Data is the residue of every action that takes place in a company, with customers, and in the marketplace. It is created when customers buy products, users interact with services, and colleagues collaborate.
In an increasingly connected world, our ability to capture and leverage data has increased exponentially. We can track interactions, transactions, and encounters in real time; but data in the wrong hands is useless, if not dangerous. In the right hands, data can drive new insights and powerfully informed decisions. When combined with advances in artificial intelligence and machine learning, data can be transformational.
This course introduces fundamental techniques and technologies from data science, predictive analytics, and machine learning that can help you get a handle on the modern information flood. Using the Python programming language, you will:
- Learn analytics skills which will enable you to evaluate, query, and visualize data using open source tools: NumPy, Pandas, Matplotlib, Seaborn, scikit-learn, and Apache Spark.
- Leverage strategies to create data-driven questions that can provide scientific or business value
- Use methods for assembling data from multiple sources and preparing powerful machine learning (ML) models
- Be exposed to common machine learning techniques used to solve supervised and unsupervised problems
- Gain hands-on experience with techniques for deploying models as part of larger systems
The data fundamentals course can be taught in one, three, four, or five day variations.
- The one day version focuses on working with data in Pandas with an introduction to machine learning techniques.
- The four day course includes data fundamentals and classical machine learning.
- The five day course includes data fundamentals, classical machine learning, and a full day of hands-on case-studies which further explore the techniques.
- The three day version omits formal coverage of Pandas (day 1) and excludes the case studies (day 5).