AI & Data Science Course Details

As a VI student, you will be given access to online learning materials in our e-learning portal.

To get you ready for learning, this essential pre-work will familiarise you with the basics of the key concepts and tools we will be using throughout the course.

Although you will learn these topics remotely before you arrive in class, you won’t be far away from the resources of the VI community. Make use of our Telegram channel to leverage connections with students, alumni, instructors and experts. At the end of your pre-work, you’ll be ready for the fast pace on campus!

After the course, you can choose to participate in follow-up sessions with your instructor, either in a group and/or individually, included as part of the course fee.

Tutorials:

• Introduction to Data Science
  • What is Data Science?
  • Data Science Life Cycle
  • What is Python and Why Learn It?
  • Introduction to Jupyter Notebook
• Introduction to Python Fundamentals  
  • Introduction to Data Types
  • Python Variables (In-built Functions)
  • Arithmetic, Relational and Logical Operators
  • Python Datatypes (String, Lists, Tuples, Dictionaries)
  • None and Casting

Learning Objectives:

• Describe data science and its life cycle, Python as a programming language and how to operate it on Jupyter Notebook editor
• Utilize Python to perform arithmetic, relation and logical operators with different data types and functions

Tutorials:

• String Methods  
  • String Indexing
  • String Concatenation
  • String Formatting
  • List Slicing
• Python Iterations, Control Flow, and Functions (if…else statements, for and while loops)

Learning Objectives:

• Utilize different methods of data slicing and manipulation on textual data, iterations for repetitive tasks and user defined functions for control and logic flow

Tutorials:

• Numpy 
  • Introduction to NumPy
  • Properties of Ndarray 
  • Basic Operations of Ndarray Object (Arithmetic Operations) 
  • Indexing and Iterations
  • Importing Packages
• Pandas 
  • Introduction to Pandas
  • Basic Operations of Series (Arithmetic Operations, Evaluating Values)
  • Basic Operations of Dataframes (Mathematical Operations)
  • Importing Files into Dataframes
  • Joins in Pandas (Merge & Concat)

Learning Objectives:

• Interpret Numpy arrays and use Numpy statistical module in Python for data indexing and iterations
• Perform essential operations of Pandas module to extract and manipulate data to prepare for statistical process and machine learning

Tutorials:

• Data Visualisation- Matplotlib & Seaborn
  • Introduction to Matplotlib
  • Barplot, Histogram/Density Plot, Line Chart, Scatter Plot, Boxplot, Heatmap
  • Graph Parameters (Changing Size, Color, Style Markers, Titles, Legends and Label Orientation) in Matplotlib
• Data Cleaning and Exploratory Data Analysis 
  • Introduction to Data Cleaning
  • Common steps in Data Cleaning
  • Exploratory Data Analysis (Filtering and Sorting, Column Manipulation, Group By/Aggregate Functions, Handling Missing Data, using Functions)

Learning Objectives:

• Build visualizations and graphs using Matplotlib and Seaborn to interpret data and gather insights to support business decisions
• Use advanced techniques to clean data to conduct exploratory analysis for better understanding of the data

Tutorials:

• Linear Regression
  • Modeling and Predictions
  • Introduction to Linear Regression
  • Introduction to Scikit-Learn Package (Fitting the Data, Evaluation of Model and Comparing Models)
  • Introduction to Statsmodels
• Feature Scaling
  • Standardisation
  • MinMax

Learning Objectives:

• Apply linear regression machine learning model to predict results of a continuous data type and evaluate its model performance
• Use advanced techniques in standardization and normalisation of the datasets to obtain better performance in machine learning models

Tutorials:

• K-nearest neighbors 
  • Introduction to Classification
  • Introduction to KNN
  • Advantages and Drawbacks of KNN
  • Training KNN using Scikit-Learn 
• Logistic Regression 
  • Binary Class
  • Probability Estimation Dilemma
  • Odds Ratio
  • Log Odds
• Decision Trees and Random Forest Classification
  • Algorithm Walk-Through
  • Advantages and Drawbacks of Decision Trees
  • Training Decision Tree using scikit-learn 
  • Training Random Forest Scikit-Learn

Learning Objectives:

• Apply different algorithms to predict binary or multiclass outcomes and evaluate the model performances among the algorithms

Tutorials:

• Prologue to GridSearch 
  • Introduction to GridSearch
  • Review of Initial EDA Strategies
  • Implement Changes and Updates to KNN Model using GridSearch
  • Find Optimal Hyperparameters of a model
  • Apply GridSearch to Classification Model
• Sklearn Pipelines
  • Inspecting Pipelines
  • Pipelines with GridSearch
  • Cross Validation

Learning Objectives:

• Create and automate hypertuning of machine learning models to obtain optimal machine learning parameters for good machine learning results

Participants will be required to attend an assessment after the last lesson as part of the funding requirements.

“Various manpower surveys have found particularly strong demand for data scientists, engineers and developers, with bumper salaries on offer to the right candidates.”
– The Business Times, 19 Apr 2022

Data science refers to the multidisciplinary approach that helps companies to analyse raw data and make informed business decisions. The analysis of data is done by using tools such as algorithms, analytics, and machine learning models. The most common data science language is Python.

