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What's Included

Fast Track Python for Newbies
$50 Value
Learn Python Image Processing by Making Instagram-Style Filters
$200 Value
Create a Raspberri Pi Smart Security Camera with Python
$200 Value
Master Data Visualization with Python
$200 Value
The Complete Python Programming Boot Camp: Beginner to Advanced
$199 Value
Learn Python with 70+ Exercises
$50 Value
Python Programming for Beginners: Learn Python in One Day
$50 Value
Machine Learning with Python
$149 Value
Python for Finance: Investment Fundamentals & Data Analytics
$195 Value

Product Details

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Lifetime
Content
3 hours
Lessons
28

Fast Track Python for Newbies

Learn This Powerful General-Purpose Programming Language Fast

By BiteSizedSchool | in Online Courses

Learning a new programming language can take huge amounts of time - especially if you have to drive to a physical classroom every day. This crash online course, however, will put you on the fast track to learning Python, teaching you everything you need to know about this powerful general-purpose language in just two sittings.

  • Access 28 lectures & 3 hours of content 24/7
  • Learn Python language fundamentals
  • Progress into more advanced topics like functions & classes
  • Fortify your learning by completing challenges
Brett Romero is a software engineer and entrepreneur who has started several businesses. Bitesizebschool.com is his latest business, where he writes about applying business principles to the real world and solving technology issues that every online business faces. He has also written desktop applications in languages running from Delphi to C# winforms/WPF, as well as built ASP.NET webforms and MVC based applications. He is also an iOS developer and currently has seven apps in the iTunes App Store.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Introduction
    • Introduction (1:17)
    • What Is Python & Installation (6:24)
    • Editor (6:57)
  • Language Fundamentals
    • Introduction (2:16)
    • Variables
    • Conditional Flow
    • List & Dictionaries (8:38)
    • While Loops (5:26)
    • For Loops (4:35)
    • Working with Strings (6:58)
    • Tuples (8:34)
    • File I/O (6:07)
    • M2 Challenge Instructions (code attached) (8:07)
    • M2 Challenge Solution (14:00)
  • Functions & Classes
    • Introduction (1:20)
    • Functions & Parameters (9:47)
    • Lamba Expression (4:04)
    • Classes (7:48)
    • Inheritance (9:03)
    • M3 Challenge Part 1 (code attached) (9:28)
    • M3 Challenge Part 2 (14:08)
  • Exception Handling
    • Introduction (1:21)
    • Try/Except/Finally (11:18)
    • Raising An Exception (8:08)
    • M4 Challenge Part 1 (code attached) (8:54)
    • M4 Challenge Part 2 (9:22)
    • Course Summary (2:55)

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3.5 hours
Lessons
27

Learn Python Image Processing by Making Instagram-Style Filters

Build the Filters You Love to Use!

By ZENVA | in Online Courses

Everybody loves their filters, and in this course you'll learn how to build your very own, just like your favorites on Instagram and Snapchat. You'll build an app that allows you to load a photo, edit its contrast, brightness, and gray-scale, and then create and apply custom filters. In addition to the practical construction, you'll also delve into the Python theory behind it all.

  • Access 27 lectures & 3.5 hours of content 24/7
  • Discuss concepts such as matrices, color models, brightness, contrast, & convolution from a mathematical perspective
  • Install Virtual Box
  • Perform matrix operations using Numpy & OpenCV
  • Discover a variety of Python libraries
Software developer and founder of ZENVA. Since 2012, Pablo Farias Navarro has been teaching online how to create games, apps and websites to over 150,000 students through the Udemy and Zenva Academy platforms, and created content for companies such as Amazon and Intel.

