DATA SCIENCE WORKSHOP @ IIT- BHILAI

TWO WEEKS DATA SCIENCE WORKSHOP 

@ IIT-BHILAI

Introduction to Data Science 1. Data, Why Data, Types of Data, Data Quality 2. Law of Diminishing Returns, Design for Scalability 3. Data Collection and Preparation, Regression and Classification Models 4. Data and decision making, Understanding cognitive bias 5. Data for persuasion and action, Integrating data and domain knowledge, storytelling with data Fundamentals of Statistics 1. Probability and Sampling Theory 2. R Programming – Setting up R Studio and its packages 3. Statistical Thinking and Statistical Models 4. Descriptive Statistics and Visualization 5. Bayesian Modeling Linux & Python Basics 1. Linux commands to navigate File Systems 2. Basics of GIT and Notebooks. 3. Introduction to Python Programming, Setup 4. Data structures, List, Dictionaries, Tuples, Functions, Namespaces, Scope, Recursive Functions and I/O –Operations Advanced Python Concepts 1. File and Formatting, Error Handling 2. Interactive Programming – Jupyter Notebooks 3. Advance Data Science Libraries – Numpy, Pandas, Scikit Big Data 1. Introduction to Hadoop – Motivation, BigData, MapReduce 2. HDFS. Hadoop Evolution – v1.0 vs v2.0, YARN and Other Distributions – Cloudera 3. Deployment Modes, Standalone, Pseudo and Full distributions use cases

Introduction to Apache Flume 1. Introduction to Flume and its architecture 2. Data Transfer between local and HDFS and some use cases 3. Introduction to Hive Programming, its architecture, data types and models, operators and UDF 4. Introduction to Pig, its architecture, components, models and operators. Introduction to Clustered Computing 1. Introduction to Spark – Why, installation and configuration 2. Spark vs. Hadoop, Spark Basics 3. Spark RDDs, pySpark, Spark Streaming 4. Spark SQL, DataFrames, UDF 5. Using Hive, MLIB, use cases Fundamentals of Machine Learning 1. Setting up the programming environment 2. Learning Models – Supervised Models, Regression 3. Unsupervised Models- Clustering 4. Recommender Systems – Collaborative Filtering 5. Other Machine Learning Techniques, Apache SystemML Fundamentals of Deep Learning 1. Setting up the programming environment. 2. Deep Learning Libraries – Caffe, Tensorflow and others 3. GPU-based Processing 4. Deep Learning Models – CNN,RNN 5. Introduction to Convolution Neural Networks (CNN) and its components and implementation 6. Introduction to Recurrent Neural Networks (RNN) and its components and implementation 7. Use cases - Image Classifications using Caffe.

The workshop held for two weeeks in July. The course curriculum for the workshop is stated above.
 Our Trainers Mr. Gowtham Balaji and Mr. Naresh Kumar headed and conducted the course to the students.

The course mainly focused on:

• Latest Technology.
• Fundamentals of the course.
• Advantages and Programming Techniques in Python.
• Implementation of Algorithms in Machine Learning and Deep Learning.
• Use Cases and Applications.

Hands on practical session was conducted and it was interactive. The doubts raised by the students were clarified  instantly.