Data Science Fundamentals Part 1: Learning Basic Concepts, Data Wrangling, and Databases with Python

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch

Jonathan has always had a passion for sharing the things he has learned in the most creative ways he can. When he is not working with students, you can find him blogging about data, visualization, and education at hopelessoptimism.com or rambling on Twitter @jonathandinu.Skill LevelBeginnerWhat You Will LearnHow to get up and running with a Python data science environmentThe essentials of Python 3, including object-oriented programmingThe basics of the data science process and what each step entailsHow to build a simple (yet powerful) recommendation engine for Airbnb listingsWhere to find quality data sources and how to work with APIs programmaticallyStrategies for parsing JSON and XML into a structured formThe basics of relational databases and how to use an ORM to interface with them in PythonBest practices of data validation, including common data quality checksWho Should Take This CourseAspiring data scientists looking to break into the field and learn the essentials necessaryJournalists, consultants, analysts, or anyone else who works with data and looking to take a programmatic approach to exploring data and conducting analysesQuantitative researchers interested in applying theory to real projects and taking a computational approach to modeling.Software engineers interested in building intelligent applications driven by machine learningPracticing data scientists already familiar with another programming environment looking to learn how to do data science with PythonCourse RequirementsBasic understanding of programmingFamiliarity with Python and statistics are a plusLesson DescriptionsLesson 1: Introduction to Data Science with Python
Lesson 1 begins with a working definition of data science (as we use it in the course), gives a brief history of the field, and provides motivating examples of data science products and applications. This lesson covers how to get set up with a data science programming environment locally, as well as gives you a crash course in the Python programming language if you are unfamiliar with it or are coming from another language such as R. Finally, it ends with an overview of the concepts and tools that the rest of the lessons cover to hopefully motivate you for and excite you about what’s to come!

Lesson 2: The Data Science Process-Building Your First Application

Lesson 2 introduces the data science process by walking through an end-to-end example of building your very first data science application, an AirBnB listing recommender.

You continue to learn how to work with and manipulate data in Python, without any external libraries yet, and leverage the power of the built-in Python standard library. The core application of this lesson covers the basics of building a recommendation engine and shows you how, with simple statistics and a little ingenuity, you can build a compelling recommender, given the right data. And finally, it ends with a formal treatment of the data science process and the individual steps it entails.

Lesson 3: Acquiring Data-Sources and Methods

Lesson 4: Adding Structure-Data Parsing and Storage

Lesson 4 picks up with the second stage of what traditionally is referred to as an extract, transform, and load (ETL) pipeline, adding structure through the transformation of raw data.

Lesson 5: Storing Data: Relational Databases (with SQLite)

Lesson 6: Data Validation and Exploration

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