This course provides participants the fundamentals of Data Analytics through combination of lectures, a series of lab session, discussion of sample case studies from the industry. Participants will experience the use of a data science tool (for demonstration), well known as a collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build, and deliver their own data products more efficiently.
What You Will Learn:
- Explain machine learning concepts and describe applications of well-known machine learning algorithms
- Apply machine learning techniques to a list of practical problems
- Apply DSS to solve problems
- Optional – Certification by IBM Cognitive Class AI
Who Should Attend:
This course is suitable for participants who are interested in the basics of Data Analytics, have little or no programming language (program coding) knowledge. The course encourages the participants to be spontaneous during the lab sessions, discussions
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Pre-workshop self-evaluation (30 questions)
- What is Big Data : Volume, Velocity, Variety, Veracity and Value
- Big Data in business: examples and case studies
- Drivers and terminologies: cloud computing, zettabytes, etc.
- Structured, Unstructured and Semi-structured data
- Relationship of Big Data and Data Science
- Big Data Use Case : adding value in business
Lab Session 1 with DSS platform
- What is Data Science
- Life of a Data Engineer or Scientist:
- Data Science tool and technology: R versus Python
- Data Science in business
- Data Science Use Case
- Reading of pdf file for case study followed by discussion
Lab Session 2 with DSS platform
- A Business Perspective & Understanding
- Approach to Analytics
- Data Requirement and Collection
- Data Preparation
- Modeling (Algorithm) – Linear Regression, Random Forests, KNN, K-Means
- Evaluation – r2, confusion matrix, ROC, AUC
- Visualization (Storytelling)
Lab Session 3 with DSS platform
- Explore multiple success stories
- Propose (Discussion) an idea for your company
- Research on a similar case and present it (create a flow and checklist)
- Introduction to Kaggle
Lab Session 4&5 with DSS platform
Practical for Lab sessions using sample data and completing the methodology flow.
Participants will need to complete these labs on their own with minimum help.
Post workshop self-evaluation (30 questions)
|Tue, Wed, Thu, Fri||09:00 AM — 06:00 PM|
|No. of Days:||4|
|No. of Participants:||10|
- In 2004, Wizlogix invested in high-end EDA tools to be used for in-house layout design services and explored into additional services such as PCB prototyping fabrication and assembly.
- In 2007, the Wizlogix Training Hub was established. Working with renowned trainers internationally, we aim to provide technical competency courses and certification programs for local engineers.
- In 2009, we further expanded to specialize in quick turn prototyping, New Product Introduction (NPI) and High Mix Low Volume (HMLV) builds.
- With the aim of expanding like an MNC, Wizlogix embarked on improving employee engagement through HR Capability Programmes in 2011.
- In 2012, we became an IPC member to gear ourselves towards a worldwide industry standard in PCB qualities acceptance.
- By 2013, Wizlogix was involved in the PCB layout of a joint commercial satellite design and development project between DSO and NTU, and the satellite design for Singapore Technologies. They are now known as ST (Satellite System). Since then, we became part of the space ecosystem in the government’s efforts towards nurturing this new industry. ...