by Kennedy Behrman, Noah Gift | Released April 2019 | ISBN: 9780135687253

9+ Hours of Video InstructionWhile there are resources for Data Science and resources for Machine Learning, there's a distinct gap in resources for the precursor course to Data Science and Machine Learning. This complete video course fills that gap--it is specifically designed to prepare students to learn how to program for Data Science and Machine Learning with Python. This is the antidote to the over-complicated universe of these hot new, growing technologies. With this course, students will learn the fundamentals of Python and get prepared specifically for Data Science.Noah Gift and Kennedy Behrman take students with zero programming background through enough Python to prepare them for their Data Science curriculum. Companies are looking for developers who can create insight-driven systems, as they are now becoming critical to business success. Very few professionals are adequately trained to handle both large-scale software engineering and Machine Learning/AI. This is an emerging field, and we are developing the training to meet this need in the marketplace.DescriptionNotebook-based Data Science programming in Python is the emerging standard but there is a dearth of quality training material available for beginners. This 9-hour video provides foundational training on the Python language for the novice or beginner programmer looking to start in the Data Science field. The video serves as the 100-level course for a Data Science undergraduate or graduate program.The course has been designed around Colab notebook-based learning. Students would be able to run every exercise shown in the videos. The material focuses on a smaller, easier subset of Python that is needed just for Data Science coding.Skill LevelBeginnerWhat You Will LearnLearn Google Colab notebook Data Science programmingLearn the essential subset of Python used in Data ScienceLearn to manipulate data using popular Python libraries such as pandas and numpyLearn to apply Python Data Science recipes to real-world projectsLearn functional programming fundamentals unique to Data ScienceWho Should Take This CourseComplete beginners to programmingStatisticians and Analysts in the data industry looking to use Python for Data ScienceSales, Product Managers, Data Analysts, Marketing who want to perform Data ScienceSoftware Engineers looking to level up into Data Science and Machine Learning tracksStudents enrolled in a Data Science programCourse RequirementsGeneral computer skills are an asset, such as moving, copying, renaming, and deleting files on the computer they will be usingExperience using text editors and/or spreadsheet applicationsComfort using web browsers and search enginesLessonsIntroductionLesson 1: Python Past and FutureLesson 2: Introduction to ColabLesson 3: Fundamentals of PythonLesson 4: Strings in PythonLesson 5: Python Data StructuresLesson 6: Data Conversion RecipesLesson 7: Execution ControlLesson 8: Functions in PythonLesson 9: Data Science LibrariesLesson 10: Functional ProgrammingLesson 11: Lazy EvaluationLesson 12: Pattern MatchingLesson 13: Sorting in PythonLesson 14: I/O in PythonLesson 15: Sharing Your WorkLesson 16: Case StudiesSummaryAbout Pearson Video TrainingPearson 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.Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson. Show and hide more

1. Introduction
   • Python for Data Science Complete Video Course Video Training: Introduction 00:02:45
2. Lesson 1: Python Past and Future
   • Learning objectives 00:00:24
   • 1.1 History of Python in data science 00:04:59
   • 1.2 Overview of Python data science libraries 00:02:52
   • 1.3 Future trends of Python in AI, ML, and data science 00:03:29
3. Lesson 2: Introduction to Colab
   • Learning objectives 00:00:25
   • 2.1 Create your first Colab document 00:10:00
   • 2.2 Manage Colab documents 00:10:48
   • 2.3 Use magic functions 00:05:21
   • 2.4 Understand compatibility with Jupyter 00:05:22
