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INSOFE offers uniquely customised training programs for corporates that are developed in co-collaboration mode. Such programs help establish Centers of Excellence and Tiger Teams in Data Science, Analytics and Big Data by getting them hands-on with cutting-edge technologies.

The technologically immersive program is a fine blend of faculty-driven lectures on niche and contemporary technologies followed by getting hands dirty with immediate application of theory on our in-house, best in the world, extreme edge tech infrastructure. The programs are delivered by INSOFE faculty who carry a formidable combination of PhDs from some of the world’s top Universities and commendable consulting experience as well as industry exposure.

The custom program can be exploited to train:

1
Future managers in evidence or data based thinking
2
Engineers to become hands-on with the big data environments, projects and tools.
3
Analysts to start applying cutting-edge machine-learning to solve problems.

INSOFE’s forte lies in its ability to deliver the program to a cohort comprising of professionals having varied experience and diverse background. Anecdotally, INSOFE has conducted training programs with participants who have had a Ph.D. in Statistics, SBU directors and other mid-level managers, all in the same batch but, successfully delivered equal knowledge transfer.

Our training programs reign in the best of the rigor of an academic world, the application of the consulting world and the bleeding-edge of the R&D world.

Are you curious to engage with INSOFE on your next custom made corporate training program?
Email:- [email protected]

Programs tailor-made for your specific technological upgradation/unique technological specifications

A program that may last for a day or a continuous development program that lasts over 30 days. Our participants come from various educational background like Ph.D in mathematics to psychology, fresh recruits to CXOs. We make sure the program suits each group as we understand industry requirements.


Training Curriculum

To read our exhaustive curriculum and bespoke module, please click here.. Our curriculum suggests that our competence lies across the length and breadth of Data Science and Big Data Analytics. However, our academic board develops programs to meet the learning objective of individual clients.

Case studies are a salient part of corporate training at INSOFE that provides an effective way of bringing theory into practice. These case studies include real-world business problems to be solved during lab sessions of the training program using concepts learned in the theory lectures.

The following are few of the case studies that have been given to the participants during various corporate training programs:

Data Visualization

The objective is to turn data into information and information into insights using tools such as R, Excel and Tableau to draw insights using simple drill-down charts and dashboards. The areas include employee performances, revenue per division, monthly/quarterly expenses, employee leaves, attrition etc.

HR Analytics

Organizations have been using workforce analytics retroactively by keeping a record of employee demographics, employee absence, recruitments, joining dates and exit dates, departments, designations, appraisals, etc. The objective is to leverage this data to gain an insight into who is likely to quit based on several factors that lead to employee attrition using Machine learning techniques to find when business should initiate proactive measures.

Predicting if the sales opportunity is a win or a loss

To satisfy the consumers and reduce the inventory stock-outs, retailers need to understand the demand for certain products in the market and ensure its availability in shorter lead times. The goal is to demonstrate how various features can be derived from the transactional data, information about the stock, procurements, etc and predict the demand of products using a blend of Time series analysis and Machine learning techniques. Additionally, the task includes evaluating issues with various error measures and adjusting it to suit the conditions.

Demand forecasting of product in Retail

To satisfy the consumers and reduce the inventory stock-outs, retailers need to understand the demand for certain products in the market and ensure its availability in shorter lead times. The goal is to demonstrate how various features can be derived from the transactional data, information about the stock, procurements, etc and predict the demand of products using a blend of Time series analysis and Machine learning techniques. Additionally, the task includes evaluating issues with various error measures and adjusting it to suit the conditions.

Staff allocations using call data analysis

Hospitals receive calls for various type of inquiries wherein the management wants to equip itself with the required staff to attend to the needs of patients. In this case study, the objective is to make them understand the needs and optimize the staff availability by identifying and categorizing the call into various call types using Machine Learning methods through Python’s NLP packages.

Customer retention analytics

Retaining customers has been the biggest challenge for telecom companies. The objective in this case is to use machine learning techniques to help companies engineer new features from call data to derive various usage related factors, call type attributes, behaviour related factors and others to apply ensemble methods to arrive at accuracy and other performance measures of the models and to evaluate the cost of misclassification and its impact on revenues.

Customer lifetime value

For B2C clients the single most important metric for understanding customers is Customer lifetime value. Enterprises collect large volumes of customer transactional data and want to draw insights about customers. The objective of this case study is to use Machine Learning techniques to draw insights from this transactional data, to prepare the data for model building and providing predictions and later, to provide insights on various error measures to validate the model performance and present it to the business users.

Customer satisfaction feedback analysis

As part of continuous improvement process, organizations periodically evaluate their performance with respect to various solutions/services they provide. Python’s NLP packages and machine learning methods are used to computationally identify and categorize opinions expressed in a piece of text, to determine whether the customer’s attitude towards a product/service, etc. is positive, negative, or neutral.

Clientele across industries

  • HP
  • CA Technologies
  • Microsoft
  • HCL
  • Deloitte
  • Cisco
  • ZS Associates
  • OPERA Solutions
  • YODLEE
  • NOVARTIS
  • Dr. Reddy’s
  • PHILIPS
  • The Advisory Board Company
  • VISTAKON – Division of Johnson & Johnson Vision care Inc.
  • TATA Steel
  • TATA Metaliks
  • Idea
  • Vodafone
  • KLA Tencor
  • Honeywell
  • DRDO
  • ITC Limited
  • IGS Energy
  • Broadridge
  • Abercrombie & Fitch
  • PEPSICO
  • QARAR
  • ZEE Entertainment
  • DE Shaw & Co.