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
