---
title: Data Science Course | Le Wagon
description: "Discover our Data Science course: 9 weeks to learn the fundamentals of Data Science and get job-ready for your career in tech!"
---
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TECH TRAINING
# Get into tech with an immersive Data Science course in Singapore
Learn in-demand skills to launch your tech career in just 9 weeks with the world's most acclaimed Data Science course.
[Apply](https://www.lewagon.com/apply?city_slug=singapore&program_slug=data) [Download syllabus](https://www.lewagon.com/singapore/data-science-course#download-local-syllabus)
4,98/5 - 5,200+ student reviews
TIPP
## Find out if you're eligible for funding.
The Tech Immersion Placement Programme (TIPP) is a grant given by the Infocomm Media Development Authority (IMDA) in Singapore to support people being placed in tech positions. Through this grant, you may be able to finance 70% or 90% of the course fee.
[Speak to us to find out if you're eligible](https://www.lewagon.com/singapore#city-advisor)
## Next sessions
Join the next batch of our Data Science course to start your journey into tech! Funding options are available. Speak to us to find out more.
### Full-Time On campus - 9 weeks
Data Science
Apr 15 - Jun 18 2024
SGD 12,900
[Apply now](https://www.lewagon.com/apply?city_slug=singapore&program_slug=data)
### Part-Time Hybrid - 24 weeks
Data Science
Apr 20 - Oct 5 2024
SGD 12,900
[Apply now](https://www.lewagon.com/apply?city_slug=singapore&program_slug=data)
## Master the fundamental skills of a Data Scientist in just a few weeks
 Data Analytics Toolkits
Master the fundamental tools of programmatic data-analysts: Python as a backbone, SQL for queries and Jupyter Notebooks for visualization.
 Python  SQL  Jupyter  Matplotlib
 Decision Science
Leverage statistics to build advanced analyses and take well-informed business decisions: statistical inferences, hypothesis testing, multivariate regression and confidence intervals.
 Pandas  Numpy  Statsmodels
 Machine Learning & Deep Learning
Master Machine Learning and Deep Learning workflows (data preparation, model selection, evaluation, and fine-tuning) and understand mathematical concepts and numerical implementations behind models.
 Scikit-Learn  Tensorflow  Google Compute
 ML Engineering & Team Projects
Package your models into replicable Python code that can be trained on big data in the cloud, using virtual machines and online databases. Monitor & retrain models when needed, and expose them to the world through APIs.
 Git  Docker  MLflow  FastAPI
## What you will learn in practice
In this Data Science course, you will learn the fundamentals you need to become a Data Scientist in just 9 weeks.
Prep work
Module 1
Module 2
Module 3
Module 4
Module 5
Module 6
Career Week
Programme
- Prep work
- Module 1
- Module 2
- Module 3
- Module 4
- Module 5
- Module 6
- Career Week
Prep work
### Prep work: Get ready to start the bootcamp
Once you’ve been accepted, you'll receive about 40 hours of online learning resources, carefully curated by Le Wagon to be intuitive and interactive. This prepwork will ensure you have the necessary foundations in Python and maths before the bootcamp starts.
- Setup your learning environment (notes, text editor, ...)
- Terminal, Git and OS basic commands
- Python foundations
- Maths foundations (in a fun & intuitive way!)
What you will do in practice
-  40 hours of online tutorials
-  Curated resources from Le Wagon to gain solid foundations
Module 1
### Data Analysis
Learn Python for data science: extract data from relational databases, manipulate big data matrix and build visualizations. Understand key maths concepts for data analysis like statistics & linear algebra.
- Source data from files, web-scraping, or APIs
- Manipulate data with Python, Pandas & Numpy
- Query / store data with SQL & Google Big Query
- Visualization with Jupyter Notebook, Matplotlib, Seaborn & Plotly
What you will do in practice
-  A database built by scraping data from online bookstores
-  Advanced analysis of football performances in SQL
-  Visual dashboard connected to stock-market APIs
Module 2
### Decision Science
Put yourself in the shoes of a data consultant, and learn how to survive the data preparation phase of a vast dataset. Extract insights by interpreting statistical results based on multivariate regression models, hypothesis testing, and confidence intervals.
