O’REILLY | Python for Data Science Complete Video Course Video Training [FCO]

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  • 01 - Python for Data Science Complete Video Course Video Training - Introduction.mp4 (76.6 MB)
  • 02 - Learning objectives.mp4 (11.2 MB)
  • 03 - 1.1 History of Python in data science.mp4 (78.1 MB)
  • 04 - 1.2 Overview of Python data science libraries.mp4 (44.4 MB)
  • 05 - 1.3 Future trends of Python in AI, ML, and data science.mp4 (77.5 MB)
  • 06 - Learning objectives.mp4 (25.0 MB)
  • 07 - 2.1 Create your first Colab document.mp4 (328.8 MB)
  • 08 - 2.2 Manage Colab documents.mp4 (451.8 MB)
  • 09 - 2.3 Use magic functions.mp4 (156.3 MB)
  • 10 - 2.4 Understand compatibility with Jupyter.mp4 (258.1 MB)
  • 11 - Learning objectives.mp4 (28.8 MB)
  • 12 - 3.1 Write procedural code.mp4 (112.9 MB)
  • 13 - 3.2 Use simple expressions and variables.mp4 (173.9 MB)
  • 14 - 3.3 Work with the built-in types.mp4 (66.6 MB)
  • 15 - 3.4 Learn to Print.mp4 (70.6 MB)
  • 16 - 3.5 Perform basic math operations.mp4 (167.1 MB)
  • 17 - 3.6 Use classes and objects with dot notation.mp4 (194.5 MB)
  • 18 - Learning objectives.mp4 (17.0 MB)
  • 19 - 4.1 Use string methods.mp4 (131.9 MB)
  • 20 - 4.2 Format strings.mp4 (98.7 MB)
  • 21 - 4.3 Manipulate strings - membership, slicing, and concatenation.mp4 (136.7 MB)
  • 22 - 4.4 Learn to use unicode.mp4 (74.4 MB)
  • 23 - Learning objectives.mp4 (22.5 MB)
  • 24 - 5.1 Use lists and tuples.mp4 (370.0 MB)
  • 25 - 5.2 Explore dictionaries.mp4 (213.3 MB)
  • 26 - 5.3 Dive into sets.mp4 (83.0 MB)
  • 27 - 5.4 Work with the numpy array.mp4 (234.4 MB)
  • 28 - 5.5 Use the Pandas DataFrame.mp4 (116.8 MB)
  • 29 - 5.6 Use the Pandas Series.mp4 (71.6 MB)
  • 30 - Learning objectives.mp4 (24.0 MB)
  • 31 - 6.1 Convert lists to dicts and back.mp4 (74.4 MB)
  • 32 - 6.2 Convert dicts to Pandas Dataframe.mp4 (104.6 MB)
  • 33 - 6.3 Convert characters to integers and back.mp4 (35.7 MB)
  • 34 - 6.4 Convert between hexadecimal, binary, and floats.mp4 (101.4 MB)
  • 35 - Learning objectives.mp4 (24.9 MB)
  • 36 - 7.1 Learn to loop with for loops.mp4 (44.9 MB)
  • 37 - 7.2 Repeat with while loops.mp4 (50.2 MB)
  • 38 - 7.3 Learn to handle exceptions.mp4 (111.9 MB)
  • 39 - 7.4 Use conditionals.mp4 (168.2 MB)
  • 40 - Learning objectives.mp4 (22.5 MB)
  • 41 - 8.1 Write and use functions.mp4 (206.5 MB)
  • 42 - 8.2 Learn to use decorators.mp4 (210.9 MB)
  • 43 - 8.3 Compose closure functions.mp4 (132.9 MB)
  • 44 - 8.4 Use lambdas.mp4 (106.2 MB)
  • 45 - 8.5 Advanced Use of Functions.mp4 (319.0 MB)
  • 46 - Learning objectives.mp4 (33.8 MB)
  • 47 - 9.1 Learn NumPy.mp4 (287.9 MB)
  • 48 - 9.2 Learn SciPy.mp4 (665.0 MB)
  • 49 - 9.3 Learn Pandas.mp4 (335.6 MB)
  • 50 - 9.4 Learn TensorFlow.mp4 (341.9 MB)
  • 51 - 9.5 Use Seaborn for 2D plots.mp4 (261.6 MB)
  • 52 - 9.6 Use Plotly for interactive plots.mp4 (262.1 MB)
  • 53 - 9.7 Specialized Visualization Libraries.mp4 (241.7 MB)
  • 54 - 9.8 Learn Natural Language Processing Libraries.mp4 (124.9 MB)
  • 55 - Learning objectives.mp4 (27.7 MB)
  • 56 - 10.1 Understand functional programming.mp4 (151.1 MB)
  • 57 - 10.2 Apply functions to data science workflows.mp4 (47.1 MB)
  • 58 - 10.3 Use map_reduce_filter.mp4 (95.2 MB)
  • 59 - 10.4 Use list comprehensions.mp4 (98.3 MB)
  • 60 - 10.5 Use dictionary comprehensions.mp4 (15.4 MB)
  • 61 - Learning objectives.mp4 (17.8 MB)
  • 62 - 11.1 Use generators.mp4 (69.4 MB)
  • 63 - 11.2 Design generator pipelines.mp4 (141.3 MB)
  • 64 - 11.3 Implement lazy evaluation functions.mp4 (59.1 MB)
  • 65 - Learning objectives.mp4 (21.0 MB)
  • 66 - 12.1 Perform simple pattern matching.mp4 (97.1 MB)
  • 67 - 12.2 Use regular expressions.mp4 (284.6 MB)
  • 68 - 12.3 Learn text processing techniques - Beautiful Soup.mp4 (87.6 MB)
  • 69 - Learning objectives.mp4 (18.2 MB)
  • 70 - 13.1 Sort in Python.mp4 (186.7 MB)
  • 71 - 13.2 Create custom sorting functions.mp4 (229.3 MB)
  • 72 - 13.3 Sort in Pandas.mp4 (302.0 MB)
  • 73 - Learning objectives.mp4 (22.1 MB)
  • 74 - 14.1 Read and write files - file, pickle, CSV, JSON.mp4 (214.7 MB)
  • 75 - 14.2 Read and write with Pandas - CSV, JSON.mp4 (336.5 MB)
  • 76 - 14.3 Read and write using web resources (requests, boto, github).mp4 (110.9 MB)
  • 77 - 14.4 Use function-based concurrency.mp4 (608.1 MB)
  • 78 - Learning objectives.mp4 (20.9 MB)
  • 79 - 15.1 Share with Github.mp4 (358.1 MB)
  • 80 - 15.2 Create Kaggle Kernels.mp4 (207.5 MB)
  • 81 - 15.3 Collaborate with Colab.mp4 (125.2 MB)
  • 82 - 15.4 Post public graphs with Plotly.mp4 (103.5 MB)
  • 83 - Learning Objectives.mp4 (28.7 MB)
  • 84 - 16.1 PyTest.mp4 (372.9 MB)
  • 85 - 16.2 Visual Studio Code.mp4 (364.6 MB)
  • 86 - 16.3 Vim.mp4 (136.8 MB)
  • 87 - 16.4 Ludwig (Open Source AutoML).mp4 (146.5 MB)
  • 88 - 16.5 Sklearn Algorithm Cheatsheet.mp4 (104.1 MB)
  • 89 - 16.6 Recommendations.mp4 (47.7 MB)

