Abidos Hotel, Dubailand, Dubai, UAE
DAY #1
Time
Agenda
09:00 – 09:30
Registration and arrival tea and coffee
Day1: Session 1 – Data Science concepts and fundamentals
09:30 – 09:40
Welcome and introductions
09:40 – 10:40
· General data science methodologies
· Data science conceptual paradigms
· Data science analytic frameworks
· Overview of overarching data science standards and best practices
10:40 – 11:00
Morning Tea and Networking
Day1: Session 2 – R-Language Programming
11:00 – 12:30
· R-Language features and attributes
· R- Language program control constructs
· R- Language data structures – working with data frames
· Working with R vectors, lists and matrices
· R Programming examples and patterns for data science
12:30 – 13:00
Discussion session
13:00 – 14:00
Lunch
Day1: Session 3 – R-Language Package-based Data Analysis
14:00 – 15:00
· Working with R-Studio
· R-packages for predictive data mining
· R-packages for associative analytics
15:00 – 15:20
Afternoon Tea and Networking
15:20 – 16:40
· Common proprietary and open-source tools data science
16:40 – 17.00
Review session for the day’s topics
Continued/…
DAY #2
Delegate arrival tea and coffee
Day2: Session 1 – SQL-based Data Science
Day 2 Opening Remarks
· Relational data science features
· Relational data modelling
· SQL constructs for analysis
· Working with Hive-QL
Day2: Session 2 – NoSQL-based Data Science
· NoSQL data science features
· Graph-NoSQL-based data science
· Commercial NoSQL tools for data science
Practical Programming Exercises
Day2: Session 3 – Data Acquisition and Cleansing Systems
· Data acquisition considerations– ingestion and pipelining
· Data cleansing and wrangling techniques
· Change-data-capture and SCM workflows
· Building data pipelines for data science applications
· Commercial toolsets and practical exercises
DAY #3
Day3: Session 1 – Regression Modelling and other Inferential Schemes
Day 3 Opening Remarks
· Regression modelling basics
· Regression modelling with R packages
· Alternative inferential analysis approaches
Day3: Session 2 – Clustering and Segmentation Schemes
· Cluster-based analysis
· Segmentation-based techniques
· R-packages for cluster and segmentation analysis
Day3: Session 3 – Machine Learning Techniques & Systems
· Machine Learning (ML) attributes and frameworks
· Deploying Machine Learning systems
· R-packages for Machine Learning
· Practical ML programming exercises
· IDEs for developing ML systems
Closing remarks
