Big Data, Data Science, and Machine Learning (ML) as terms are known to all interested in technology. As digital transformation has generated gigantic amount of data the IT industry is constantly going through disruptions. As per an estimate by IBM, humanity has created somewhat around 2.5 quintillion bytes of raw data that can be used for meaningful insights. Businesses of all sizes are utilising this data to drive business decisions and foster innovation and development. As the number of organisations relying on big data is increasing, job opportunities are also expanding. World economy is expected to employ whopping 3 million professionals in big data by 2020.
These technologies equally employ computer applications, mathematics, and engineering graduates with aspirants having above average analytical skills. Since these technologies are closely related, yet quite different, aspirants often wonder which skills they need to get their desired job roles. In this article, we’ll break down the career opportunities available in these technologies.
Big Data is a collection of raw data in the range of petabyte and above, so it can be structured, semi-structured, and unstructured data and is based on the fundamental pillars of volume, velocity, and variety. Unless processed, Big Data adds no value to a business and since it cannot be processed using basic data analysis approaches, we require Machine Language algorithms with specialized data modelling tools to analyse, process and optimize these massive data sets before these are utilized to understand trends and take informed and better business decisions. Big Data Engineer or Architect as a career is suited for learners who have a nice hold of programming skills and Big Data infrastructures.
Skills you’ll need to master:
- Programming skills (Java, Python, SQL)
- Analytical skills
- Database skills
- Mathematics and Statistics
- Data structure and algorithms
- Machine Learning
- Parallel programming
Data Structures seems easy! Machine Learning Is the Right Pathway
Machine Learning is the technology that enables computers to learn from existing data patterns and behaviours and react to similar situations without human intervention or instructions. It uses algorithms and mathematical models to analyse data, learn from it and transforms the historical user data into actionable insights for an enhanced customer experience.
Facebook, Netflix, or Amazon are the best examples of use of Machine Learning. These platforms use advanced ML algorithms analyse gathered user data and come up with pretty amazing recommendations based on our taste and likes. Learners with keen interest in data structures, algorithms, and mathematical models are more likely to make career in Machine Learning.
Skills you’ll need:
- Programming skills (Java, Python, R)
- Statistics and Probability
- Data modeling and evaluation skills
- Strong foundation in API
Data Science is a discipline that utilizes a combination of mathematical, statistical, and computational tools to acquire, process, and analyze Big Data. In certain occasions, it may also apply ML techniques to Big Data. Data scientists and data analysts use statistical inference and data visualization techniques along with their domain expertise to extract hidden insights and converts them into business-oriented directives.
Thus, Big Data and ML fit right into the broader canvas of Data Science that takes into consideration the entire concept of Big Data processing. Learners comfortable playing with vast amounts of data and weaving valuable insights from them will find Data Science a fruitful career.
Core skills you’ll need for Data Science
- Expert programming skills (Java, Python, C/C++, Perl, SQL)
- Domain expertise
- In-depth knowledge of Statistics and Probability
- Data modeling and evaluation skills
- ETL and data profiling
- Ability to work with data analytical tools (SAS, Spark, Hadoop, Pig, Hive)
IT employees are constantly looking to transit their careers with trending skills like Data Science, Machine Learning, and Big Data. Most of them are skilled programmers and are versed in databases and networking. So only thing aspirants require is to learn newer skills. The best way to stay in touch is to brush up mathematical, and programming skills; practice programming in more than two languages. Apart from this, learners need to understand the foundations of data structures and algorithms while using different tools involved in data mining, analysis, modeling, evaluation, and visualization.