AI for Industry

AI Singapore’s AI for Industry (AI4I) – Practical Foundations in AI with Python is a hybrid programme with AI curriculum on DataCamp and face to face workshops/online interactions led by AI Singapore mentors.

The objective of the programme is to enable technically inclined individuals understand and use AI appropriately and be able to program basic AI and data applications in Python.

Programme Details

Duration: up to 12 months (self-directed learning)
Course Fee: SGD$535 (GST included)

  • Hybrid programme with AI curriculum on DataCamp
  • At least 2 face-to-face sessions at AI Singapore during the programme
  • Complete a project within the 12-month duration (optional)
  • 1 year subscription to DataCamp (For Continuous Learning)
  • Guidance from AI Singapore’s mentors
  • Access to Intel AI Academy materials
  • Access to AISG Kelaberetiv forums and community
  • CITREP+ Supported Programme
  • Candidates who successfully complete the program within 12 months will be awarded “the Foundations in AI” Certificate

AI for Industry (AI4I) Modules

Python is a general-purpose programming language that is becoming more and more popular for doing data science. Companies worldwide are using Python to harvest insights from their data and get a competitive edge. Unlike any other Python tutorial, this course focuses on Python specifically for data science. In our Intro to Python class, you will learn about powerful ways to store and manipulate data as well as cool data science tools to start your own analyses. Enter DataCamp’s online Python curriculum.

Intermediate Python for Data Science

The intermediate python course is crucial to your data science curriculum. Learn to visualize real data with matplotlib’s functions and get to know new data structures such as the dictionary and the Pandas DataFrame. After covering key concepts such as boolean logic, control flow and loops in Python, you’re ready to blend together everything you’ve learned to solve a case study using hacker statistics.

Python Data Science Toolbox (Part 1)

It’s now time to push forward and develop your Python chops even further. There are lots and lots of fantastic functions in Python and its library ecosystem. However, as a Data Scientist, you’ll constantly need to write your own functions to solve problems that are dictated by your data. The art of function writing is what you’ll learn in this first Python Data Science toolbox course. You’ll come out of this course being able to write your very own custom functions, complete with multiple parameters and multiple return values, along with default arguments and variable-length arguments. You’ll gain insight into scoping in Python and be able to write lambda functions and handle errors in your very own function writing practice. On top of this, you’ll wrap up each Chapter by diving into using your acquired skills to write functions that analyze twitter DataFrames and are generalizable to broader Data Science contexts.

Python Data Science Toolbox (Part 2)

In this second course in the Python Data Science Toolbox, you’ll continue to build your Python Data Science skills. First you’ll enter the wonderful world of iterators, objects that you have already encountered in the context of for loops without having necessarily known it. You’ll then learn about list comprehensions, which are extremely handy tools that form a basic component in the toolbox of all modern Data Scientists working in Python. You’ll end the course by working through a case study in which you’ll apply all of the techniques you learned both in this course as well as the prequel. If you’re looking to make it as a Pythonista Data Science ninja, you have come to the right place.

Importing Data in Python (Part 1)

As a Data Scientist, on a daily basis you will need to clean data, wrangle and munge it, visualize it, build predictive models and interpret these models. Before doing any of these, however, you will need to know how to get data into Python. In this course, you’ll learn the many ways to import data into Python: (i) from flat files such as .txts and .csvs; (ii) from files native to other software such as Excel spreadsheets, Stata, SAS and MATLAB files; (iii) from relational databases such as SQLite & PostgreSQL.

Cleaning Data in Python

As a Data Scientist, on a daily basis you will need to clean data, wrangle and munge it, visualize it, build predictive models and interpret these models. Before doing any of these, however, you will need to know how to get data into Python. In the prequel to this course, you have already learnt many ways to import data into Python: (i) from flat files such as .txts and .csvs; (ii) from files native to other software such as Excel spreadsheets, Stata, SAS and MATLAB files; (iii) from relational databases such as SQLite & PostgreSQL. In this course, you’ll extend this knowledge base by learning to import data (i) from the web and (ii) a special and essential case of this: pulling data from Application Programming Interfaces, also known as APIs, such as the Twitter streaming API, which allows us to stream real-time tweets.

