Look to the future. With data science skills, you can learn to create powerful predictions through modeling and machine learning, and drive business strategy. GA’s hands-on data science courses in Hong Kong prepare you for high-growth opportunities in business intelligence and more. 

Power Business Solutions: Learn Data Science in Hong Kong

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“General Assembly has great community involvement, strong relationships with local businesses, and instructors with great experience teaching and working in the field.”

Joseph Kambourakis, Data Science Immersive Instructor, GA Boston

Alumni Story: Breaking New Ground With Data

Anson Au

Leveraging the predictive power of data, General Assembly students are tackling problems in even the most traditional of industries. Anson Au (part-time Data Science, Hong Kong) for one, is IT manager at Alliance Construction Materials, a concrete supply company in Hong Kong. Demand for concrete is high, but so are excess orders and last-minute cancellations.

Au wanted to find a technical solution to help anticipate these complications. He found one as a student in GA’s Data Science course at our Hong Kong campus, applying the skills and techniques he learned to Alliance’s real data sets.

“One of the great things that Mart, our instructor, did was show us how to approach a set of data and how to attack and analyse it,” he said. By the end of the course, Au had design a new, improved algorithm that has since helped save the company hundreds of thousands of dollars. 

Break into Hong Kong's data science industry.

Harness one of today’s most in-demand skill sets: data science. Learn the essentials in our part-time course, or make a career change with our full-time Immersive.

Learn In-Demand Skills in Our Data Science Courses and Bootcamps in Hong Kong

Big Data

The term “big data” refers to the enormous amount of data we generate each day — from social media, to online purchases, and beyond. Get an intro to big data and its ecosystem of tools and technologies, and discover the potential and power of massive data sets.

Deep Learning

Deep learning — a subfield within machine learning — focuses on developing layers of artificial networks that mimic the brain’s activity. These groundbreaking AI developments power speech and image recognition technologies and strengthen the connection between technology and human intuition.

Machine Learning

Kirubakumaresh Rajendran, Data Science Immersive instructor at GA Sydney, says, “Machine learning is a branch of artificial intelligence (AI) that concentrates on building systems that can learn from and make decisions based on data. Instead of explicitly programming the machine to solve the problem, we show it how it was solved in the past and the machine learns the key steps that are required to do the same task on its own from the examples. Machine learning is revolutionizing every industry by bringing greater value to companies’ years of saved data. Leveraging machine learning enables organizations to make more precise decisions instead of following intuition.

“At General Assembly, our Data Science Immersive program trains students in machine learning, programming, data visualization, and other skills needed to become a job-ready data scientist. Students learn the hands-on languages and techniques, like SQL, Python, and UNIX, that are needed to gather and organize data, build predictive models, create data visualizations, and tackle real-world projects. In class, students work on data science labs, compete on the data science platform Kaggle, and complete a capstone project to showcase their data science skills. They also gain access to career coaching, job-readiness training, and networking opportunities.”

Read “A Beginner’s Guide to Machine Learning” by Kirubakumaresh Rajendran.

Python

Python is a high-level, object-oriented programming language used to collect data from multiple sources, run statistical and machine learning models, and create visualisations of those insights. Because of its ease of use and readability, Python is both a great tool for beginners and a go-to for data experts.

Ridgeline Plots

Josh Yazman, Data Analytics instructor at GA Washington, D.C., says, “Ridgeline plots, which are essentially a series of density plots (or smoothed-out histograms), can help balance the need to communicate risk without overemphasizing error in situations where error bars only slightly overlap.Instead of showing an error bar, which is the same size from top to bottom, a ridgeline plot gets fatter to represent more likely values and thinner to represent less likely values. This way, a small amount of overlap doesn’t signal lack of statistical significance quite as loudly. Decision-makers need to understand this error to make the most of survey results, so it's important for data scientists and analysts to communicate confidence intervals when visualizing estimated results.

“In General Assembly’s full-time, career-changing Data Science Immersive program and part-time Data Science course, students learn about sampling, calculating confidence intervals, and using data visualizations to help make actionable decisions with data. Students can also learn about the programming language R and other key data skills through expert-led workshops and exclusive industry events across GA’s campuses.”

Read “A Beginner’s Guide to Ridgeline Plots” by Josh Yazman.

Spark

Joseph Kambourakis, a Data Science instructor at GA Boston, says, “Apache Spark is an open-source framework used for large-scale data processing. Apache Spark is important to the big data field because it represents the next generation of big data processing engines. The framework is made up of many components, including four programming APIs and four major libraries. By providing a flexible language platform and having concise syntax, the data scientist can write more programs, iterate through their programs, and have them run much quicker. Since Spark’s release in 2014, it has become one of Apache’s fastest growing and most widely used projects of all time.

“At GA, we teach both the concepts and the tools of data science. Because hiring managers from marketing, technology, and biotech companies, as well as guest speakers like company founders and entrepreneurs, regularly talk about using Spark, we’ve incorporated it into the curriculum to ensure students are fluent in the field’s most relevant skills. Spark is a great tool to teach because the general curriculum focuses mostly on Python, and Spark has a Python API/library called PySpark. When we teach Spark in our Data Science Immersive, we cover resilient distributed data sets, directed acyclic graphs, closures, lazy execution, and reading JavaScript Object Notation (JSON), a common big data file format."

Read “A Beginner’s Guide to Spark" by Joseph Kambourakis.

SQL

Michael Larner, a Data Analytics instructor at General Assembly Los Angeles, says, "Put simply, SQL is the language of data — it’s a programming language that enables us to efficiently create, alter, request, and aggregate data from those mysterious things called databases. It gives us the ability to make connections between different pieces of information, even when we’re dealing with huge data sets. Modern applications are able to use SQL to deliver really valuable pieces of information that would otherwise be difficult for humans to keep track of independently. In fact, pretty much every app that stores any sort of information uses a database. This ubiquity means that developers use SQL to log, record, alter, and present data within the application, while analysts use SQL to interrogate that same data set in order to find deeper insights.

"At General Assembly, we know businesses are striving to transform their data from raw facts into actionable insights. To accomplish this, we give students the opportunity to use SQL to explore real-world data such as Firefox usage statistics, Iowa liquor sales, or Zillow’s real estate prices. Our full-time Data Science Immersive and part-time Data Analytics courses help students build the analytical skills needed to turn the results of those queries into clear and effective business recommendations. On a more introductory level, after just a couple of hours of in one of our SQL workshops, students are able to query multiple data sets with millions of rows."

Read “A Beginner’s Guide to SQL" by Michael Larner.