Data Science Bootcamp
Get hands-on with Python programming, machine learning, and more to drive powerful predictions through data.
What is Python programming?
Ever wondered how YouTube’s recommendation engine works? Or how TikTok knows exactly what to show you next? These predictive functionalities are driven by training a computer how to learn using large data sets. Machine learning is powering innovation in everything from insurance-tech to lending models to fraud detection.
The most popular machine learning language is Python. Python is a powerful general-purpose coding language used in a variety of professions, ranging from data science to web development. Compared to many languages, Python is easy to learn and to use. Its functions can be carried out with simpler commands and less text than most competing languages. This is why it is one of the most in-demand coding languages in the world.
In this course, students will get hands-on with Python programming, machine learning, and more to drive powerful predictions through data.
Why Learn Data Science Skills?
Highly in Demand
According to the recent Robert Half Salary Guide, data scientists are now one of the top five in-demand hiring roles in Singapore.
High Demand = High Pay
A data scientist in Singapore can fetch up to S$168,000 in salary.
Flexible Job Paths
The versatile role allows you to venture into virtually any industry including software, artificial intelligence, advertising, ecommerce.
Course Details
Vertical Institute’s Data Science Bootcamp is an introduction to the interdisciplinary field of data science and machine learning, which lies at the intersection of business, computer science, statistics. You will learn to use Python to help you acquire, clean, transform and model data, deriving decision-making predictions and insights. The course will be a hands-on approach to the fundamental data analysis techniques and machine learning algorithms that enable you to build robust predictive models of real-world data and test their validity. For the capstone project, you will apply machine learning techniques to solve a real-world problem in finance and practice communicating your data-driven insights.
What to expect?
- Learn new concepts and tools through expert-led lectures, discussions, assignments and project work
- Make use of our e-learning portal to access course materials, assignments and submit work
- Apply what you’ve learned to create a capstone project solving a real-world data problem
- Participate in hands-on exercises with real world data sets to apply newly learned tools and concepts
- Receive individualised feedback and support from your instructional team
- Be part of the VI community where members can leverage connections with alumni, instructors and experts
Curriculum
Pre-Work
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 familiarize 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.
Module 1: Data Science Fundamentals
- 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
Module 2: String Methods & Python Control Flow
- String Methods
- String Indexing
- String Concatenation
- String Formatting
- List Slicing
- Python Iterations, Control Flow, and Functions (if…else statements, for and while loops, try and except)
Module 3: NumPy & Pandas
- Numpy
- Introduction to NumPy
- Properties of Ndarray
- Basic Operations of Ndarray Object (Arithmetic Operations, Matrix Product)
- 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)
Module 4: Data Cleaning, Visualization & Exploratory Data Analysis
- Data Visualization- 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)
Module 5: Linear Regression and Feature Scaling
- 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
- Standardization
- MinMax
Module 6: Classification Models
- 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
Module 7: Capstone Project Discussion & Summary
- 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
Instructors
Han Qi
Han Qi is a Machine Learning Engineer in fintech firm DC Frontiers.
Always curious about skill development and how to learn efficiently, he enjoys sharing his knowledge and inspiring others to do the impossible.
Previously, he was working in AI Singapore and A*STAR. He graduated with a double Bachelors in Electrical Engineering and Business Administration from National University of Singapore and also Raffles Institution.
Clarence
Clarence is a data analyst at OCBC Bank.
As an analytics and tech enthusiast, he has done projects in front-end development, automation and analytics projects during his time in IBM, Standard Chartered Bank and OCBC Bank. He also taught data analytics at General Assembly.
Clarence graduated with a Masters in IT in Business (Analytics) and Bachelor of Accountancy from SMU. He often mentor students about careers in the tech industry during his free time.
Bootcamp Schedule
The bootcamp is 21 hours long and conducted on weekends or weekday evenings.
Due to COVID-19 measures, classes will be conducted 100% online, done face-to-face with our Instructor via Zoom. You will have intimate access to our instructional team that’s ready to answer your questions and a strong peer community; all immediately available through messaging and web chat.
September Schedule:
The next intake dates are to be confirmed. Kindly email us to indicate your interest.
Course Fee
Price: $2,500 SGD
Frequently Asked Questions
Q. What is Python programming?
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together.
Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.
Q. How long is this course?
This introductory bootcamp consists of 7 modules to be completed. Our course schedule can either be in a bootcamp (3 full days) or a part-time (7 three-hour lessons). The course is rigorous in nature with class practices, assignments, and capstone project work.
Q. Who are the instructors?
VI courses are created and led by industry practitioners from tech giants such as Apple, Amazon and Alibaba. They combine in-depth experience as practitioners with a passion for nurturing the next generation of tech talent.
Q. How are VI's courses conducted?
All our courses are held on weekends or weekday evenings to minimize disruption to regular business hours.
Our students will have access to our e-learning portal where they can access course materials and submit assignments before class.
Q. What will I gain from this course?
Upon successful completion of the course, students will be able to:
- Acquire knowledge about fundamental programming concepts and the Python programming language
- Code comfortably in Python and understand control flow and conditional programming
- Ingesting data from various files to a pandas dataframe; Manipulate data using pandas operations and methods
- Perform exploratory data analysis with python and pandas dataframe and series
- Understand which data visualization to use to effectively present data
- Build and refine machine learning models to predict patterns from data sets
- Tune data parameters for advanced model evaluation
- Build and refine machine learning models to predict patterns from data sets
- Tune data parameters for advanced model evaluation
- Communicate data driven insights to a technical and non-technical audience alike
Instructors & Students from
