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What's this Python Workshop About? Data scientists need to know how to code, and Python is the most useful and versatile programming language for doing data science. In this hands-on workshop, you’ll learn foundational skills for adding Python to your data science toolbox. Professional instructors from GalvanizeU will guide you through analyzing data in Python by writing scripts and taking advantage of handy libraries. You’ll leave this session equipped to write your own Python scripts to analyze data, and instructor recommendations about next steps to take on your pathway to data science. *Note: this intro to Python is free. *TBD: Location and Exact Date, more Details Coming Soon. Prerequisites (Beginner Level): This is a workshop for beginners. This workshop does not require programming experience. Participants who are familiar with concepts of data analysis and statistics will be better equipped to apply their skills after the conclusion of the workshop. What You'll Learn: • How to view and analyze data sets with Python • How to write and run Python scripts • Which libraries and packages are most useful for analyzing data in Python • Why Python is a flexible, versatile language for doing data science • Which resources you should next utilize to develop your skills Workshop Schedule: 6:00 pm - Networking & Announcements 6:30 pm - Why Use Python for Data Science? 6:50 pm - Working with Python’s NumPy, SciPy 7:50 pm - Working with Pandas (DataFrames) 8:30 pm - Statistical Analysis in Python 9.00 pm - Wrap-Up and Additional Resources Meet Your Instructor: Nir Kaldero is the Director of Science, Head of Galvanize Experts (gX) at Galvanize. As a data scientist and economist, Nir serves on the faculty of the Masters’ of Engineering in Big Data. Before joining Galvanize, Nir was the Lead Instructor for Data Science at General Assembly, a Scholar in the Economics Department at the University of California, Berkeley, and an independent data science consultant. Nir completed PhD coursework in Economics, with application to Industrial Organizations, Finance and Marketing at the University of California, Berkeley, and holds a BA in Economics and Business Administration from IDC Herzliya in Tel Aviv, Israel.
Data Scientist are finding themselves working with increasingly large and complex data in their day to day work. The standard tool-set of a data scientist however has not evolved to meet this need. There currently exists a divide in the tools of engineers (such as Java and Hadoop) which have been developed to handle production tasks and those of data scientists (Python and R) which facilitate rapid prototyping and modeling. While there has been much improvement in the tooling for dealing with data at scale with the development of higher abstractions such as Pig, Hive, Spark, and Scalding, there hasn’t been an equivalent adoption in the workflow of many data scientists. Part of this is due to awareness and part of this is due to availability resources. Due to the fact that most of these tools are in languages the data scientists may not be comfortable with (Java, Scala) there is a perceived high barrier to entry. This talk will teach the best practices of using Spark for practicing data scientists in the context of a data scientist’s standard workflow. By leveraging Spark’s APIs for Python and R to present practical applications, the technology will be much more accessible by decreasing the barrier to entry. Prerequisites: Intermediate What To Bring: Laptop Meet Your Instructor: Jonathan Dinu is currently the VP of Academic Excellence at Galvanize. Previously, he founded Zipfian Academy, which recently has been acquired by Galvanize. He first discovered his love of all things data while studying Computer Science and Physics at UC Berkeley. In a former life, he worked for Alpine Data Labs developing distributed machine learning algorithms for predictive analytics on Hadoop. Jonathan has always had a passion for sharing the things he has learned in the most creative ways he can. At Galvanize, he gets to combine his two favorite things: humans and code. When he is not working with students you can find him blogging about data, visualization, and education at hopelessoptimism.com (http://hopelessoptimism.com/)