4. Lesson 3: Fundamentals of Python
   • Learning objectives 00:00:29
   • 3.1 Write procedural code 00:03:27
   • 3.2 Use simple expressions and variables 00:07:16
   • 3.3 Work with the built-in types 00:02:36
   • 3.4 Learn to Print 00:02:16
   • 3.5 Perform basic math operations 00:04:56
   • 3.6 Use classes and objects with dot notation 00:05:37
5. Lesson 4: Strings in Python
   • Learning objectives 00:00:17
   • 4.1 Use string methods 00:05:38
   • 4.2 Format strings 00:04:05
   • 4.3 Manipulate strings: membership, slicing, and concatenation 00:06:20
   • 4.4 Learn to use unicode 00:02:22
6. Lesson 5: Python Data Structures
   • Learning objectives 00:00:22
   • 5.1 Use lists and tuples 00:14:22
   • 5.2 Explore dictionaries 00:08:21
   • 5.3 Dive into sets 00:03:58
   • 5.4 Work with the numpy array 00:07:58
   • 5.5 Use the Pandas DataFrame 00:04:09
   • 5.6 Use the Pandas Series 00:02:17
7. Lesson 6: Data Conversion Recipes
   • Learning objectives 00:00:24
   • 6.1 Convert lists to dicts and back 00:02:31
   • 6.2 Convert dicts to Pandas Dataframe 00:02:38
   • 6.3 Convert characters to integers and back 00:01:16
   • 6.4 Convert between hexadecimal, binary, and floats 00:02:57
8. Lesson 7: Execution Control
   • Learning objectives 00:00:25
   • 7.1 Learn to loop with for loops 00:01:35
   • 7.2 Repeat with while loops 00:01:28
   • 7.3 Learn to handle exceptions 00:02:40
   • 7.4 Use conditionals 00:05:49
9. Lesson 8: Functions in Python
   • Learning objectives 00:00:22
   • 8.1 Write and use functions 00:07:03
   • 8.2 Learn to use decorators 00:06:46
   • 8.3 Compose closure functions 00:04:08
   • 8.4 Use lambdas 00:03:26
   • 8.5 Advanced Use of Functions 00:05:57
10. Lesson 9: Data Science Libraries
    • Learning objectives 00:00:34
    • 9.1 Learn NumPy 00:08:14
    • 9.2 Learn SciPy 00:15:40
    • 9.3 Learn Pandas 00:09:32
    • 9.4 Learn TensorFlow 00:06:39
    • 9.5 Use Seaborn for 2D plots 00:06:19
    • 9.6 Use Plotly for interactive plots 00:07:19
    • 9.7 Specialized Visualization Libraries 00:04:41
    • 9.8 Learn Natural Language Processing Libraries 00:04:35
11. Lesson 10: Functional Programming
    • Learning objectives 00:00:28
    • 10.1 Understand functional programming 00:06:03
    • 10.2 Apply functions to data science workflows 00:01:36
    • 10.3 Use map/reduce/filter 00:04:01
    • 10.4 Use list comprehensions 00:05:58
    • 10.5 Use dictionary comprehensions 00:01:12
12. Lesson 11: Lazy Evaluation
    • Learning objectives 00:00:18
    • 11.1 Use generators 00:03:08
    • 11.2 Design generator pipelines 00:05:46
    • 11.3 Implement lazy evaluation functions 00:02:48
13. Lesson 12: Pattern Matching
    • Learning objectives 00:00:21
    • 12.1 Perform simple pattern matching 00:02:43
    • 12.2 Use regular expressions 00:12:47
    • 12.3 Learn text processing techniques: Beautiful Soup 00:02:17
14. Lesson 13: Sort in Python
    • Learning objectives 00:00:18
    • 13.1 Sort in Python 00:06:48
    • 13.2 Create custom sorting functions 00:07:05
    • 13.3 Sort in Pandas 00:09:35
15. Lesson 14: I/O in Python
    • Learning objectives 00:00:22
    • 14.1 Read and write files: file, pickle, CSV, JSON 00:07:42
    • 14.2 Read and write with Pandas: CSV, JSON 00:09:20
    • 14.3 Read and write using web resources (requests, boto, github) 00:03:29
    • 14.4 Use function-based concurrency 00:15:50
16. Lesson 15: Sharing Your Work
    • Learning objectives 00:00:21
    • 15.1 Share with Github 00:06:02
    • 15.2 Create Kaggle Kernels 00:03:27
    • 15.3 Collaborate with Colab 00:02:35
    • 15.4 Post public graphs with Plotly 00:01:45
17. Lesson 16: Case Studies
    • Learning Objectives 00:00:29
    • 16.1 PyTest 00:06:50
    • 16.2 Visual Studio Code 00:06:07
    • 16.3 Vim 00:03:01
    • 16.4 Ludwig (Open Source AutoML) 00:04:22
    • 16.5 Sklearn Algorithm Cheatsheet 00:03:50
    • 16.6 Recommendations 00:03:36
18. Summary
    • Summary 00:00:53
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