Workshops at Data Science Congress – 29 May 2018

Access: Platinum Pass & Premium Workshop Pass

Please Note: Our Premium workshop & Platinum pass Delegate can choose either Hall #4 workshop or Hall #5 workshop for Full Day. Cannot switch two workshops for example Hall #4 delegate cannot access Hall#5 workshop since one will miss the flow of the workshop

Hall # 5

Deep Learning Fundamentals

Type: Premium | Duration: Half Day | Certificate: Yes | Date: 29 May 2018 | Time: 10:00 AM to 1:00 PM


This hands-on workshop will provide an introduction to deep learning to the participants who are already aware of data science and machine learning techniques but have not worked on deep learning.  The course will cover the different types of network architectures that make the foundations of deep learning.

Following topics will be covered:

1. What is deep learning and what are the use cases of it?

2. Introduction to Feed Forward Neural Networks  including the hands-on session

3. Building an Image Classifier using Convolutional Natural Networks

4. Applying Recurrent Neural Network and LSTM Network for text classification

5. How to build your own deep learning projects?

Prerequisites:

Participants need to have basic data science knowledge and a basic understanding of Python, and iPython Network.

Prerequisite software:

Keras, Tensorflow, Python 3.5 and IPython installed on your computer. If you want to use a VM provided by us with all the required software, then no software installation is needed. 

Laptop configuration with OS:

Students should bring a laptop with Virtualbox (or VMWare) installed, 4GB of RAM and 10GB of storage.

DSC Hall : Hall 5

Instructor: Dr. Satnam Singh, Chief Data Scientist, Acalvio Technologies

Certification: Aegis School of Data Science

Practical Approach to Deep Learning with Google TensorFlow

Type: Premium | Duration: Half Day | Certificate: Yes | Date: 29 May 2018 | Time: 2:00 PM to 6:00 PM


Agenda: 

  • Introduction to Deep Neural Network
  • Design Approach for Business Use Cases
  • Developing Deep Neural Network (CNN, RNN) with TensorFlow
  • Testing Deep Neural Networks
  • Hyper parameter Tuning for Deep Neural Networks

Software requirement:

JetBrains PyCharm Community Edition, Python 3.x, Google TensorFlow

No specific Laptop configuration required

DSC Hall : Hall 5

Instructor: Mr. Utpal Chakraborty

Certification: Aegis School of Data Science

Hall # 4

Enterprise search – the rise of NLP, NLU and NLG to augment IR

Type: Premium | Duration: Half Day | Certificate: Yes | Date: 29 May 2018 | Time: 10:00 AM to 1:00 PM


Agenda: 

  • Enterprise search – what is it? past, present, future
  • Knowledge engineering and enterprise search – modeling and indexing of enterprise data and knowledge
  • Information retrieval
  • NLU – interpreting search intent
  • NLG – interpreting knowledge back to user
  • Usecases – Chatbot, Cognitive search etc.

No specific Laptop configuration or Software required

DSC Hall : Hall 4

Instructor: Dr. Soudip Roy Chowdhury

Certification: Aegis School of Data Science

Security Data Science Bootcamp

Type: Premium | Duration: Half Day | Certificate: Yes | Date: 29 May 2018 | Time: 2:00 PM to 6:00 PM


Agenda:

This hands-on session will provide an introduction to information security data science to the participants who are already aware of data science and machine learning techniques but have not worked on real-world problems.  The course will cover the entire data science pipeline in Information Security from data preparation, exploratory data analysis, data visualization, machine learning, model evaluation. 

Following topics will be covered:

1. What are the data sources and use cases for security data science?

2. How to preprocess raw security data using Splunk and visualize data using queries in Splunk?

3. How to build a data processing pipeline in Python and iPython notebook to find anomalous network behavior and endpoints?

In the Bootcamp, we will do several hands-on data experiments on InfoSec use cases: 

a. Data exfiltration detection using anomaly detection 

b. Detect Command and Control (C&C) Center

Prerequisites:

Participants need to have basic data science knowledge and a basic understanding of Python.

Prerequisite software:

Splunk, Python 2.7 and IPython installed.  

Laptop configuration with OS:

Virtualbox (or VMWare) installed, 4GB of RAM and 10GB of storage.

