22
May 2020

1-Day Data Science Modelling & Inference(LIVE Stream)

Course Information

Start Date22 May 2020, Friday
End Date22 May 2020, Friday
Time09:00 am - 05:00 pm
VenueOnline LIVE Stream via ZOOM
Fee$600 (Excluding GST)Inclusive of e-certificate, e-materials,
Contact6720 3333 (Ms Rina Lim) training.aventis@gmail.com
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LIVE Stream

INTRODUCTION

Learn Inference and Modeling, Two of the Most Widely Used Statistical Tools in Data Analysis.

Build Models, Make Inferences, and Deliver Interactive Data Products

Get the skills today’s employers demand. Statistical inference and modeling are indispensable for analyzing data affected by chance, and thus essential for data scientists. Once you understand this, you will be able to understand two concepts that are ubiquitous in data science: confidence intervals and p-values.

Learn the Critical Elements of Data Science Modelling and Inference

This course will show you how inference and modeling can be applied to develop the statistical approaches. You will learn concepts necessary to define estimates and margins of errors and learn how you can use these to make predictions relatively well and also provide an estimate of the precision of your forecast. Even if you’ve never written a line of code in your life, you’ll be able to follow this course and understand how you can use apply Data Science skills to any business and industry.

Key Takeaways
  • Learn inference and modeling: two of the most widely used statistical tools in data analysis.
  • The concepts necessary to define estimates and margins of errors, parameters, estimates and standard errors in order to make predictions
  • 2 Key concepts that are ubiquitous in data science: confidence intervals, and p-values.
  • How to assess the Adjusted R-Squared for all types of predictive modelling
  • How to create a Simple Linear Regression (SLR)
  • How to use models to aggregate data from different sources
Who Should Attend?
  • Individuals with an interest or need to understand and design data science
  • Adult learners with some computer knowledge and a keen interest in data
Testimonials

Jackie is an innovative leader, he contributes great and strategic ideas to the current market. I had the pleasure of working with Jackie in creating a user-centric mobile application. As far as it goes, Jackie would be an asset to any team. Mark Ong, Senior UX Designer at Clearisk

Jackie is an active thought leader who voices his insights and experience. He is an academic genius who has conquered the book of knowledge and an innovator who will define new categories. – Poon Da Qian, CEO, BUTLER

I received timely and insightful feedback from him as needed and learnt the crucial steps to take note when dealing with datasets. He has been a friendly, helpful and knowledgeable mentor along the way and I highly recommend those who need a kickstart in data science/analytics to get his projects he offers to mentor you. Nicholas Tan

Data Scientist: Mr Jackie Tan (Co-Founder of UpLevel & FundMyLife. Forbes 30 Under 30)

Jackie is the co-founder of UpLevel, a data science startup that helps companies co-design projects to solve data problems, discover data science talents and train these talents. Jackie is also the co-founder of fundMyLife, a financial planning online platform that uses an algorithm to connect users with financial advisors based on the questions they ask. Named Forbes 30 under 30 for his work in fintech, Jackie is also known as a leader in fintech in South East Asia.

Jackie has personally trained and coached over 300 professionals in the area of data science, Python programming and coding. Jackie holds a Bachelor of Science (Honours) and is currently pursuing his PhD with Nanyang Technological University. Jackie has also mentored 4 groups of startup to achieve milestones for Startup SG Founder grants of over $100,000 funding.

Jackie has written extensively on entrepreneurship on startup publications such as e27 and Tech in Asia, and has spoken on innovation, data science, and entrepreneurship on platforms such as TEDx, SGInnovate, and Deep Learning in Finance.

Course Outline
  1. What is statistics?
  2. Why do we need to learn statistics?
  3. What can we do with statistics?
  4. Basic concepts in statistic metrics
  5. Concepts and developing an intuition in statistical testing
  6. Basics of univariate analysis
  7. Basics of bivariate analysis
  8. Where to get data?
  9. Creating your own simple linear regression
  10. Where to go from here?
  11. Useful resources and guides