Data Science - Knowledge Boundaries

DSaaS – How It works?

Data science as a service (DSaaS) is a form of outsourcing that involves the delivery of information gleaned from advanced analytics applications run by data scientists at an outside company to corporate clients for their business use. A DSaaS provider collects data from clients, prepares it for analysis, runs analytical algorithms against the refined data and returns the findings generated by the algorithms to the customers.

Data science as a service is a potential way for organizations to cope with a shortage of data scientists and other skilled data analysts. Businesses increasingly are looking to predictive modeling, data mining and other forms of analytics to provide business insights they can profit from. But as the awareness of the benefits of advanced analytics grows, the number of trained data scientists isn't keeping pace, leaving many enterprises unable to find enough to lead all the analytics projects their business operations need. In addition, the scarcity of data scientists has driven up the cost of hiring for the position. DSaaS gives organizations access to analytics resources for specific data science applications without requiring them to hire or train their own analysts.

What We Do

Our Data Science consulting and business analytics solutions leverage the power of predictive analytics to derive real-time insights and reduce customer churn. We help our clients solve the toughest data challenges, predict demand for products and services to improve customer satisfaction and guide business strategies based on knowledge and foresight.Our Data Science service enables organizations to:

  1. Develop customized statistical models and algorithms
  2. Leverage advanced customer, operational and IoT analytics
  3. Generate and deploy intelligent insights in near real time

We help our clients reduce revenue leakages and boost bottom line productivity using advanced data science solutions.Our team of experts enables you to find innovative ways to strategize and optimize operations while exploring new market opportunities.

Enterprise Operations Analytics

Understand your enterprise data at a glance to identify hidden opportunities,enable better decision making, and increase efficiency across all departments.

  1. Demand Analytics – improvedemand prediction by correlating historical sales information with internal and external data (weather, events, social, economic data etc.)
  2. Asset Analytics – predict type of failure and time to failure using warnings, sensor, log, image, and external data.
  3. Security and Risk Analysis – leverage behavioural anomaly identification,net promoter score, and satisfaction prediction to minimize churn
  4. Sales Analytics – enhance efficiency with opportunity conversion analytics, pricing analytics, demand and sales forecast, and sales and incentive analytics
  5. HR Analytics – find the right fit with integrated talent management analytics from employee sourcing to employee exit
  6. Finance Analytics – forecast and optimize the use of financial resources using advanced analytics

Our Data Engineering Expertise


  2. Neo4j
  3. MongoDB
  4. Spreadsheets
  5. SQL databases
  6. NoSQL databases

Semi Structured

  1. Time series
  2. Stock price data every day/hour/month
  3. Population data every Year
  4. Web APIs
  5. Email
  6. Sensor data


  1. Websites
  2. Text & Images
  3. Audios & Videos
  4. Social Networks
  5. Log files

Data Science Capabilities

Data Collection

  1. Structured and Unstructured
  2. Semi-structured
  3. RDBMS & Big Data
  4. Distributed File System (HDFS)
  5. Flat file (text, csv, json, logs)
  6. Emails, Websites & Web APIs

Data Processing

  1. Data Cleansing
  2. Data Profiling
  3. Normalization, Text Mining
  4. Data Extractor
  5. Data Transformation
  6. Load Data to Data Warehouse

Feature Engineering

  1. Locality Sensitive Hashing (LSH)
  2. Principal Component Analysis (PCA)
  3. Singular Value Decomposition (SVD)
  4. Text Transformation (word2vect, TF-IDF)
  5. Vectorization, Indexer
  6. Feature Scaling

Optimization & Evaluation

  1. Cross Validation
  2. Hyper parameter Tuning
  3. Gradient Descent, SGD
  4. Ensemble & Boosting
  6. Log-loss, F-measure, Precision-Recall

Machine Learning

  1. Regression Algorithms
  2. Classification Algorithms
  3. Support Vector Machine (SVM)
  4. KD-Tree, Decision tree, Random Forest
  5. K Nearest Neighbors (KNN)
  6. K-means, Latent Drichlet Allocation


  1. Model Deployment
  2. Model Serving
  3. Model Pipeline
  4. Managed Deployment
  5. Monitoring
  6. Evaluation

If you would like more information on a few of Augmented Reality apps that NiiD has completed, please click here. Additionally, you can contact us for a free quote contact us.