This discipline has become incredibly popular due to the abundance of benefits it offers. For instance, data science helps to transform problems into research, followed by coming up with practical solutions. Data-driven decisions can also significantly boost sales and give businesses an edge over its competitors.

Yes, you can get up to 70% funding support from SkillsFuture Singapore (SSG) for our accredited programs. After subsidy, you can use SkillsFuture Credits, Post-Secondary Education Account (PSEA), or NTUC UTAP funding for the remaining course fees. Eligible Singaporeans & PRs may receive funding support for our SSG-accredited training programs.

For details, visit: https://www.skillsfuture.gov.sg/funding-individuals

Yes. For self-sponsored Singaporeans aged 25 years old and above, you can use your SkillsFuture Credit to offset the remaining course fees after IBF funding.

To check your SkillsFuture credit balance, please follow these steps:

1. Go to https://myskillsfuture.gov.sg
2. Click on ‘Submit SkillsFuture Credit Claims’
3. Login with your SingPass
4. Click on the arrow (>) at the top right hand corner. You will be able to see a drop-down list of your Available SkillsFuture Credits.

After you have registered for a course, a VI representative will reach out to guide you with the SkillsFuture Credits

*Please note that our bootcamps are not eligible for “Additional SkillsFuture Credit (Mid-Career Support)’. You will only be able to use available credits from ‘SkillsFuture Credit’ and ‘One-off SkillsFuture Credit Top-Up’.

Yes, Singaporeans aged 21-31 can utilize their PSEA to cover remaining fees after SSG funding and using SkillsFuture Credit.

To check your PSEA balance, call the 24-hour automated hotline at 6260 0777. After course registration, a VI representative will assist with the PSEA claim application.

For more details, visit: https://www.moe.gov.sg/financial-matters/psea

Yes, you can utilise both at the same time. You can use your PSEA to offset the remaining balance after SFC has been applied.

For illustration purposes only:

*Dependent on PSEA’s balance

Yes, all our courses are eligible for Union Training Assistance Programme (UTAP) Funding. NTUC Union members can use UTAP to offset 50% of unfunded course fees (capped at $500 per year).

This claim must be done after completion of the course. Please refer to the UTAP FAQ for more information.

For illustration purposes only:

Enhanced Training Support for SMEs

SMEs that meet all of the following eligibility criteria:

1. Registered or incorporated in Singapore
2. Employment size of not more than 200 or with annual sales turnover of not more than $100 million

SME-sponsored Trainees:

1. Must be Singapore Citizens or Singapore Permanent Residents.
2. Courses have to be fully paid for by the employer.
3. Trainee is not a full-time national serviceman.

Singapore citizen aged 40 and above – Up to 70% of the course fees

Singapore citizen aged 40 and below including PRs – Up to 50% of the course fees

For details , please visit – https://www.skillsfuture.gov.sg/initiatives/employers/enhanced-training-support-for-smes

Absentee Payroll (AP) funding is a grant to help employers defray the manpower costs incurred when you send your employees for training courses funded by SSG. For training outside working hours, AP funding is given to you to defray the overtime pay that was paid to your employees.

With effect from 1 Jan 2022, SSG will introduce a fixed Absentee Payroll (AP) rate at $4.50 per hour. There will also be a $100,000 cap on the maximum AP funding each organisation can claim each calendar year to ensure that the benefits are more evenly distributed across companies.

For details, visit – https://www.skillsfuture.gov.sg/funding-employers

We offer interest-free instalment plans to ensure tech education is accessible to all Singaporeans and PRs. You may reach out to our admissions team for more information via:

Email: [email protected]
Phone: 6950 7023

This program is suitable for beginners with no pre-requisites.

Upon successful completion of this WSQ-accredited course, participants will be awarded a data science certification recognised by employers in Singapore. VI alumni use their course certificate to demonstrate skills to employers and their LinkedIn network.

Our programmes are well-regarded by top companies, who contribute to our curriculum and use our courses to train their own teams.

The below graph shows the popularity of various coding languages. Python is the most popular coding language today and highly demanded by employers.

1. Python is the fastest growing programming language.
2. Python is extremely versatile, with multiple uses.
3. Python is in high demand skill for jobs.
4. Python is easy to read, write and learn.
5. Python developers are paid competitively.
6. Python has an incredibly supportive community.

You will need to install Anaconda to run Python programming.

You will need to have your own personal laptop/PC to attend the bootcamp.

Your Instructional team will guide you step-by-step on the installation process before class begins.

If you miss a lesson, you are allowed to attend a makeup class. This would be the corresponding class of another intake that same week, subject to availability. If you cannot make it for the makeup class, then you can simply watch the recorded lesson video.

Your attendance is only counted if you attend a makeup class.

‘Singapore Tech Salaries May Soar Up to 30% in Two Years’ – The Straits Times, 2022

Businesses today understand the importance of data and are now placing immense emphasis on data science skills to drive innovation and make data-driven decisions. 

Data science is one of the most coveted skills by employers across various industries such as finance and tech. The high demand and low supply of such skills also makes data science a highly lucrative career option. 

Kickstart a career in Data Science with the beginner-friendly Data Science Bootcamp by Vertical Institute. The course allocates 3-4 instructional team members per class to ensure dedicated peer and mentor support for your Data Science journey. You will graduate with an IBF-accredited Data Science certification, as well as a polished, portfolio-ready project to showcase  your new Python and machine learning skills.