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With an industry of 33.5% compound annual growth rate, one can think of several applications with data science at its core. The scenario of data science is growing and spreading at a fast pace, not just domestically but internationally too. More than 40% of the analytics revenue comes from countries like USA and UK. This shows that analytics business has found much application of data science to boost the business quality.
DATA SCIENCE
Data science is a field which brings different subjects and fields of expertise together like mathematics, statistics, computer science etc. Other than these there are micro, specialty skills too, which one needs to hone in. Apart from technical skills, one needs to have the business acumen to understand the working of a business unit and be aware all the recent market trends.
Data science is used in industries like digital marketing, E-commerce, healthcare, education, transport, entertainment etc. Analytics is used by all forms of business like private, public and non-profit organizations, as the main theme is to provide value to the customers and increase efficiency likewise.
STEPS IN DATA SCIENCE
Data science includes different activities and techniques combined together for just one objective, to know what’s hidden in the data pile. Data can come from many sources like external media and web, governmental survey datasets and internal databases of one’s own company. Whatever be the source data needs to be worked upon diligently and with smartness to dig out the meaning from it.
The steps involved are:
- Frame the objectives: This is the very first step of data analysis. Here the management must know what they want from their data analytics team. This step also includes definitions of parameters for measuring the performance of the insights recovered.
- Deciding the business resources: For solving any problem there must be enough resources available too. If a firm is not in a position to spend its resources on a new innovation or channel of workflow then one shouldn’t waste time in meaningless analysis. Several metrics and levers should be prepositioned to give a direction to the data analysis.
- Data collection: More amounts of data leads to more chances of solving a problem. Having limited amounts of data and restricted to only a few variables can lead to stagnation and half baked insights. Data should be collected from varied resources like web, IoT, social media etc and using varied means like GPS, satellite imaging, sensors etc.
- Data cleaning: This is the most critical step as erroneous data can give misleading results. Algorithms and automation programs prune the data from inconsistencies, wrong figures, and gaps.
- Data modeling: This is the part where machine learning and business acumen comes to use. This involves building algorithms that can co-relate to the data and give outcomes and recommendations needed for strategic decision making.
- Communicate and optimize: Results found are communicated and action is taken for it, and the performance of the decision taken is checked. If the models worked then data project goes successful, if not, then models and techniques are optimized and begin again.
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