Professional Certificate in Machine Learning (Python) – Statistical Thinking and Exploratory Analysis

Overview

The job of a data scientist is to glean knowledge from complex and noisy datasets. Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning.

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Run 1: 27, 31 May & 1 Jun 2019

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In this course, you will learn the foundations of probability and statistics. You will learn both the mathematical theory, and get a hands-on experience of applying this theory to actual data using Python.

Next Course Starts On27 May 2019 (Mon) See Full Schedule
Fee SGD1712.00* (as low as SGD113.60* after maximum funding) Learn more
Duration

2 weekday evenings and a full Saturday (15 hours)

Level
Basic
Learning Objectives

By the end of this course, students will be able to:

•    Review basic statistical concepts and probability theory.
•    Explore elements of descriptive analysis and data presentation.
•    Explore elements of inferential analysis and parameter estimation.

Work towards a bigger goal

This module is part of:

Professional Certificate in Machine Learning (Python)

Learn more

Who Should Attend

•    Aspiring data science professionals seeking to apply Python to real world data problems e.g. business intelligence analysts, data engineers
•    Anyone with an interest in learning about the fundamentals of data science programming!
•    Anyone whose work interfaces with data analysis who wants to learn key concepts, formulations, algorithms, and practical examples of what is possible in machine learning and artificial intelligence
•    Managers who need the vision and understanding of the many opportunities, costs, and likely performance hurdles in predictive modeling, especially as they pertain to large amounts of textual (or similar) data
•    Professionals looking for a deeper understanding and hands-on experience with SMU School of Information System's adjunct faculty and industry expert
•    No prior experience or background required in the field

Assessment

Assessment

•In-class group work

•Individual assessment

Learning Activities

Instructional Methods and Learning Activities

Lectures, Discussions, Case Studies

Fees and Funding

Total Nett programme fee (self-sponsored)

  • S$1,712 (after GST) for non-Singaporean Citizens and non-PRs
  • S$513.60 (after GST) for Singaporeans below 40 and Permanent Residents
  • S$193.60 (after GST) for Singaporeans age 40 and above
  • S$113.60 (after GST) for Singaporeans on Workfare Training Support (WTS)
  • SME sponsored Singaporeans/PRs (non-WTS) only need to pay only S$193.60 (after GST) per pax.

All self-sponsored Singaporeans aged 25 and above can use their $500 SkillsFuture Credit to offset the Total Nett Programme Fee.

Funding

SkillsFuture Series 
Course fee grant at 70% of course fees (excluding GST) for participants who are successfully enrolled by SMU into approved courses under the Programme. Participants must be Singapore Citizens or Singapore Permanent Residents.


SkillsFuture Mid-Career Enhanced Subsidy ("MCES")
Up to 90% of course fees for Singapore Citizens aged 40 years and above


Enhanced Training Support for SMEs ("ETSS")
Up to 90% of course fees; and 80% of basic hourly salary capped at $7.50/hr for local employees of SMEs


SkillsFuture Credit
Singapore Citizens aged 25 and above, and self-funding may use their SkillsFuture Credit (up to S$500) to defray part of the course fee. Please click User Guide on how to submit your claim. SkillsFuture Credit claims may be submitted by logging in via MySkillsFuture.sg.

 

SkillsFuture Course Code*: CRS-N-0048198

 

SkillsFuture Course Name*: Professional Certificate in Machine Learning (Python): Module 2 - Statistical Thinking and Exploratory Analysis

 

* Important Note: Participants claiming SkillsFuture credits should locate the course in Training Exchange using the Course Code / Name

Schedule

Start Date(s)
Intake Information

Course dates:

Run 1 :

27 May 2019, 

31 May 2019,

1 Jun 2019

Speaker/Trainer Bio

Mr. Johnson Poh

Principal Data Scientist, Development Bank of Singapore

Johnson is currently Adjunct Faculty at Singapore Management University's School of Information Systems and his focus areas include applied statistical computing, machine learning as well as big data tools and techniques. His industry experience spans across finance, consulting and government sectors, serving as Head of Data Science and Principal Data Scientist in DBS, Booz Allen Hamilton and Ministry of Defence respectively. An avid programmer and data enthusiast, Johnson enjoys developing apps and data products. Most recently, he was awarded first prize in Singapore’s largest coding competition, Hackathon@SG 2015 as well as the CapitaLand Data Challenge 2016. Johnson completed his bachelor’s degree at University of California, Berkeley, majoring in the subjects of Pure Mathematics, Statistics and Economics. He received his postgraduate degree in Statistics at Yale University.

Additional Details

• Good Bachelor's Degree
• Diploma Holders with at least 3 years of working experience

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