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Data matters: Introduction to Data Science and R Programming for Data Analysts

Untitled Document

Date : 13-14 December 2017

Fees: RM 100

Venue: Kulliyyah of Information and Communication Technology, International Islamic University Malayisa.

Organizers
Dr.V.Vijayakumar, Professor & Associate Dean, VIT University, Chennai, India
Prof.Ramesh Ragala, Senior Assistant Professor, VIT University,Chennai,India

Key Topics to be covered:

1. Introduction to Data Science

2. Role of R language in Data Science

3. Decision Tree and Random Forest using R

4. Linear and Logistic Regression using R

5. Clustering and Model Evaluation using R

6. Implementation of Classification Algorithms using R

7. Time Series Analysis using R

8. Social Media Analysis using R

9. Text mining using R

10. Making Graphs using R


Target Audiences:

- All Students including MBA

- Faculty Members

- Research Scholars

- Industry Professionals

Objective of Workshop:

Data Science is the systematic discovery of meaningful patterns in data to support decision-making. Analysis of data not only requires the appropriate techniques and methods but also the right tools. One of the most powerful tools used by Data Scientists all over the world is R. R Programming Language is the most widely used language in Data Science. It is an Open Source Software and allows to integrate our own packages. It has plenty and boundless packages for all complex problems. This main objective of this workshop is enlightening the data analytics with help of R tool.

Goals of the Workshop:

The goal of this workshop is to provide you the basic skills for exploration of few machine learning algorithms, interpretation of data and insights from the data with visualization, which are important for solving a range of Data Science Problems.

Learning Outcome of the Workshop:

At the end of the workshop, the participants are able to:

- Analyse large datasets and make data driven decisions.

- Reconnoitre knowledge and essential skills needed to address the challenges of data intensive decision making environments.

- Walk you through several predictive analytics techniques.

Pre-Requisite:

Basic knowledge of statistics and programming languages (optional)


 

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