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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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What is this class about?

This course is about learning from data, in order to gain useful predictions and insights. We will take a dive into the fields of Data Science and Machine Learning using the Python programming language and Python-based open source libraries. Through real-world examples and on-the-spot coding exercises, we will take you through the entire data science process:
• data cleaning/imputation/normalization in order to go from raw often cluttered data to an informative and usable data-set
• exploratory data analysis (and data visualization) to get some intuition about the data
• predictive modeling based on supervised (e.g. classification and regression) and unsupervised (e.g. k-means) machine learning and statistical tools.
In the end, we will cover more advanced topics such as ensemble models, dimensionality reduction and deep learning.

Why take this course?

The course is suitable for anyone wanting to learn from data. Today we see that the more and more companies use data that they have collected in the past, or are collecting now, to make predictions or gain useful insights. More and more data is collected to the point that some of these companies might be overwhelmed by it, or in extremis not know what to do with it all. This opens up opportunities, but it is only through careful analysis and application of statistical and machine learning methods that we can learn from the data without also getting overwhelmed.

Expected Learning Outcomes

After completion of this course, you will have learned to
• Use 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.



The couse is suitable for anyone wanting to learn insights from data. When data science is a new activity in your organisation, or you have new people joining your existing data science team, this course if for you. And even if you just want to brush up on your existing data science skills, this course is valuable for you.