Rapid advances in data collection and storage have enabled many organizations to accumulate vast amounts of data. Traditional analysis tools and techniques cannot be used because of the large sets. Data Science is a blend of traditional data analysis methods with sophisticated algorithms for processing huge amount of sets. It has also made a way to discovering new types of data.

Let’s look at some well-known applications for data analysis-

  • Business: when we are doing any business, we need to be sure about the point-of-sale of our products reaching customers. To be specific, consider bar code scanners and smart card technologies, that we use in today’s world, have allowed retailers to estimate the data about the customer’s purchases at the counters. Retailers use this information, along with other business and customer service records, to build a better understanding of the needs of the customers and improve their businesses.
  • Medicine, science and engineering: Researchers in this field are rapidly extracting data that is key to further discoveries. For example, satellites in space send us data about whatever is happening in today’s world. Data that the satellite provides ranges from multiple terabytes to petabytes, which is definitely a huge amount.

We have seen some basic applications of data science, now let’s turn our focus towards the challenges-

  • Scalability: The advances in data generation and collection – sets with sizes of gigabytes, terabytes, or even petabytes – are becoming common. If some algorithm could handle such massive amount, we can make an algorithm in such a way that we can divide one huge block into several small blocks. This method is known as scalability. Scalability ensures ease of access to individual records in an efficient manner.
  • High Dimensionality: Nowadays, handling sets with hundreds and thousands of attributes are common. In bioinformatics, the ICU analysis produces a huge dimension of measurements and many features to track the human health. Also, for some analysis algorithms, the computational complexity increases as dimensionality increases.
  • Heterogeneous and complex data: traditional data analysis often deals with sets having attributes of the same type. Now, as data is booming in many industries, data has become heterogeneous and complex.
  • Non-Traditional Analysis: Current data analysis tasks often require the valuation of thousands of hypotheses and the development of some of these techniques has been motivated by the desire to automate the process of hypothesis evaluation.

As we know the data is interrelated, making use of attributes, we can distribute it into categories:

  1. Distinctness: Equal and not equal
  2. Order: <, >, <=, >=
  3. Addition: + and-
  4. Multiplication: * and /

As we can observe, there are so many areas that are in need of data scientists, it becomes very important to learn and build a career in such an emerging field. The future jobs depend on data science to a maximum extent; in the field of science, commerce, engineering etc.