Real-World Data Science

Real-world data science is messy, and very different from reading or learning from a book. Having developed and deployed data analytics projects around the APAC region since 2012, we know this well. In our courses, we draw from our “battlefront” not only technical knowledge but lessons learned to share with managers, data scientists and infrastructure engineers. We focus on telling it like it is, on showing what it really takes to deliver quality data science.

For Managers

Come in knowing about enterprise IT, walk away ready to manage big data and data science projects. Our programme on Data Analytics for Managers balances broad pictures of the big data and machine learning landscape with dives into available industry tools, infrastructure, workflows and project management best practices.

We focus on removing hype and doing a straight comparison of tools. With this approach, you are empowered to execute, to decide on the best for you and your team.

For Infrastructure Engineers

A data infrastructure engineer focuses on implementing the models fine-tuned by the data scientists, ensuring the infrastructure supports the data science workflow from ideation through all the way to production. Learn how to build consistent and reproducible analytic environments and data processing platforms to support your Data Science teams.

For Analysts and Programmers

Data science should not be shrouded in mysterious mathematical equations; it is a practice which analysts should be able to apply to everyday work problems.

Applicability and accessibility are crucial, so our programmes blend real-life examples with lessons on using flexible, powerful and popular tools such as Python and R.

Learn not only strong fundamentals in data science programming, but also what translating your skills to the workplace really means.


Learning Journey



Click on the programme title for more details.


Programme
Fee Per Participant Dates
Data Science, Machine Learning and Artificial Intelligence for Senior Executives

Data is the new oil. Understanding how data and machine learning is allowing organisations to understand customers and how the revival of artificial intelligence with techniques like deep learning are rapidly changing the competitive landscape and ecosystem is key to remaining relevant. In this programme, the instructor will share his experiences in building cloud, big data and analytics companies over the last two decades. You will gain insights into how an organisation’s data can be ingested by leveraging current best practices and why leading data companies such as Google, Facebook are created and/or depending on open source tools such as Hadoop, Spark, R, and Python. During the programme, there will be opportunities to explore and discuss and remove the myth and hype around big data, data analytics, machine learning and artificial intelligence.

$650
(before GST)
$695.50
(includes GST)
Wednesday,
16 Aug 2017
Data Visualization Begins With Me

Knowing how to best express your results from your data science and machine learning algorithms is key to convincing your team and management your point of view. The programme will provide an overview on visualization and its principles.

$400
(before GST)
$428
(includes GST)
Wednesday,
23 August 2017
Programme
Fee Dates
Chatbot Development Begins With Me

Chatbots on the mobile phone or web site is becoming a popular method to interact and service an organisation’s customers. This 1-day programme will enable you to build a basic Chatbot with no programming requirement. It is designed for organisations wanting to build and launch a Chatbot service on their web or as a mobile service.


$750
(before GST)
$802.50
(includes GST)

Wednesday,
19 July 2017
Data Analytics Begins With Me

Basic Data Analytics taught with a simple but powerful graphical user interface (“GUI”) drag-and-drop tool targeted at business analysts requiring more sophisticated capabilities than what a spreadsheet can offer.

Understanding and using data is increasingly an important part of an executive function. Whether you are a business analyst, customer service officer, physiotherapist, procurement officer or HR executive – the ability to understand where potential data is coming from, and to properly collect it, clean it and then analyze the data will be an important and critical skill.

The programme focuses on these core skills and provides you the knowledge to participate in the organisation’s data analytics process without any coding requirements.

You will learn to use the free and open source data analytics tool – Orange – a powerful and popular Python-based data analytics tool. All tasks in Orange are done with point-click-drag-and-drop only.

We will cover basic concepts of data analysis, processing of data, and visualization of raw data and processed data to gain insights, followed by building simple models for classification and prediction.

$750
(before GST)
$802.50
(includes GST)
Thursday,
17 August 2017
Data Visualization Begins With Me

Knowing how to best express your results from your data science and machine learning algorithms is key to convincing your team and management your point of view. The programme will provide an overview on visualization and its principles.

