data science 101

fundamental data science & machine learning with Python + Jupyter

Looking to kick-start your career in data science but not sure where to start?

In this workshop, we'll work through the basics of data science, 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:

Lisa joined GE Digital as a data scientist after graduating with her Masters in Analytics from Georgia State University. At the GE Digital Data Science Team, she applies her skills in machine learning and statistics to transform company data into business insights.

In her free time, Lisa can be found winning hackathon challenges and making the world a better place, one app at a time.

topics + tools

data wrangling basics + tools

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

fundamental python libraries

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

foundations of machine learning

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

schedule + lesson plan

thurs, february 28th 2019 | 8 am-4 pm

workshops: 8 am-4 pm  |  happy hour: 4-6 pm  |  healthcare analytics panel: 6-9 pm

morning

8:00 am - 9:00 am

Breakfast + Registration

9:00 am - 10:00 am

Introduction to Data Science and Machine Learning

  • What is data science? What do data scientists do?
  • Difference between AI, machine-learning, and deep-learning
  • Types of machine learning

Data Science Project Workflow

  • What does a data science project look like?
  • What is the workflow to build a data science project from scratch?

Introduction to Data Science + Machine Learning

  • What is data science? What do data scientists do?
  • Difference between AI, machine-learning, and deep-learning
  • Types of machine learning

Data Science Project Workflow

  • What does a data science project look like?
  • What is the workflow to build a data science project from scratch?

10:00 am - 10:30 am

Data Scientist Toolbox

  • Data Scientist Toolbox
    • Common data science tools
    • Python and Anaconda/Jupyter Notebook

Confirm Python and Anaconda Installation

10:30 am - 12:00 pm

Start Your First Data Science Project with Jupyter Notebook

  • Import your data
  • Data inspection
  • Exploratory data analysis and data visualization

12:00 pm - 1:00 pm

Lunch + Networking

afternoon

1:00 pm - 2:00 pm

Data Wrangling + Feature Engineering

  • Feature engineering
  • Dealing with missing values
  • Normalization

2:00 pm - 3:30 pm

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
  • Troubleshoot

3:30 pm - 4:00 pm

Conclude

  • Additional Resource to Continue Learning Data Science and Python
  • Q&A

4:00 pm - 6:00 pm

Post-Workshop Happy Hour

  • Drinks and light refreshments at South City Kitchen

Option to work remotely from Atlanta Tech Village

  • Conference rooms TBD

6:00 pm - 9:00 pm

Healthcare Analytics Panel

  • Dinner and open bar included
  • Register for Healthcare Analytics Panel HERE
    • Purchase bundled tickets for additional savings

venue + parking

location TBA soon!