Description



By Kennedy Behrman, Noah Gift
Publisher: Addison-Wesley Professional
Release Date: April 2019
ISBN: 9780135687253

Video Description

9+ Hours of Video Instruction

While 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.

Description

Notebook-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, complete with interactive quizzes, 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 Level

• Beginner

What You Will Learn

• Learn Google Colab notebook Data Science programming
• Learn the essential subset of Python used in Data Science
• Learn to manipulate data using popular Python libraries such as pandas and numpy
• Learn to apply Python Data Science recipes to real-world projects
• Learn functional programming fundamentals unique to Data Science

Who Should Take This Course

• Complete beginners to programming
• Statisticians and Analysts in the data industry looking to use Python for Data Science
• Sales, Product Managers, Data Analysts, Marketing who want to perform Data Science
• Software Engineers looking to level up into Data Science and Machine Learning tracks
• Students enrolled in a Data Science program

Course Requirements

• General computer skills are an asset, such as moving, copying, renaming, and deleting files on the computer they will be using
• Experience using text editors and/or spreadsheet applications
• Comfort using web browsers and search engines

Lessons

• Introduction
• Lesson 1: Python Past and Future
• Lesson 2: Introduction to Colab
• Lesson 3: Fundamentals of Python
• Lesson 4: Strings in Python
• Lesson 5: Python Data Structures
• Lesson 6: Data Conversion Recipes
• Lesson 7: Execution Control
• Lesson 8: Functions in Python
• Lesson 9: Data Science Libraries
• Lesson 10: Functional Programming
• Lesson 11: Lazy Evaluation
• Lesson 12: Pattern Matching
• Lesson 13: Sorting in Python
• Lesson 14: I/O in Python
• Lesson 15: Sharing Your Work
• Lesson 16: Case Studies
• Summary

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.

Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.

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O’REILLY | Python for Data Science Complete Video Course Video Training [FCO]

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13.1 GB
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O’REILLY | Python for Data Science Complete Video Course Video Training [FCO]

Torrent hash: 5EFA31C7080A132823679A10D1830A88D47EA1FD