Date

November 30, 2017

Location

Bangalore

Capacity

100 Tickets

Speakers

7+ Professional Speakers

About the event

India Analytics Summit 2017 is coming to Bangalore on November 30, 2017, allowing excellent networking opportunities on top of intensive classes, case studies, demos and workshops that were offered. Previous iterations of India Analytics and Big data Summit have allowed us to select and bring back only the finest speakers and top scoring classes to make sure that your Analytics & Big Data conference is unparalleled.

We explore how data analytics impacts businesses, also considering its connections with predictive analytics and the new area of real-time analytics. There are also interactive panels for attendees to engage with the presenters and bring their issues to the panelists.

We cover the topics that matter most to today's big data & analytics leaders along with creativity-enhancing inspiration & innovative action points and takeaways.

Training

Training Name : Data Storytelling with Visualization

Date : 29th November, 2017

Pricing : INR 9,000 + GST


Agenda

SessionTime
Visualization as a Data Story Telling technique9:00 AM to 10:00 AM
PowerBI – Getting Around
Demo / Teaser Session
10:00 AM to 10:30 AM
Connecting to a data file and Data Preparation10:30 AM to 11:30 AM
Basic reporting with different charts
Bar, Pie, Line, Scatter, Bubble, Treemap, Funnel
11:30 AM to 1:00 PM
DAX Commands2:00 PM to 3:00 PM
Custom Visuals3:00 PM to 4:30 PM
Power BI on Cloud and Quick Insights4:30 PM to 5:30 PM



X

Trainer Profile


Consultant with around 20 years expertise in delivering projects / programs / consulting engagements

Information Technology professional with good acumen in Business Analytics and Business Intelligence. Insightful knowledge of project analysis, design, re-engineering, process rationalization, cost control, capacity planning, performance measurement and quality. Proven ability in devising strategies aimed at enhancing overall organizational growth, sustained profitability of business and improved delivery performance.

Experience
Delivery responsibility for planning, scoping, estimating, tracking & ensuring implementation of program within pre-set budgets and deadlines

Management and P&L responsibility of global clients

Providing leadership & direction to multiple development teams on projects

Training

• 1000+ hours of training on Data Science / Mining
• Technical Knowledge in R, POWER BI, SAS, Tableau, Azure ML, IOT

Qualifications:

Bachelor of Engineering – Government College of Technology
Business Analytics and Business Intelligence - Great Lakes Institute of Management Studies , Chennai

Program Committee

Akhilesh Sharma

Agile Transformation Leader

Societe Generale Global Solution Centre

Conference or Event Schedule


X Topic Abstract

Many industries today are leading the way forward in data analytics, which are disrupting and revolutionising new capabilities in Machine Learning and AI. Providing real-time analysis is no longer a costly endeavour and businesses today are keen to have a real-time view of their organisation to better operations, providing better and enhanced customer experience and increasing sales through identifying key patterns.

Speaker Profile

Sanket is a highly skilled programmer, innovator and has an experiential algorithmic and software architectural background. He is the Founder of BlobCity, which offers the world's only No-Query Database system. The revolutionary database was conceptualised and single handedly written by him. An electrical engineer by academics with a passion for software, he has been a mentor, trainer and technology evangelist, speaking at numerous conferences, events and user groups. He has been a consultant to multinationals with architecting complex systems, managing large teams and quality assurance delivery. In his early stint with technology and software, he has created a Sudoku solver that can solve and generate Sudoku's of any size in record time. Today he has several Sudoku books published in the market that are created by his algorithm.


X Topic Abstract

How can Organizations believe in business analytics when it has so much uncertainties? Should analytics be centralized within an organization or should it be imbibed into the mind and soul of every employee? We have already invested in data warehouse and business intelligence and we are quite mature in our analytical process, but where do we go next?

Business Analytics, Machine Learning, Artificial Intelligence or Data Science has seen its share of success and failure with hype within organizations. Companies are moving to data driven or data enabled decisions while trying to overcome the hurdle of data intimidation.

