SkillShare – Data Science and Business Analytics with Python
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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.
Hands-on Class Project
Create a PDF report of a data analysis in Python with at least one visualization.
Code available on here on Skillshare or on Github with interactive links.
Assignment: Use a dataset you have from a project you are working on. Prepare and analyze this data and create at least one meaningful visualization. The data could be sales, expenses, or your FitBit data! Make sure to anonymize the data in case anything is sensitive information! (If you don’t have any data, I have some data listed and even a data set you can use below!)
Deliverable: Create a Jupyter Notebook describing your analysis process that contains at least one visualization that tells a compelling story.
Details: The project will consist of loading data and performing the exploratory data analysis and visualizations outlined in the class. The project is relatively straight-forward, as the class will follow an applied structure that can be revisited for parts of the project analysis.
Students are encouraged to use their own datasets for the analysis, as these yield the most benefit in learning. Alternatively, it is also possible to search for data sets in the following places: Check course page for more detail….
Covered Topics
• Technology
• Data Visualization
• Python
• Science
• Scipy
• Data Analysis
• Data Science
About Author
Jesper Dramsch, PhD
a world-class scientist for machine learning working between physical data, data science and AI.
In my classes, you’ll learn state-of-the-art methods to work with and gain insights from data. This takes the form of exploring data and gaining insights with modelling and visualizations. Whether you’re a beginner, intermediate, or expert, these classes will deepen your understanding of data science.
I am trained as a geophysicist and shifted into data science and machine learning research and Python programming 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 70,000 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. Now I am part of the coordinated organisation ECMWF.
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