Shreemoy Nayak is an alumnus of UpGrad and BITS Pialni Big Data Engineering program.
I am a mechanical engineer currently working as a project engineer. My job is to execute the projects at the site and deliver them on time. Though my job doesn’t involve data analysis at the scale of banking or FMCG industry, it demands a certain level of awareness for planning and executing a project. During my 2-year experience, I’ve come across situations, more often than not, when things don’t go as planned and the engineer-in-charge needs to make informed decisions to complete the job in time while making sure the expenditure doesn’t exceed the sanction.
In these cases, a sound knowledge of exploiting the data, both financial and otherwise, from previous and ongoing projects those are similar to the current project, comes in handy. In today’s world data-driven decision making has taken the front seat and Big Data is going to be the next big thing.
With a sound knowledge of statistics, I searched for online courses related to data analysis and came across this PG Program in Big Data Engineering offered by BITS Pilani in association with UpGrad on Facebook. Initially, I was apprehensive. But 7 months into this program and I am sure I’ve made the right decision.
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The program starts with a preprogram preparatory module which mainly covers programming with Java, basic Data Structure and SQL. This is very helpful for those who are from a non-computer science background. I personally found the preparatory module very helpful. The curriculum of this program is well designed to transform you from a novice to a professional in data engineering, provided you put in the effort it demands and ready to walk the extra mile. There are professors from BITS Pilani, Industry experts and UpGrad teaching staffs who will take you through every relevant detail in Big Data Engineering through video lectures, case studies and assignments. Above all, there is a discussion forum where you can interact with peers and get your doubts cleared. UpGrad organizes fortnightly webinars where industry experts explain practical uses of all the theories you have learnt during the weeks. The program is adequately paced so that one can easily manage the coursework along with his/her job.
In a way, this program is different from other online courses available on the internet as every student gets a dedicated student mentor who constantly keeps in touch with you and helps you with any problem you face during the program. He constantly motivates you to utilize the resources to the maximum and reminds you of the deadlines if they are close.
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Being a mechanical engineer if I can come this far, even when my job has no direct relation with data engineering, then any person with any prior knowledge or current involvement with data engineering can achieve a lot from this program. This program also offers placement opportunities like any full-time university program. So, for any aspiring Big Data enthusiast, I’ll strongly recommend to take-up this course.
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If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.
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What are the real-life use cases of Big Data?
What are the advantages of Big Data?
The growing amount of data accessible creates both benefits and challenges. In principle, having more data about consumers should help businesses to better personalize goods and marketing activities to ensure customer happiness and repeat business. For the benefit of all stakeholders, companies that gather a huge quantity of data are given the ability to undertake deeper and richer analyses. Big Data technologies can be utilized to create a base of operations or staging zone for new data, which can be used for industrial purposes. Its benefits include improved customer service, increased operational efficiency, and better decision-making.
What are the disadvantages of Big Data?
Even though Big Data helps us to provide improved analysis, huge data can lead to overload and noise, which reduces its use and efficiency. Larger amounts of data must be handled in Big Data, and it must be figured out which data reflects the signals against noise. The major problem is determining what makes the data meaningful. Furthermore, the data's type and format may need initial treatment such as cleaning before it can be used. While structured data (which is made up of numeric values) is simple to store and sort, unstructured data (such as mails, videos, and text documents) may necessitate the adoption of more advanced techniques before becoming helpful.
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