Course Description

Big data generates a lot of opportunities for business leaders to make better decisions. However, interpreting data from various data is not a piece of common knowledge for most of the business professional. How can we leverage on the data for making the optimal marketing decision? How to increase operational efficiency? How to maximize business revenue?

Participants will acquire knowledge on a practical framework including data analytic life cycle from how to select, use and turn the data into a useful analysis. Besides that, participants will be equipped with practical skills to implement predictive modelling using various data analysis methods such as linear regression model, logistic regression models and decision trees. Participants will also acquire the skills related to the implementation of unsupervised learning algorithms such as k-means clustering and other analytical methods such as correlation analysis and association rules to derive insights from their data.

Course Objective

The objective of this course is to equip participants with various key analytics concepts encapsulating data analytics, be intensively familiar with the motivations behind the evolution of data analytics and be able to explain the value of data analytics to organizations. R programming language will be used to cover the data analytics including data manipulation and data visualization. In addition, big data analysis will also be incorporated to help participants to distinguish between the traditional approach versus the big data approach and big data architecture and eco-system. Furthermore, to acquire successful analytics, we must take the backend support of the database into consideration. SQL and NoSQL databases will be incorporated to distinguish between SQL and NoSQL Databases. This including technical skills to set up MongoDB, query with MongoDB, connect MongoDB to R, and perform data analysis.

Skills Acquired

Participants will be equipped with knowledge of data analytics using R programming language. In addition, they will be equipped with practical end-to-end data analytics skillsets such as engineering classification and regression models using Linear Regression, Logistic Regression, Decision Trees, and Neural Network. Participants will also acquire practical skills of performing data manipulation in MongoDB.