Python for Data Science: Foundational Python from the Ground Up

Python for Data Science: Foundational Python from the Ground Up
Python for Data Science: Foundational Python from the Ground Up
English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 6h 38m | 3.86 GB

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.

Notebook-based Data Science programming in Python is the emerging standard but there is a dearth of quality training material available for beginners. This 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.

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
Table of Contents

1 Python for Data Science Complete Video Course Video Training – Introduction
2 Learning objectives
3 1.1 History of Python in data science
4 1.2 Overview of Python data science libraries
5 1.3 Future trends of Python in AI, ML, and data science
6 Learning objectives
7 2.1 Create your first Colab document
8 2.2 Manage Colab documents
9 2.3 Use magic functions
10 2.4 Understand compatibility with Jupyter
11 Learning objectives
12 3.1 Write procedural code
13 3.2 Use simple expressions and variables
14 3.3 Work with the built-in types
15 3.4 Learn to Print
16 3.5 Perform basic math operations
17 3.6 Use classes and objects with dot notation
18 Learning objectives
19 4.1 Use string methods
20 4.2 Format strings
21 4.3 Manipulate strings – membership, slicing, and concatenation
22 4.4 Learn to use unicode
23 Learning objectives
24 5.1 Use lists and tuples
25 5.2 Explore dictionaries
26 5.3 Dive into sets
27 5.4 Work with the numpy array
28 5.5 Use the Pandas DataFrame
29 5.6 Use the Pandas Series
30 Learning objectives
31 6.1 Convert lists to dicts and back
32 6.2 Convert dicts to Pandas Dataframe
33 6.3 Convert characters to integers and back
34 6.4 Convert between hexadecimal, binary, and floats
35 Learning objectives
36 7.1 Learn to loop with for loops
37 7.2 Repeat with while loops
38 7.3 Learn to handle exceptions
39 7.4 Use conditionals
40 Learning objectives
41 8.1 Write and use functions
42 8.2 Learn to use decorators
43 8.3 Compose closure functions
44 8.4 Use lambdas
45 8.5 Advanced Use of Functions
46 Learning objectives
47 9.1 Learn NumPy
48 9.2 Learn SciPy
49 9.3 Learn Pandas
50 9.4 Learn TensorFlow
51 9.5 Use Seaborn for 2D plots
52 9.6 Use Plotly for interactive plots
53 9.7 Specialized Visualization Libraries
54 9.8 Learn Natural Language Processing Libraries
55 Learning objectives
56 10.1 Understand functional programming
57 10.2 Apply functions to data science workflows
58 10.3 Use map_reduce_filter
59 10.4 Use list comprehensions
60 10.5 Use dictionary comprehensions
61 Learning objectives
62 11.1 Use generators
63 11.2 Design generator pipelines
64 11.3 Implement lazy evaluation functions
65 Learning objectives
66 12.1 Perform simple pattern matching
67 12.2 Use regular expressions
68 12.3 Learn text processing techniques – Beautiful Soup
69 Learning objectives
70 13.1 Sort in Python
71 13.2 Create custom sorting functions
72 13.3 Sort in Pandas
73 Learning objectives
74 14.1 Read and write files – file, pickle, CSV, JSON
75 14.2 Read and write with Pandas – CSV, JSON
76 14.3 Read and write using web resources (requests, boto, github)
77 14.4 Use function-based concurrency
78 Learning objectives
79 15.1 Share with Github
80 15.2 Create Kaggle Kernels
81 15.3 Collaborate with Colab
82 15.4 Post public graphs with Plotly
83 Learning Objectives
84 16.1 PyTest
85 16.2 Visual Studio Code
86 16.3 Vim
87 16.4 Ludwig (Open Source AutoML)
88 16.5 Sklearn Algorithm Cheatsheet
89 16.6 Recommendations