Defining Trends for Data Science in 2015
The term ‘data’ is often used everywhere, even typing this blog means creation of data. So, what is this data and what are we supposed to do out of it? The answer to this question means the creation of data again, confusing isn’t it? Data Science appears the moment we think about handling of data! That is a fact and to support it, by 2020 or 8 more years from now – more than 40 zettabytes (1 ZB = 10007 bytes) of data will be created. The data is so massive, equivalent to 5,200 GB per person and to record and maintain such data one would need an equally gigantic infrastructure. The benefits drawn from Data Science will be helpful for analytics, future predictions and knowledge purposes.
Extraction of data also plays a vital role in data science. Data Scientists spend hours of time in analyzing structured or unstructured data to sum up on predictions and patterns. Let us look at the key trends that have helped to acquire and manage data under the umbrella of data science.
- Presentational skills for Data Visualization are a priority – We live in the 21st century where presentational skills matter. People can relate and process data quickly and easily if presented in a visual form. Tools like Tableau and Qlikview are some of the essentials that help in processing and presenting data in a structured format.
- Ensemble has the presence that it needs – Ensemble modeling has been on the top, its algorithm exceptionally helps in processing data through applications. Kaggle is mostly infused with an Ensemble model for data acquiring.
- Python is pacing ahead in time – Python has gained momentum lately for data science. Its libraries- NumPy, Pandas and SciPy are at the forefront for data science. Gathering information to build a module in a product using data analytics? Consider the presence of Python in your armament. Python is freely available to download and is supported by a vast community base as well.
- In-memory database and NoSQL garners the limelight – NoSQL databases like Neo4j and MongoDB have been getting a lot of attention over the past few years for their superb performance delivery, schema flexibility, scalability and analytics capabilities. In-Memory database evolving in 2015 have greater chances of being led in the future as well. Aerospike and Mem-SQL are some of the names that give excellent features of data retrieval. I think, further we can also observe that In-Memory Computing is a close relative of In-Memory Databases.
- Data Security to play an important role – Securing the system against cyber-attacks is a major concern. Big corporations churning data have employed advanced security systems to tackle cyber-attacks that can alter or destroy structured/unstructured data.
- Hadoop is home for everything – The Hadoop ecosystem has a lot to offer for data science. Developments of Hadoop for Big Data back in 2014 were the main highlights of strengthening the base for 2015. Hadoop storage systems enable quicker analytics without any delay and are a perfect fit for data science.
The above-mentioned trends are just the tip of the iceberg, by the end of 2015; there would be other trends that would populate the list in this dynamically evolving domain.
Big Data today has come to a stage where extracting structured or unstructured data is not an issue but managing and churning results is. One would need a team of experienced engineers, statisticians and analysts working as a cohesive team to unlock the key insights from the data. IDC estimates in one of its reports that by 2020, as much as 33% of all data will include information that will carry the potential to fetch billions of dollars if analyzed. Today, 90% of media files- images, sound and videos are considered as dark data. Furthermore, this data is untouched and not used for analytics. Data Science has much deeper digging to do, there is a lot to explore and understand how to utilize recovered data apart from storing it, but the journey has surely begun and we are already mapping speculations backed by research firms like Gartner, IDC or Forrester for Data Science.