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Data Science  Bootcamp

Singapore – 3 days / 21 hours Bootcamp

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Introduction

Python is a powerful programming language used in a variety of professions, ranging from data science to web development. Explore the fundamental tools and techniques behind what the Harvard Business Review has dubbed “The sexiest job of the 21st century.” 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? 

1. Highly in demand

Data scientists are in demand because there is a shortage of qualified data science professionals on the market today. According to a survey by the MIT Sloan Management Review, 43 percent of companies report a lack of appropriate analytic skills as a key challenge. In addition, based on LinkedIn’s research, data scientist jobs have 37% hiring growth over the last three years.

2. High demand means high pay

As an early career data scientist, you can earn up to $108,000* a year.

*source: Glassdoor

3. Flexible job paths

Unlike their data analytics counterpart, data science covers broader insights that concentrate on which questions should be asked. As a data scientist your task mainly include designing of data model processes, creating algorithms and predictive models to extract the data the business needs. This versatile yet crucial role allow you to venture into several different industries including retail, banking and finance and even medicine. 

 

Course Details

Vertical Institute’s Data Science Bootcamp is designed by industry practitioners to equip individuals with the most in-demand skills and best-practices in Python programming. You will learn to use Python to help you acquire, clean, transform and model your data. A significant portion of 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. You will also practice communicating your results and insights. 

Who is this for?

This programme is for

  • Anyone interested in data science
  • Individuals, students and professionals looking to explore a career path in data science
  • Anyone interested in learning Python Language

What to expect?

As a student, you’ll:

  • Learn new concepts and tools through expert-led lectures, discussions, assignments and project work
  • Participate in hands-on exercises with real-world data sets to apply newly learned tools and concepts
  • Make use of our e-learning portal to access course materials, assignments and submit work
  • Receive individualized feedback and support from your instructional team
  • Apply what you’ve learned to create a capstone project: a presentation detailing your approach to and findings from solving a real-world data problem
  • Receive pre and post-course support from both our instructional and admissions team
  • Be part of the VI community where members can leverage connections with students, 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 Introduction & Introduction to Python Fundamentals
  • Welcome to data science  
  • Introduction to Python Fundamentals 
    • Why Python?
    • Introduction to Data Types
    • Introduction to Jupyter Notebook
    • Python Variables
    • Arithmetic, relational and logical operators
    • Python Datatypes (String, lists, tuples, dictionaries)
    • Indexing and printing
    • Documentation of codes
Module 2: String Methods & Python Control Flow
  • Python 
    • String functions (indexing, concatenation and formatting)
    • Control flow (if…else statements, for and while loops, try and except)
    • Importing Packages
Module 3: Python & Numpy / Pandas
  • Numpy 
    • Ndarray object
    • Basic operations of ndarrays 
    • Indexing and iterations
  • Pandas 
    • Dataframe & series
    • Basic operations of series (arithmetic operations, evaluating values)
    • Basic operations of dataframes (mathematical operations)
    • Importing files into dataframes
    • Joins in Pandas
Module 4: Data Cleaning, Visualization & Exploratory Data Analysis
  • Data Visualization- Matplotlib & Seaborn
    • 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
  • Intro to Modeling: Linear Regression
    • Modeling and Predictions
    • Introduction to Linear Regression
    • Introduction to scikit-learn package (fitting the data, comparing models)
    • Introduction to Statsmodels
    • Feature Scaling
  • Pre-processing tools
    • 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 Iris dataset
  • Logistic Regression 
    • Binary Class
    • Probability estimation dilemma
    • Odds Ratio
    • Log Odds
  • Decision Trees and Random Forest Classification
    • Algorithm walk-through
    • Training decision tree using scikit-learn using the Titanic dataset
    • Training random forest scikit-learn
Module 7: Advanced Model Evaluation & Capstone Project Presentations
  • GridSearch 
    • Review of initial EDA strategies
    • Intuition behind GridSearch
    • Implement changes and updates to KNN model using GridSearch
    • Find optimal hyperparameters of a model
  • Final Project Presentation 

Instructor

Daniel

Daniel

 Daniel is a data scientist at Traveloka.

He has spent his career building technologies in the travel industry, previously being with TripAdvisor. He was also a past data science instructor at General Assembly.

Daniel is passionate about data skills and education, and loves that he can combine both disciplines. As a scholar, he graduated from Singapore Management University with a Bachelor’s degree in Information Systems.

Bootcamp Schedule

August Schedule:

Lesson 1: Saturday (29 August 2020)

Lesson 2: Saturday (5 September 2020)

Lesson 3: Saturday (12 September 2020)

Time: 9:00am – 5:00pm

LocationSingapore Management University

Due to COVID-19 measures, classes will be conducted 100% online, done face-to-face with our Instructor via Zoom.

Course Fee

Course fees are discounted during this COVID-19 period to provide support for individuals and businesses.

Usual Price: $2,500.00 SGD

Discounted Price: $880.00 SGD

Frequently Asked Questions

Q. What is Python?

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 part-time and classroom-based, 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. Are there any pre-requisites for this course?

This program is designed for beginners with no prerequisites. Students completely new to data science will have access to pre-work in our e-learning portal to help you prepare for the course. If you do have existing experience with the Python programming, our program will increase your proficiency with best programming practices.

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
Q. Is there a certificate granted at the end of the course?

Upon successful completion of the course, participants will be awarded a digital certificate by Vertical Institute. VI alumni use their course certificate to demonstrate skills to employers and their LinkedIn network.

Our data science course is well-regarded by top companies, who contribute to our curriculum and use our courses to train their own teams.

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