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The Python Mega Course: Build 10 Real-World Applications
$195 Value
Introduction to Python Training
$200 Value
Python Data Analysis with NumPy & Pandas
$200 Value
Learn Python for Data Analysis & Visualization
$200 Value
Master Clustering Analysis for Data Science Using Python
$200 Value
Keras Bootcamp for Deep Learning & AI in Python
$200 Value
Image Processing & Analysis with OpenCV and Learning in Python
$200 Value
Master PyTorch for Artificial Neural Networks & Deep Learning
$200 Value
Data Mining with Python: Real-Life Data Science Exercises
$200 Value
Solve 100 Python Exercises to Boost Your Python Skills
$195 Value
Data Visualization with Python & Bokeh
$195 Value
Advanced Python Training
$200 Value
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23.0 hours
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242
The Python Mega Course: Build 10 Real-World Applications
Explore the Power of Python By Actually Building Apps With Python
By Ardit Sulce | in Online Courses
The best way to learn Python is by using Python, and this massive course will teach you while you develop real-life applications. Over the course, you'll truly begin to appreciate the many, many uses of Python as you build web applications, database applications, web visualizations, and much more. By course's end, you will have built 10 applications that you can be proud of, and have the tools to go off on your own into the world of Python programming.
- Access 242 lectures & 23 hours of content 24/7
- Build a name generator, a website URL timed blocker, a web map generator, a portfolio website w/ Flask, a GUI-based desktop application, & more apps
- Under & use object-oriented design
- Use Python to build applications w/ Flask, Tkinter, Numpy, Folium & more
- Explore scraping data, computer vision, sending automated emails & more using Python
- Schedule Python programs based on computer events
Ardit Sulce received his master's degree in Geospatial Technologies from the Institute of Geoinformatics at University of Muenster, Germany. He also holds a Bachelor's degree in Geodetic Engineering. Ardit offers his expertise in Python development on Upwork where he has worked with companies such as the Swiss in-Terra, Center for Conservation Geography, and Rapid Intelligence. He is the founder of PythonHow where he authors written tutorials about the Python programming language.
Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: beginner
Requirements
- Internet access required
Course Outline
- Your First Program
- Introduction
- Course Introduction - 2:20
- The Tools You Need - 1:08
- Installing Python 3 and an IDE - 6:43
- The Basics: Small Program
- Your First Python Code - 3:12
- Your first Python Program - 6:34
- Summary
- FAQs
- The Basics: Data Types
- Variables - 3:12
- Exercise: Assign Values and Print
- Solution: Assign Values and Print
- Simple Types: Integers, Strings, and Floats - 3:21
- List Types - 1:31
- Type attributes - 2:54
- How to find what code you need - 4:34
- Bonus: Steps of Learning Python - 1:28
- Dictionary Types - 3:43
- Tuple Types - 2:26
- How are datatypes used in the real world - 1:07
- Summary: Integers, Floats, Lists, Dictionaries, and Tuples
- Exercise: Create Integers, Strings, and Floats
- Solution: Create Integers, Strings, and Floats
- Exercise: Sum Up Numbers
- Solution: Sum Up Numbers
- Exercise: Create List
- Solution: Create List
- Exercise: Create Complex List
- Solution: Create Complex List
- Exercise: Calculate Maximum
- Solution: Calculate Maximum
- Exercise: Count Values
- Solution: Count Values
- Exercise: Modify String
- Solution: Modify String
- The Basics: Operations with Data Types
- Python shell and terminal tips - 0:52
- More operations with lists - 5:56
- Accessing list items - 2:04
- Accessing list slices - 2:49
- Accessing items and slices with negative indexes - 2:02
- Accessing characters and slices in strings - 1:30
- Accessing items in dictionaries - 1:52
- Summary: Positive/Negative Indexes, Slicing
- The Basics: Functions and Conditionals
- Creating Your Own Functions - 5:25
- Print or return - 3:59
- Intro to conditionals - 0:58
- If conditional example - 3:48
- Bonus Code: Using "and" and "or" in a Conditional
- Conditional explained line by line - 3:09
- More on conditionals - 2:21
- Elif conditionals - 1:15
- White space - 3:30
- Summary: Functions and Conditionals
- The Basics: Processing User Input
- User input - 7:55
- String formatting - 2:44
- String formatting with multiple variables - 1:35