$400
(before GST)
$428
(includes GST)
Wednesday,
23 August 2017
Programme
Fee Dates
Data Analytics for Managers

This programme provides an exploratory tour of big data, data analytics, data science, machine learning and artificial intelligence. It introduces the various tools and techniques used by data science teams today. A roadmap of how to build an internal data science competency in an organisation will be discussed.


$1,500
(before GST)
$1,605
(includes GST)
Friday,
18 August 2017
Data Analytics & Visualization for Managers

This programme provides an exploratory tour of big data, data analytics, data science, machine learning and artificial intelligence. It introduces the various tools and techniques used by data science teams today. A roadmap of how to build an internal data science competency in an organisation will be discussed. The programme provides exposure to building static and interactive graphics using Tableau and Python including a demonstration on the building of actual analytics models in Python.

$2,100
(before GST)
$2,247
(includes GST)
1) 5 - 7 July 2017
(Closed)
2) 26 – 28 July 2017
(Closed)
3) 2 – 4 August 2017
(Closed)
4) 13 - 15 Sep 2017
Introduction to Python Programming

This programme is designed as a refresher training on Python or for learning Python before attending the Introduction to Python for Data Science programme. It will equip you with sufficient knowledge to undertake the data science journey in the Python programming language.

While this is an introduction to Python programming, the main focus will be on the use of Python techniques such as interactions with data sources and libraries such as Pandas, NumPy for data munging and Bokeh for visualization. These fundamentals are the pre-requisites to building data analytics models with more advanced Python libraries.

The programme will also cover the use of Python libraries and maintenance of libraries conflicts with Anaconda environments and Python virtualenv. This is an important topic as the rapid innovation of the Python data science ecosystem often requires the use of multiple libraries with incompatible versions, and the use of virtualenv or Anaconda environments helps maintain developer sanity.

$1,500
(before GST)
$1,605
(includes GST)
Thursday
24 August 2017
to
Friday
25 August 2017
Introduction to Python for Data Science

New libraries for data manipulation, visualization and data modeling have made Python an increasingly exciting alternative to R as a data science language.

This programme aims to quickly bring up to speed a programmer or business analyst who already knows how to programme in Python to begin using Python as a data science tool.

The programme will define data science and explore the first two things a data scientist must do – cleaning and visualizing data. It will then cover the Data Science Workflow - training models and testing them through the application of machine learning models to various industry-relevant data science problems. The tools used will be Scikit-learn and Keras.

$1,500
(before GST)
$1,605
(includes GST)
Thursday,
7 September 2017
to
Friday
8 September 2017
Introduction to R for Data Science

R is the gold standard programming language today for data scientists. R provides a wealth of libraries for data manipulation, visualization and data modeling. R packages allows a beginner data scientist to build simple to sophisticated models quickly and easily in a few lines of code.

The programme aims to quickly bring up to speed a programmer or business analyst who already knows how to programme in other language or have done advanced Excel macros to begin using R as a data science tool.

The programme will define data science and explore the first two things a data scientist must do – cleaning and visualizing data. You will learn and use R's dplyr, ggplot and ggvis packages for these tasks. It will then cover the Data Science Workflow - training models and testing them through the application of machine learning models to various industry-relevant data science problems. The tool used will be the Caret package.

At the end, you should have a working knowledge of how to solve data science problems with R.

$1,500
(before GST)
$1,605
(includes GST)
Thursday
21 September 2017
to
Friday 22 September 2017
Programme
Fee Dates

Data Visualization in Python

Knowing how to best express your results from your data science and machine learning algorithms is key to convincing your team and management your point of view. This programme will teach you visualization techniques using Python as part of your data science workflow.

In this programme, you will be shown how to leverage various Python libraries such as Matplotlib, Bokeh, Seaborn and others to enable you to focus on how to communicate with visualizations for maximum impact.

Pre-requisites: Must be familiar with the Python programming language, and statistics 101 at a pre-university level.


$750
(before GST)
$802.50
(includes GST)
Friday,
29 September 2017

Deep-Learning Applications in Python and Keras

Deep learning is an exciting new technique from the research work done by the artificial intelligence community. In particular, image recognition techniques used by self-driving cars or language translation engines like Google Translate are all based on deep learning.