Pablo is a member of the Intel Innovator Program in the Asia Pacific, and has run live programming workshops in San Francisco, Brisbane and Bangalore. Pablo holds a Master in Information Technology (Management) degree from the University of Queensland (Australia) and a Master of Science in Engineering degree from the Catholic University of Chile.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Introduction
    • Introduction (3:22)
    • Project Files
  • Theory Basics
    • 1-1 Images (7:45)
    • 1-2 Matrices (9:10)
    • 1-3 Matrix Addition and Subtraction (6:54)
    • 1-4 Constraints on Matrix Addition and Subtraction (3:22)
    • 1-5 VirtualBox (19:08)
    • 1-6 Numpy Matrices (3:38)
    • 1-7 Scalar Multiplication (7:23)
    • 1-8 Numpy Matrix Operations (7:50)
    • 1-9 OpenCV Basics (8:24)
    • 1-10 Color Models (12:03)
    • 1-11 Colorspace Conversion (5:59)
    • 1-12 Brightness and Contrast (14:20)
    • 1-13 Brightness and Contrast (6:57)
    • 1-14 Kernels (8:24)
    • 1-15 Intro to Convolution (9:21)
    • 1-16 Convolution Example (9:18)
    • 1-17 Convolution Detailed Example (8:34)
    • 1-18 Convolution (6:32)
  • Application
    • 2-1 Intro to App (2:04)
    • 2-2 Main UI Loop (5:42)
    • 2-3 UI (6:01)
    • 2-4 Contrast and Brightness (8:48)
    • 2-5 Filters (11:49)
    • 2-6 Grayscale (7:53)
    • 2-7 Saving Images (8:05)
  • Conclusion
    • Conclusion (2:14)

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Content
4.5 hours
Lessons
36

Create a Raspberri Pi Smart Security Camera with Python

By ZENVA | in Online Courses

The best way to learn a programming language is through practical, hands-on experience, and this course will teach you Python through building a fully-functional security camera. The application you're building will detect movement in video footage and subsequently execute an action, such as sending out an email or SMS. Between theory and actual hands-on work, by course's end you'll have a nuanced understanding of Python.

  • Access 36 lectures & 4.5 hours of content 24/7
  • Create a fully-functional smart security camera using a Raspberry Pi or the webcam on your device
  • Learn summation notation
  • Explore image similarity metrics like sum squared errors, mean squared errors, & structural similarity
  • Understand how to perform video processing w/ Python
Software developer and founder of ZENVA. Since 2012, Pablo Farias Navarro has been teaching online how to create games, apps and websites to over 150,000 students through the Udemy and Zenva Academy platforms, and created content for companies such as Amazon and Intel.

Pablo is a member of the Intel Innovator Program in the Asia Pacific, and has run live programming workshops in San Francisco, Brisbane and Bangalore. Pablo holds a Master in Information Technology (Management) degree from the University of Queensland (Australia) and a Master of Science in Engineering degree from the Catholic University of Chile.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Introduction
    • Intro (3:54)
    • Project Files
  • Image Processing Basics
    • 1-1 Images (7:45)
    • 1-2 Matrices (9:10)
    • 1-3 Matrix Addition and Subtraction (6:54)
    • 1-4 Constraints on Matrix Addition and Subtraction (3:22)
    • 1-5 VirtualBox (19:08)
    • 1-6 Numpy Matrices (3:38)
    • 1-7 Scalar Multiplication (7:23)
    • 1-8 Numpy Matrix Operations (7:50)
    • 1-9 OpenCV Basics (8:24)
    • 1-10 Color Models (12:03)
    • 1-11 Colorspace Conversion (5:59)
    • 1-12 Brightness and Contrast (14:20)
    • 1-13 Brightness and Contrast (6:57)
    • 1-14 Kernels (8:24)
    • 1-15 Intro to Convolution (9:21)
    • 1-16 Convolution Example (9:18)
    • 1-17 Convolution Detailed Example (8:34)
    • 1-18 Convolution (6:32)
  • Security Camera
    • 1-1 Summation Notation (18:39)
    • 1-2 Numpy Matrix Elements Sum (8:33)
    • 1-3 L1 Norm (10:11)
    • 1-4 Numpy L1 Norm (6:26)
    • 1-5 Sum of Squared Errors (7:58)
    • 1-6 Numpy Sum of Squared Errors (5:00)
    • 1-7 Mean Squared Error (10:20)
    • 1-8 Numpy Mean Squared Error (4:26)
    • 1-9 Structural Similarity (9:49)
    • 1-10 Comparison of MSE and SSIM (10:09)
  • Raspberri Pi
    • 2-1 Raspberry Pi (5:19)
    • 2-2 Handling Videos in OpenCV (16:18)
    • 2-3 Frame-by-frame Security Camera (10:09)
    • 2-4 Improved Security Camera (11:53)
    • 2-5 Twilio (7:46)
    • 2-6 Security Camera Notification with Twilio (11:00)
  • Conclusion
    • Conclusion (2:44)

