• CMMi Level 3,
  • ISO 27001:2005 & ISO 9001:2008 Certified Company
  • A CMMi Level 3 Certified
  • An ISO 9001:2008 Certified
  • An ISO 27001:2005 Certified

Data Science & Analytics

Data Science is a detailed study of the flow of information from the colossal amounts of data present in an organization’s repository. It involves obtaining meaningful insights from raw and unstructured data which is processed through analytical, programming, and business skills.

Data Science

In recent years, there is a huge growth in the field of Internet of Things, due to which 90 percent of the data has been generated in the current world. Every day, 2.5 quintillion bytes of data are generated, and it is more accelerated with the growth of IoT. This data comes from all possible sources such as:

  • Sensors used in shopping malls to gather shoppers’ information
  • Posts on social media platforms
  • Digital pictures and videos captured in our phones
  • Purchase transactions made through e-commerce

This data is known as big data

Companies are flooded with colossal amounts of data. Thus, it is very important to know what to do with this exploding data and how to utilize it.

It is here, the concept of Data Science comes into the picture. Data Science brings together a lot of skills like statistics, mathematics, and business domain knowledge and helps an organization find ways to:

  • Reduce costs
  • Get into new markets
  • Tap on different demographics
  • Gauge the effectiveness of a marketing campaign
  • Launch a new product or service

And the list is endless!

Therefore, regardless of the industry vertical, Data Science is likely to play a key role in your organization’s success.

How Does Data Science Work?

Data science involves a plethora of disciplines and expertise areas to produce a holistic, thorough and refined look into raw data. Data scientists must be skilled in everything from data engineering, math, statistics, advanced computing and visualizations to be able to effectively sift through muddled masses of information and communicate only the most vital bits that will help drive innovation and efficiency.

Data scientists also rely heavily on artificial intelligence, especially its subfields of machine learning and deep learning, to create models and make predictions using algorithms and other techniques.

Data science generally has a five-stage lifecycle that consists of1:

  1. Capture: Data acquisition, data entry, signal reception, data extraction
  2. Maintain: Data warehousing, data cleansing, data staging, data processing, data architecture
  3. Process: Data mining, clustering/classification, data modeling, data summarization
  4. Communicate: Data reporting, data visualization, business intelligence, decision making
  5. Analyze: Exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis


  • Anomaly detection (fraud, disease, crime, etc.)
  • Automation and decision-making (background checks, credit worthiness, etc.)
  • Classifications (in an email server, this could mean classifying emails as “important” or “junk”)
  • Forecasting (sales, revenue and customer retention)
  • Pattern detection (weather patterns, financial market patterns, etc.)
  • Recognition (facial, voice, text, etc.)
  • Recommendations (based on learned preferences, recommendation engines can refer you to movies, restaurants and books you may like)

We, at Prata, are devoted to data science services as we see many improvements that it can bring to businesses, regardless of the industry they represent.

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