Data science refers to the science of using multiple methods related to reasoning, algorithms and other technological based techniques for extracting meaningful information from a raw chunk of data.

Understanding Data Science and Data Product in business

Data science is amalgamation of three main components:

• Data inference
• Algorithm development
• Technology

Main component that is data is utilized with capability and techniques in ways to generate business value. Data science, through insight and development of data product can have multiplicative returns on investment.
This is the art of blending the business world and the data world. The final result, the “Data Product” is the outcome of input and process of utilization of that data to generate results.

Data product is technical functionality designed to integrate directly into core application.
Data mining to find and understand trends and inferences, smarter decisions and insights are all the rungs of the ladder to the business growth. Data insights help provide advice to make smarter business decisions.

Exploration of the data is vital factor to mine out insights and understand patterns and characteristics in data.

• Inferential models
• Segmental analysis
• Time series forecasting
• Synthetic control experiments

All these contribute towards insight into the newer trends and facts to provide strategic guidance.

In this sense the Data Scientists are actual consultants to business stakeholders. They play a pivotal role in developing data product by:

• building out algorithms
• Testing
• Refinement
• Technical deployment in to production systems

Machine Learning in Data Science

Recommendation engine ingests the user data and makes personalized recommendations. Machine training feeds the data into a model and it automatically recognizes future purchases. It incorporates data modeling to make algorithms in predictions and deciphering patterns. Tagged data is used to train predictive models which automatically characterize tagged data to predict tags for unknown data points.

• Common methods for training models range from basic regression to complex neutral nets. This is supervised learning. In supervised learning Machine learning uses supervised learning where output to input values are known. In Supervised learning Output values can be predicted; based on the previous records of getting output values by consuming input values. Common Supervised learning algorithms are Regression and classification
• Second modeling paradigm is Unsupervised learning which tries to surface modeling patterns and associations in data without any concrete ground truth without any tagged observations. In unsupervised learning Machine learning uses unsupervised learning where output to input values are not known. Here Algorithms keep on learning new insights from data. In Unsupervised learning there are lesser chances of getting accurate results. Common unsupervised learning algorithms are Clustering and Association rules

• Cluttering
• Principal component analysis
• Hidden models
• Topic models
• Collaborative filtering
• Contextual bandits are some other machine learning methods

Data scientist chooses any tool from various machine learning techniques during solution analysis.

Determined by algorithms, various search engines suggest items to the target users.

For example Amazon, Netflix, Spotify, autonomous driving software and many other sites use these techniques towards the prospective target.

Machine learning methods are used for product recommendations, churn predictions, logistics planning and automatic personalized marketing.
Netflix data mines movie viewing patterns and based on viewers’ interest, makes decision about content production.
Purchase behaviors of clients are juxtaposed to segment clients into different overlapping cohorts.

Gmail spam filter is one example of data product – An algorithm behind the scenes processing incoming mails and check for spam messages.
It can be said that algorithms enable the machines to recognize the traffic, lights and pedestrians.

Core languages associated with data science include SQL, Python, R , SAS, Tableau .
To some extent Java, Scala, Julia and few others are also used. Python is great for data mining and data munging.
R has great visualization packages and great tool for prototyping data science.
Tableau is useful for creating interactive visualization reports or stories.
SQL is useful in integrating data sources, data exploration and data debugging.

Data science in retail:

The business of retail is now getting online. Gone are the days, when one has to stray around from one shop to another, searching for your kid’s favorite toy or a particular dress design worn by a famous actress in a movie for endless hours. Now a day, almost everything is just one click away, and data science is making this possible in a very big way.
It can improve retail both online and offline and strides in e-commerce indicate paradigm shift in retail market trends.

• Online product recommendations are based on past revenue
• Data collection by noticing time of browsing
• Good of the prospective client
• Browser data (mobile or desktop)
• Type of internet browser
• Behavioral data. For example history of purchases and recent activity, social media behavior gives a deeper preview of a client’s interests

E-commerce accounts for more than two trillion dollars in scales.

At Amazon, past behavior and predicted purchases of customers are used to plan on hastening the next day shipping to next hour shipping! The shipped products even without order are marketed to others at a discounted rate or kept at a final hub.

Personalized product recommendations, new products creation or updation are all utilized to attract highest number of clients.
Customer service and sales can also focus on increasing their product expertise to better serve their customers.

In hotel industry, customer segmentation, energy consumption, investment management and resource allocation can be revolutionized by using big data analysis. It uses video, audio and web data.

With analytical exploitation of their data, hoteliers deepen their knowledge of guests to develop a more granular understanding of:

• segment behavior
• needs and expectations
• Profitable customer segments
• identify opportunities to attract new customers

A new way to retail

In US alone, in 2017, retail industry had sales of over 5 trillion dollars. Retail industry is turning to niche technology that is big data for achieving better outcome.

Regardless of only making up around 10% of all acquisitions, ecommerce accounts for more than $2 trillion dollars in sales. To be precise, groceries and big-box stores optimize separately for online and offline.
Whether it’s online or offline, Data Science has opened a whole new dimension for the new age of retailing.