Python Programming for Actuaries and Data Scientists

Register Today!






Become proficient in Python with an 8-week course to develop practical skills in Python programming with a focus on actuarial related data. Students will acquire skills to perform actuarial tasks including model validation,  research, and risk analysis.  The course will also introduce  students to fundamental concepts of data mining and machine learning to model large amounts of data.

Joshua Pam is an expert in Python programming. He earned a BS in Mathematics from Marywood University and a Masters in Science from Adelphi University with experience in statistical consulting.

Python for Actuaries and Data Scientists Offers The Following:

  1. Weekly 2 hour lectures to gain a proficient understanding and use of Python programming
  2. Suggested weekly assignments to reinforce skills learned in class
  3. Online office hours for students to receive personal help from Joshua Pam in areas of confusion and to master Python skills
  4. Mini-Sessions in which students can schedule private meetings with Joshua Pam to review areas of confusion and master Python skills
  5. Students are welcome to join The Introduction to Python for Actuaries and Data Scientists Discussion Group open only to program members
  6. Q & A Email Correspondence in which students can email Joshua Pam questions with quick reply back
  7. All weekly lectures recorded and available to students

Schedule

Weekly Live Classes & Subjects

June 9th 
7:00 PM – 9:00 PM EDT

Lecture 1

  1. Getting started with Jupyter Notebooks and object oriented programming
  2. Data in Python
  3. Fundamental data mining concepts and terminology

June 16th
7:00 PM – 9:00 PM EDT

Lecture 2

  1.  Quality and structure of data
  2.  Data preprocessing (cleaning)
  3.  Measures of similarity/dissimilarity

June 23rd
7:00 PM – 9:00 PM EDT

Lecture 3

  1. Creating plots/graphs in Python
  2. Visualizing non-record (spatio-temporal) data

June 30th
7:00 PM – 9:00 PM EDT

Lecture 4

  1. Exploratory data analysis
  2. Correlation analysis
  3. Pattern recognition
  4. Anomaly detection

July 7th
7:00 PM – 9:00 PM EDT

Lecture 5

  1. Regression: Ordinary least-squares, ridge, and lasso
  2. Goodness-of-fit, bootstrapping, and aggregation

July 14th
7:00 PM – 9:00 PM EDT

Lecture 6

  1. Classification: Logistic regression, k-nearest neighbors, and decision trees
  2. Accuracy, precision, recall, and f1-score
  3. Cross-validation

July 21st
7:00 PM – 9:00 PM EDT

Lecture 7

  1. Clustering: Centroids and K-means
  2. Silhouette coefficient and Jaccard index

July 28th
7:00 PM – 9:00 PM EDT

Lecture 8

  1. Stochastic processes and simulation
  2. Time-series forecasting

July 30th
7:00 PM – 9:00 PM EDT

Lecture 9

  1. Review of course material and Q&A
  2. Capstone project: Write a detailed report on financial data w/ annotated Python code

Join Python Programming for Actuaries and Data Scientists Today!

REGISTER