This training will teach you about the relevant packages and tools to do data science in Python. We will cover the tools and packages that help you solve your day-to-day data science needs. Specifically, you will learn how to work with pandas, matplotlib, and scikit-learn; we will teach how to use Jupyter notebooks. Added to that, we will show you how the command line can speed up some everyday tasks relevant to data science.
"The training gave me a lot of grip/insights on the subject. How to use pandas / clean up your data and plotting it were for me the most interesting parts." - Configuration Manager, KPN
Q: Is Data Science with Python training right for me?
- Yes - if you want to learn how to use Python to clean and reshape data
- Yes - if you want to know how to develop data science models
- Yes - if you want to know how to visualize your data and present your results with plotting
Q: What will I achieve by completing this training?
Through instructor-led discussion and interactive, hands-on exercises, you will learn how to use Python for Data Science.
You will learn:
- Using git and the CLI when doing data science
- Jupyter notebooks
- Numpy basics
- Plotting basics
- Introduction to machine learning with scikit-learn
You will gain hands-on experience in:
- Working with Jupyter notebooks
- Data cleaning and generating general insights with pandas
- Visualizing results with matplotlib and seaborn
- Building machine learning models
You will develop the skills to:
- Work with Pandas DataFrames
- Apply and select the best statistical methodologies (for example: regression, cluster analysis, decision trees) for solving business problems
- Report and share generated insights
- Fit, select and evaluate machine learning algorithms
Q: What else should I know?
- In order to benefit from this course participants are expected to have general knowledge of Python programming concepts (e.g. be familiar with concepts mentioned under Basics on learnpython.org)
- You will need to bring your own laptop for this training with the following requirements:
- Anaconda with Python 3.5 should already be installed
- Ability to install software
- Ability to connect to internet