With the advent of digital, mobile and IoT technology, people are producing data at a rate of 2.5 quintillion bytes per day. By 2020, it’s estimated that 1.7MB of data will be created every second for every person on earth. Ninety percent of data in the world has been created in the last few years. What will we do with this data, and who will do it?
Big Data is Moneyball for Business
As data sets continue to explode, businesses are racing to gather, analyze and use these massive amounts of digital information to improve their business operations. Putting this information to good use in real-time often means the difference between capitalizing on information for a granular view of the target audience, or losing customers to competitors who do.
- Business Intelligence – is a critical weapon in the fight for market share. Business intelligence puts an organization’s big data to work charting and predicting activity and challenges, ultimately for increased productivity and profitability.
- Innovation – By analyzing a high-level view of the myriad interactions, patterns, and anomalies taking place within an industry and market, big data is used to drive new, creative products and tools to market.
Drowning in the Data Lake
According to the Bureau of Labor Statistics, jobs relating to Big Data in the mathematics field are expected to grow by as much as 33.8% by 2026. IBM, for example, predicts that the annual demand for data scientists, data developers and data engineers will lead to 700,000 new recruitments by 2020. Last year, 151,000 data scientist jobs went unfilled, according to a LinkedIn Workforce Report.
If you’re a talented technical candidate looking to broaden your career horizons or future-proof your skill-set, you can increase your marketability by knowing what types of skills employers are looking for in Big Data. The field encompasses more than a single subject or language, but is instead a combination of subjects, skills and technologies that include:
- Programming skills
- Data Structure & Algorithms
- Analytical skills
- Database Skills
- Machine Learning
- NLP, OS and Cryptography
- Parallel Programming
The Harvard Business Review called the data scientist ‘the sexiest job of the 21st century’. As problem solvers and analysts, data scientists are the professionals identifying patterns, noticing trends and making new discoveries, often working with real-time data, machine learning and AI.
Essential big data skills include programming fluency to conduct numerical and statistical analysis with massive data sets. Some of the languages you should invest time and money in learning are Python, R, Java, and C++ among others.
Familiarity with frameworks such as Apache Spark, which is used for creating distributed data analytics applications, and data architecture/environments such as Hadoop, SQL, RedShift or DB2, is a highly valued asset.
Skill in manipulating data is another important capability that data scientists should possess. The need to build data pipelines, manipulate ETL processes, and prep data for analysis remain some of the most time-consuming tasks for data scientists, though automation of these tasks is on the horizon.
For analysis, the ability to wield algorithms effectively is another critical skill. Knowledge of classic machine learning algorithms like regressions, K-means, and SVMs (among others) are essential for modern data scientists. Increasingly, data scientists need to know how to put together a deep learning setup.
In our next installment, we’ll review resources and ways to enhance the skill set needed for success in Big Data.
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