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Lifetime
Content
3 hours
Lessons
20

Master Data Visualization with Python

Present Data More Effectively Through the Power of Python

By ZENVA | in Online Courses

Python is an especially valuable tool for visualizing data, and this course will cover a variety of techniques that will allow you to visualize data using the Python library, Matplotlib. Beginning with an intro to statistics, you'll extend into a variety of plots that will cover most use-cases.

  • Access 20 lectures & 3 hours of content 24/7
  • Explore bar charts, line plots, & scatter plots
  • Learn about advanced plots such as Quiver plots, 3D lines, & 3D surfaces
  • Discover subplots & how they're used
Software developer and founder of ZENVA. Since 2012, Pablo Farias Navarro has been teaching online how to create games, apps and websites to over 150,000 students through the Udemy and Zenva Academy platforms, and created content for companies such as Amazon and Intel.

Pablo is a member of the Intel Innovator Program in the Asia Pacific, and has run live programming workshops in San Francisco, Brisbane and Bangalore. Pablo holds a Master in Information Technology (Management) degree from the University of Queensland (Australia) and a Master of Science in Engineering degree from the Catholic University of Chile.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Course Introduction
    • Introduction (2:35)
    • Source Code Files
  • Projects
    • Introduction to Statistics (10:29)
    • Mean and Variance (11:36)
    • Five Number Summary (12:32)
    • Development Environment Setup
    • Bar Charts (11:16)
    • Histograms (10:46)
    • Pie Charts (11:22)
    • Box Plots (10:20)
    • Line Plots (10:43)
    • Scatter Plots (10:58)
    • Quiver Plots (10:01)
    • 3D Line Plots (10:09)
    • 3D Surface Plots (9:42)
    • Sub Plots (12:00)
  • Course Conclusion
    • Conclusion (1:47)

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Lifetime
Content
4 hours
Lessons
46

The Complete Python Programming Boot Camp: Beginner to Advanced

Master Python One Step At a Time

By Joydip Ghosh | in Online Courses

This beginner course will get you up to speed with the very basics of Python, one of the most important programming languages used in a wide variety of industries today. Give your resume a boost while learning practical skills that can take your career a long way!

  • Access 46 lectures & 4 hours of content 24/7
  • Set up Python & install the IDE
  • Learn about operators in Python
  • Use for loops & while loops
  • Discover the different types of statements in Python
  • Master Python fundamentals
Joydip Ghosh loves learning new skills, and since 2012 has been teaching people everything he knows. Through his online learning business, he creates courses that teach you how to become the better version of yourself with all kinds of skills.

Joydip specializes in coding languages like C, Java, C++, and PHP, as well as ethical hacking and Big Data. He also has business expertise.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Introduction to Python
    • Introduction To Python - Install The IDE (2:41)
  • Veriable
    • Python Veriables in Details (15:01)
  • Operators
    • introduction_operator (0:50)
    • arithmatic_opertaor (2:28)
    • assignment_operator (5:10)
    • relational_operator (4:06)
    • logical_operator (2:57)
  • Statement and Loop
    • Introduction to desicion making (0:45)
    • if statement (2:57)
    • if_else statement (1:54)
    • ifelifelse statement (3:20)
    • nested statement (3:28)
    • break and continue statement (5:54)
    • For Loop (4:40)
  • Number
    • Introduction to Number (1:56)
    • abs() method (0:36)
    • ceil() method (1:37)
    • floor() method (1:16)
    • exp() method (0:41)
    • max() method (0:39)
    • min() method (0:29)
    • pow() method (0:53)
    • sqrt() method (0:34)
    • modf() method (0:53)
    • log() method (0:35)
  • List
    • List introduction (1:54)
    • Access Update delete List (2:28)
    • max() method (0:51)
    • min() method (0:42)
    • count() method (0:52)
    • index() method (0:45)
    • append() methods (0:44)
    • insert() methods (1:15)
    • reverse() methods (0:45)
    • sort() methods (1:09)
    • List Operation (4:17)
    • List Slicing (3:59)
  • Tuple
    • Tuple Introduction (1:51)
    • Access Update Delete Tuple (2:46)
    • Tuple Methods (3:59)
    • Tuple Operation (5:04)
    • slicing Tuple Items (4:33)
  • Dictionary
    • Dictionary Introduction (3:18)
    • Dictionary methods (6:54)
    • Access Update Delete Dictionary (5:28)
  • Function
    • Introduction to function (2:07)
    • Argument (6:47)
    • Pass by Value and Pass by Reference (6:19)
  • Home Assignment # Gause the Number
    • Assignment Statement - What you have to do??? (1:36)
    • Some hints for you! (0:52)
    • Sollution!!! (10:08)
  • Model
    • All about Python Module (6:00)
  • Exception Handling
    • Introduction to Exception (3:53)
    • Exception Handling (6:26)
  • Python Package
    • Python Package (10:06)

