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

Tutorials:

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

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)

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)

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

Tutorials:

• K-nearest neighbors 
  • Introduction to Classification
  • Introduction to KNN
  • Advantages and Drawbacks of KNN
  • Training KNN using Scikit-Learn using Loan ApprovalDdataset
• 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 using the Loan Approval dataset
  • Training Random Forest Scikit-Learn

• 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 using Loan Approval Dataset
• Sklearn Pipelines
  • Inspecting Pipelines
  • Pipelines with GridSearch
  • Cross Validation
• Capstone Project Discussion

For this individual open-book capstone project, students will culminate their learning by applying the new tools and concepts learnt to create a portfolio project that includes:

• A clearly articulated problem statement
• A summary of the data acquisition, cleaning, and parsing stages
• A clear explanation of your predictive model and the processes you took to create it

View our alumni portfolio projects.

“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.

The IBF Standards Training Scheme (“IBF-STS”) provides funding for training and assessment programmes accredited under the Skills Framework for Financial Services.

For our training programmes that are IBF-accredited, eligible Singaporeans & PRs may receive funding support under the IBF Standards Training Scheme (IBF-STS), subjected to all eligibility criteria being met.

For more information on the funding support, please visit: https://www.ibf.org.sg/home/for-individuals/skills-and-jobs-development/training-support/IBF-STS

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.
5. 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’.

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

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.

Yes, you can utilise both at the same time. UTAP can only be applied after SFC has been applied.

For illustration purposes only:

The IBF funding support works on a nett fee model. This means that the subsidy is applied upfront, and you will only need to pay the balance course fees after the subsidy. For example, if you are eligible for 70% subsidy, you only need to pay the remaining 30% upfront.

To be eligible, you’ll have to meet the following prerequisites.

For Self-Sponsored:

All Singaporeans or Singapore Permanent Residents (PRs) that are currently residing in Singapore and successfully complete the course will be eligible.

• Be a Singaporean Citizen or PR residing in Singapore
• Minimum of 75% attendance (this means that you must attend at least 6 out of 7 lessons)
• Pass the final assessment

Any balance course fees can be offset using your SkillsFuture Credits & NTUC UTAP funding.

For Company-Sponsored:

• Be from Financial Institutions that are regulated by the Monetary Authority of Singapore (MAS) (either licensed / exempted from licensing) or Fintech companies that are registered with the Singapore Fintech Association.
• Be a Singaporean Citizen or PR residing in Singapore
• Minimum of 75% attendance (this means that you must attend at least 6 out of 7 lessons)
• Pass the final assessment

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 IBF-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 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.