on-demand course

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

with Jonathan Dinu

August 2017

Beginner

20h 54m

English

Pearson

Closed Captioning available in English, Japanese, Korean, Chinese (Simplified), Chinese (Traditional)

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Course outline

Data Science Fundamentals Part 1: Introduction
7m 0s
Topics
1m 37s
1.1 Welcome to the Course
3m 14s
1.2 Why Data Science and Why Now?
7m 46s
1.3 The Potential of Data Science
24m 18s
1.4 Getting Set Up with a Data Science Development Environment
7m 41s
1.5 A Python (3) Primer
22m 19s
1.6 Python 2 versus Python 3
7m 49s
1.7 Test Your Knowledge: Wordbuzz
27m 38s
1.8 Wordbuzz: Putting it all Together
12m 29s
1.9 Python Review and Resources
8m 47s
1.10 Python for Data Science
14m 59s
1.11 What‚Äôs to Come
10m 10s
Topics
1m 17s
2.1 Introduction to the Data Science Process
7m 16s
2.2 Defining Your Problem
6m 46s
2.3 Acquiring Data
21m 1s
2.4 Wrangling Data
28m 5s
2.5 Exploring Data
28m 47s
2.6 Recommendations through Triangle Closing
19m 54s
2.7 Python Development Workflow
14m 0s
2.8 Triadic Closure in Python
27m 35s
2.9 Challenges of Recommendation Systems
11m 5s
2.10 Obtaining an Evaluation Baseline
18m 37s
2.11 Inspecting and Evaluating Results
12m 4s
2.12 Present and Disseminate
15m 20s
2.13 The Data Science Process Applied—Cheaper Beds, Better Breakfasts
6m 27s
Topics
1m 41s
3.1 The Data Science Mindset
14m 57s
3.2 The Data Science Technology Stack
15m 29s
3.3 Where to Get Data: Sources and Services
16m 45s
3.4 How the Web Works
19m 54s
3.5 Making HTTP Requests with Python
14m 32s
3.6 Adding Context with Open Data
9m 25s
3.7 Parsing Data with Python—JSON and XML
28m 12s
3.8 Data and File Formats
15m 13s
3.9 Working with APIs
24m 19s
3.10 Parametric API Requests with Python
28m 11s
3.11 Exploring the Foursquare API
13m 57s
3.12 Downloading Foursquare Venues
22m 4s
Topics
1m 12s
4.1 Introduction to the ETL Pipeline
10m 46s
4.2 Data Models—Adding Structure to Data
23m 56s
4.3 Building Abstractions—Object Oriented Programming
11m 47s
4.4 Creating Classes in Python
19m 44s
4.5 Defining Methods and Updating State
20m 48s
4.6 Magic Methods, Class Attributes, and Introspection
25m 16s
4.7 Exploring and Structuring the Foursquare Response
23m 36s
4.8 Data Models Applied—Representing Foursquare Entities with Classes
21m 58s
4.9 Modeling Behavior with Methods
15m 16s
4.10 Customizing Model Interfaces with Setter Methods and Virtual Attributes
24m 28s
4.11 Keeping Things DRY with Inheritance
28m 51s
4.12 Object-Oriented Programming Use Cases
20m 45s
4.13 The Case for (and against) OOP
13m 4s
Topics
1m 21s
5.1 Introduction to Databases with SQLite
26m 53s
5.2 Inspecting Databases with the SQLite shell
13m 12s
5.3 The Database Landscape
12m 4s
5.4 What's in a Schema? Mapping Data Models to Data Tables
22m 32s
5.5 Introduction to Object Relational Mappers
6m 36s
5.6 ORMs in Python with peewee
19m 26s
5.7 Creating and Querying Records with peewee
25m 19s
5.8 End-to-end ETL in Python
9m 20s
5.9 Advantages and Disadvantages of ORMs
4m 52s
5.10 Extract, Transform, Load—Putting It All Together
10m 8s
Topics
1m 21s
6.1 Introduction to Exploratory Data Analysis
17m 15s
6.2 Understanding your Data Quickly with Graphical Tools
17m 27s
6.3 Inspecting Databases and Building Schemas with peewee
20m 15s
6.4 Data Quality Checks with peewee
18m 19s
6.5 Finding Missing Data and Null Values with peewee
12m 13s
6.6 Dealing with Missing Data
10m 38s
6.7 EDA for Insight—Describing Data
4m 39s
6.8 Inspecting Queries and Displaying Results in peewee
18m 5s
6.9 Groups and Aggregates with peewee
14m 23s
6.10 Ranking and Sorting Venues
20m 14s
6.11 SQL Relations and Joins
8m 25s
6.12 Joins with peewee
27m 42s
6.13 Querying Across Datasets with Joins
26m 26s
6.14 Translating peewee to SQL
6m 58s
6.15 A Visual Introduction to Joins with SQL
14m 49s
Data Science Fundamentals Part 1: Sunmary
3m 21s

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

1.2 Why Data Science and Why Now?

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Publisher Resources

ISBN: 9780134660141

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

with Jonathan Dinu

Introduction

Lesson 1: Introduction to Data Science with Python

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

Lesson 3: Acquiring Data—Sources and Methods

Lesson 4: Adding Structure—Parsing Data and Data Models

Lesson 5: Storing Data—Persistence with Relational Databases

Lesson 6: Validating Data—Provenance and Quality Control

Summary

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

1.2 Why Data Science and Why Now?

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.