Data Science Fundamentals Part 1
Addison Wesley (Hersteller)
978-0-13-465945-9 (ISBN)
Data Science Fundamentals LiveLessons teaches you the foundational concepts, theory, and techniques you need to know to become an effective data scientist. The videos present you with applied, example-driven lessons in Python and its associated ecosystem of libraries, where you get your hands dirty with real datasets and see real results.
Description
This first course of the two-course series focuses on the fundamentals of acquiring, parsing, validating, and wrangling data with Python and its associated ecosystem of libraries. After an introduction to Data Science as a field and a primer on the Python programming language, you walk through the data science process by building a simple recommendation system. After this introduction, you dive deeper into each of the specific steps involved in the first half of the data science process–mainly how to acquire, transform, and store data (often referred to as an ETL pipeline). You learn how to download data that is openly accessible on the Internet by working with APIs and websites, and how to parse this XML and JSON data. With this structured data, you learn how to build data models, store and query data, and work with relational databases. Along the way, you learn the fundamentals of programing with Python (including object-oriented programming and the standard library) as well as the best practices of building sustainable data science applications.
Skill Level
Beginner
What You Will Learn
How to get up and running with a Python data science environment
The essentials of Python 3, including object-oriented programming
The basics of the data science process and what each step entails
How to build a simple (yet powerful) recommendation engine for Airbnb listings
Where to find high-quality data sources and how to scrape websites if no existing dataset is available
How to work with APIs programmatically, including (but not limited to) the Foursquare API
Strategies for parsing JSON and XML into a structured form
How to build data models and work with database schemas
The basics of relational databases with SQLite and how to use an ORM to interface with them in Python
Best practices of data validation, including common data quality checks
How to query data in a database, including joining data tables and aggregating data
The fundamentals of exploratory data analysis
How to find and handle missing or malformed data
The importance of creating reproducible analyses and how to share them effectively
Who Should Take This Course
Aspiring data scientists looking to break into the field and learn the essentials necessary
Journalists, consultants, analysts, or anyone else who works with data and are looking to take a programmatic approach to exploring data and conducting analyses
Quantitative researchers interested in a programmatic and systematic approach to working with data and data pipelines
Software engineers interested in the fundamentals and best practices of working with data
Practicing data scientists already familiar with another programming environment looking to learn how to do data science with Python
Course Requirements
Basic understanding of programming
Familiarity with Python and relational databases are a plus
Lesson Descriptions
Lesson 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 3 begins the deep dives into each of the specific stages of the data science process, starting with the first: data acquisition. The lesson covers the basics of finding the appropriate data source for your problem and how to download the datasets you need once you have found them.
Beginning with an overview of how the infrastructure behind the Internet works, you learn how to programmatically make HTTP requests in Python to access data through APIs and websites, as well as the basics of two of the most common data formats: JSON and XML. The lesson ends by setting up the additional data you need for the other lessons: Foursquare Venues.
Working with the Foursquare dataset, you learn how to interact with APIs and do some minor web scraping. You also learn how to find and acquire data from a variety of sources and keep track of its lineage all along the way. You learn to put yourself in the data science mindset and how to see the data (hidden in plain sight) that we interact with every day.
Lesson 4: Adding Structure–Parsing Data and Data Models
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.
You see how to work with a variety of data formats, including XML and JSON, by parsing the data we have acquired to eventually load it into an environment better-suited to exploration and analysis: a relational database. But before we load our data into a database, we take a short diversion to talk about how to conceptually model structure in data with code. You get a primer in object-oriented programming and learn how to leverage it to create abstractions and data models that define how you can interface with your data.
Lesson 5: Storing Data–Persistence with Relational Databases
Lesson 5 starts with an introduction to one of the most ubiquitous data technologies–the relational database. The lesson serves as a capstone to the ETL pipeline of the previous videos where you learn the fundamentals of databases with SQLite and how to interface with them in Python. You learn the ins and outs of the various strategies for storing data in a database and see how to map the abstractions you created in Python to database tables through the use of the peewee object-relational mapper (ORM). By being able to query and manipulate data with Python while persisting data in a database reliably, the interface ORMs provide gives you the best of both worlds.
Lesson 6: Validating Data–Provenance and Quality Control
Lesson 6 starts by showing you how to effectively query your data to understand what it contains, uncover any biases it might contain, and to deal with missing values and bad data. After you have validated the quality of the data, you learn the fundamentals of exploratory data analysis (EDA) and see how to use descriptive statistics to learn how your data is distributed. Finally, you learn to explore your data to uncover insights and trends in the data by joining and aggregating data tables with peewee and also learn how these queries in Python map to traditional SQL.
About Pearson Video Training
Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.
