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PYTH01 Data Science with Python on your request on your request Contact Us

Data Science with Python

Data Science with Python


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This course is a hands-on course on Data Science and Machine Learning using the Python programming language and Python-based open source libraries.

The course starts with a fast-paced, crash course of Python and an introduction to the most important scientific libraries used in Data Science. We make use of Jupyter notebooks, an interactive analysis and learning environment, and take you through a complete data science process.

Next will be a deep-dive into the use of the NumPy, Scipy and pandas libraries, which will form the basis of two more extensive hands-on exercises on Exploratory Data Analysis and Predictive Modelling.

Afterwards we will move on to Machine Learning, covering both supervised learning (Classification and Regression) as well as unsupervised learning. Here we will also cover data cleaning, data imputation, data normalization and standardization. We will make use of the Scikit-Learn Python library for Machine Learning.

Next up, we will cover more advanced topics such as ensembles, dimensionality reduction and the use of neural networks in Deep Learning.

Learning objectives:

• Crash course in the Python programming language as foundation for the remainder of the course
• Understand the use of Python libraries in data wrangling and data visualization
• Combine various libraries into a complete data science or machine learning process
• Get an understanding on how to create and improve models for your data that provide you with insights hidden in your data



DAY 1 – Crash Course on Python
• Use of Jupyter notebooks
• Fast-paced introduction to Python
• Introduction to scientific libraries: SciPy, NumPy, pandas and Matplotlib

DAY 2 – Exploratory Data Analysis and Predictive Modelling
• Deep dive into NumPy
• Data analysis with pandas
• Advanced data visualisation with Matplotlib
• Exploratory Data Analysis Exercise
• Predictive Modelling Exercise

DAY 3 – Machine Learning with Scikit-Learn
• Data cleaning, data imputation, data normalization and standardization
• Supervised learning – Classification Exercise
• Supervised learning – Regression Exercise
• Unsupervised learning
• Spam Filter Exercise using Natural Language Text Processing

DAY 4 – Advanced Data Science & Deep Learning
• Ensemble models
• Dimensionality reduction using Principal Component Analysis
• Neural Networks
• Deep Learning
• Image Recognition Exercise



• Previous experience in programming is required (preferably in Python or in another language such as Java, Scala, R, …)
• Basic understanding of data analytics and linear algebra is a plus, but not strictly required.