This page is secured with 256 bit encryption

Bereket Kindo PhD has over 10 years of experience in the data science and statistics profession. In the early part of his data science career, he mainly used R and SAS for statistical analysis and development of machine learning models, with occasional use of Python. In the more recent 5 years, Bereket has been using Python as the primary programming language for his Lead Data Scientist role at Humana.

Bereket has a doctoral degree in Statistics with a focus in Bayesian machine learning, a masters degree in Actuarial science and a Bachelor's degree in Mathematics.

In addition to his technical skills, Bereket is a strong communicator, often explaining and illustrating complex programming and statistical concepts clearly.

Tentative Schedule for the day - Python for R Users

• 8:30 AM - 9:30 AM - Registration, breakfast and assistance with software loading
• 9:30 AM - 11:00 AM - Section 1: Introduction to Python
• 11:00 AM - 11:15 AM - Break
• 11:15 AM - 12:45 PM - Section 2: Data loading, cleaning and exploration
• 12:45 PM - 1:15 PM - Lunch
• 1:15 PM - 2:45 PM - Section 3: Data summarization and visualization
• 2:45 PM - 3:00 PM - Break
• 3:00 PM - 4:30 PM - Section 4: Statistical tests & models and Closeout

Prerequisite: This course assumes basic knowledge of programming concepts such as variable assignment, using functions, vector/array construction and indexing. While the preferred prerequisite is a working knowledge of R or other statistical computing languages (e.g., SAS, Matlab), those with no prior background in programming are also welcome. We will make a concerted effort to make it beginner friendly. For those who do not have previous programming background in any language, we recommend reading the first 5 chapters of “Think Python” by Allen B. Downey. This book is freely accessible at Think Python Link.

Section 0:  Link to a pre-recorded video: https://youtu.be/C7ndPsUyTEY.  If you need additional support, please arrive 30 minutes prior to the start of the course for assistance. Python libraries of focus: NumPy, Pandas, SciPy, StatModels, Scikit-learn, Matplotlib, and Seaborn.

Section 1: Introduction to Python

• Python variable assignments and data types
• Mathematical and logical operators in Python
• Python lists, and NumPy arrays:
  • Creating, slicing, sub-setting, manipulating, lists and arrays.
  • Multidimensional arrays
• Python functions
  • Constructing Functions and methods. Differences between functions and methods
• Control flow:
  • Conditional evaluation: if else; if elif else
  • Loops (for, and while loops)
• Python dictionaries

Section 2: Data loading, cleaning, exploration, summarization, and visualization 

• Import and clean data.
• Summary statistics
• Data manipulation with the Pandas Package (iloc, loc, selecting columns, adding columns, removing columns, filtering, and summarizing by a categorical column)
• Joining/merging data using Pandas

Section 3: Data Summarization, and visualization

• Summary statistics
• Scatterplots, line graphs, and bar graphs using matplotlib library
• Statistical data visualization using seaborn library

Section 4: Statistical tests & models

• Performing t-tests, proportion tests, and chi-square tests using Python
• Linear models using StatModels library.