- Summary: Processing User Input
- The Basics: Loops
- For loops, how and why - 5:46
- For loop over a function
- Looping through a dictionary - 1:12
- Bonus code: Dictionary loop and string formatting
- While loops: how and why - 2:59
- While loop example with user input - 3:12
- While loop with break and continue - 3:22
- Summary: Loops
- Putting the Pieces Together: Building a Program
- Problem statement - 3:48
- Approaching the problem - 1:46
- Building the maker function - 5:14
- Constructing the loop - 4:39
- Making the output user-friendly - 3:28
- List Comprehensions
- Simple list comprehension - 3:27
- List comprehension with If conditional - 1:22
- List comprehension with if-else conditional - 1:37
- Summary: List Comprehensions
- More on Functions
- Functions with multiple arguments - 1:45
- Keyword and non-keyword arguments, default and non-default parameters - 3:00
- Functions with an arbitrary number of non-keyword arguments - 3:32
- Functions with an arbitrary number of keyword arguments - 1:34
- Summary: More on Functions
- File Processing
- The concept of processing files with Python - 1:12
- Reading text from a file - 3:07
- The cursor - 1:49
- Closing a file - 1:34
- Opening files using "with" - 1:45
- Different file paths - 1:25
- Writing text to a file - 3:59
- Appending text to an existing File - 3:30
- Summary: File Processing
- Imported Modules
- Builtin modules - 5:53
- Standard Python modules - 8:34
- Third-party modules - 5:49
- Third-party module example - 2:45
- Summary: Imported Modules
- Application 1: Build an Interactive Dictionary
- Program demonstration - 4:10
- The data source - 4:54
- Loading JSON data - 3:52
- Returning the definition of a word - 3:25
- Counting for non-existing words - 2:51
- Implementing case sensitivity - 3:09
- Similarity ratio between two words - 4:39
- Best match out of a list of words - 6:07
- Recommending the best match - 9:42
- Confirmation from the user - 10:17
- Optimizing the final output - 7:51
- Exercise: Fixing a program bug (1)
- Solution
- Exercise: Fixing a program bug (2)
- Solution
- Numpy
- What is Numpy - 8:07
- Creating Numpy Arrays from Images and Vice-Versa - 12:30
- Indexing, Slicing and Iterating - 4:57
- Stacking and Splitting - 5:44
- Data Analysis with Pandas
- What is Pandas - 6:37
- Installing Pandas
- Getting Started with Pandas - 8:37
- Getting Started with Jupyter Notebooks - 9:18
- Note
- Loading CSV Files - 4:20
- Exercise: Loading JSON Files
- Solution
- Note on Adding Excel Files
- Loading Excel Files - 0:58
- Loading TXT Files - 2:30
- Set Header Row - 2:34
- Set Column Names - 0:56
- Set Index Column - 4:45
- Indexing and Slicing - 5:35
- Deleting Columns and Rows - 2:30
- Updating and Adding New Columns and Rows - 7:31
- Note on Nominatim
- Example: Geocoding Addresses with Pandas and Geopy - 15:11
- Application 2: Create Webmaps with Python and Folium
- Demonstration of the Web Mapping Application - 1:24
- Creating an Open Street Map with Python - 6:34
- Adding Markers to the Map - 5:10
- Adding Markers to the Map from CSV Data - 9:12
- Rule-based Coloring of Markers - 4:31
- More on Rule-based Styling - 4:27
- Calculating the Map Center from the Input Data - 7:56
- Adjusting the Code for the Latest Version of Folium - 8:12
- Adding a Choropleth Map from GeoJson - 20:59
- Adding a Layer Control Panel - 4:28
- Fixing Programming Errors
- Syntax errors - 8:22
- Runtime errors - 10:58
- Fixing difficult errors - 5:38
- The structure of a good programming question - 5:59
- Error handling - 7:59
- Application 3: Build a Website Blocker
- Demonstration of the Website Blocker Application - 3:48
- Application Architecture - 3:44
- Setting up the Script - 9:08
- Setting up the Infinite Loop - 11:00
- Implementing the First Part - 12:16
- Implementing the Second Part - 18:55
- Scheduling the Python Program on Windows - 12:39
- Scheduling the Python Program on Mac and Linux - 6:15
- Application 4: Build a Website with Python and Flask
- Demonstration of the Website - 1:42
- Building Your First Website - 8:07
- Returning HTML Templates - 4:09
- Adding a Navigation Menu - 8:32
- Adding CSS Styling - 5:59
- Creating a Python Virtual Environment - 6:22
- Deploying the Website to a Live Server - 21:52
- Maintaining the Website - 7:26
- Graphical User Interfaces with Tkinter
- Introduction to Tkinter - 2:35
- Setting up a GUI with Widgets - 9:11
- Connecting GUI Widgets with Callback Functions - 9:33
- Interacting with Databases with Python
- Introduction to Working with Databases - 3:04