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Lifetime
Content
5.5 hours
Lessons
93

Learn Python with 70+ Exercises

Get Hands-On, Beginner-Friendly Practice with Python

By Vignesh Rajendiran | in Online Courses

Totally new to programming? No worries, this course is geared towards beginners, showing you detailed examples of how to code with Python, one of the most common and powerful general-purpose languages around. If you've ever had an interest in programming, here's where to start!

  • Access 93 lectures & 5.5 hours of content 24/7
  • Learn Python basics through exercises
  • Discover variables, strings, lists, & more
  • Explore tuples, dictionaries, decision statements, & more
Vignesh Rajendiran has a bachelor's degree in Mechanical Engineering, and changed his profession to become a software developer due to his passion for computer programming. He learned to program online (mostly self-taught) and now he is working as a software engineer at a multi-national company. Vignesh teaches on many online platforms to share his knowledge with others, who are also trying to achieve their passion towards programming.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Getting started with python
    • Intoduction to python (2:32)
    • Python 2.x Vs Python 3.x (2:30)
    • Downloading python (2:17)
    • Installing python (1:48)
    • Both python versions in same system (3:36)
    • Text editors (7:43)
  • Basics of python
    • Guide to exercises
    • arithmetic operator (1:43)
    • Notes - Arithmetic Operator
    • Exercise - Arithmetic Operator
    • comparision operator (1:47)
    • Notes - Comparision Operator
    • Exercise - Comparision Operator
    • logical operator (2:43)
    • Notes - Logical Operator
    • Exercise - Logical Operator
  • Variables
    • Variables (4:01)
    • Notes - Variables
    • Exercise - Variables
  • Input and print statement
    • Input Statement (2:09)
    • Notes - Input Statement
    • Print Statement (14:13)
    • Notes - Print Statement
    • Exercise - Input/Print Statement
    • Exercise - Input / Print Statement
  • String
    • Strings (8:13)
    • Notes - Strings
    • Exercise - Strings
    • Exercise - Strings
  • List
    • List (12:36)
    • Notes - List
    • Exercise - List
    • Exercise - List
  • Tuples and set
    • Tuples and set (8:31)
    • Notes - Tuples and set
    • Exercise - Tuples
    • Exercise - Set
  • Dictionary
    • Dictionary (8:36)
    • Notes - Dictionary
    • Exercise - Dictionary
    • Exercise - Dictionary
    • Exercise - Dictionary
  • Decision statements
    • If statement (17:48)
    • Notes - If statements
    • Exercise - Decision Statements
    • Exercise - Decision Statements
    • Exercise - Decision Statements
    • Exercise - Decision Statement
  • Conditional Statements
    • For loop (15:28)
    • Basic For Loop Explanation (7:41)
    • Nested For Loop (6:22)
    • Notes - For Loop
    • While Loop (16:53)
    • While Loop Explanation (3:53)
    • Notes - While Loop
    • Exercise - Conditional Statements
    • Exercise - Conditional Statements
    • Exercise - Conditional Statements
    • Exercise - Conditional Statements
    • Exercise - Conditional Statements
    • Exercise - Conditional Statements
    • Exercise - Conditional Statements
    • Exercise - Conditional Statements
    • Exercise - Conditional Statements
    • Exercise - Conditional Statements
    • Exercise - Conditional Statements
    • Exercise - Conditional Statements
    • Exercise - Conditional Statements
    • Exercise - Conditional Statements
  • Control Statements
    • Break Statements (12:34)
    • Notes - Break Statements
    • Continue Statements (6:27)
    • Notes - Continue statements
    • Pass Statements (2:17)
    • Notes - Pass Statements
    • Exercise - Break Statements
    • Exercise - Continue Statement
    • Exercise - Pass Statement
  • Functions
    • Functions (10:09)
    • Notes - Functions
    • Exercise - Functions
    • Exercise - Functions
    • Lambda functions (2:54)
    • Notes - Lambda Functions
    • Exercise - Lambda Functions
  • Exceptions
    • Exceptions (20:13)
    • Notes - Exceptions
    • Exercise - Exceptions
    • Exercise - Exceptions
  • Modules
    • Math Range Module (11:04)
    • Notes - Math range Modules
    • Exercise - Math Range Module
    • Exercise - Math Range Module
  • Code along - Project 1
    • Project 1 intro (1:33)
    • Project 1 part 1 (8:59)
    • Project 1 part 2 (9:04)
    • Project 1 part 3 (7:51)
    • Project 1 part 4 (7:30)
  • Code along - Project 2
    • Project 2 introduction (1:29)
    • Project 2 Part 1 (11:19)
    • Project 2 Part 2 (4:31)
    • Project 2 Part 3 (17:37)
    • Project 2 Part 4 (6:02)
    • Project 2 Part 5 (11:21)
    • Project 2 Part 6 (4:10)
    • Project 2 Part 7 (8:09)
    • Project 2 Part 8 (5:10)
  • Answers for the Exercises
    • Answers for the exercise

