"Most organizations early on in the data-science learning curve spend most of their time assembling data and not analyzing it. Mature data science organizations realize that in order to be successful they must enable
their members to access and use all available data—not some of the data, not a subset, not a sample, but all data. A lawyer wouldn’t go to court with only some of the evidence to support their case—they would go with all appropriate evidence.
...The fundamental building block of a successful and mature data science capability is the ability to ask the right types of questions of the data. This is rooted in the understanding of how the business runs... The mature data science organization has a
collaborative culture in which the data science team works side by side with the business to solve critical problems using data. ... [it] includes one or more people with the skills of a data artist and a data storyteller. Stories and visualizations are where
we make connections between facts. They enable the listener to understand better the context (What?), the why (So what?), and “what will work” in the future (Now what?)."
Peter Guerra and Kirk Borne in Ten Signs of Data Science Maturity (2016)
Explore the world of Business Intelligence through
the use cases supporting different functions within that company. This workshop provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance.
In Part 1, you
will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you
will learn to communicate results with sharp visualizations and interactive, web-based dashboards.
After completing the use cases, you will be able to work with business data in the R programming environment and realize
how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence.
What You Will Learn
- Extract, clean, and transform data
the quality of the data and variables in datasets
- Learn exploratory data analysis
- Build regression models
- Implement popular data-mining algorithms
- Visualize results using popular graphs
- Publish the results as a
dashboard through Interactive Web Application frameworks
Program Schedule (27th August 2019)
8:30 – 9:00
9:00 – 10:30
- Extract, Transform and Load
- Data Cleaning
11:00 – 12:30
- Exploratory Data Analysis
- Linear Regression for
12:30 – 2:00
2:00 – 3:30
- Data Mining with Cluster Analysis
- Time Series Analysis
3:30 – 4:00
4:00 – 5:00
- Visualizing the Data's Story
- Web Dashboards with Shiny
Early Bird Rate
Register Before 1st August 2019
After 1st August 2019
Assoc. Prof. Dr. Rayner Alfred
Computer Science (Knowledge Discovery and Machine Learning)
Email: firstname.lastname@example.org / email@example.com
Email us to register and reserve your seat.