- Statsmodels for multivariate linear/logistic regressions
- Packaging and object-oriented programming in Python
- Notebook-based presentations with interactive graphs
What you will do in practice
-  40h-long data consulting challenge based on real data from a marketplace
-  Individual presentations of your key findings to your client
Module 3
### Machine Learning
Implement the Machine Learning workflow with Scikit-Learn (data preparation, feature engineering, model selection, evaluation, and fine-tuning) and understand maths intuitions and numerical implementations of ML models.
- Scikit-learn and XGBoost libraries
- Supervised learning (linear, KNN, SVM, Trees, Ensembles)
- Unsupervised learning (PCA, K-means, t-SNE, DBSCAN)
- Structured data (tabular, time-series with SARIMAX...)
- Unstructured data (images, text with Naive-Bayes, Tf-idf, LDA...)
What you will do in practice
-  Machine Learning models perfectly fine-tuned to your tasks
-  Pipelines combining data processing and model predictions
-  Image compression model by color clustering
-  Spam detection algorithms
-  Prediction model for house prices
Module 4
### Deep Learning
Unveil the magic behind Deep Learning! Understand the architecture of neural networks (neurons, layers, stacks) and their parameters (activations, losses, optimisers). Build your own neural networks (dense, recurrent, or convolutional), to work on images, sequences and texts. Learn how to re-use and transfer learning from pre-trained “big-models” from latest open-source research! Get your hands dirty with auto-encoders, batch data processing pipelines, and GPU training.
- TensorFlow
- Keras
- Google Colab
What you will do in practice
-  Dense neural network for fraud transaction detection
-  Transfer learning for image classification
-  Auto-encoders for image compression and denoising
-  Recurrent networks for weather forecasting
-  World embedding for sentiment analysis or text auto-completion
Module 5
### Machine Learning Engineering (MLOps)
Turn your best handcrafted models into a replicable python package that can be trained on big data in the cloud, using virtual machines and online databases. Monitor your model performance as new data comes in, retrain it when needed, and expose its predictions to the world via APIs or websites.
- VS code & command line
- Google Cloud, Virtual Machines, SSH for the trainer
- MLflow & Prefect for DAG orchestration
- Docker & Fast API for the backend
- Streamlit for the frontend
What you will build in practice
-  ML model for predicting taxi fares, trained on big data on the cloud with GPUs
-  Visual web dashboard showing live predictions (on charts, maps, etc.)
-  Trained models in production capable of self-healing
Module 6
### Project weeks
Projects Phase is the ultimate experience of the course. Collaborate efficiently in teams of 3-4 people on a real data science project that you will either join or pitch to your class. Use either open-data repositories (government initiatives, Kaggle, Paper with Code, etc...) or bring your own private dataset. With full-time mentoring from expert teachers, let your wildest dream come true!
What you will build in practice
-  Make an app with a live demo of model predictions
-  Create an in-depth analysis of a business dataset
-  Replicate latest AI research papers with Big Models!
Career Week
### Career Week: start your career in data science!
Meet data science experts working in startups or large companies, prepare your CV and do mock interviews to prepare your job search. Go deeper into essential data topics.
- Preparation for your job search
- Connection to our 20,000+ alumni and 985+ hiring partners
What you will do in practice
-  1:1 coaching
-  Review of CV and cover letter
-  Technical interviews preparation
## How to apply to our Data Science course
Our Data Science course is very complete and intense. But don’t worry, if you don’t have the suggested requirements we will help you get there!
1
Suggested requirements
**Programming**: you'll need to be comfortable with data types & variables, conditions, loops, functions and data structures.
**Mathematics**: you'll need a High School level of Maths, meaning you will be comfortable with functions, derivatives, and systems of linear equations.
