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Python has been regarded as one the most used programming languages in the last decade. With the development of AI, Python’s popularity remains even stronger. Python’s practicability lies not only in general data analysis, but also in web building, machine learning, deep learning, and artificial intelligence. Its language structure is easy to start, and was thought to be significantly easier compared with compiled language like C++. In this Python Basic Course, we will provide the fundamentals which we think are important and necessary to learn Python. The course covers Python data structure, basic functions, conditional test and looping, as well as Numpy and Pandas modules and data visualization in Python. Having programming experience is not a prerequisite for attending this course. However, we assume you have basic idea and knowledge about data analysis, and can use a personal computer. After completing this course, we hope the student will have elementary to intermediate understanding about how to use Python to do data analysis.
The course takes 2 days on weekend, about 10 hours in total. Course fee is 16 USD.
Python Basic Course have the following sections.
Section 1 Get started with Python environment
Installation of Anaconda and set up Python environment
Section 2 Variables and simple data types
String variables
Numbers
Section 3 Working with lists
Introducing lists
Changing, Appending,Removing items of Lists
Sorting lists
Looping through a list
Making Numerical Lists
List comprehension and Working with Part of a List
Tuples
Using a while Loop with Lists
Set operation
String Format Manipulation in Python
Section 4 Conditional test and If statements
Conditional Tests
if Statements
Using if Statements with Lists
Section 5 Dictionaries
Working with Dictionaries
Looping Through a Dictionary
Nesting dictionaries
Python Dictionary get() Method
Section 6 User input and while loop
User input
Introducing while loops
Using break and continue in while loops
Using a while Loop with Dictionaries
Section 7 Functions
Defining a function
Passing arguments to function
Functions: Return a simple value
Functions: Return a dictionary
Using a Function with a while Loop
Passing a List to function
Passing an Arbitrary Number of Arguments to function
Storing Your Functions in Modules
Lambda function
Map function
Partial functions
Writing generator functions with the yield statement
zip() function in Python
enumerate()
OS Module in Python
decorators
Section 8 Classes
Creating and Using a Class
Working with Classes and Instances
Inheritance of Classes
Working with Attributes and Methods for the Child Class
Importing Classes
Section 9 Files and Exceptions
Reading from a File
Writing to a File
Introducing try-except Blocks Exception
Handling the FileNotFoundError Exception
Using Try, Except, else,pass and Finally in Python
Storing Data using json() module
Refactoring
Section 10 The NumPy library
Introducing NumPy library
Creation of Array in NumPy
Basic operations of Numpy ndarray
Copies and Views, difference between NumPy arrays and Python lists
Broadcasting of NumPy arrays
Random Number Generation with Python and NumPy
Reading and Writing NumPy Array Data on Files
Difference between reshape() and resize() method in Numpy
Section 11 Introducing Pandas library
Getting Started with Pandas in Python
Introduction of Pandas Data Structures: The Series
Pandas Series Operations
Introduction of Pandas Data Structures: The DataFrame
Basic manipulation of Pandas DataFrame
Working with Index of Pandas Data Structures
Operations and Functions of Pandas Data Structures
Statistics Functions of Pandas Data Structures
Sorting and Ranking of Pandas Data Structures
Handling “Not a Number” Data with Pandas Data Structures
Hierarchical Indexing and Leveling of Pandas Data Structures
Accessing Rows and Columns of DataFrame
Ways to filter Pandas DataFrame by column values
Section 12 Reading and Writing Data with Pandas library
Reading Data in CSV or Text Files with Pandas
Using Regular Expressions to Parse TXT Files with Pandas
Writing Data to CSV Files with Pandas
Reading and Writing Data on Microsoft Excel Files with Pandas
Reading and Writing HTML Files with Pandas
Reading Data from XML with Pandas
Reading and Writing JSON Data with Pandas
Section 13 Pandas in Depth: Data Manipulation
Merging Datasets with Pandas
Concatenating and Combining Datasets with Numpy and Pandas
Pivoting,Stacking,Unstacking,Long and Wide forms of Datasets with Pandas
Removing, Mapping Operations with Pandas
Rename Indexes of Axes with Pandas
Detecting and Filtering Outliers with Pandas
Discretization and Binning of Datasets with Pandas
Permutation,Random Sampling with Pandas
Data Aggregation,Grouping with Pandas
Reshape Wide long form pandas
Section 14 Data Visualization with matplotlib
Getting started using matplotlib
Adding text to charts in matplotlib
Adding grid to charts in matplotlib
Adding legend to charts in matplotlib
Save your charts in matplotlib
Handling Date values of charts in matplotlib
Line charts
Histograms
Bar charts
Pie charts
Plotting Google Map using gmplot package
Plotting Google Map using folium package
Donut Chart matplotlib
Stack Plot
Box plot
Plot Subplots Within Other Subplots
You can also watch video from our YouTube channel for a preview of Python programming.
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