Python for Data Science

PYTHON FOR DATA SCIENCE

fundamental data science and machine learning with Python

Thurs, Oct 17 | GSU Buckhead Campus, Tower Place 200 | 7:30 AM - 4:00 PM

Location: Registration in Lobby | Breakfast in 1201 | Workshop in Room #1203

Python Basics

Learn the workflows, tools, and approaches that data scientists use to analyze and transform data it into insights

Fundamental Packages

Walk through the foundation of Python and commonly used Python packages including: pandas, matplotlib, and scikit-learn.

Machine Learning

Apply machine learning techniques at the beginner to intermediate level with Python and Jupyter Notebook.

OVERVIEW

Looking to kick-start your programming career in Python and not sure where to start?  Or just looking to develop a more solid foundation in the Python programming language?

In this workshop, we'll work through the basics of leveraging data science using Python, from framing the problem and preparing the data to machine learning basics like building, scoring and improving your data model.

If you're a data science beginner, or want to get hands-on experience with the fundamental Python packages, this is the workshop for you.

EXPERIENCE LEVEL

Beginner
%

AUDIENCE

Data Scientist
%
LISA-SU

LISA SU

DATA SCIENTST, FULLSTORY

Lisa is a Data Scientist at FullStory, an analytics platform that enables businesses to provide their users with a perfect online experience. Her focus is on creating product features that benefit customers, as well as developing internal tools to help make FullStory more “bionic” — smarter, faster, and more scalable. Before FullStory, Lisa was a Senior Data Scientist at General Electric (GE).

IMG_2972 (2)

MO MEDWANI

DATA SCIENCE CONSULTANT + INSTRUCTOR

AGENDA

7:30 AM - 4:00 PM

REGISTRATION + BREAKFAST

MORNING SESSION - Data Science + Machine Learning

Coffee Break Included in Each Session

  • How to install Python and Jupyter/Anaconda Notebook
  • Introduction to Python Programming
  • Learn Python using Pandas, Matplotlib, Scikit-learn,
  • Create DataFrames using a dictionary or list
  • Data Cleaning & Preparation for Analysis
  • Data Imputation
  • Aggregation, Wrangling, Rearranging, and Reshaping data
  • Join, Concatenate, Pivot, and Melt Functions
  • Data Manipulation
  • Time Series Data Analysis
  • Data visualization with Matplotlib

LUNCH + NETWORKING

AFTERNOON SESSION - Predictive Modeling

Coffee Break Included in Each Session

  • Feature engineering

  • Dealing with missing values

  • Normalization

  • Build a model with Scikit-learn

  • Split data into train/test set

  • Train a model with training data

  • Evaluate the performance of the model

  • Concepts of various evaluation metrics

  • Precision/recall

  • Accuracy

  • F1-score

  • ROC curve

  • Tune the Model for Better Performance

  • Model Persistence

  • Save and re-use trained model

  • Troubleshooting