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.
- 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.
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
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.