Now the Jobs related to Big Data Scientist is topped in Data science segment, be it as a part of bigger picture of Data Science recruiters are calling having various job titles like Data Scientists, Data Analysts, and Data Engineers. Though all these job titles deal with Big Data and sound similar they do have significant difference between profiles. You cannot imagine there is fixed boundaries in between this titles and job profiles but all are data science related tasks. The basic profile of the Data scientists is combine quantitative and statistical modelling expertise with business acumen and a talent for finding hidden patterns.
Big Data Analyst or Big Data Scientist
Why Big Data Analytics, What does Big Data Analyst / Scientist mean?
To Improve the Decision-making power, Big Data Analytics helps many businesses to progress in their performance. As we all know about big data consists of both Structured, Semi Structured and Unstructured data from various offline sources (Backup files, Text Documents, Databases etc.,), online (Log files, ERP Software, Websites etc.,) and Data Piping devises like Electronic Devices, IoT Sensors or Instruments, etc. A Data Developer with his programming tools can gather such data from various sources, but Analytics is a must for processing the data. Through processing the Data Analyst / Scientist can derive some insights from such data to know what they mean and how the information inferred can be used for business development.
A Data Analyst works as a consultant for full life cycle activities of analysis and design. that include requirements analyses and reports on big data stored and maintained by an organization. A big data analyst is an individual that having capability to reviews and develop business intelligence and reporting, and he continuously monitors performance and quality control plans to identify improvements.
What Skills you need to Learn to become a Big Data Analyst ?
Big data Analysts / Scientist uses Technologies which required for automated and Big Data Analytics Software strategies or techniques to manual Analysis to analyse large amounts of raw and unstructured data with the intent to find business insight, intelligence or any other useful information in it.
A Big data Analyst having good knowledge in big data concepts, possesses and skills in using NoSQL Database querying languages. Big data analyst usually works under the data scientists who take care about the planning and design based on the Enterprise requirements. Analyst should have a good understanding of data mining and extraction techniques.
For the Structured SQL Data or Big data analysis the analytics part remains the same whether you are dealing with small datasets, large datasets or even unstructured datasets. What you want to do in big data is the ability to draw relevant information from the humungous amounts of data being processed every minute keep in different environment which require for big data. This requires technology to join hands to analytics or data scientists.
A Data Analyst can be known as a Data Architect or an Analytics Engineer. Data Analysts is key position in the in-Data Science. Architect display the data in graphs, charts, tables and supply the data to dashboards from they work with range of data sets and prepares tasks associated with setting up data and collecting statistical reports out of them.
Key skills needed for being a big data Analyst :
- Find the following skills need to master to become a successful data analyst
1) Programming Knowledge :
- Even you are not a Software Programmer a big data analyst needs to be very comfortable with coding knowledge in some Java / Python / or any other supportive language. While traditional data analyst might be able to get away without being a full-fledged programmer, the main reasons for this requirement is that big data is still in an evolution phase. Not many standard processes are set around the large complex datasets a big data analyst has to deal with. Al ways need a lot of customization required on daily basis to deal with the different data sets coming from different locations.
2) Which languages you learn to became successful data scientist
- As you are not a developer but you should ne familiar with at minimum you should know R, Python, and Java. If you are good in bellow languages you having good control on getting the information easily from complex big data, just looking to this language to keep in your hand to use whenever you required.
- R, Python, Java, C++, Ruby, SQL, Hive, SAS, SPSS, MATLAB, Weka, Julia, Scala. As you can not knowing a language should not be a barrier for a big data scientist. At the minimum one needs to know.
3) you should have Knowledge in Data Bases and Data Warehousing
- Understanding of relational and non - relational database systems is a must. Examples of relational database include – Mysql, Oracle, DB2 and non-relational database include – NoSql : Hbase, HDFS, MongoDB, CouchDB, Cassandra, Teradeta, etc.
4) Bigdata Scientist should know Computational frameworks
- First you should have strong knowledge about Bigdata Hadoop, HDFS and MapReduce. And these technologies help in Big Data processing which can be streamed to a great extent with frameworks such as Apache Spark, Apache Storm, Apache Samza, Apache Flink etc.,
5) Who should have knowledge in Quantitative Aptitude and Statistics
- Any Data Analytics platform apart from the technologies and technics which use to pull the data to analitics is Mathematics, It divided in to many parts like statistics, probability and linear algebra etc., While the processing of Big Data requires great use of technology, Statistics is a basic building block of data science and understanding of core concepts like summary statistics, probability distribution, random variables, Hypothesis testing framework is important if you are data scientist of any genre.
6) The Big Data Analyst Critical Skill is Business Knowledge
- The most critical skill of a big data scientist is to have a good knowledge of the domain or business the project is about. To keep the analysis focused and understand the Key words to validate, sort, relate, evaluate the data, one is working on having domain knowledge relevant. There are analysts good in business and statistics but not in programming. Some are good in programming but they don’t know what information is looking for and how they want it. Many Developers are fail in the context of the business goal. In fact, the reason big data analysts are so much in demand is that its very rare to find resources who can understanding of technical aspects, statistics and business requirements.
Difference between the Data Science and Data Scientist
Data Science: The science trying to extract insights and information from data. dealing with Unstructured, Semi Structured and Structured data is called Data Science, and the person who dealing that Job is Data Scientist or Analyst. Data Science is a field that comprises of everything that related to data cleansing, preparation, and analysis and pull the Information from that data.
Data Science is under the umbrella combinations of statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, in simple terms the ability to look at things differently, and the activity of cleansing, preparing and aligning the data.
Data Analyst and Scientist :Data Analytics is the science of preparation of reports or dash boards with the raw data on the purpose of drawing conclusions about that information.
Data Analytics involves applying an algorithmic or mechanical process to derive insights. For example, running through a number of data sets to look for meaningful correlations between each other.
It is used in a number of industries to allow the organizations and companies to make better decisions as well as verify and disprove existing theories or models.
The focus of Data Analytics lies in inference, which is the process of deriving conclusions that are solely based on what the researcher already knows.