Jonathan Dinu is an author, researcher, and most importantly, an educator. He is currently pursuing a Ph.D. in Computer Science at Carnegie Mellon's Human Computer Interaction Institute (HCII), where he is working to democratize machine learning and artificial intelligence through interpretable and interactive algorithms. Previously, he founded Zipfian Academy (an immersive data science training program acquired by Galvanize), has taught classes at the University of San Francisco, and has built a Data Visualization MOOC with Udacity. In addition to his professional data science experience, he has run data science trainings for a Fortune 500 company and taught workshops at Strata, PyData, and DataWeek (among others). He first discovered his love of all things data while studying Computer Science and Physics at UC Berkeley, and 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. 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.
Introduction
Lesson 1: Introduction to Data Science with Python
Topics
1.1 Welcome to the Course
1.2 Why Data Science and Why Now?
1.3 The Potential of Data Science
1.4 Getting Set Up with a Data Science Development Environment
1.5 A Python (3) Primer
1.6 Python 2 versus Python 3
1.7 Test Your Knowledge: Wordbuzz
1.8 Wordbuzz: Putting It All Together
1.9 Python Review and Resources
1.10 Python for Data Science
1.11 What’s to Come
Lesson 2: The Data Science Process—Building Your First Application
Topics
2.1 Introduction to the Data Science Process
2.2 Defining Your Problem
2.3 Acquiring Data
2.4 Wrangling Data
2.5 Exploring Data
2.6 Recommendations through Triangle Closing
2.7 Python Development Workflow
2.8 Triadic Closure in Python
2.9 Challenges of Recommendation Systems
2.10 Obtaining an Evaluation Baseline
2.11 Inspecting and Evaluating Results
2.12 Present and Disseminate
2.13 The Data Science Process Applied: Cheaper Beds, Better Breakfasts
Lesson 3: Acquiring Data—Sources and Methods
Topics
3.1 The Data Science Mindset
3.2 The Data Science Technology Stack
3.3 Where to Get Data—Sources and Services
3.4 How the Web Works
3.5 Making HTTP Requests with Python
3.6 Adding Content with Open Data
3.7 Parsing Data with Python—JSON and XML
3.8 Data and File Formats
3.9 Working with APIs
3.10 Parametric API Requests with Python
3.11 Exploring the Foursquare API
3.12 Downloading Foursquare Venues
Lesson 4: Adding Structure—Parsing Data and Data Models
Topics
4.1 Introduction to the ETL Pipeline
4.2 Data Models—Adding Structure to Data
4.3 Building Abstractions—Object-Oriented Programming
4.4 Creating Classes in Python
4.5 Defining Methods and Updating State
4.6 Magic Methods, Class Attributes, and Introspection
4.7 Exploring and Structuring the Foursquare Response
4.8 Data Models Applied—Representing Foursquare Entities with Classes
4.9 Modeling Behavior with Methods
4.10 Customizing Model Interfaces with Setter Methods and Virtual Attributes
4.11 Keeping Things DRY with Inheritance
4.12 OOP Use Cases
4.13 The Case for (and against) OOP
Lesson 5: Storing Data—Persistence with Relational Databases
Topics
5.1 Introduction to Databases with SQLite
5.2 Inspecting Databases with the SQLite Shell
5.3 The Database Landscape
5.4 What's in a Schema?—Mapping Data Models to Data Tables
5.5 Introduction to Object Relational Mappers
5.6 ORMs in Python with peewee
5.7 Creating and Querying Records with peewee
5.8 End-to-end ETL in Python
5.9 Advantages and Disadvantages of ORMs
5.10 Extract, Transform, Load—Putting It All Together
Lesson 6: Validating Data—Provenance and Quality Control
Topics
6.1 Introduction to Exploratory Data Analysis
6.2 Understanding Your Data Quickly with Graphical Tools
6.3 Inspecting Databases and Building Schemas with pewee
6.4 Data Quality Checks with peewee
6.5 Finding Missing Data and Null Values with peewee
6.6 Dealing with Missing Data
6.7 EDA for Insight–Describing Data
6.8 Inspecting Queries and Displaying Results in peewee
6.9 Groups and Aggregates with peewee
6.10 Ranking and Sorting Venues
6.11 SQL Relations and Joins
6.12 Joins with peewee
6.13 Querying Across Datasets with Joins
6.14 Translating peewee to SQL
6.15 A Visual Introduction to Joins with SQL
| Erscheint lt. Verlag | 31.1.2022 |
|---|---|
| Reihe/Serie | LiveLessons |
| Verlagsort | Boston |
| Sprache | englisch |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| ISBN-10 | 0-13-465945-7 / 0134659457 |
| ISBN-13 | 978-0-13-465945-9 / 9780134659459 |
| Zustand | Neuware |
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