Business analytics and data science have become important skills across all industries. Knowing both how to perform analytics, as well as, sense checking analyses and understanding concepts is key in making decisions today.
Python has become the lingua franca of data science and is, therefore, the topic of this class. This class assumes Python knowledge if you’d prefer a high-level introduction without programming application to data science I have another class: The No-Code Data Science Master Class.
Programming can be intimidating, however, Python excels due to its readability and being freely available for all platforms including Linux, Mac and Windows. This class will assume some prior knowledge of Python syntax, but to establish a common learning environment some of the basics will be covered. We will cover the full data science workflow including:
- Loading data from files (e.g. Excel tables) and databases (e.g. SQL servers)
- Data cleaning
- Exploratory data analysis
- Machine learning
- Model validation and churn analysis
- Data visualization and report generation
In this class, we will use freely and openly available Python libraries including: Jupyter, NumPy, SciPy, Pandas, MatPlotLib, Seaborn, and Scikit-Learn and you will also learn how to quickly learn new libraries.
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Who am I?
Jesper Dramsch is a machine learning researcher working between physical data and deep learning.
I am trained as a geophysicist and shifted into Python programming, data science and machine learning research during work towards a PhD. During that time I created educational notebooks on the machine learning contest website Kaggle (part of Alphabet/Google) and reached rank 81 worldwide. My top notebook has been viewed over 64,000 times at this point. Additionally, I have taught Python, machine learning and data science across the world in companies including Shell, the UK government, universities and several mid-sized companies. As a little pick-me-up in 2020, I have finished the IBM Data Science certification in under 48h.
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