Course Outline
WHAT YOU’LL LEARN
Python, Data Science & Data Visualization -12 lectures
Lecture 1
• Introduction to Python Programming Language
• Different Popular Python Usages
• Data Analysis & Visualization
• Web & API Development with Django and Flask
• Software & Desktop Apps Development
• Task Automation
• Artificial Intelligence, Machine Learning AI/ML
• Internet of Things IoT
Lecture 2
• Python 3x Setup & Installation
• Python Programming in Windows Command Prompt
• Basic DOS Commands
• Python Console using python & ipython commands
• Writing Code in Python Console
• Functions print() & quit()
• Writing Code in Python .py File using VS Code Editor
• Run .py Code File
• Python Package Manager PIP Install and Import Packages
Lecture 3
• Introduction to Anaconda 3x Data Science Platform
• What Anaconda Offers
• Anaconda Package Manager Conda Install and Import Packages
• PIP vs Conda
• Install Packages by Reading a File pip/conda install -r req.txt
• Anaconda Prompt Console
• Anaconda Offers In-Memory Environments
• Benefits of Isolated OS Independent Environments
• Create, List, Activate, Deactivate, Remove Environments
• Jupyter Notebook Web Interface & Its Benefits
• Create and Use .ipynb File
Lecture 4
• Variables & Data Types in Python
• Global vs Local Variables
• Global Variables Outside a Function Use Anywhere
• Local Variables Inside a Function Use Within Function
• Implicit vs Explicit
• Text : str
• Numeric : int, float, complex
• Sequence : list, tuple, range
• Mapping Dictionary : dict
• Set : set, frozenset
• Boolean : bool
• Binary : bytes, bytearray, memoryview
Lecture 5
• Setting Data Type Implicitly assign value to a variable
• Setting Data Type Explicitly use constructor functions
• str(), int(), float(), complex(), list(), tuple(), range(), dict()
• set(), frozenset(), bool(), bytes(), bytearray(), memoryview()
• Getting Data Type print(type(x))
Lecture 6
• Basic Data Storage Structures
• Variable for Single Value, Arrays/Collections for Multiple Values
• Lists vs Tuples
• Tuples Values must be Unique and Constant cannot be modified
• Python uses Indentation to Indicate a Block of Code
• Spaces as You Want but atleast one, Once defined have to follow
• Tab is Best for Indentation
• Comments in Python using Hash #
• String Literal as Multiline Comment “”” Comment Inside “””
Lecture 7
• Boolean and Comparison Operators
• Logical Operators
• Arithmetic Operators
• Assignment Operators
• Identity Operators
• Membership Operators
• Bitwise Operators
• Conditional Statements If Else and Elif
Lecture 8
• For and While Loops in Python
• Python Normal Functions and Anonymous/Lambda Functions
• Python JSON (JavaScript Object Notation)
• Exception Handling, RegEx
• Python Classes and Objects
Lecture 9
• Inheritance in Python
• Python User Input, Dates and String Formatting
• Python Modules and Scope
• Python Iterators
• Python File Handling Read, Write/Create, Delete Files
Lecture 10
• A Introduction to NumPy, Pandas, Plotly & Dash Packages
• Dictionary Standard Advanced Multiple Values Storage Structure
• DataFrames Pandas Advanced Multiple Values Storage Structure
• Concept of Objects and Composed Objects
• Arrays & DataFrames as Objects
• Arrays & DataFrames Helper Functions
• Attached as Composed Objects to Main Object
Lecture 11
• NumPy Numerical Python Crash Course
• NumPy used for Working with Arrays
• Play with Numbers Fast as Compared to Raw Python Functions
• Python Array vs NumPy Array
• NaN (not a number), INF (infinity)
• Statistical Operations
• Shape, Reshape, Ravel, Flatten
• array, arrange, zeros, ones, linspace
• Sequence, Repetitions and Random Numbers
• Random Arrays randint, seed NumPy Random Package
• randint().min() .max() .mean() .argmax() .argmin() .reshape()
• Indexing & Masking
• Find Value by Indexing & Conditional Filters to Grab Elements
• Where
• File Read and Write
• Concatenate and Sorting
• Working with Dates
Lecture 12
• Pandas Advanced Data Manipulation Package Crash Course
• DataFrame and Series
• File Reading and Writing
• Import Pandas, Fetch DataSets in CSV File
• Functions read_csv(), head() First 5 Rows, tail() Last 5 Rows
• Info, Shape, Duplicated and Drop
• Columns
• NaN and Null Values
• Imputation, Lambda Functions
• Pivoting and Unpivoting Columns of DataSets
• Reshape and Clean Data in Jupyter Notebook Better Approach