As part of this session, we like to share our approach on working with organizations with a top-down approach on how to factor in analytics as part of their innovation cycle. Understand opportunities to debate, whether its sales, operational or customer satisfaction. Understand the need for data strategy for sourcing, sampling and transformation. Identify hotspots and fail early and start over.

Speaker Profile

Consultant with around 20 years expertise in delivering projects / programs / consulting engagements

Information Technology professional with good acumen in Business Analytics and Business Intelligence. Insightful knowledge of project analysis, design, re-engineering, process rationalization, cost control, capacity planning, performance measurement and quality. Proven ability in devising strategies aimed at enhancing overall organizational growth, sustained profitability of business and improved delivery performance.

Experience

Delivery responsibility for planning, scoping, estimating, tracking & ensuring implementation of program within pre-set budgets and deadlines
Management and P&L responsibility of global clients
Providing leadership & direction to multiple development teams on projects

Training

• 1000+ hours of training on Data Science / Mining
• Technical Knowledge in R, POWER BI, SAS, Tableau, Azure ML, IOT

Qualifications:

Bachelor of Engineering – Government College of Technology
Business Analytics and Business Intelligence– Great Lakes Institute of Management Studies , Chennai


X Topic Abstract

We see recommendation systems all around us. Amazon and other e-commerce companies recommends items/goods that we are likely to buy based on our past behavior. Netflix suggests what videos we can watch. Pandora even builds our personalized music streams, based on what we are likely to listen. Almost every website has (or ideally, should have) a recommendation system based on user browsing history, past purchases, past searches and preferences. It turns out most existing recommendation systems are based on three paradigms

• Collaborative Filtering (CF) and its variants - either model-based or memory based CF, based on ratings given by users to items.
• Content-based recommendation engines - based on information about features of items.
• Hybrid recommendation engines - combining content based and CF or exploiting more information about users in content based recommendation.

There are three desirable properties of recommendation systems:

• Accuracy – must be close to what the user is likely to pick/read/watch/click.
• Sparse data handling - Many users may never have rated any items, so system must figure out what to recommend to those users. Another issue with respect to users is that some users may be "Gray Sheep" - implying they do not fit into the segmentation/categorization of users and have distinct characteristics.
• Avoiding cold start - how can new items (that have never been rated) be recommended.
• Scalability - algorithms must scale to large number of items/users without too much degradation in performance.

The memory-based CF systems are highly scalable, but may suffer from cold start and data sparsity problems. Model based CF systems such as the Naïve Bayes recommendation engine [1] often outperform memory-based CF systems with respect to accuracy. The matrix factorization based recommendation systems, which can be seen as the most advanced ones, have the best accuracy, but may suffer from performance degradation issues at extreme scale.

Many content based recommendation systems use reviews and text analytics of the reviews to understand user’s preference and consequently, able to avoid items which users have condemned or criticized. These are limited in that they treat reviews as bag-of-words and may lose contextual information. They also suffer from scalability issues. Most content based recommendation engines also need hand-crafted features resulting in excessive manual labour for recommendation. Content based recommendation engines also tend to get struck in what is known as a "well of similarity" [2], where the items form a restricted theme scope.

Deep Learning Based Recommendation Engines

Previous work in using AI to build recommendation engines include [A] where the authors used a two layer Restricted Boltzmann Machine (RBM) to model user ratings in a CF environment. This was able to handle large number of ratings/users and also help in capturing hierarchical latent factors of users and items. However, this still has the disadvantage that it does not make use of content and hence suffers from cold-start issues.

[3] is the other prior work which builds a collaborative deep learning model to understand review texts or content using a Bayesian Stacked Denoising Auto Encoder (SDAE). It also has several short comings including the fact that it uses bag-of-words to represent review text – this may result in loss of semantics as word orderings may be important. Further, semantically similar reviews using different words may also not be treated as similar.

We have built a recommender system based on intent prediction using deep learning for an e-commerce client building on some ideas from [4]. The intent of this talk is to motivate the need for deep learning based recommendation systems and to explain how we have built one such system. Key takeaways for the audience include:

1. Introduction to deep learning - different networks such as RBMs, Conv nets, auto-encoders.
2. Introduction to recommendation systems - why deep learning is required for hybrid systems as well as how different deep learning networks such as conv nets or RBMs can be used to build hybrid recommendation systems.
3. end-to-end view of recommendation and learning to rank systems on TensorFlow including model management and scaling.