Cleaning Data in Python

A vital component of data science involves acquiring raw data and getting it into a form ready for analysis. In fact, it is commonly said that data scientists spend 80% of their time cleaning and manipulating data, and only 20% of their time actually analyzing it. This course will equip you with all the skills you need to clean your data in Python, from learning how to diagnose your data for problems to dealing with missing values and outliers. At the end of the course, you’ll apply all of the techniques you’ve learned to a case study in which you’ll clean a real-world Gapminder dataset!

Pandas Foundations

Pandas DataFrames are the most widely used in-memory representation of complex data collections within Python. Whether in finance, scientific fields, or data science, a familiarity with Pandas is essential. This course teaches you to work with real-world data sets containing both string and numeric data, often structured around time series. You will learn powerful analysis, selection, and visualization techniques in this course.

Manipulating DataFrames with Pandas

In this course, you’ll learn how to leverage pandas’ extremely powerful data manipulation engine to get the most out of your data. It is important to be able to extract, filter, and transform data from DataFrames in order to drill into the data that really matters. The pandas library has many techniques that make this process efficient and intuitive. You will learn how to tidy, rearrange, and restructure your data by pivoting or melting and stacking or unstacking DataFrames. These are all fundamental next steps on the road to becoming a well-rounded Data Scientist, and you will have the chance to apply all the concepts you learn to real-world datasets.

Merging DataFrames with Pandas

As a Data Scientist, you’ll often find that the data you need is not in a single file. It may be spread across a number of text files, spreadsheets, or databases. You want to be able to import the data of interest as a collection of DataFrames and figure out how to combine them to answer your central questions. This course is all about the act of combining, or merging, DataFrames, an essential part of any working Data Scientist’s toolbox. You’ll hone your pandas skills by learning how to organize, reshape, and aggregate multiple data sets to answer your specific questions.

Intro to SQL for Data Science

The role of a data scientist is to turn raw data into actionable insights. Much of the world’s raw data—from electronic medical records to customer transaction histories—lives in organized collections of tables called relational databases. Therefore, to be an effective data scientist, you must know how to wrangle and extract data from these databases using a language called SQL (pronounced ess-que-ell, or sequel). This course teaches you everything you need to know to begin working with databases today

Introduction to Databases in Python

In this Python SQL course, you’ll learn the basics of using Structured Query Language (SQL) with Python. This will be useful since whether you like it or not, databases are ubiquitous and, as a data scientist, you’ll need to interact with them constantly. The Python SQL toolkit SQLAlchemy provides an accessible and intuitive way to query, build & write to SQLite, MySQL and Postgresql databases (among many others), all of which you will encounter in the daily life of a data scientist.

Introduction to Data Visualisation with Python

This course extends Intermediate Python for Data Science to provide a stronger foundation in data visualization in Python. The course provides a broader coverage of the Matplotlib library and an overview of Seaborn (a package for statistical graphics). Topics covered include customizing graphics, plotting two-dimensional arrays (e.g., pseudocolor plots, contour plots, images, etc.), statistical graphics (e.g., visualizing distributions & regressions), and working with time series and image data.

Interactive Data Visualisation with Bokeh

Bokeh is an interactive data visualization library for Python (and other languages!) that targets modern web browsers for presentation. It can create versatile, data-driven graphics, and connect the full power of the entire Python data-science stack to rich, interactive visualizations.

Statistical Thinking in Python (Part 1)

After all of the hard work of acquiring data and getting them into a form you can work with, you ultimately want to make clear, succinct conclusions from them. This crucial last step of a data analysis pipeline hinges on the principles of statistical inference. In this course, you will start building the foundation you need to think statistically, to speak the language of your data, to understand what they are telling you. The foundations of statistical thinking took decades upon decades to build, but they can be grasped much faster today with the help of computers. With the power of Python-based tools, you will rapidly get up to speed and begin thinking statistically by the end of this course.

Statistical Thinking in Python (Part 2)

After completing Statistical Thinking in Python (Part 1), you have the probabilistic mindset and foundational hacker stats skills to dive into data sets and extract useful information from them. In this course, you will do just that, expanding and honing your hacker stats toolbox to perform the two key tasks in statistical inference, parameter estimation and hypothesis testing. You will work with real data sets as you learn, culminating with analysis of measurements of the beaks of the Darwin’s famous finches. You will emerge from this course with new knowledge and lots of practice under your belt, ready to attack your own inference problems out in the world.

