 3. Apache HadoopHadoop is perhaps the most widely used tool in the Big Data industry. This framework is essential to learn to collect data from various server (node) in all forms text, image, etc. Using Hadoop’s Map Reduce functions, analysts can perform analysis on data received directly by Hadoop. Let alone analytics, learning Hadoop alone can open high-paying development roles. The average salary of a Hadoop developer is $110,000. Hadoop is a Java-based application, so you will need to get your hands dirty with Java. 

 4. Apache Spark– Spark is a better alternative for Hadoop. It overcomes the limitations of Map Reduce analytic capabilities by allowing in-memory data processing. You can perform analysis and modelling on streaming data. Learning Spark will add substantial value in a Big Data career.  

 5. Machine Learning This is perhaps the most complex part in analytics. KNN, Decision trees, logistic and linear regression, etc. are frequently used algorithms that you need to work with. ML algorithms help predictive models learn iteratively and improve the accuracy of the model’s outcomes.  

Next step in moving head in a Big data career is building a portfolio, especially if you’re about to start. Once you have a portfolio, show it publicly. You can publish your portfolio online. 

 Get a certification 

 is still evolving and hiring the right talent for analytics roles is difficult for companies. Big Data Analytics certification increase your value in the job market and make you stand out, proving that you hold expertise in Big Data analytics. Certifications are available for all levels of analytics professionals. 

 For beginners, a certification adds a lot of value. Associate Big Data Analyst (ABDA) certification from DASCA, for instance, validates the candidate’s expert-level proficiency in R, Hadoop, data analysis, and machine learning. Plus, intermediate knowledge of Python. ABDA is a globally-recognized entry-level certification.  

Similarly, you can go for intermediate and advanced Big data analytics certifications corresponding to your experience. Following are some prominent certifications in the Big Data industry. 

 1. Certified Analytics Professional ( CAP) – Entry level 

 2. Dell EMC Data Science Specialist ( DCS- DS) – Advanced level 

 3. IBM Data Science Professional – Entry & Intermediate level 

 4. Senior Big Data Analyst ( SBDA) – Intermediate level 

 5. Senior Data Scientist ( SDS) – Advanced level 

 5. Microsoft ( MCSE) Data Science: Data Management & Analytics – Entry level

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