Speaker Profile

Dr. Vijay Srinivas Agneeswaran has a Bachelor’s degree in Computer Science & Engineering from SVCE, Madras University (1998), an MS (By Research) from IIT Madras in 2001, a PhD from IIT Madras (2008) and a post-doctoral research fellowship in the LSIR Labs, Swiss Federal Institute of Technology, Lausanne (EPFL). He is now a Senior Director of Technology and heads data sciences team of SapientRazorfish in India. He has spent the last ten years creating intellectual property and building products in the big data area in Oracle, Cognizant and Impetus. He has built PMML support into Spark/Storm and realized several machine learning algorithms such as LDA, Random Forests over Spark. He led a team that designed and implemented a big data governance product for a role-based fine-grained access control inside of Hadoop YARN. He and his team have also built the first distributed deep learning framework on Spark. He is a professional member of the ACM and the IEEE (Senior) for the last 10+ years. He has four full US patents and has published in leading journals and conferences, including IEEE transactions. His research interests include distributed systems, data sciences as well as Big-Data and other emerging technologies. He has been an invited speaker in several national and International conferences such as O'Reilly's Strata Big-data conference series. He was an editorial speaker at the Strata Data conference in London in May 2017. He is also in the program committee of Strata Data, Singapore, 2017 as well as Strata Data, San Jose, 2018.


X Topic Abstract

Data is the new resource of modern time, that is available to every organization. Some get it as a part of its enterprise data collection process while others put an elaborate data collection plan to collect it. In some sense, data has the traits of a perishable good. If not used on time or within a prescribed time duration, it’s value diminishes to dust. Imagine this, it takes 25 % of the acquisition cost to retain an existing customer. How do you keep your existing customers engaged with you especially when they have every information about competing products and offerings at their finger tip.

Customers do not like to give proactive feedbacks on what they like or dislike. Besides, disgruntled customer would often churn out even before you got enough time to course correct yourself. Customers are our compass. They give us a sense that we are headed in the right direction. How do we know if our customers are having good experience from our product and services. How do we know which customers are at risk of churning out. It is only when every company can get visibility of customer expereince through Analytics

Speaker Profile

Soumen is an experienced leader, having a strong background in innovation management, technology development, warranty analytics, vehicles health management and telematics. Recently he went on an international assignment to GM China to lay out the telematics strategy for GM China connected (OnStar Enabled) vehicles that would provide value added service from a quality standpoint from the OnStar platform to Engineering Quality function and the End Customers.

Presently, he is the EGM, Operational Excellence for GM International Quality. In this role, he is responsible for developing strategies that will improve Cost of Quality (CoQ) across 8+ business units of GM International ( India, Korea, Australia, Thailand, Egypt, Middle East,South Africa etc ) . The role involves using multivariate data analysis approaches and working with the cross-functional teams (such as Finance, Supplier Quality, Engineering Quality, Aftersales, Parts Department and , Quality Strategy functions) to identify high impact (cost reduction opportunities of several $M) project/s in each of these business units that would impact EBIT/margins from the region.

Then work with the Global Cross Functional team to develop a RASIC and execute the project as per the business plan using DFSS and DMAIC processes. He has both technical and business expertise in technology strategy development,technology management big data analytics and data science research and has authored several publications and patents in those areas. See details below

Specialties: Project Management Professional (PMP) From PMI, USA, DFSS Black Belt certified from GM, Certified Manager of Quality /Organizational Excellence Certification from ASQ USA


X Topic Abstract

The latest projections put out by the UN population division suggest that India's population, currently estimated at 1.34 billion, is projected to rise to 1.51 billion by 2030 and further to 1.66 billion by 2050 before declining to 1.52 billion by century end. Food systems are central to human societies. Can we rely on them to feed 1.5 billion people in 2030 nutritiously and sustainably?

