AI for Industry

AI for Industry (AI4I) – Practical Foundations in AI with Python is a hybrid programme to help technically inclined individuals understand and use AI appropriately and be able to program basic AI and data applications in Python. The AI curriculum is conducted on DataCamp’s platform* and will also include face to face workshops/online interactions led by AI Singapore mentors.


Engineers, software developers, managers, executives who are technically inclined and keen to learn programming to develop basic AI and data applications.

* course fees apply.

The next batch of AI4I will be launched by the first quarter of 2020
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AI for Industry (AI4I) Modules

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.

Intermediate Python for Data Science

Learn to visualize real data with matplotlib’s functions and get to know new data structures such as the dictionary and the Pandas DataFrame. You will cover key concepts such as boolean logic, control flow and loops in Python,

Python Data Science Toolbox (Part 1 & 2)

Be able to write custom and lambda functions as well as handle errors. Use your acquired skills to write functions that analyze twitter DataFrames and are generalizable to broader Data Science contexts.

Importing Data in Python (Part 1 & 2)

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

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

This course will equip you with all the skills you need to clean your data, from learning how to diagnose your data for problems to dealing with missing values and outliers. Then, you’ll apply all of the techniques you’ve learned to a a real-world Gapminder dataset!

Pandas Foundations

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

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. 

Merging DataFrames with Pandas

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.

Analyzing Police Activity with pandas

During the course, you will practice cleaning messy data, creating visualizations, combining and reshaping datasets, and manipulating time series data. Analyzing Police Activity with pandas will give you valuable experience analyzing a dataset from start to finish.

Intro to SQL for Data Science

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 Relational Databases in SQL

In this course, you’ll experience this firsthand by working with a real-life data set that was used to investigate questionable affiliations of universities. Column by column, table by table, you’ll get to unlock and admire the full potential of databases. In between, you’ll learn how to create tables and specify their relationships as well as how to enforce data integrity.

Introduction to Data Visualisation with 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, statistical graphics and working with time series and image data.

Interactive Data Visualisation with Bokeh

Bokeh is an interactive data visualization library for Python 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 & 2)

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. 

Joining Data in SQL

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.

Introduction to Shell for Data Science

Sometimes called “the universal glue of programming”, it helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds that may be halfway around the world. 

Conda Essentials

 This course explains how to use its core features to manage your software so that you and your colleagues can reproduce your working environments reliably with minimum effort.

Supervised Learning with scikit-learn

 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.

Machine Learning with the Experts: School Budgets

In this course, 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

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.

Machine Learning with Tree-Based Models in Python

You’ll learn how to use Python to train decision trees and tree-based models with the user-friendly scikit-learn machine learning library. You’ll understand the advantages and shortcomings of trees and demonstrate how ensembling can alleviate these shortcomings, all while practicing on real-world datasets.

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.

Network Analysis in Python (Part 1)

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. 

Frequently Asked Questions

About the Course

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.

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.

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 two 3-hour workshops. All sessions are conducted by AI Singapore’s mentors.

Must I complete ‘Introduction to Python' to apply for AI4I?

You may register for the free trial by signing up here: Google Form Registration Link 

The DataCamp team will process the registrations by batch on the 15th and 30th (or the next working day) of each month. Successful candidates will receive an automated email from DataCamp. Please ensure that you prioritise the completion of the required course. The trial accounts remain active for 30 days from the day the trial is granted and cannot be renewed.

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.

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. 

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.

What if I decide to drop out of the course?

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

Course Requirements

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

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

What is the criteria for successful completion of the course?

You will have to complete

  • Complete ‘Data Scientist with Python’ track
  • Achieved 75% (74,783 XP out of 99,710 XP) of the total XP 
  • Attended 2 face-to-face workshops

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. 

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

Cost and Funding

What are the fees to join this training programme?

$642 (GST included) before CITREP+ funding. 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.

Can I use my SkillsFuture Credits?

No, we currently do not accept SkillsFuture Credits

Are there subsidies available?

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 the funding, the participants must satisfy the claim conditions before submitting the application.

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. Therefore, kindly ensure that your information is accurate and complete. Please refer to the Application Procedure outlined here.

Can companies sponsor employees to join the programme?

Yes. For more information on CITREP+ funding for companies, you may visit here.


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

Please visit this article for more information.

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Mailing List Sign Up

Registration Process (currently closed)

Step 1

Register an account in AI Makerspace.

Step 2

Complete AI4I Pre-work under the tab ‘LearnAI’

Step 3

After completion, you will receive an email invitation in your registered email address to apply for AI for Industry (AI4I).

Step 4

Complete the registration form and make payment using the registration link in the email invitation.

Step 5

You will receive the payment receipt and the welcome email within 7 business days.

Step 6

You will be able to access Datacamp premium on 10 Sep 2019, Eastern Time to start the AI4I programme.

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