Some of us love numbers, love playing with them, working on them. So here we came with another career option that is all related to digits – Big data analytics.
They are the masters of handling and processing large and varied data sets (big data) to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can actually help a business to earn more and end up on more-informed decisions.
Big Data Analytics work for various business benefits, including new revenue opportunities, more effective marketing, better customer service, improved operational efficiency and competitive advantages over rivals.
So this makes a perfect image of an analytics that he is a very vital person for a company.
Their work makes people in business and research field make better and faster decisions using data that was previously inaccessible or unusable.
Now we have come up to so many advanced analytics techniques like:
- text analytics
- machine learning
- predictive analytics
- data mining
- natural language processing
These techniques are used by businesses to gain new insights resulting in significantly better and faster decisions.
So there are some you need to have or need to develop to be in this field. choose this career accordingly. If you intend to become a successful data analyst, you must start by ensuring you get a good background in mathematics, technology, business intelligence, data mining and statistics. Other skills include:
- Analytical Skills are important. These skills refer to the ability to gather, view and analyze all forms of information in details. They also mean the ability to view a challenge or situation from different perspectives.
- Mathematical skills which includes having good knowledge of figures and numbers, interpret any mathematical information, be conversant with trend and to be able to work with graphical information.
- Then there have to be technical and computer skills. You should possess a basic knowledge of statistics. Also, you need to be familiar with some computer software and tools including; scripting language (Python), Querying Language (SQL, Hive, Pig), Spreadsheet (Excel) and Statistical Language (SAS, R, SPSS).
- The other most important thing is to be attentive to details. This ability is especially important at the point of solving problems and making decisions. One who pays attention to details to work better and stands lower risk of making errors.
- Business skills are also important. You need to possess certain business skills such as decision-making and problem solving to function well as a data analyst.
- Communication skills are also vital as in any career these days. You must be able to facilitate meetings, make the right requests and be an active listener in order to assimilate new information.
Top Analytics courses in India
This is the only College in Delhi University offering this type of course.
The course is designed to provide in-depth subject knowledge on basic and advanced statistics in addition to learning tools and techniques. Participants are encouraged to solve case studies to understand concepts better along with a capstone assignment post each module.
Jigsaw Academy hires subject matter experts from the industry to build out a curriculum which is most relevant to a specific role in the industry. For example, the flagship course offered by Jigsaw Academy on Data Science with R was designed by an aero-scientist who was working at one of the world’s foremost aircraft manufacturer while he was creating the course. This model ensures the course content meets the career objectives of not only aspiring students but also aspiring professionals at various stages of their careers.
The crux of AnalytixLabs courses is training students for the various job roles in the analytics industry. Hence the courses are designed keeping in mind the job responsibilities of these roles and are validated by professionals from that field itself.
Big data analytics uses and challenges
Big data analytics applications often include data from both internal systems and external sources, such as weather data or demographic data on consumers compiled by third-party information services providers.
We see challenges in every field , so one should be afraid to accept them and overcome them.
In addition, streaming analytics applications are becoming common in big data environments, as users look to do real-time analytics on data fed into Hadoop systems through Spark’s Spark Streaming module or other open source stream processing engines, such as Flink and Storm.