Joining Data in PostgreSQL

Now that you’ve learned the basics of SQL in our Intro to SQL for Data Science course, it’s time to supercharge your queries using joins and relational set theory! In this course you’ll learn all about the power of joining tables while exploring interesting features of countries and their cities throughout the world. You will master inner and outer joins, as well as self-joins, semi-joins, anti-joins and cross joins – fundamental tools in any PostgreSQL wizard’s toolbox. You’ll fear set theory no more, after learning all about unions, intersections, and except clauses through easy-to-understand diagrams and examples. Lastly, you’ll be introduced to the challenging topic of subqueries. You will see a visual perspective to grasp the ideas throughout the course using the mediums of Venn diagrams and other linking illustrations.

Supervised Learning with scikit-learn

At the end of day, the value of Data Scientists rests on their ability to describe the world and to make predictions. Machine Learning is the field of teaching machines and computers to learn from existing data to make predictions on new data – will a given tumor be benign or malignant? Which of your customers will take their business elsewhere? Is a particular email spam or not? In this course, you’ll learn how to use Python to perform supervised learning, an essential component of Machine Learning. You’ll learn how to build predictive models, how to tune their parameters and how to tell how well they will perform on unseen data, all the while using real world datasets. You’ll do so using scikit-learn, one of the most popular and user-friendly machine learning libraries for Python.

Machine Learning with the Experts: School Budgets

Data science isn’t just for predicting ad-clicks-it’s also useful for social impact! This course is a case study from a machine learning competition on DrivenData. You’ll explore a problem related to school district budgeting. By building a model to automatically classify items in a school’s budget, it makes it easier and faster for schools to compare their spending with other schools. In this course, you’ll begin by building a baseline model that is a simple, first-pass approach. In particular, you’ll do some natural language processing to prepare the budgets for modeling. Next, you’ll have the opportunity to try your own techniques and see how they compare to participants from the competition. Finally, you’ll see how the winner was able to combine a number of expert techniques to build the most accurate model.

Unsupervised Learning in Python

Say you have a collection of customers with a variety of characteristics such as age, location, and financial history, and you wish to discover patterns and sort them into clusters. Or perhaps you have a set of texts, such as wikipedia pages, and you wish to segment them into categories based on their content. This is the world of unsupervised learning, called as such because you are not guiding, or supervising, the pattern discovery by some prediction task, but instead uncovering hidden structure from unlabeled data. Unsupervised learning encompasses a variety of techniques in machine learning, from clustering to dimension reduction to matrix factorization. In this course, you’ll learn the fundamentals of unsupervised learning and implement the essential algorithms using scikit-learn and scipy. You will learn how to cluster, transform, visualize, and extract insights from unlabeled datasets, and end the course by building a recommender system to recommend popular musical artists.

Deep Learning in Python

Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition and artificial intelligence (including the famous AlphaGo). In this course, you’ll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting edge library for deep learning in Python.

Network Analysis in Python (Part 1)

From online social networks such as Facebook and Twitter to transportation networks such as bike sharing systems, networks are everywhere, and knowing how to analyze this type of data will open up a new world of possibilities for you as a Data Scientist. This course will equip you with the skills to analyze, visualize, and make sense of networks. You’ll apply the concepts you learn to real-world network data using the powerful NetworkX library. With the knowledge gained in this course, you’ll develop your network thinking skills and be able to start looking at your data with a fresh perspective!

Who Is This For?

AI4I is specially designed for engineers, software developers, managers, executives who are technically inclined and keen to learn programming to develop basic AI and data applications.

Eligibility Criteria

Singaporean or Singapore PR

Singaporean and Singapore PR can apply for the programme

Age 17 years old and above

Parental consent required for participants under 18 years old

Completed DataCamp‘s ‘Introduction to Python‘ course

You will be required to submit your DataCamp's certificate for this module as part of the registration process.
Please email DataCamp at to request for a 30 day free trial. Please include "AI4I - Your Estimated Course start month" as your email subject.

Registration portal will open April 2019

Timeline for AI for Industry Batch #2

Programme officially starts!

Receive access to DataCamp premium content here!

25 Feb 2019

Attend 2 AI Workshops

We will be conducting 2 workshops - 'AI for Everyone' and 'Hands-on Machine Learning' once/twice a month. Attend 1 session of each workshop best suited to your schedule.

Every Month

Completion of Programme

Fulfill the completion criteria:

  • Complete DataCamp's "Data Scientist with Python" career track
  • Attend 2 workshops 

Drop us an email at when you complete the programme.