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2.5 hours
Lessons
84

Python Programming for Beginners: Learn Python in One Day

Improve Your Productivity by Learning to Code Python

By Mellon Training | in Online Courses

Python is an incredibly efficient language as you'll learn in this quick course that you can complete in as little as a day. Python is frequently used in scientific fields for academic research and applied work due in part to this great efficiency. This example-driven course is created to give you a deeper understanding of the language.

  • Access 84 lectures & 2.5 hours of content 24/7
  • Learn Python by completing projects
  • Get a single, all-inclusive learning experience for Python 2.X or Python 3.X
  • Discover the essential Python fundamentals in as little as one day
Andreas Exadaktylos has over 16 years of experience in teaching as an instructor, especially in Microsoft Excel, and has a passion for software products that make people’s lives easier. He runs his own computer learning school, Futurekids, as well as the online video tutorial platform Mellon Training, and enjoys building websites and utilizing SEO techniques to improve them.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Course Overview
    • Introduction (2:17)
    • Be-a-great-student (0:53)
    • Why Learn Python (1:41)
    • Python 2 and Python 3 (0:53)
    • Download the Python Project Files + Course Study Tracker
  • Getting Started
    • Downloading and Installing Python (1:23)
    • Using the Python Shell and IDLE (2:43)
    • Hello World! - Writing our First Program (2:17)
    • How to Find Help (2:31)
    • Summary
    • Exercise:1
    • Answer of Exercise:1
  • Variables and Operators
    • Variables (3:09)
    • Basic Operators (6:18)
    • Booleans and Chained Comparison Operators (3:03)
    • Summary
  • Simple Data Types
    • Strings (5:45)
    • Useful String Methods (7:44)
    • Numbers- Integers and Floats (1:55)
    • Integers in Python 2 (2:01)
    • Type Casting (3:10)
  • Making your Program Interactive
    • Input() (4:16)
    • Using int() to Accept Numerical Input (2:18)
    • Print() (3:30)
    • More Print() Formatting Methods (6:14)
    • How do you write Comments (2:20)
  • Lists
    • What is a List (5:58)
    • Working with Lists (4:30)
    • Making Numerical Lists (2:27)
    • Tuples (2:16)
  • Dictionaries
    • Working with Dictionaries (3:17)
    • Dictionaries vs Lists (2:53)
    • Nesting (2:33)
  • Statements and Loops
    • If Statements (4:36)
    • for Loops (3:08)
    • while Loops (5:12)
    • Break and Continue (3:28)
    • Handling Errors with Try and Except (2:46)
    • Using if Statements with Lists (3:29)
    • Using a while Loop with Lists and Dictionaries (3:35)
    • Using for Loops with Lists (1:11)
    • Looping Through a Dictionary (3:52)
  • Functions
    • Build-In Functions (3:25)
    • Defining your Own Function (4:14)
    • Global and Local Scopes (4:08)
  • Working with Files
    • Opening and Reading Text Files (4:54)
    • Writing to a File (3:02)
    • Organizing Files (2:44)
    • Deleting and Renaming Files (2:08)
    • Storing Data (3:30)
  • Modules
    • Modules (2:46)
    • Creating our Own Module (3:35)
    • Installing third-party modules (1:53)