DSC Hall : Hall 4

Instructor: Dr. Satnam Singh

Certification: Aegis School of Data Science

Access: Standard Workshop Pass | Pro Pass | Platinum Pass

Please Note: Delegate can choose either Hall #3 workshop or Hall #2 workshop for Full Day. Cannot switch two workshops for example Hall #3 delegate cannot access Hall#2 workshop since one will miss the flow of the workshop

Hall # 3

Hands-on Introduction to SPARK

Type: Standard | Duration: Full Day | Certificate: Yes | Date: 29 May 2018 | Time: 10:00 AM to 6:00 PM


Agenda:

  • SPARK – Introduction, Basic Concepts and Environment, Applications
  • Basics of SPARK programming using Python
  • SPARK Datasets, Dataframes and SQL programming
  • SPARK Streaming and Machine Learning: Hands-on Introduction

Pre-requisite:

Good working knowledge or experience with Python programming language is essential to participate in this workshop.

Software Requirement:

Delegate need to install the latest version of Oracle Virtual Box and import a virtual machine (VM) that will be provided a  days before the workshop.

Laptop configuration:

Laptop with minimum 4 GB RAM. 8 GB or more preferred

DSC Hall: Hall 3

Instructor: Dr. Vinay Kulkarni

Certification: Aegis School of Data Science

Hall # 2

Machine Learning Using R

Type: Standard| Duration: Full Day | Certificate: Yes | Date: 29 May 2018 | Time: 10:00 AM to 6:00 PM


Objectives:
1. Introduce the basic concepts of Machine Learning with a focus on different types of algorithms
2. Provide hands on implementation experience on sample problems
3. Make participants aware about machine learning tools and frameworks
4. Provide an overview of key machine learning application areas
Outline:
Introduction and landscape
Supervised Learning (hands-on)
Unsupervised Learning (hands-on)
Other types of Learning (hands-on)
Pre-requisites:
R-Studio

DSC Hall: Hall 2

Instructor: Dr Shamsuddin Ladha

Certification: Aegis School of Data Science

Access: Standard Workshop Pass | Pro Pass | Platinum Pass

Hall # 1

Hand-on Text Analytics

Type: Standard| Duration: Half Day | Certificate: Yes | Date: 29 May 2018 | Time: 10:00 AM to 1:00 PM


  • Hands on how to process the text data and get value out of it

Software requirement: Python3, Anaconda

DSC Hall: Hall 1

Instructor: Mr. Bhavik Gandhi

Certification: Aegis School of Data Science

Recommendation Science

Type: Standard| Duration: Half Day | Certificate: Yes | Date: 29 May 2018 | Time: 2:00 AM to 5:00 PM


Software requirement:

  • Anaconda with Python 3

Laptop Configuration:

  • The configuration would be i5 with 8 GB ram and any OS

Pre-reading material:

Need basic understanding of Python, jupyter and anaconda, some links below

DSC Hall: Hall 1

Instructor: Mr. Bhavik Ghandhi

Certification: Aegis School of Data Science

₹50KAll Days
Platinum Pass
  • Get Access to
  • Premium Workshops - 29th May
  • Key Talks and Panel Discussion
  • Exhibition
  • Product & Solution Demo
  • Leaders Lunch - VIP Lounge
  • The Data Night (CXO Networking)
₹15KAll Days
Pro Pass
  • Get Access to
  • Standard Workshops - 29th May
  • Key Talks and Panel Discussion
  • Exhibition
  • Product & Solution Demo
  • Delegate Lunch
₹20KFull Day
Premium (Only Workshop)
  • Workshops on - 29 May 2018
  • Attend any of the following workshops:
  • Deep Learning workshop by Nvidia DLI
  • or
  • Natural Language Understanding
₹10KFull Day
Standard (Only Workshop)
  • Workshops on - 29 May 2018
  • Attend any of the following workshops:
  • Apache Spark
  • or
  • Machine Learning Using R Language
₹5KHalf Day
Standard (Only Workshop)
  • Workshops on - 29 May 2018
  • Attend any of the following workshops:
  • Social Media Analytics - Using IBM Watson
  • or
  • Machine Learning Models Deployment as a Webservice