By 24 Feb 2020

Make a reimbursement claim from CITREP

Within 3 months of receiving the certificate

Upcoming Batches

Batch #3: end April 2019
Batch #4: end June 2019

Frequently Asked Questions

Who is this programme for and what are the outcomes upon completion?

AI4I is a hybrid programme meant to provide technical executives, managers, developers a learning experience in building data and AI application using  Python. Upon completion, you will be able to build basic data/AI applications.

Must I attend the face-to-face workshops? What if I am unable to attend them due to my work commitments ?

In order to complete the programme, you will need to achieve 75% attendance for the face-to-face workshops. We will be conducting multiple sessions for each workshop to enable our participants to fulfill this requirement. Each workshop will have 1 session during work hours and 1 session after work hours. 

What are the fees to join this training programme?

$535 (GST included). As AI4I is CITREP+ endorsed, you may receive funding support from CITREP+ here. Please select the course title, ‘Practical Foundations in AI with Python‘, when applying for CITREP+ funding. Please note that you should only submit your claim after you have completed your course.

Do I have to give up my current job to join the training programme?

No. This course is designed to enable professionals to continue to learn and up-skill themselves.

I am currently working. May I know what is the commitment expected for the course?

The course is flexible where you set your own learning pace. You are required to complete the course within 12 months in order to be eligible for CITREP+ funding. There are up to three 3-hour workshops. All sessions are conducted by AI Singapore’s mentors.

What if I decide to drop out of the course?

Please refer to our refund policy in the Terms & Conditions.

Will I get a certificate at the end of the course?

Yes, upon completion of the course within 12 months, you will be awarded the “Foundations in AI Certificate” issued by AI Singapore.

What is the criteria for successful completion of the course?

You will have to complete ‘Data Scientist with Python’ track with 75% (74,783 XP out of 99,710 XP) of the total XP and achieve 75% attendance in the face-to-face sessions.

Do I need to be a Singaporean/ Singaporean PR to apply?

Yes, only Singaporeans or Singaporean PRs will be accepted into our programme.

I am not a Singaporean / Singapore PR but I would like to attend the programme. What can I do?

Our programme is based on DataCamp’s content. Hence, you may sign up with DataCamp directly without signing up for our programme. What is different is that in our AI4I – Practical Foundations in AI with Python programme, we have included guidance from AI Singapore’a mentors as well as up to three face-to-face sessions

Must I complete ‘Introduction to Python for Data Science’ to apply for AI4I?

Yes. You are required to complete ‘Introduction to Python with Data Science’ on the Desktop version with minimum 75% (3,525 XP) of the total of 4,700 XP.

Can I use my SkillsFuture Credits?

No, we currently do not accept SkillsFuture Credits

What is XP?

XP is a way of gauging how well you are doing or how engaged you are in DataCamp. It is calculated automatically based on courses, exercises or other actions you complete in DataCamp. Your total or cumulative XP will appear in the top right-hand corner of your screen and on your profile page. Whenever you choose the option to take a Hint or Show Solution, XP will be deducted from your potential additionally awarded XP.

After completing this programme, will I be able to find a job as an AI Engineer/Developer?

This programme is the entry point to being an AI Engineer / Developer. The goal to is build foundational skills to start your AI learning journey. 

If I have an existing DataCamp subscription, do I have to cancel my subscription?

Please visit this article for more information.

Is the extension in timeline to complete the programme applicable to Batch 1?

Yes. For participants in Batch 1, you are required to complete the programme by 6 Nov 2019. You may drop us an email at and indicate that you hace completed the programme. We will issue the AI4I certificate at the end of each month.

Are there subsidies available?

The AI4I is a CITREP endorsed programme. Under the CITREP scheme, the participants have to pay the full course fee to the course provider / training centre. To claim for for funding, the participants must satisfy the claim conditions before submitting the application to claim.

How do I apply for the CITREP claim?

We will enroll participants into IMDA’s ICMS according to the information provided by the participants in their registration form. Please refer to the Application Procedure outlined here

I have signed up for Batch 2 but have not received the DataCamp premium access. When will I receive the access?

You will receive access on 25 Feb, 9am – 10am, GMT-6. Please drop us an email if you have not received the access by 1 March 2019. 

How do I apply for the CITREP claim?

We will enroll participants into IMDA’s ICMS according to the information provided by the participants in their registration form. Please refer to the Application Procedure outlined here

Please indicate your interest here

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