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Lifetime
Content
5 hours
Lessons
48

Machine Learning with Python

Explore A New Tech Frontier Through Python

By Stone River eLearning | in Online Courses

Python has a range of machine learning applications, making it invaluable for developers, especially ones building engines for the web. This course focuses on building procedural machine learning processes from the ground up so you can get a more nuanced understanding of how machines process data.

  • Access 48 lectures & 5 hours of content 24/7
  • Get thorough explanations & comments to understand why things are being done in certain ways
  • Take code walkthroughs to see a machine learning app work in real time
  • Write machine learning processes from scratch
Stone River eLearning was founded in 2011, and has taught over 300,000 students through its website and reseller partners since its founding. Their courses focus on programming, development and design.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Course Introduction
    • Course Introduction (2:02)
  • Machine Learning Concepts
    • Section Introduction (0:38)
    • Supervised and Unsupervised Learning (8:34)
    • Semi-Supervised Learning (4:25)
    • Section Summary (0:23)
  • First ML Application
    • Section Introduction (1:49)
    • Installing the Environment (2:54)
    • Hello World (7:34)
    • Installing Aaconda and Deep Learning Libraries (10:18)
    • Email Spam Checker - Part 1 (7:09)
    • Email Spam Checker - Part 2 (13:39)
    • Email Spam Checker Results (8:35)
    • Iris 70:30 - Part 1 (8:56)
    • Iris 70:30 - Part 2 (9:18)
    • Section Summary (0:44)
  • Data Analysis
    • Section Introduction (0:32)
    • Data Analysis - Example 1 (12:57)
    • Data Analysis - Example 2 (10:47)
    • Data Visualization (8:44)
    • Section Summary (0:44)
  • Linear Algebra
    • Section Introduction (0:59)
    • Parametric Algorithms (6:52)
    • Linear Algebra (9:35)
    • Linear Regression Calculation - Part 1 (12:42)
    • Linear Regression Calculation - Part 2 (5:03)
    • Regression on Larger Dataset - Part 1 (10:18)
    • Regression on Larger Dataset - Part 2 (7:38)
    • Regression on Larger Dataset - Part 3 (10:09)
    • Section Summary (0:38)
  • Natural Language Processing
    • Section Introduction (1:03)
    • Natural Language Processing - Part 1 (8:57)
    • Natural Language Processing - Part 2 (3:57)
    • Tokenizing Content (11:21)
    • Processing Unique Words (13:11)
    • Summarizing Headlines - Part 1 (9:18)
    • Summarizing Headlines - Part 2 (11:54)
    • Summarizing Headlines - Part 3 (8:31)
    • Section Summary (0:50)
  • Clustering
    • Section Introduction (1:03)
    • Cluster Introduction (8:31)
    • EM and M Clustering (6:18)
    • Clustering Code Walkthrough (9:07)
    • Clustering Iris Data - Part 1 (8:57)
    • Clustering Iris Data - Part 2 (8:43)
    • Clustering Iris Data - Part 3 (8:15)
    • Dendrogram Graphs (10:01)
    • Section Summary (0:59)
    • Course Summary (2:00)

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Access
Lifetime
Content
6.5 hours
Lessons
104

Python for Finance: Investment Fundamentals & Data Analytics

Use Python to Conduct Real-World Financial Analysis

By 365 Careers | in Online Courses

Python can be especially useful in finance as it has powerful analytical and predictive modeling capabilities. In this course, you'll learn just how Python can be applied in the world of finance. Even if you know absolutely nothing about coding, you'll learn in-demand, real-world skills that can make your resume jump out or help you make a difference in your own financial portfolio.

