Topics

• Primitive Data Types
• Getting started with JavaScript
• Functions
• Arrays
• AJAX
• Getting started with Python Language
• List comprehensions
• Arithmetic (Math)
• List
• Getting started with jQuery
• Functions
• Promises
• Getting started with Microsoft SQL Server
• Loops
• Variable Scope and Binding
• Date
• Files & Folders I/O
• String Methods
• Indexing and Slicing
• Simple Mathematical Operators
• Map Function
• Getting started with Node.js
• Strict mode
• JSON
• Classes
• Set
• Regular Expressions (Regex)
• Variable coercion/conversion
• Getting started with algorithm
• Getting started with pandas
• Getting started with React
• Incompatibilities moving from Python 2 to Python 3
• Getting started with numpy
• Getting started with vim
• Getting started with JSON
• Tuple
• String Formatting
• Getting started with scikit-learn
• Getting started with azure
• Installing Node.js
• Arrays
• Element Visibility
• Reserved Words
• Algorithm Complexity
• Creating DataFrames
• Performance Tips
• Indexing and selecting data
• The dis module
• Exceptions
• Grouping Data
• Getting started with html5-canvas
• Deque Module
• Generating random data
• Getting started with kivy
• *args and **kwargs
• Pandas IO tools (reading and saving data sets)
• Python and Excel
• Basic Plots
• Getting started with processing
• Common Pitfalls
• Stack
• Database Access
• Accessing Python source code and bytecode
• JSON
• Big-O Notation
• Bellman–Ford Algorithm
• Dimensionality reduction (Feature selection)
• Feature selection
• Magics
• GRANT and REVOKE
• Getting started with octave
• Python Serial Communication (pyserial)
• Performance optimization
• Unit Testing: Best Practices
• Deliver HTML or any other sort of file
• Simple manipulation of DataFrames
• Testing frameworks
• Animations and interactive plotting
• Knapsack Problem
• Selection Sort
• Applications of Greedy technique
• Regular Expressions
• Plotting with Matplotlib
• Applications of Dynamic Programming

Examples

• Using the DOM API
• Array spread / rest
• Getting Started
• List Comprehensions
• Basic use of map, itertools.imap and future_builtins.map
• Iterating over lists
• Introduction
• Break and Continue in Loops
• Getting Started
• Global Variables
• Changing the sequence you are iterating over
• File modes
• Writing to a file
• Basic Slicing
• Division
• Rounding
• Get the current time and date
• Hello World HTTP server
• Test the starting and ending characters of a string
• Testing what a string is composed of
• Hello World command line
• Displaying the top JavaScript questions of the month from Stack Overflow's API
• Avoid repetitive and expensive operations using conditional clause
• Create a new Date object
• INSERT / SELECT / UPDATE / DELETE: the basics of Data Manipulation Language
• Matching the beginning of a string
• Creating variables and assigning values
• Lambda (Inline/Anonymous) Functions
• Installation or Setup
• Argument passing and mutability
• Datatypes
• Reading csv file into DataFrame
• Loops with an "else" clause
• Basics of String Formatting
• Alignment and padding
• Default values for instance variables
• Closure
• Hello World
• Array operators
• Array Access
• Installing Node.js on Windows
• Shallow cloning an array
• Sets versus multisets
• Errors due to reserved words
• Named Functions
• What is ReactJS?
• Create a sample DataFrame
• Create a sample DataFrame using Numpy
• Exceptions are Objects too
• Create a DataFrame from a list of tuples
• Delete a column in a DataFrame
• Hello World
• Select column by label
• Nonlocal Variables
• What is Python bytecode?
• Disassembling modules
• Select by position
• Rename a column
• Converting a string to a boolean
• Adding a new column
• Slicing with labels
• Basic grouping
• Grouping numbers
• Converting to boolean
• Create a DataFrame from a dictionary of lists
• Create a sample DataFrame with datetime
• Concatenate and Merge lists
• Tuple
• Reshaping an array
• Boolean indexing
• Train a classifier with cross-validation
• Sample datasets
• Aggregating by size versus by count
• Installation & Setup
• Big-Theta notation
• Descriptive statistics
• Save and Load a DataFrame in pickle (.plk) format
• map()
• Packing and Unpacking Tuples
• JSON versus JavaScript literals
• Using *args when calling functions
• Remove duplicate values in list
• Broadcasting array operations
• JSON Syntax Rules
• Introduction to classes
• Scatter Plots
• Primitive Types Cheatsheet
• The round() function tie-breaking and return type
• List multiplication and common references
• Trigonometric Functions
• Integer and String identity
• String function - str() and repr()
• Toggle possibilities
• Hello World
• Changes to properties
• long vs. int
• Cyclic object values
• SQLite
• Limit DOM Updates
• Display the bytecode of a function
• Exploring the code object of a function
• Line plots
• Dictionaries are unordered
• Heatmap
• Low-Variance Feature Removal
• Hello World
• Create excel charts with xlsxwriter
• Populate an array with the contents of a CSV file
• Create a DataFrame from a list of dictionaries
• Delete / drop rows from DataFrame
• Grant/revoke privileges
• Comparison of the asymptotic notations
• Variable leaking in list comprehensions and for loops
• Initialize serial device
• Read from serial port
• Check what serial ports are available on your machine
• Local vs Global Scope
• Code profiling
• Get the first/last n rows of a dataframe
• Hello World
• Reading commands from a script file
• Matrices
• Deliver HTML at specified path
• Basic animation with FuncAnimation
• Save animation to gif
• Interactive controls with matplotlib.widgets
• Display the source code of an object
• Hello World in the REPL
• Implementation of Selection sort in C#
• Select distinct rows across dataframe
• Filter out rows with missing data (NaN, None, NaT)
• The "half loop" do-while
• REGEXP / RLIKE
• Core modules
• Read JSON from file
• Built-in line and cell magics
• Numpy n-dimensional array: the ndarray
• Rotate
• Save canvas to image file
• Fibonacci Numbers
• What to do in case of a crash