- Connecting and Inserting Data to SQLite via Python - 13:11
- Selecting, Inserting, Deleting, and Updating SQLite Records - 6:58
- Introduction to PostgreSQL Psycopg2 - 8:46
- Selecting, Inserting, Deleting, and Updating PostgreSQL Records - 12:53
- Application 5: Build a Desktop Database Application
- Demonstration of the Database Application - 2:25
- User Interface Design - 5:54
- Building the Front-end Interface - 27:00
- Building the Back-end - 24:28
- Connecting the Front-end to the Back-end, Part 1 - 17:31
- Connecting the Front-end to the Back-end, Part 2 - 21:59
- Creating a Standalone Executable Version of the Program - 5:00
- Object Oriented Programming
- Object Oriented Programming Explained - 4:59
- Turning this Application into OOP Style, Part 1 - 13:01
- Turning this Application into OOP Style, Part 2 - 14:06
- Creating a Bank Account Object - 21:06
- Inheritance - 12:08
- OOP Glossary - 8:12
- Python for Image and Video Processing with OpenCV
- Introduction - 2:29
- Installing OpenCV for Python - 2:48
- Loading, Displaying, Resizing, and Writing Images with Python - 14:00
- Face Detection - 19:38
- Capturing Video - 19:45
- Application 6: Build a Webcam Motion Detector
- Demonstration of the Motion Detector Application - 1:59
- Detecting Objects from the Webcam - 30:20
- Recording Motion Time - 20:38
- Interactive Data Visualization with Python
- Introduction to Bokeh - 2:02
- Installing Bokeh
- Your First Bokeh Plot - 13:52
- Plotting Triangles and Circle Glyphs (Practice)
- Solution
- Using Bokeh With Pandas - 4:51
- Plotting Education Data (Practice)
- Solution
- Note on Loading Excel Files
- Plot Properties
- Plot Weather Data (Practice)
- Solution
- Visual Attributes
- Time-Series Plots - 6:36
- More Visualization Examples with Bokeh - 4:21
- Plotting Time Intervals of the Motion Detector - 14:05
- Hover Tool Implementation - 9:57
- Webscraping with Python Beautiful Soup
- Section Introduction - 1:57
- The Concept Behind Webscraping - 4:30
- Scraping a Webpage with Requests and BeautifulSoup - 16:22
- Application 7: Scrape Real Estate Property Data
- Demonstration of the Webscraping Application - 2:28
- Understanding the Problem and Loading the Webpage in Python - 7:15
- Extracting Divisions of All Properties - 11:34
- Extracting Addresses and Property Details - 14:39
- Extracting Elements with no Unique Identifiers - 12:07
- Saving the Extracted Data in CSV Files - 8:27
- Crawling Through Webpages - 17:15
- Application 8: Build a Web-based Financial Graph
- Demonstration of the Financial Analysis Application - 1:59
- Downloading Various Datasets with Python - 11:31
- Understanding Stock Market Data - 3:25
- Understanding Stock Market Data Candlestick Charts - 5:39
- Building Chart Candlesticks with Bokeh Quadrants - 10:13
- Building Chart Candlesticks with Bokeh Rectangles - 22:28
- Building Candlestick Segments - 5:02
- Stylizing the Chart - 4:21
- The Concept Behind Embedding a Bokeh Chart in a Webpage - 11:04
- Embedding the Bokeh Chart in a Webpage - 15:33
- Deploying the Chart Website to a Live Server - 8:22
- Application 9: Build a Data Collector Web App
- Demonstration of the Web Application - 2:59
- Steps for Building a PostgreSQL Database-enabled Web Application - 6:08
- Building the Front-end: HTML Part - 14:52
- Building the Front-end: CSS Part - 10:11
- Building the Back-end: Getting User Input - 17:31
- Building the Back End: Creating the PostGreSQL Database Model - 18:17
- Building the Back End: Storing User Data to the Database - 19:14
- Building the Back End: Emailing Database Values Back to the User - 11:14
- Building the Back End: Sending Statistics to Users - 16:00
- Deploying the Web Application to a Live Server - 29:31
- Bonus Lecture: User Downloads and Uploads - 20:51
- Application 10: Student Project on Building a Geocoder Web Service
- Demonstration of the Geocoding Web Service Application and Project Requirements - 7:31
- Solution, Part 1 - 16:21
- Solution, Part 2 - 5:51
- End of the Course - 0:47
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Lifetime
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3.0 hours
Lessons
141
Introduction to Python Training
Familiarize Yourself with the World's Most Popular Programming Language
By Webucator | in Online Courses
Python is a popular, general-purpose programming language primarily used in web and app development. In this Python training course, students learn to program in Python. The course is aimed at students new to the language who may or may not have experience with other programming languages.