2
Book an interview with our advisor
When you apply, we'll get back to you to schedule a 30 minute video interview. We'll talk about your professional project and your motivation.
3
Pass our technical quiz
You will receive a Programming & Mathematics quiz to help you and the Admissions team better understand your current level.
4
Payment options & prepwork
The last step will consist of finding the most suitable financing option for you. Then, you'll jump into the prepwork which consists of a 40h training.
[Apply now](https://www.lewagon.com/apply?city_slug=singapore&program_slug=data)
DISCOVER
## Explore our Data Science course
Press play to learn more about our Data Science course and the success stories of our graduates!
### Everything you need to know about the Data Science course
### Discover the experiences of our alumni
### Find out about our career support and student outcomes
### Would you like more details about our Data Science course?
Understand the goal of the course
Get our syllabus week by week
Understand our methodology
4.98 / 5 [2234 reviews](https://www.coursereport.com/schools/le-wagon#reviews)
4.98 / 5 [2290 reviews](https://www.switchup.org/bootcamps/le-wagon#tablist-tab-review)
4.9 / 5 [710 reviews](https://careerkarma.com/schools/le-wagon/)
Download our Data Science course syllabus
## See what our graduates do now
  Alejandro Seif
- Data Scientist
  Xin Er Chong
- Data Engineer
  Marcus Tan
- Data Scientist
  George Lee
- Data Scientist
## Do you have questions? These answers might help!
## Do you offer career support?
Once the Data Science course ends, you will benefit from our career services. Our team will help you prepare for tech interviews, meet the best local recruiters and connect with relevant alumni. You will also have access to a complete guide to kick-start your tech career after the course: boost your portfolio, prepare for technical interviews, leverage our 20,000 alumni community, but also to lots of useful Slack channels to find jobs or freelance opportunities.
We will make sure to introduce you to the right people depending on your goal and you will meet with inspiring alumni who will come back to share their post-bootcamp experiences, like how they found a job, started their own company or freelancing career.
These are some career services we will provide:
- Networking events, career workshops and office hours with alumni or tech recruiters
- Coaching sessions with our Talent Manager or local alumni
- Resources our Career Playbook
- Introductions to our network of hiring partners through our Hiring Newsletter
- Career week with practical workshops (from building a portfolio to inspiring talks)
## How can I pay for the Data Science course?
We have partnered with Infocomm Media Development Authority (IMDA) to offer subsidies of up to 70% of the course fee as part of the Tech Immersion Placement Programme (TIPP). This grant is to support eligible Singaporeans to be placed in tech positions. Contact us to find out if you're eligible!
## Can I join the Data Science course without a technical background?
The Data Science course requires some basic knowledge of programming and mathematics.
**How much programming do I need to know?** Well, you must be comfortable with data types & variables, conditions, loops, functions and data structures like arrays and dictionaries (also called hashes in some programming languages). If you know those topics in other languages than Python (like Ruby, JavaScript, C++, etc.), you have the right programming prerequisites!
**How much mathematics do I need to know?** In order to join our Data Science course, you also need a minimum level in Mathematics and to be familiar with concepts covered in high school's scientific section. We need you to be comfortable with functions, their derivatives & systems of linear equations. To get up to speed, some additional preparation work will be given to you before the bootcamp start to get a refresh of all these concepts as well as more advanced knowledge on linear algebra and statistics.
## What are my opportunities after the Data Science course?
After our training, you can launch your tech career in a few months. According to the results of an employability study conducted on Le Wagon Alumni during the second half of 2021, 93% of alumni found a job offer, launched their freelance activity or created their own startup within 6 months after their training. That’s an average of 47 days to find your next opportunity!
## Get started for free
Sign up for our free courses and take your first steps into the world of tech. From Python and SQL to JavaScript - our learning platform provides practical exercises to get you started on your journey!
[Start learning for free](https://start.lewagon.com/)
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