Understanding the Data Science Process

1. Introduction to Data Science

The history of data science and its inception can be tracked by the introduction of big data[1], a term coined in 2005 by Roger Mougalas from O’Reilly Media. According to Mougalas, big data is a large set of almost impossible data to manage and process using traditional business intelligence tools.’  

This is where the need for computational theories and tools to assist humans in the extraction of useful information from rapidly growing volumes of data arose. Data science put simply is “the process of extracting valuable insights and discovering knowledge from inherent patterns in data”.

2. Data Collection

Every year, piles upon piles of data accrue in a business’s databases.  These databases could be structured or unstructured. The first step in the data science process is understanding the domain `of the business and understanding the goal from the customer’s perspective. The data scientist might have to be consulted by a subject matter expert(SME), an expert in a field, say marketing. The combined expertise of a data scientist and SME acts as a crucial link between Marketing and Artificial Intelligence used in businesses to streamline consumer data and personalize promotional advertising campaigns.

Databases can be structured or unstructured. ML algorithms utilize labeled data for supervised learning. A highly structured database can be easily automated for supervised learning, but the ‘art’ of data labeling, which involves mapping the data into target datasets, invariably lies in the experience of a data scientist. Challenges in Data Labeling involve taking bad quality data and converting it into a more useful form for effective pattern recognition. Consequently, data labeling is an integral step in data preparation and preprocessing. DL is a subset of ML better equipped for unsupervised learning in machine perception tasks that involve unstructured data such as blobs of pixels or text.

3. Modelling

The core of data science is data processing, which includes using various learning algorithms for pattern discovery and extraction. Our goal here is to take the large chunks of low-level data and mapping it into a more compact, abstract, or useful form.  This could mean a short report, a descriptive model, or a predictive model of the process. Advances in the field of AI have shifted the ‘static’ nature of these algorithms into ones that can gain experience from new information and gradually improve over time. Many enterprises have thereby jumped onto the AI bandwagon to gain a competitive upper hand, transforming their business processes and exponentially increasing the demand for skilled individuals in the landscape of AI careers.

4. Deployment

After minimizing the initial errors and tuning the model, it is ready to be deployed. This phase is commonly known as data analytics.  The AI model can do three things[4]

Descriptive analytics – Using historical data the AI-powered system finds insights and patterns in large datasets. 

Predictive analytics – The technology could go a step further by analyzing the data from various real-time sources to offer predictions about consumer preferences, product development, and marketing channels.

Prescriptive analytics – Furthermore, the AI algorithm suggests actions geared towards making improvements in the process, as per defined objectives (conversion rate of leads).

Getting started with AI in data analytics can be daunting if not done correctly. Due to the rate at which the field is growing, it is almost impossible to keep up. Fortunately, boot camps like Data Science Bootcamp by Sentiligent AI provide a gateway into the world of data science by solving real-world problems. After all, the applications of data science are what make it such a revered and demanding field.

5. Verification

Instead of making nebulous decisions, the decision-making process is now data-driven. But there is one last step before we can establish the efficacy of the results. The model’s performance has to be verified. With each iterative deployment, the accuracy of the model increases.

 The machine ‘learns’ much like a child learns with experience and the process is progressively optimized. It is important to closely monitor the process in the initial stages to establish an accurate and reliable model that can be automated.

Once the model is verified it can be used to cater to all future data for making predictions with re-evaluation techniques involved to keep the model up-to-date.

6. Conclusion

From the Venn Diagram, it is seen that the data science process is an intersection of Hacking skills, Math & Statistics Knowledge, and Substantive Expertise. Several data science platforms like ADAPT AI  integrate the various phases and enable you to automate machine learning tasks. DL Platforms like ADVIT work exceptionally well at tackling video/image recognition tasks in self-driving cars that use computer vision to see.

The information age is driven by data and data science opens up a world of possibilities for businesses all over the world to better understand their customers. As decisions are driven by data and not emotions, the world economy is bound to be transformed by this seemingly arcane science; the science of data.

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