- Access 141 lectures & 3 hours of content 24/7
- Understand how Python works & what it's good for
- Work with & manipulate strings in Python
- Perform math operations & work w/ Python sequences
- Learn flow control processing
Webucator is a global training company founded in 2003 that has trained over 60,000 students. They provide onsite and online training on technologies such as Microsoft ASP.NET, Microsoft Office, Azure, Windows, Java, Adobe, Python, SQL, JavaScript, Angular and much more. Webucator has trained more than 63,677 students from over 11,907 organizations are a Microsoft Certified Partner for Learning Solutions (CPLS).
Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: all levels
Requirements
- Internet access required
Access
Lifetime
Content
1.0 hours
Lessons
40
Python Data Analysis with NumPy & Pandas
Enhance Your Data Analysis Skills Using NumPy, Pandas, & Matplotlib
By Webucator | in Online Courses
If you or your team are using or plan to use Python for data science or data analytics, then this is the Python course for you. The course starts by teaching you how to use Jupyter Notebook, a great tool for writing, testing, and sharing quick Python programs. You will learn NumPy, which makes working with arrays and matrices (in place of lists and lists of lists) much more efficient, and pandas, which makes manipulating, munging, slicing, and grouping data much easier. You will also learn some simple data visualization techniques with matplotlib.
- Access 40 lectures & 10 hours of content 24/7
- Write, test & share quick Python programs using Jupyter Notebook
- Use NumPy to work w/ arrays & matrices of numbers
- Work w/ pandas to analyze data
- Learn some simple data visualization techniques w/ matplotlib
Webucator is a global training company founded in 2003 that has trained over 60,000 students. They provide onsite and online training on technologies such as Microsoft ASP.NET, Microsoft Office, Azure, Windows, Java, Adobe, Python, SQL, JavaScript, Angular and much more. Webucator has trained more than 63,677 students from over 11,907 organizations are a Microsoft Certified Partner for Learning Solutions (CPLS).
Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: all levels
Requirements
- Internet access required
Access
Lifetime
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2.0 hours
Lessons
33
Learn Python for Data Analysis & Visualization
Gear Yourself with Necessary Skills for Expert-Proven Data Science Techniques
By Tony Staunton | in Online Courses
Python Pandas are one of the most used libraries in Python when it comes to data analysis and manipulation. Whether in finance, scientific fields, or data science, familiarity with Pandas is a must-have. This course teaches you how to work with real-world data sets for analyzing data in Python using Pandas. Not only will you learn how to manipulate and analyze data you will also learn powerful and easy to use visualization techniques for representing your data.
- Access 33 lectures & 2 hours of content 24/7
- Use Pandas DataFrames to work w/ tabular data
- Use conditional filtering to select relevant information from datasets
- Create graphical plots such as bar, line, area, scatter, & more
Tony Staunton is a Python development and productivity consultant, helping over 20,000 students. Tony created and ran his own software business and won several awards from, a most innovative startup to the best product.
Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: intermediate
Requirements
- Internet access required
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Lifetime
Content
5.0 hours
Lessons
35
Master Clustering Analysis for Data Science Using Python
Learn All About Clustering Algorithms with Python Examples & Datasets
By Nouman Azam | in Online Courses
This course is for you if you want to gain a deeper understanding of the clustering algorithms without having to learn all the complicated maths. The approach in this course is very practical and will start everything from scratch. We will immediately start coding after a couple of introductory tutorials and minimal theories. All the coding will be done in Python which is one of the fundamental programming languages for engineer and science students and is frequently used by top data science research groups worldwide.