  • Access 104 lectures & 6.5 hours of content 24/7
  • Build correlations between stocks, estimate risk & rate of return
  • Explore the difference between diversifiable & non-diversifiable risk
  • Perform regression analysis
  • Measure a regression's explanatory power w/ R^2
  • Use Monte Carlo in a corporate finance context for options & stock pricing
  • Apply the Black Scholes formula
365 Careers is a firm specializing in high-end business, financial, data science, and office productivity training programs. Our growing list of courses includes Excel (Microsoft Excel for Beginner and Advanced users), PowerPoint, Word, Outlook, Accounting, Finance 101, Investment Banking, Financial Modeling, Company Valuation, Financial Planning & Analysis, Job Hunting, Strategy, Management, Marketing, Decision Making and Negotiation, and Python trainings. Our goal is to provide to our students the practical instruments they will need in order to perform successfully at their future workplace.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Welcome! Course Introduction
    • What Does the Course Cover? (5:10)
    • Download Useful Resources
  • Introduction to Programming with Python
    • Programming Explained in 5 Minutes (5:04)
    • Why Python? (5:11)
    • Why Jupyter? (3:29)
    • Installing Python and Jupyter (4:22)
    • Jupyter's Interface - the Dashboard (3:15)
    • Jupyter's Interface - Prerequisites for Coding (6:15)
  • Python Variables and Data Types
    • Variables (3:41)
    • Numbers and Boolean Values (3:05)
    • Strings (5:43)
  • Basic Python Syntax
    • Arithmetic Operators (3:23)
    • The Double Equality Sign (1:33)
    • Reassign Values (1:08)
    • Add Comments (1:25)
    • Line Continuation (0:49)
    • Indexing Elements (1:18)
    • Structure Your Code with Indentation (1:44)
  • Python Operators Continued
    • Comparison Operators (2:10)
    • Logical and Identity Operators (5:35)
  • Conditional Statements
    • Introduction to the IF statement (3:04)
    • Add an ELSE statement (2:39)
    • Else, if, for Brief - ELIF (5:33)
    • A Note on Boolean Values (2:13)
  • Python Functions
    • Defining a Function in Python (2:03)
    • Creating a Function with a Parameter (3:49)
    • Another Way to Define a Function (2:35)
    • Using a Function in another Function (1:49)
    • Combining Conditional Statements and Functions (3:06)
    • Creating Functions Containing a Few Arguments (1:13)
    • Notable Built-In Functions in Python (3:56)
  • Sequences
    • Lists (4:02)
    • Using Methods (3:22)
    • List Slicing (4:31)
    • Tuples (3:13)
    • Dictionaries (4:04)
  • Using Iterations in Python
    • For Loops (2:26)
    • While Loops and Incrementing (2:26)
    • Create Lists with the range() Function (2:22)
    • Use Conditional Statements and Loops Together (3:05)
    • All In - Conditional Statements, Functions, and Loops (2:27)
    • Iterating over Dictionaries (3:07)
  • Useful Tools
    • Object-Oriented Programming (5:00)
    • Modules and Packages (1:05)
    • The Standard Library (2:47)
    • Importing Modules (4:10)
    • Must-Have Packages for Finance and Data Science (4:53)
    • Working with Arrays (6:02)
    • Generating Random Numbers (2:52)
    • Importing and Organizing Data in Python - Part I (3:44)
    • Importing and Organizing Data in Python - Part II (7:01)
    • Importing and Organizing Data in Python - Part III (4:19)
  • PART II Finance: Calculating and Comparing Rates of Return in Python
    • Considering Both Risk and Return (2:33)
    • What Are We Going to See Next (2:34)
    • Calculating a Security's Rate of Return (5:31)
    • Calculating a Security's Rate of Return in Python - Simple Returns - Part I (5:23)