The world is increasingly volatile – 2016 alone was full of surprises. What unexpected events might take us down unforeseen paths in the future?

Food systems are integral to the health of people and the sustainability of the planet. Developing inclusive, sustainable, efficient, nutritious and healthy food systems will be essential to achieve the Sustainable Development Goals (SDGs). Currently, nearly half of the India's population does not eat a properly nutritious diet.

The agriculture sector is a significant contributor of greenhouse gases, deforestation and water scarcity. In some regions, up to 40% of food is lost or wasted. The volatility of weather events and food prices are growing. An observer focused entirely on the challenges in food systems might say "the future is bleak"; whereas one focused on innovations to meet the SDGs might say "the future is bright".

Looking at the positive picture, India is the world’s 2nd largest producer of fruits and vegetables. The government expects the processing in this sector to grow by 25 per cent of the total produce by 2025. India is the largest producer of milk in the world. But it will be challenging to feed 1.5 billion people in 2030. We will need to bring innovative technologies in farming and food processing industry so as we can demand the current and future demand.

At the conference, I will speak about various digital trends that are shaping the future of food industry. The use of technologies like data science and artifical intelligence is shaping the industry by removing the guess work out of farming. We will look at the how making smart farms a reality through precision farming and increasing farm productivity through unexpected data partnerships.

I will present several examples that will demonstrate how the agricultural industry is pursuing the audacious goal of "ending hunger, achieving food security and improved nutrition, and promoting sustainable agriculture." The future of agriculture lies in smart farming and digital transformation, with systems of intelligence including cloud computing, big data platforms, IoT, predictive analytics and other new capabilities.

Speaker Profile

With more than 14 years of experience in IT industry, Swapnil has spent more than 8 years in the Supply Chain Analytics field. He is an ambitious, creative and highly motivated individual, who has a passion for the Supply Chain with focus on Retail and Manufacturing industries and an uncompromising commitment to quality and outstanding customer service. He areas of research includes Supply Chain management and web analytics. His expertise lies in figuring out ways to do what others say can't be done. He holds MBA from S.P. Jain Institute of Management & Research, M.S. (BITS, Pilani) and B.E. (Visvesvaraya Technological University).


X Topic Abstract

This presentation will focus on BT’s journey in Big Data Analytics and challenges faced while building predictive analytics. The session will focus on the learnings and best practices that would help in building predictive analytical models. Key focus areas are how to build the data science capability within the team, predictive model lifecycle management and deployment which are key to leveraging the insights gained out of analytics.

Speaker Profile

Bala Jayapal is a Lead Data Architect at BT (British Telecom) and has 15 years of Industry experience in the area of “Data & Analytics”. His focus areas include the Logical Data warehouse architecture, Big Data Governance, Data Management strategies for Fast Data such as IOT & Virtual Reality. In BT, he’s a champion for initiatives such as the Data Marketplace & Shadow BI reengagement. He has worked in various domains such as Retail, Banking, Media, Insurance & Telecom. In the past, he has been associated with various firms such Tesco, AIG, Deloitte Consulting & Cognizant. He has built several Enterprise Data models using Industry standard frameworks and has architected & implemented several large scale Teradata EDW programs.
balakumaran.jayapal@bt.com


X Topic Abstract

A US based mid-sized Non-Banking Financial Company (NBFC) that operates in a niche market segment, wanted to leverage the power of advanced data analytics to optimize their business actions for marketing efficiencies and risk management. The NBFC is in the business of extending credit (in the form of personal loans) to the customers who are indexed high in terms of consumer credit risk.

To achieve this, a business integrated advanced analytics and machine learning approach was adopted to design a solution that entailed identification of the right prospects for targeting, leading to optimization of marketing and operational cost. This also mitigated the long-term credit losses incurred from customers who have the tendency to default on loan.