Python and the Keras package will be used to introduce you to the basics of neural networks and deep learning. The programme will focus on two specific areas where deep learning has shown its strengths – Convolution Neural Networks for image recognition and classification, and Recurrent Neural Networks (RNN) for text analysis.


$750
(before GST)
$802.50
(includes GST)
Tuesday
29 August 2017


Laurence Liew

Laurence Liew is a veteran of the open source, Linux and high performance computing, grid and cloud community and has been promoting the use of Linux/HPC/grid/cloud since 1998.

Laurence currently heads up industry and engineering development under the Singapore National Research Foundation’s AI.SG initiative and is also an Adjunct Associate Professor at the National University of Singapore School of Continuing and Lifelong Education (NUS SCALE).

Prior to joining NUS, Laurence introduced Singapore to advanced analytics and machine learning through companies such as Numascale where he architected an in-memory analytics appliance, and Revolution Analytics Singapore where he introduced enterprise R analytics to organisations in Singapore and Asia. In particular, he conceptualized and built the Revolution’s Centre of Excellence for Analytics in Singapore in partnership with the Infocomm Development Authority of Singapore (IDA) in 2013. He also ran Revolution's development team responsible for putting Revolution R in the cloud. Revolution was subsequently acquired by Microsoft in 2015.

Laurence was involved in building the very first commercial Linux supercomputing cluster for an A*STAR research institute in 1999 and has since implemented and consulted for many organisations in Asia Pacific, Japan, Europe and US, on HPC, grid, cloud and now big data analytics.

Laurence is a member of the Singapore IT Masterplan 2025 committee and mentor to several startups in Singapore and a long time Editor of Transactions on Computational Sciences by Springer Journal.

Laurence graduated from NUS with First Class Honours in Engineering, and holds a Master in Knowledge Engineering from NUS.


Mohamed Najib Bin Ninaba

Najib Ninaba is a 17-year veteran of technology and startups, particularly, in high performance computing, big data and cloud with almost a singular focus on Free and Opensource Software (FOSS) throughout the years since 1999.

Prior to joining NUS, Najib co-founded Scalable Systems Pte Ltd back in 2003 where he led the development for high-performance cluster infrastructures and was the lead consultant for HPC professional services, working with partners such as Intel, Quadrics, and Myrinet. He built a close working relationship with the San Diego Supercomputer Center/National Partnership for Advanced Computational Infrastructure (SDSC/NPACI) ROCKS team where Najib became the first external contributor/committer outside of the ROCKS core team and fellow collaborator for NPACI Rocks Clustering Toolkit. Scalable Systems was acquired by Platform Computing in 2006 when Najib became the Development Manager for Platform Computing Singapore.

At Platform Computing Singapore, Najib was responsible for leading the engineering team to develop and maintain system infrastructure management software. He ran several key product development projects across Singapore, China and North America as well as partner products integration projects. Under his technical leadership, his engineering team developed the open source software, Project Kusu, which became the original base for the Platform Cluster Manager solution. Platform Computing was later acquired by IBM in 2012.

After Platform Computing, Najib went on to become the Development Manager for Revolution Analytics Singapore where he built and managed the engineering team that focused on cloud, hadoop and virtual machine technologies. The team designed and developed RevoCloudR, a complete self-service provisioning service-as-a-software portal that deploys Revolution R Analytics stack including Cloudera Hadoop on Amazon Web Services and OpenStack. He also led the integration solution projects with partners such as Cloudera, IBM Netezza and Intel. Revolution Analytics was acquired by Microsoft in 2015.

Jeanne Choo

Jeanne Choo , a bio-statistician by training but R and Python hacker by day. Prior to joining NUS, Jeanne worked in the analytics industry in London.

Jeanne draws on both technical and teaching experience to conduct workshops. Technically, her R skills came from training as an Oxford biologist wrangling with real-world datasets in the UK education technology sector. Jeanne has conducted R coding labs in London for the likes of Google and Huawei. Jeanne won the Best Data Hack Award at HackLondon (largest hackathon university in the UK) in 2015 where she built a prize-winning R Shiny application which predicted Bitcoin value using historical data. Jeanne works from the idea that personal experiences make each workshop participant's learning curve unique and is always open to requests, suggestions and productive digressions.

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