- Access 35 lectures & 5 hours of content 24/7
- Implement different clustering algorithms in python
- Know when to use a specific algorithm
- Handle issues of varying cluster sizes, densities, shapes & noise
Nouman Azam received his Ph.D. Degree in Computer Science from the University of Regina in 2014. Prior to that, he completed his M.Sc. in Computer Software Engineering from National University of Sciences and Technology, Pakistan, and Bachelor's in Computer Sciences from National University of Computer and Emerging Sciences, Pakistan in 2007 and 2005, respectively
Nouman has over 10 years of teaching experience. He has taught almost all the major computer science subjects including introduction to computers, computer organization and architecture, operation systems, computer networks, image processing, digital logic design, discrete structures, and many others. He has extensive knowledge of tools such as MATLAB, QTSpim, C++, Java and Other academic tools used for teaching and instructing purposes.
Nouman has over 10 years of teaching experience. He has taught almost all the major computer science subjects including introduction to computers, computer organization and architecture, operation systems, computer networks, image processing, digital logic design, discrete structures, and many others. He has extensive knowledge of tools such as MATLAB, QTSpim, C++, Java and Other academic tools used for teaching and instructing purposes.
Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: advanced
Requirements
- Internet access required
Access
Lifetime
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3.0 hours
Lessons
35
Keras Bootcamp for Deep Learning & AI in Python
Master Keras: An Important Framework for Deep Learning & Artificial Intelligence
By Minerva Singh | in Online Courses
This is a full 3-hour Python Keras Neural Network & Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using one of the most important Deep Learning frameworks—Keras. This course is your complete guide to the practical machine and deep learning using the Keras framework in Python. This means, this course covers the important aspects of Keras (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Keras based data science.
- Access 35 lectures & 3 hours of content 24/7
- Get started w/ Jupyter notebooks for implementing data science techniques in Python
- Understand the basics of Keras syntax
- Create artificial neural networks & deep learning structures w/ Keras
Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.
Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: beginner
Requirements
- Internet access required
- Prior exposure to Python-based data science will be beneficial
- Prior exposure to basic statistical concepts & implementation will be useful
- Prior exposure to common machine learning terms such as cross-validation
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5.0 hours
Lessons
61
Image Processing & Analysis with OpenCV and Learning in Python
Dig Deep Into Both Conventional & Data Science-Centric Image Processing and Computer Vision Tasks
By Minerva Singh | in Online Courses
This 5-hour course is a full Python-based image processing and computer vision boot camp that will help you implement basic image processing and computer vision tasks using Jupyter Notebooks. With 61 lectures, this course covers the important aspects of Keras and Tensorflow. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of Tensorflow and Keras is revolutionizing Deep Learning. By gaining proficiency in Keras and Tensorflow, you can give your company a competitive edge and take your career to the next level.
- Access 61 lectures & 5 hours of content 24/7
- Carry out basic image pre-processing & computer vision tasks w/ Python
- Implement common machine learning algorithm on image classification
- Read in image data into the Jupiter/iPython environment
Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.
Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: all levels
Requirements
- Internet access required
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Lifetime
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6.0 hours
Lessons
52
Master PyTorch for Artificial Neural Networks & Deep Learning
Get Introduced to Deep Neural Networks & Become a Pro in Practical PyTorch-Based Data Science
By Minerva Singh | in Online Courses
This is a complete neural network and deep learning training with PyTorch in Python. It's a full 6-hour PyTorch Bootcamp that will help you learn basic machine learning, how to build neural networks and explore deep learning using one of the most important Python Deep Learning frameworks. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and the advent of frameworks such as PyTorch is revolutionizing deep learning. By gaining proficiency in PyTorch, you can give your company a competitive edge and take your career to the next level.
- Access 52 lectures & 6 hours of content 24/7
- Learn implement deep learning models w/ PyTorch
- Implement PyTorch based deep learning algorithms on imagery data
- Configure the Anaconda Environment for getting started w/ PyTorch
- Implement common machine learning algorithms for Image Classification
Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.
Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: beginner
Requirements
- Internet access required
Access
Lifetime
Content
6.0 hours
Lessons
40
Data Mining with Python: Real-Life Data Science Exercises
Get Hands-On Python Experience with Exercises in Visualization, Machine Learning, Apache Spark, SQL, NLP, Matplotlib & More!
By Ardit Sulce | in Online Courses
This course prepares you for a career in web development by using real-world examples of data mining and datasets with Python. You'll practice with hands-on projects, such as learning how to find data about a house when looking to become a homeowner. You'll be able to look through house data to find useful information from a text dataset, as well as clean the data, filter noise, and make data available for analysis. You'll also perform cluster analysis, classification, and regression, and learn how to use Apache Spark, the number one framework used for distributed processing.