Consumer credit bureau (CCB) data was used for building the solution. An ensemble solution approach was undertaken that involved innovative feature extraction, dimensionality reduction and application of advanced statistical and machine learning techniques. Although the solution was custom made for the NBFC, the design turned out to be repeatable, scalable and customizable for similar business objectives in the consumer lending space. Techniques such as Principal Component Analysis (PCA), Variable Clustering, and Information Value were used for dimensionality reduction to arrive at an optimum set of attributes. This was followed by building an ensemble machine learning solution that involved response and risk segmentation using techniques such as CHAID and modeling using techniques such as Logistic Regression. Sampling, in-time, and out-of-time validation ensured robustness and reliability of the solution. The solution improved the customer response rate by 40%, decreased the cost per acquisition by 20% and significantly mitigated the over-all credit risk of the portfolio.

Speaker Profile

Satyamoy is the head of client solutions at Analyttica Datalab Inc, a fast growing analytics start-up headquartered in Delaware, US with offices in Bangalore and Delhi. He is part of the management of Analyttica, undertaking a leadership role in driving build through growth of the company. His role spans across different industry verticals with primary focus on Banking and Financial Services.

He is a seasoned analytics professional with deep Financial Industry experience. His past experiences include 12+ years in managerial and leadership roles in Citigroup and GE.

He is passionate about creating business value, driven by analytical innovations and connecting content to context, through the right leverage of 'Business Knowledge', 'Data Science' and 'Technology'.

Satyamoy is also passionate about coaching and developing talent. He is leveraging the same in driving the build and launch of Analyttica's flagship product platform Analyttica TreasureHunt® . He is recipient of several project and client excellence awards and trained in different levels of corporate leadership and executive development programs.

Satyamoy is an MS in Industrial Engineering and has done his executive general management from IIM Bangalore.


X Topic Abstract

I am going to talk on the topic "How to use data efficiently". In this talk, I would like to share my understanding and learning experiences on the data-driven applications along with some use cases which will cover the major concepts of descriptive and predictive analysis.

Speaker Profile

An innovative and creative mind who is passionate about computers and programming. I have a strong background in development in the areas of functional programming, data visualization, and rich user-friendly applications.

I have worked in a number of business domain organizations on several client projects and deliver them successfully as client required.

I have also given a public talk on Data and Visualisation for an organization like CDAC and continuing with many others.

Specialties/Skills
* Programming Languages : Python, PHP, OOPS and Data Structures
* Programming Libraries : Pandas, Numpy, Requests, Sqlalchemy etc.
* Data Visualization : D3.js
* Web Technologies : HTML 4, CSS 3, Bootstrap, JavaScript, jQuery,
* Platforms : Ubuntu, Red Hat, Fedora, Windows
* Databases: MySQL, Sqlite
* Version Contorl : Git
* Code Editor : SublimeText


X Topic Abstract

1. The IOT Connectivity "SOUP" (Bluetooth, ZigBee, LPWAN, 4GLTE, 5G, LTE Advanced, Wifi, Sigfox, LoRa, Open Internet)

2. Rich Design Space for Devices - Gateway Communication
2.1 IoT Network Design Space - Three dimensional Analysis (Battery Life, Device's Data Rate, Device to Gateway Range)

3. Data Processing and Storage
3.1 Typical IoT Data Processing Scenarios
3.2 Three Key Challenges of IoT Data

3.2.1.Limited resources (power, bandwidth, storage)
3.2.2.Missing and noisy data
3.2.3.Outliers and anomalies

4. Architects Vs Data Scientist - The Three B's (Battery, Byte, Bandwidth Options)

Speaker Profile

Sachin is a former Telecom Systems Architect and Engineer in wireless domain with 18+ years of experience.

With newly acquired Data Science skills, Sachin had been instrumental in providing some of the complex Solutions & spear heading PoC development for HiTech & Communications leveraging Advanced Analytics & Machine Learning Algorithms.

Sachin worked closely with customers on the technical requirements to provide technical solutions - identified requirements, including key business issues/drivers, and future technology requirements and solutions and established strong relationships with strategic clients and industry partners.

Sponsors

Benefits of sponsorship

This is a great opportunity to strategically brand your organization. As a sponsor, you will receive a tremendous amount of visibility and numerous other benefits at the conference.

