LiveLessons – Data Science Fundamentals Part 1: Learning Basic Concepts, Data Wrangling, and Databases with Python
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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.
If nothing else, by the end of this video course you will have analyzed a number of datasets from the wild, built a handful of applications, and applied machine learning algorithms in meaningful ways to get real results. And all along the way you learn the best practices and computational techniques used by a professional data scientist. More specifically, you learn how to acquire data that is openly accessible on the Internet by working with APIs. You learn how to parse XML and JSON data to load it into a relational database. You get hands-on experience with the PyData ecosystem by manipulating and modeling data. You explore and transform data with the pandas library, perform statistical analysis with scipy and numpy, build regression models with statsmodels, and train machine learning algorithms with scikit-learn. All throughout the course you learn to test your assumptions and models by engaging in rigorous validation. Finally, you learn how to share your results through effective data visualization.
Table of Contents
Introduction
Data Science Fundamentals Part 1: Introduction 00:07:00
Lesson 1: Introduction to Data Science with Python
Topics 00:01:37
1.1 Welcome to the Course 00:03:14
1.2 Why Data Science and Why Now? 00:07:48
1.3 The Potential of Data Science 00:24:21
1.4 Getting Set Up with a Data Science Development Environment 00:07:41
1.5 A Python (3) Primer 00:22:33
1.6 Python for Data Science 00:14:59
1.7 What’s to Come 00:10:10
Lesson 2: The Data Science Process-Building Your First Application
Topics 00:01:17
2.1 Introduction to the Data Science Process 00:07:17
2.2 Defining Your Problem 00:06:46
2.3 Acquiring Data 00:21:03
2.4 Wrangling Data 00:28:05
2.5 Exploring Data 00:28:48
2.6 The Simplest Recommender-Triangle Closing 00:21:29
2.7 Triadic Closure in Python, Part 1 00:25:24
2.7 Triadic Closure in Python, Part 2 00:27:36
2.8 Evaluate Results, Part 1 00:11:05
2.8 Evaluate Results, Part 2 00:18:37
2.8 Evaluate Results, Part 3 00:18:30
2.9 Present and Disseminate 00:16:32
2.10 The Data Science Process Applied: Cheaper Beds, Better Breakfasts 00:06:27
Lesson 3: Acquiring Data-Sources and Methods
Topics 00:01:41
3.1 The Data Science Mindset, Part 1 00:14:56
3.1 The Data Science Mindset, Part 2 00:15:34
3.2 Where to Get Data-Sources and Services 00:16:46
3.3 How the Web Works, Part 1 00:19:54
3.3 How the Web Works, Part 2 00:14:32
3.4 Downloading and Parsing Data with Python, Part 1 00:09:25
3.4 Downloading and Parsing Data with Python, Part 2 00:28:12
3.4 Downloading and Parsing Data with Python, Part 3 00:15:25
3.5 Working with APIs, Part 1 00:13:44
3.5 Working with APIs, Part 2 00:10:43
3.5 Working with APIs, Part 3 00:28:16
3.6 Data Blending-Downloading Venues from Foursquare, Part 1 00:13:58
3.6 Data Blending-Downloading Venues from Foursquare, Part 2 00:22:24
Lesson 4: Adding Structure-Parsing Data and Data Models
Topics 00:01:13
4.1 Ideas and Implementations 00:11:59
4.2 Data Models-Adding Structure to Data 00:23:56
4.3 Building Abstractions-Object-Oriented Programming, Part 1 00:11:47
4.3 Building Abstractions-Object-Oriented Programming, Part 2 00:19:46
4.4 A Brief Pythonic Diversion-Classes, Part 1 00:21:30
4.4 A Brief Pythonic Diversion-Classes, Part 2 00:14:29
4.4 A Brief Pythonic Diversion-Classes, Part 3 00:11:00
4.5 The Case for (and against) OOP, Part 1 00:23:37
4.5 The Case for (and against) OOP, Part 2 00:22:01
4.5 The Case for (and against) OOP, Part 3 00:15:15
4.5 The Case for (and against) OOP, Part 4 00:14:25
4.5 The Case for (and against) OOP, Part 5 00:29:01
4.5 The Case for (and against) OOP, Part 6 00:20:45
4.5 The Case for (and against) OOP, Part 7 00:13:04
Lesson 5: Storing Data-Persistence with Relational Databases
Topics 00:01:20
5.1 Data Models Applied-Relational Databases with SQLite, Part 1 00:26:56
5.1 Data Models Applied-Relational Databases with SQLite, Part 2 00:25:58
5.2 What’s in a Schema-Mapping Data Models to Data Tables 00:23:10
5.3 Querying Data(bases)-Thinking Relationally, Part 1 00:06:36
5.4 Querying Data(bases)-Thinking Relationally, Part 2 00:19:43
5.5 Querying Data(bases)-Thinking Relationally, Part 3 00:25:31
5.6 Querying Data(bases)-Thinking Relationally, Part 4 00:09:18
5.7 ORMs versus SQL 00:04:52
5.8 Extract, Transform, Load-Putting It All Together 00:10:11
Lesson 6: Validating Data-Provenance and Quality Control
Topics 00:01:21
6.1 A Brief Historical Diversion 00:10:42
6.2 Defensive Data Analysis-Quality Checks 00:06:50
6.3 Getting to Know Your Data 00:17:27
6.4 Data Quality Checks with peewee, Part 1 00:20:15
6.4 Data Quality Checks with peewee, Part 2 00:18:25
6.4 Data Quality Checks with peewee, Part 3 00:12:13
6.5 Dealing with Missing Data 00:10:44
6.6 EDA for Insight: Describing Data, Part 1 00:04:39
6.6 EDA for Insight: Describing Data, Part 2 00:18:05
6.6 EDA for Insight: Describing Data, Part 3 00:14:24
6.6 EDA for Insight: Describing Data, Part 4 00:20:17
6.7 Querying Across Datasets with Joins 00:08:25
6.8 Joins with peewee, Part 1 00:27:42
6.8 Joins with peewee, Part 2 00:26:49
6.9 Translating peewee to SQL 00:06:58
6.10 A Visual Introduction to Joins with SQL 00:14:49
Summary
Data Science Fundamentals Part 1: Summary 00:03:21
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