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EVENT DETAILS |
This bootcamp is designed to introduce you to data exploration using the Python programming language. Through this intense, weeklong program you will begin your mastery of the skills necessary to manipulate, visualize, and explore datasets to extract valuable insights.
Expert Instructor
This course is taught byTed Petrou, an expert in exploring data with Python through Pandas. He is the author ofPandas Cookbook, a thorough step-by-step guide to accomplish a variety of data analysis tasks with Pandas.
Small Class Size
This is a small class with at most 8 participants, which will allow all students to fully participate and ask questions that will be answered quickly.
When
July 30th - Aug 3rd: 9 a.m. - 5 p.m.
Structure of Course
Learning is accomplished by working through difficult assignments and receiving and reviewing modeled solutions. Using a 'flipped classroom', students will prepare and read each day's material before coming to class. In class, students will rotate from instructor guided lessons to student-focused exercises and projects. The instructor will personally review code and give feedbackoncourse assignments. Approximately 300 short answer questions with detailed solutions will be available.
Syllabus
Before the Course:
Students will need to set aside 10 - 20 hours to set up the programming environment and to complete a thorough overview of the fundamentals of Python. An additional webinar will be held the week before the bootcamp to ensure all students are completing this assignment.
Day 1:Introduction to Pandas - Selecting Subsets of Data
Perhaps the most popular and widely used open-source data wrangling tool of the times, the Pandas library and its main data structures, the Series and DataFrame will be introduced. Selecting subsets of data is a very common yet confusing task that must be mastered in order to be effective with Pandas.
Day 2:Split-Apply-Combine
Insights within datasets are often hidden amongst different groupings. The split-apply-combine paradigm is the fundamental procedure to explore differences amongst distinct groups within datasets.
Day 3: Tidy Data
Real-world data is messy and not immediately available for aggregation, visualization or machine learning. Identifying messy data and transforming it into tidy data (as described by Hadley Wickham) provides a structure to data for making further analysis easier.
Day 4: Exploratory Data Analysis
Exploratory data analysis is a process to gain understanding and intuition about datasets. Visualizations are the foundations of EDA and communicate the discoveries within. Matplotlib, the workhorse for building visualizations will be covered, followed by pandas effortless interface to it. Finally, the Seaborn library, which works directly with tidy data, will be used to create effortless and elegant visualizations.
Day 5:AppliedMachine Learning
After tidying, exploring, and visualizing data, machine learning models can be applied to gain deeper insights into the data. Workflows for preparing, modeling, validating and predicting data with Python's powerful machine learning library Scikit-Learn will be built.
Post-Course Education
It is vital for the student to continue building data explorations immediately after course completion. To facilitate this, a specific curriculum will be outlined that will guide the student towards an entry-level position as a data analysts or data scientist.
Instructor
Ted Petrouis the author ofPandas Cookbookand founder of bothDunder Dataand theHouston Data ScienceMeetup group. He worked as a data scientist at Schlumberger where he spent the vast majority of his time exploring data. Ted received hisMaster'sdegree in statistics from Rice University and used his analytical skills to play poker professionally and teach math before becoming a data scientist.
tags: data science, machine learning, python, programming
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