    • Calculating a Security's Rate of Return in Python - Simple Returns - Part II (3:28)
    • Calculating a Security's Rate of Return in Python - Logarithmic Returns (3:39)
    • What Is a Portfolio of Securities and How to Calculate Its Rate of Return (2:39)
    • Using 'Loc' and 'Iloc' - Note
    • Calculating the Rate of Return of a Portfolio of Securities (8:34)
    • Popular Stock Indices that Can Help us Understand Financial Markets (3:31)
    • Calculating the Rate of Return of Indices (5:03)
  • PART II Finance: Measuring Investment Risk
    • How Do We Measure a Security's Risk (6:05)
    • Calculating a Security's Risk in Python (5:55)
    • The Benefits of Portfolio Diversification (3:28)
    • Calculating the Covariance Between Securities (3:34)
    • Measuring the Correlation between Stocks (3:59)
    • Calculating Covariance and Correlation (5:00)
    • Considering the Risk of Multiple Securities in a Portfolio (3:19)
    • Calculating Portfolio Risk (2:39)
    • Understanding Systematic vs. Idiosyncratic Risk (2:58)
    • Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio (4:28)
  • PART II Finance: Using Regressions for Financial Analysis
    • The Fundamentals of Simple Regression Analysis (3:55)
    • Running a Regression in Python (6:35)
    • Are All Regressions Created Equal? Learning How to Distinguish Good Regressions (4:55)
    • Computing Alpha, Beta, and R Squared in Python (6:14)
  • PART II Finance: Markowitz Portfolio Optimization
    • Markowitz Portfolio Theory - One of the main Pillars of Modern Finance (6:34)
    • Obtaining the Efficient Frontier in Python - Part I (5:35)
    • Obtaining the Efficient Frontier in Python - Part II (5:18)
    • Obtaining the Efficient Frontier in Python - Part III (2:07)
  • PART II Finance: The Capital Asset Pricing Model
    • The Intuition behind the Capital Asset Pricing Model (CAPM) (4:44)
    • Understanding and Calculating a Security's Beta (4:14)
    • Calculating the Beta of a Stock (3:38)
    • The CAPM Formula (4:20)
    • Calculating the Expected Return of a Stock (CAPM) (2:16)
    • Introducing the Sharpe Ratio and the Way It Can Be Applied in Practice (2:21)
    • Obtaining the Sharpe Ratio in Python (1:22)
    • Measuring Alpha and Verifying How Good (or Bad) a Portfolio Manager Is Doing (4:13)
  • PART II Finance: Multivariate Regression Analysis
    • Multivariate Regression Analysis - a Valuable Tool for Finance Practitioners (5:42)
    • Running a Multivariate Regression in Python (6:20)
  • PART II Finance: Monte Carlo Simulations as a Decision-Making Tool
    • The Essence of Monte Carlo Simulations (2:31)
    • What Is a Normal Distribution? - Note
    • Monte Carlo Applied in a Corporate Finance Context (2:30)
    • Monte Carlo: Predicting Gross Profit - Part I (6:03)
    • Monte Carlo: Predicting Gross Profit - Part II (2:56)
    • Forecasting Stock Prices with a Monte Carlo Simulation (4:27)
    • Another Way to Calculate Simple and Log Returns - Note
    • Monte Carlo: Forecasting Stock Prices - Part I (3:39)
    • Monte Carlo: Forecasting Stock Prices - Part II (4:38)
    • Monte Carlo: Forecasting Stock Prices - Part III (4:17)
    • An Introduction to Derivative Contracts (6:32)
    • The Black-Scholes Formula for Option Pricing (4:51)
    • Monte Carlo: Black-Scholes-Merton (6:00)
    • Monte Carlo: Euler Discretization - Part I (6:21)
    • Monte Carlo: Euler Discretization - Part II (2:09)

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Terms

  • Unredeemed licenses can be returned for store credit within 15 days of purchase. Once your license is redeemed, all sales are final.