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Advanced Python Techniques for Data Users (2-day class)

New session times will be displayed below upon confirmation.

Effective immediately in response to COVID-19, all Technology Training classes will be delivered online until further notice.


In advance of each session, Tech Training will provide you with a Zoom link to your class, along with any required class materials.
 


 

Already know Python? Now it is time to acquire the knowledge needed to start using Python in data analytics tasks to scale up and automate processes.

Prerequisites:

  • Basic knowledge of Python, or other programming languages
  • Able to write a Python script that gives the character count for the text "Hello world"
  • Given two lists (e.g. x=['a', 'b', 'c'] and y=['d', 'a', 'e']), being able to find:
    • the common elements in the two lists
    • the elements in x but not in y and vice versa

Taught by Arafat Mokhtar, a Business Intel Analyst at Stanford School of Medicine, this class will help you to leverage your Python skill and venture into the field of data-analytics. We will introduce concepts of data manipulation and analysis such as Dataframe and visualization techniques using Python data science libraries such as Pandas/Numpy/Matplotlib. We will also briefly discuss machine learning libraries such as Sklearn.

By the end of this course, you will be able to accomplish common data analytics tasks such as: preparing, aggregating and summarizing data, finding patterns, and developing ways to automate manual processes -- all with Python coding.

Planned topics

Day 1 - Morning:

1. Review Python common functionalities and data structures used in data science
2. Learn the most important Python libraries in data science (Pandas, Numpy, Matplotlib)
3. Hands on: Python functionalities and dataframes

Day 1 - Afternoon:

1. Read and write data from/to different formats (excel, csv, text, json, etc.)
2. Cleanse and select important records from dataframes
3. Deal with missing data: identify, replace, and eliminate records
4. Sort dataframes by multiple columns
5. Hands on: Data manipulations with Pandas

Day 2 - Morning:

1. Leverage the functions apply, lambda, filter, and map
2. Merge/Join dataframes by foreign keys
3. Learn pivot tables in Pandas
4. Hands on: Data aggregation and summarization

Day 2 - Afternoon:

1. Learn data visualizations with the libraries Matplotlib and Seaborn
2. Introduction to the Machine Learning library Sklearn
3. Apply linear and logistic regression with Sklearn
4. Hands on: Data predictions



Instructor information:

Arafat Mokhtar is a Business Intel Analyst at Stanford School of Medicine, who supports the Human Resources Group with data collections, validation, cleansing, and analytics to provide actionable data insights used by leadership management to make data-driven decisions on the organization workforce.

He develops code in Python scientific stack (Pandas, Numpy, Matplotlib, Sklearn, etc) to automate data analytics processes. He also proposes data solutions and develops measurable business metrics.

He has a Ph. D in Particle Physics from Tel Aviv University, along with several years of Python and R work experience.

 




University IT Technology Training classes are only available to Stanford University staff, faculty, or students. A valid SUNet ID is needed in order to enroll in a class.

Custom training workshops are available for this program

Technology training sessions structured around individual or group learning objectives. Learn more about custom training


University IT Technology Training sessions are available to a wide range of participants, including Stanford University staff, faculty, students, and employees of Stanford Hospitals & Clinics, such as Stanford Health Care, Stanford Health Care Tri-Valley, Stanford Medicine Partners, and Stanford Medicine Children's Health.

Additionally, some of these programs are open to interested individuals not affiliated with Stanford, allowing for broader community engagement and learning opportunities.