Python for Data Science Training
Unleash the power of machine learning and artificial intelligence with the key python libraries and projects ones needs to know to become a data science master. Conquer data visiaulisation with Pandas, Matplotlib and Seaborn. Quickly analyse and present data with Jupyter Notebooks and learn how to use the key algorithms of scikit-learn.
- Introduction & Review of Python Syntax - Quick introduction to Python and revision of the fundamentals of Python Programming
- Jupyter Notebooks - Learn how to use Jupyter Notebooks for interactive data science & scientific computing
- Numpy - Master the basics of data analysis in Python with Numpy. Expand your skill set by learning scientific computing.
- Pandas - Learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames. Learn how to tidy, rearrange, and restructure your data using versatile pandas DataFrames.
- Visualisations - Level up your data science skills by creating visualizations using matplotlib and manipulating data frames with Pandas. master complex data visualization techniques using Matplotlib and Seaborn and create versatile and interactive data visualizations using Bokeh.
- Machine Learning
- Supervised learning with scikit-learn: Learn how to build and tune predictive models with supervised learning and understand how to evaluate their performance on unseen data. Learn how to build a model to automatically classify items.
- Unsupervised learning with scikit-learn: Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
- Neural Networks using Keras 2.0: Learn the fundamentals of neural networks and how to build deep learning models.
- Network Analysis - Master the skills to analyze, visualize, and make sense of networks using the NetworkX library.
- Working with databases & text processing - Learn to import data into Python from various sources, such as Excel, SQL, SAS and from the web. Master the basics of querying tables in relational databases such as MySQL, Oracle, SQL Server, and PostgreSQL.