- Access 40 lectures & 6 hours of content 24/7
- Learn to create, evaluate & use data models to make predictions
- Dive into setting up & starting new projects
- Practice w/ pop quizzes embedded in lectures for you to test yourself
John Bura has been programming games since 1997 and teaching since 2002. John is the owner of the game development studio Mammoth Interactive. This company produces XBOX 360, iPhone, iPad, android, HTML 5, ad-games and more. Mammoth Interactive recently sold a game to Nickelodeon! John has been contracted by many different companies to provide game design, audio, programming, level design and project management. To this day John has 40 commercial games that he has contributed to. Several of the games he has produced have risen to number 1 in the Apple's app store. In his spare time John likes to play ultimate Frisbee, cycle and work out.
Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: all levels
Requirements
- Internet access required
Access
Lifetime
Content
2.0 hours
Lessons
206
Solve 100 Python Exercises to Boost Your Python Skills
Sharpen Your Python Skills with Beginner & Intermediate Python Assignments
By Ardit Sulce | in Online Courses
In this course, you'll solve 100 different Python assignments completely on your own, assuring that you're able to write Python code independently without someone else's help. Suitable for beginners and intermediates alike, the course progresses through different difficulty levels and helps you test your solutions and learn new things as you progress. Each exercise is scored so you'll know your Python expertise at the end.
- Access 206 lectures & 2 hours of content 24/7
- Develop real-life coding skills
- Solve exercises of varying difficulty
- Build programs that do certain actions
- Fix bugs in existing programs
- Make changes to improve existing code
Ardit Sulce received his master's degree in Geospatial Technologies from the Institute of Geoinformatics at University of Muenster, Germany. He also holds a Bachelor's degree in Geodetic Engineering. Ardit offers his expertise in Python development on Upwork where he has worked with companies such as the Swiss in-Terra, Center for Conservation Geography, and Rapid Intelligence. He is the founder of PythonHow where he authors written tutorials about the Python programming language.
Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: intermediate
Requirements
- Internet access required
Access
Lifetime
Content
6.0 hours
Lessons
77
Data Visualization with Python & Bokeh
Learn How to Use Bokeh's Python Library to Build Advanced & Modern Data Visualization Web Applications
By Ardit Sulce | in Online Courses
Bokeh is a Python library for building advanced and modern data visualization web applications, and this course is your complete guide to this library. With 77 lectures, this course will guide you step-by-step, starting from plotting simple datasets to building rich and beautiful data visualization web apps that plot data in real-time and allow web users to interact and change the behavior of your plots via the internet from their browsers. Bokeh is a brand new data science library that is gaining traction fast, so it's smart to be ahead of the competition and pack the skills in your portfolio.
- Access 77 lectures & 6 hours of content 24/7
- Build advanced data visualization web apps using the Python Bokeh library
- Create widgets that let users interact w/ your plots
- Integrate & visualize data from Pandas DataFrames
- Create interactive modern web plots that represent your data impressively
Ardit Sulce received his master's degree in Geospatial Technologies from the Institute of Geoinformatics at University of Muenster, Germany. He also holds a Bachelor's degree in Geodetic Engineering. Ardit offers his expertise in Python development on Upwork where he has worked with companies such as the Swiss in-Terra, Center for Conservation Geography, and Rapid Intelligence. He is the founder of PythonHow where he authors written tutorials about the Python programming language.
Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: intermediate
Requirements
- Internet access required
Access
Lifetime
Content
1.0 hours
Lessons
99
Advanced Python Training
Go from Complete Beginner to a Python Language Expert
By Webucator | in Online Courses
If you've already got the hang of Python's basic concepts, it's time to sharpen your knowledge and take your skills to a new level of expertise. In this Python training course, students already familiar with Python programming will learn advanced Python techniques. It will cover actual mapping, advanced sorting, expressions, databases, object-oriented coding, and more.
- Access 99 lectures & 1 hour of content 24/7
- Work w/ the Collections module
- Map & filter lambda functions
- Work w/ regular expressions in Python
- Practice w/ databases, CSV files, JSON, & XML
Webucator is a global training company founded in 2003 that has trained over 60,000 students. They provide onsite and online training on technologies such as Microsoft ASP.NET, Microsoft Office, Azure, Windows, Java, Adobe, Python, SQL, JavaScript, Angular and much more. Webucator has trained more than 63,677 students from over 11,907 organizations are a Microsoft Certified Partner for Learning Solutions (CPLS).
Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: all levels
Requirements
- Internet access required
Terms
- Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.