Big data is in the limelight, and the need to save data in the best possible way is dire. Organizations generate the huge amounts of data every day, and it has become critical to manage it to develop useful insights. Therefore, Big Data is one of the IT front-runners.
Big Data helps companies put their best foot forward by improving decision-making abilities, thereby helping them gain an edge over their competition. Thanks to this trend, companies constantly seek professionals with a big data certification. These professionals are likely to have the skills and the expertise to analyze large datasets.
Given below are few of the Big Data job roles that you could be eligible for if you were certified in Big Data.
Data analysts are problem solvers who study large data streams and develop automated systems to recover data and compile it to make a report. Mostly, they work in tandem with their teams, but they can also work independently if they get complete control. Their job is to turn numbers into meaningful English sentences.
Businesses usually have data in different forms of market research, transportation costs, logistics, and sales figures. A data analyst uses that data and generates insights to improve decision making. These insights could help understand and solve business problems such as what price new materials should have, how to minimize transportation costs or how to schedule people effectively.
Database administrators manage a company’s database and solve day to day problems. Their responsibility is to notify updates, manage modifications, keeping strong backups to maintain the stability of a database. A database administrator understands the latest technologies and techniques.
A DBA either has a computer science degree with an extensive experience in a particular database product or has on-the-job training with a working knowledge of different database products. As a DBA, you are expected to know major database products such as SAP, Oracle-based database management software, and structured query language.
Structured or unstructured, skilled data analysts study various types of data and deliver their findings to organizations so that they can use it to make smarter decisions. A data scientist should be business-savvy with the skills to conduct effective data evaluations.
They should also be able to make recommendations about growing trends and advise companies on the actions required. One who holds strong analytics capabilities and wants to work in the corporate world could be a strong contender for this job.
Big Data professionals who have an aptitude for managing projects can consider applying for this role. Their role in decision making is influential after using data that operational heads provide to them.
Project managers help in planning business strategies, tasks, and works that need to be done to keep up continuity throughout a project. Sometimes, project managers might need to know statistics.
Responsibilities of Project managers
- Ensure that database tools and services remain effective throughout their use
- Monitor data compilation and make sure it occurs in accordance with legal regulations
- Make sure that data remains safe backed-up
- Help build new databases
- Check data entry procedures
Most technological companies, financial companies, and consulting and reporting companies hire individuals with this profile. Being a business intelligence specialist, you would be doing market research and would be creating reports using structured data. During report making, BI specialists use machine learning, SQL, and statistical tools to evaluate data, turn it into a meaningful form, and present it to the management.
Completing a big data course is not the end of the road for you. While the certification will get you through the door, there are still some things you’ll need to know to bag the jobs of your dreams.
Here are a few tips and tricks to get the job of your dreams.
The learning process should never end
A data scientist should never stop learning. New tools constantly hit a market, and data science evolves every day. As a data scientist, you need to learn the latest tools and skills to enhance your abilities to deal with any data.
A strong background in Python, SAS, and SQL would scale your career in Big Data. These languages make the core of data analysis, and you need to have a strong grasp of them to prove your analytical skills.
Share your work
If you have been around in the data science field for a while, then you probably have a portfolio. These projects are vital to developing your skills as a data scientist. They refine your skills and help you understand the practical aspects of your knowledge. That said, sharing your portfolio online so others can check your work would certainly enhance your validity in the market.
Advertise your work to the public by posting your portfolio on public platforms and let people grade you. This could help you in two ways. First, you will learn new tips and tricks that will make you a better analyst. Second, people in your industry will realize that you are willing to learn from others and improve your skills. This quality in itself would make you a valuable employee. You can either share your work on Github, Linkedin or you can create a blog.
Polish your profile
When you share your work with data science world, you will get easier access to new opportunities. With each portfolio you create, the better your expertise gets. As a result, your portfolio becomes more sophisticated and complex, thereby improving your skills as a data scientist.
Many data science and programming communities tend to pay attention to tutorial or blog posts that give you a chance to present your work. You could try any of these sites for the same.
/r/machine learning — articles related to machine learning and tutorials as well
/r/data science — articles and tutorials related to data science.
/r/python — articles on Python
Networking is must
Networking helps you develop an interest in other aspects of Big Data. With networking, you not only meet new and interesting people but will also be able to discover new ideas.
Networking includes contacting online communities, arranging meetings with people in your field, and attending sessions with experts in your domain. You should even read other articles written by other members of data science communities to understand data science trends and share your viewpoint in a comments’ section to stay in touch.
The scope for Data Science career opportunities increases each day. Companies put an effort to make sense of data and need the help of experienced data scientists to do this. A successful data scientist has advanced analytical skills, strong technical understanding, and business development skills to evaluate large data sets and turning raw intelligence into accurate and actionable insights.