Data Science Rex (DSR) aims to help our clients build up their Data Science & AI capabilities so that they can continuously tap on their data for value. In order to build capabilities that assist our clients to use Data Science and Artificial Intelligence effectively, DSR has a three-phase roadmap. They are the following:
Phase 1 - Identify
Identify through short sharing sessions
DSR believes in helping our clients adopt capabilities that are practical and enduring. In this phase, DSR aims to spark and identify the possibilities with data, and possible data projects. This is done through the following:
1) talks/sharing that do not go more than 2-hours long.
2) Group discussion with stakeholders.
After the above exercises are done, use cases can be discovered and the relevant skills and knowledge needed is identified.
- What is Artificial Intelligence & how is it relevant to me?
- Data Literacy for Everyone
- Machine Learning and its relevance to Artificial Intelligence
- Importance of Data Visualization and How to Design
- Data Collection & Its Importance
In this talk, participants will get to understand what Artificial Intelligence is and how it is relevant to their day-to-day. Participants can find relevant examples on where they see Artificial Intelligence applications. Through the talk, participants are brought through very briefly the history and development of Artificial Intelligence. And to round off the talk, participants will get to know how Artificial Intelligence may impact our lives going forward and what can we do about it.
Data is getting more ubiquitous these days. This is the first time humanity is capturing lots of data and trying to make sense of it.
In this talk, the aim is to share with participants why is it important to understand data, important ideas about what data really is, how can we make it work for us.
In this talk, participants will have a more in-depth look at what Machine Learning is about and its relationship to Artificial Intelligence. Participants will get to know the different branches of Machine Learning and what each of these branches provides to business
Given the rise of data, where companies are collecting tons of data, making sense of it can be a challenge. This is where data visualization comes in, providing a useful tool for companies to make sense of data. But here in lies the challenge. Bad visualizations are everywhere. How does one go about designing good data visualization?
In this talk we will share the relevance of data visualization in the workplace. Some tips will be shared with the participants on how to go about designing good visualization, visualization that can cross the deep chasm from data to insights.
In this talk, the aim is to give audiences an appreciation that understanding how data is collected is very important. A lack of understanding may result in the wrong insights made and the costs of it can be astronomical. Some working examples will be shared to establish appreciation.
Phase 2 - Improve
After Phase 1, with the possible data projects identified with the stakeholders, it is time to train the personnel. Phase 2 is where training of the relevant skills is conducted, so that stakeholders have the necessary knowledge to take up the data projects. This is done through short courses.
- Data Literacy for Everyone
- Data Visualization and Its Importance
- Python for Data Analysis
- Machine Learning for Business
In this course, DSR go in-depth into what data really is, what benefits can we derive from it and how can we make it work for us. The aim is to give participants important ideas about data, how can they use it for better decision-making and what are the possible data preparation work to do that.
We are in the Big Data era. Making sense of the huge amounts of data can be a challenge. In this course, DSR aims to improve participants ability to distill and present insights effectively through visualization. This will allow participants to gain value from data quickly, after asking the right questions.
In this course, DSR aims to train participants in using Python for Data Analysis. Participants will get to learn the importance of Data Analysis, what are the best practices and also the relevant codes to conduct data analysis, together with best practices shared by industry practitioners.
In this course, DSR will share with the participants on how to use Machine Learning to improve business outcomes, either through automation or better decision making. Participants will get to understand the different stages in a machine learning project and the best practices of implementing machine learning in business.
Phase 3 - Integrate
Integrate through mentoring
In Phase 3, personnels are armed with the skills and knowledge from Phase 2 and can now take on the use case/project but like any new projects, there will bound to be challenges along the way and this is where an experienced mentor can help. The experienced mentor can guide personnels away from pitfalls, reducing the costs of experimentation.
In this Phase, to help integrate the project into the organization, mentoring sessions are needed.