Sponsorship Levels

Platinum Sponsor (Limited to 2)

Gold Sponsor (Limited to 3)

Knowledge Partner (Limited to 1)

Silver Sponsor (Limited to 4)

Bronze Sponsor (Limited to 4)

Conference Bag Sponsor (Limited to 1)

Track Sponsor (Limited to 2)

A-La-Carte (Open)

       

Brand Your Organisation

Standard Price : Rs 30,000

You can also choose to strategically brand your organisation as per the below combo offer.

Package includes:

Full day attendance to the event

Website branding

Logo on brochure

Speaking Slot – 30 minute presentation slot or panel discussion (optional)

Name tag branding

Brochure inserts



Sponsor

Knowledge Partner

Media Partner

Past Sponsors

Testimonials

Event Speakers

Vijay Srinivas Agneeswaran

Sr Director of Technology & Head Data Science

SapientRazorfish

Soumen De

EGM & Lead - VE OpEx & Continuous Improvement

GM Technical Center - India

Bala Jayapal

Lead Data Architect

British Telecom

Swapnil Saurav

Senior Manager

JDA Software

Satyamoy Chatterjee

SVP, Head of Client Solutions

Analyttica Datalab Inc.

Sanket Sarang

Founder & CTO

BlobCity

Karthik Siva

Senior VP

Voksedigital

Mukul Taneja

Senior Data Specialist

Gramener

Sachin Mudholkar

VP Technology

Relatas

Event Price list (30th)

Conference
(30th November)

Super Early Bird : Rs 7,999
(Till 01st October 2017)
Early Bird : Rs 8,999
(Till 01st November 2017)
Standard Price : Rs 9,999
(Till 30th November 2017)


Training
(29th November)

Standard Price : Rs 9,000
(Till 29th November 2017)

Brand your Organisation

Standard Price : Rs 30,000

You can also choose to strategically brand your organisation as per the below combo offer.

Package includes:

Full day attendance to the event

Website branding

Logo on brochure

Speaking Slot - 30 minute presentation slot or panel discussion (optional)

Name tag branding

Brochure inserts

Please mail us : contact@unicomlearning.com

Event FAQS

India Analytics Summit in Bangalore is open to anyone who has an interest in Big Data & Analytics or any related field.

No

Yes, all conference attendees must register in advance to attend the event.

As this is predominantly an event for the IT Industry and Open Source Community, if you are interested in attending. Please note that there will be a charge to attend as a student (can avail special discount as a student)..

Yes you can, please contact contact@unicomlearning.com with what you would like to be changed and we can assist.

Please fill the below details of your colleague and send us an email asking to block the seats.

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Event Location

Hilton Bangalore Embassy GolfLinks
Embassy Golf Links Business Park,
Off Intermediate Ring Road,
Bengaluru, Karnataka 560071
Phone: 080667 99999

contact@unicomlearning.com

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Unicom Address

Shanmugha Arcade,
3rd Floor, 39,
NGEF Lane,
Indira Nagar 1st Stage,
Bengaluru – 560038,
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Phone : +91-95388 78795, +91-95388 78799, +91-8025257962

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Disclaimer and Indemnity

"Confirm your CANCELLATION in writing up to 15 working days before the event and receive a refund less a 10% service charge. Regrettably, no refunds can be made for cancellations received less than 15 working days prior to the event.

However, SUBSTITUTIONS are welcome at any time and is done at no extra cost. The organisers reserve the right to amend the programme if necessary.

Important Disclaimer: The organizers reserve the right to make substitutions or alterations and/or cancel a speaker(s) if deemed necessary by circumstances beyond its control.

INDEMNITY: Should for any reason outside the control of UNICOM Training & Seminars (P) ltd (hereafter called UNICOM), the venue or the speakers change, or the event be cancelled due to industrial action, adverse weather conditions, or an act of terrorism, UNICOM will endeavour to reschedule, but the client hereby indemnifies and holds UNICOM harmless from and against any and all costs, damages and expenses, including attorneys fees, which are incurred by the client. The construction validity and performance of this Agreement shall be governed by all aspects by the laws of India to the exclusive jurisdiction of whose court the Parties hereby agree to submit."