Careers in Big Data

Would you learn a language that only a few can understand? Would you buy a pen that can write only on one brand of paper, made by one particular company? Would you learn music that you can play only on ONE brand of guitar?

Go for a certification that prepares you for most kinds of data science jobs, anywhere in the world, on almost any technology or platform and in any type of company.
That’s why a certification that is NOT by a technology company is the BEST certification for you.
This is because a technology company ONLY puts its own platform or tools in the curricula, and you do not get the opportunity to learn all other platforms or technologies being used in Data Science today.

• Prepares you for working on technologies and tools of the future. You don’t want to fall behind when new technologies arrive.
• Upskills you for working on multiple technology platforms – open-source as well as proprietary. You don’t want to limit your career opportunities. Your employers also don’t want to hire professionals with narrow skill sets.
• Prepares you for a wider variety of roles and responsibilities as a professional. You want to be versatile and useful for most types of companies.
• Equips you with an understanding of both – the usage and technology sides of big data management. You want to rise faster in your career by ensuring you know all aspects of data science technologies and their applications

So, you must always choose a data science certification that has a vendor-neutral curriculum based on a global standards’ framework, and equips you for cross-platform professional work.

If you have what it takes, Data Science presents the best career opportunities today:

• More jobs available

  The demand-supply gap for Data Scientists is huge globally, and for professionals with the right qualifications, training and experience, landing a Data Scientist's job is easier than most other roles at senior levels

• Higher salaries

  Data science jobs across all levels usually pay higher salaries than other tech jobs at the same levels

• Wide career options

  Most organizations and companies require Data Scientists, so there is a growing demand

• Future-rich

  Data Science and Analytics are also jobs of the future, as the importance, need and demand for data scientists will only grow

Data Science is a growing field with use across all industries. For students of technology, statistics and disciplines like econometrics, there is probably no career better than Data Science. Even for students of other disciplines with no career interest in technology or data science, Data Science knowledge is still important, because it helps them perform better in their careers.

In the years to come, knowing Data Science will become as important for everybody as knowing computers and the Internet is today. Graduating students with knowledge and qualifications in Data Science are more attractive for all employers. There is no doubt that even for students looking at non-technical or non-data science careers, a qualification in Data Science is among the most important supporting qualifications to have.

For students majoring in information technology, computer engineering or computer science, there is probably no career better than Data Science because this field is:

• The bridge to all modern technologies : Data Science is critical for artificial intelligence, blockchain, business intelligence and supercomputing. So, if technology students want to make careers in these fields, Data Science is a good starting point.
• One that offers more jobs : The demand-supply gap for Data Analysts, Data Engineers and Data Scientists is huge globally, and for professionals with the right qualifications, training and experience, landing a Data Science job is easier than for others.
• Higher-paying : Data science jobs across all levels usually pay more than other tech jobs at the same levels
• One that offers multiple career options : There is growing demand for Data Analysts, Data Engineers and Data Scientists across industries and sectors, with requirements from most organizations and companies.
• Future-rich : Data Science and Analytics are jobs of the future, as the importance, need and demand for big data analytics and big data application development will only grow.

Data Science is a growing field with use across all industries. Data analytics, one sub-domain, is critical for all organizations today, and this field has job opportunities open even for students majoring in non-technology disciplines like economics, statistics, mathematics and econometrics. The fact is that for students who love numbers and business and have an interest in computers and technology, there is probably no career better than big data analytics today.

On the other hand, even for students of other disciplines with no career interest in data science or technology, knowledge of Data Science – particularly of big data analytics – is still important, because it helps them perform better in their careers. Make no mistake – in the years to come, knowing Data Science will become as important for all jobseekers and students as knowing computers and the Internet is today. Trends around the world clearly indicate that graduating students with knowledge and qualifications in Data Science are more attractive for all employers. Increasingly, a qualification in Data Science is emerging as the most important supporting qualification to have even for students looking at not-technical or non-data science careers.

• Choose what you want to do in Data Science: Data Science is a huge field. You can be a Data Analyst if you have a non-technology background in mathematics, statistics or quant. You can be a data engineer if you are a technology major with a background in programming and coding.
• Your university degree helps: A strong educational background is needed for developing and absorbing the cross-disciplinary knowledge that Data Science professionals need. A bachelor’s degree in computer science, statistics, mathematics, engineering or related fields will do the job for you.
• Obtain an international certification: Get a vendor-neutral, cross-platform, third party certification in Big Data Analytics or Big Data Engineering (your choice!) from an internationally respected body.
• Avoid MOOC/online training certificates: If you want to stand out, get a serious certification from a proper standards and certification body. Certificates from online training companies are very common and everybody seems to have them, so employers do not respect them. They work only if you just want to have some knowledge. But if you want to make a career in data science, you need a credible, respected qualification.
• Learn R Programming: Knowing R programming is essential because R is cut out for statistical analysis done in data science. Learning R becomes easier if one knows SPSS. Good certification programs like ABDA™/SBDA™/ABDE™/SBDE™ include this in their curricula.
• Learn to code: Python is the most common coding language deployed in data science, though Java, Perl, and C/C++, too have wide circulation, no doubt. Similarly, SQL is important, though NoSQL is doing the rounds these days. Good certification programs like ABDA™/SBDA™/ABDE™/SBDE™ include this in their curricula.
• Gain platform knowledge: Knowing Hadoop, Apache Spark, Hive, Pig and cloud technologies is a must because data scientists need to know the tools for sharing vast amounts of streaming data, along with techniques of data exploration, data filtration, data sampling, summarization and data visualization. Good certification programs like ABDA™/SBDA™/ABDE™/SBDE™ include this in their curricula.
• Learn about Machine Learning and AI: Knowing AI, Neural networks, supervised machine learning, decision trees, logistic regression, reinforcement learning, natural language processing, outlier detection, computer vision, recommendation engines, survival analysis and others enhances attractiveness for the most challenging Data Science jobs. Good certification programs like ABDA™/SBDA™/ABDE™/SBDE™ include this in their curricula.
• Learn data visualization tools: Data Scientists must be able to visualize data using data visualization tools such as ggplot, d3.js and Matplottlib, and Tableau. Good certification programs like ABDA™/SBDA™/ABDE™/SBDE™ include this in their curricula.