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Course Details

Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language. Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis.

  • Course Duration 35 Days, Daily 1 Hours
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What are the Python Course Pre-requisites

There are no hard pre-requisites. Basic understanding of Computer Programming terminologies is sufficient. Also, basic concepts related to Programming and Database is beeficial but not mandatory.

Objectives of the Course
  • To understand the concepts and constructs of Python
  • • To create own Python programs, know the machine learning algorithms in Python and
    work on a real-time project running on Python
Who should do the course
  • Big Data Professionals
  • IT Developers
  • Those who are showing interest to build their career in Python

Core Python

Introduction to Languages
  1. What is Language?
  2. Types of languages
  3. Introduction to Translators
  4. Compiler
  5. Interpreter
  6. What is Scripting Language?
  7. Types of Script
  8. Programming Languages v/s Scripting Languages
  9. Difference between Scripting and Programming languages
  10. What is programming paradigm?
  11. Procedural programming paradigm
  12. Object Oriented Programming paradigm
Introduction to Python
  1. What is Python?
  2. WHY PYTHON?
  3. History
  4. Features – Dynamic, Interpreted, Object oriented, Embeddable, Extensible, Large standard libraries, Free and Open source
  5. Why Python is General Language?
  6. Limitations of Python
  7. What is PSF?
  8. Python implementations
  9. Python applications
  10. Python versions
  11. PYTHON IN REALTIME INDUSTRY
  12. Difference between Python 2.x and 3.x
  13. Difference between Python 3.7 and 3.8
  14. Software Development Architectures
Python Software’s
  1. Python Distributions
  2. Download &Python Installation Process in Windows, Unix, Linux and Mac
  3. Online Python IDLE
  4. Python Real-time IDEs like Spyder, Jupyter Note Book, PyCharm, Rodeo, Visual Studio Code, ATOM, PyDevetc
Python Language Fundamentals
  1. Python Implementation Alternatives/Flavors
  2. Keywords
  3. Identifiers
  4. Constants / Literals
  5. Data types
  6. Python VS JAVA
  7. Python Syntax
Different Modes of Python
  1. Interactive Mode
  2. Scripting Mode
  3. Programming Elements
  4. Structure of Python program
  5. First Python Application
  6. Comments in Python
  7. Python file extensions
  8. Setting Path in Windows
  9. Edit and Run python program without IDE
  10. Edit and Run python program using IDEs
  11. INSIDE PYTHON
  12. Programmers View of Interpreter
  13. Inside INTERPRETER
  14. What is Byte Code in PYTHON?
  15. Python Debugger
Python Variables
  1. bytes Data Type
  2. byte array
  3. String Formatting in Python
  4. Math, Random, Secrets Modules
  5. Introduction
  6. Initialization of variables
  7. Local variables
  8. Global variables
  9. ‘global’ keyword
  10. Input and Output operations
  11. Data conversion functions – int(), float(), complex(), str(), chr(), ord()
Operators
  1. Arithmetic Operators
  2. Comparison Operators
  3. Python Assignment Operators
  4. Logical Operators
  5. Bitwise Operators
  6. Shift operators
  7. Membership Operators
  8. Identity Operators
  9. Ternary Operator
  10. Operator precedence
  11. Difference between “is” vs “==”
Input & Output Operators
  1. Print
  2. Input
  3. Command-line arguments
Control Statements
  1. Conditional control statements
  2. If
  3. If-else
  4. If-elif-else
  5. Nested-if
  6. Loop control statements
  7. for
  8. while
  9. Nested loops
  10. Branching statements
  11. Break
  12. Continue
  13. Pass
  14. Return
  15. Case studies
Data Structures or Collections
  1. Introduction
  2. Importance of Data structures
  3. Applications of Data structures
  4. Types of Collections
  5. Sequence
  6. Strings, List, Tuple, range
  7. Non sequence
  8. Set, Frozen set, Dictionary
Strings
  1. What is string
  2. Representation of Strings
  3. Processing elements using indexing
  4. Processing elements using Iterators
  5. Manipulation of String using Indexing and Slicing
  6. String operators
  7. Methods of String object
  8. String Formatting
  9. String functions
  10. String Immutability
  11. Case studies
List Collection
  1. What is List
  2. Need of List collection
  3. Different ways of creating List
  4. List comprehension
  5. List indices
  6. Processing elements of List through Indexing and Slicing
  7. List object methods
  8. List is Mutable
  9. Mutable and Immutable elements of List
  10. Nested Lists
  11. List_of_lists
  12. Hardcopy, shallowCopy and DeepCopy
  13. zip() in Python
  14. How to unzip?
  15. Python Arrays:
  16. Case studies
Tuple Collection
  1. What is tuple?
  2. Different ways of creating Tuple
  3. Method of Tuple object
  4. Tuple is Immutable
  5. Mutable and Immutable elements of Tuple
  6. Process tuple through Indexing and Slicing
  7. List v/s Tuple
  8. Case studies
Set Collection
  1. What is set?
  2. Different ways of creating set
  3. Difference between list and set
  4. Iteration Over Sets
  5. Accessing elements of set
  6. Python Set Methods
  7. Python Set Operations
  8. Union of sets
  9. functions and methods of set
  10. Python Frozen set
  11. Difference between set and frozenset ?
  12. Case study
Dictionary Collection
  1. What is dictionary?
  2. Difference between list, set and dictionary
  3. How to create a dictionary?
  4. PYTHON HASHING?
  5. Accessing values of dictionary
  6. Python Dictionary Methods
  7. Copying dictionary
  8. Updating Dictionary
  9. Reading keys from Dictionary
  10. Reading values from Dictionary
  11. Reading items from Dictionary
  12. Delete Keys from the dictionary
  13. Sorting the Dictionary
  14. Python Dictionary Functions and methods
  15. Dictionary comprehension
Functions
  1. What is Function?
  2. Advantages of functions
  3. Syntax and Writing function
  4. Calling or Invoking function
  5. Classification of Functions
  6. No arguments and No return values
  7. With arguments and No return values
  8. With arguments and With return values
  9. No arguments and With return values
  10. Recursion
  11. Python argument type functions :
  12. Default argument functions
  13. Required(Positional) arguments function
  14. Keyword arguments function
  15. Variable arguments functions
  16. ‘pass’ keyword in functions
  17. Lambda functions/Anonymous functions
  18. map()
  19. filter()
  20. reduce()
  21. Nested functions
  22. Non local variables, global variables
  23. Closures
  24. Decorators
  25. Generators
  26. Iterators
  27. Monkey patching

Advanced Python

Python Modules
  1. Importance of modular programming
  2. What is module
  3. Types of Modules – Pre defined, User defined.
  4. User defined modules creation
  5. Functions based modules
  6. Class based modules
  7. Connecting modules
  8. Import module
  9. From … import
  10. Module alias / Renaming module
  11. Built In properties of module
Packages
  1. Organizing python project into packages
  2. Types of packages – pre defined, user defined.
  3. Package v/s Folder
  4. py file
  5. Importing package
PIP
  1. Introduction to PIP
  2. Installing PIP
  3. Installing Python packages
  4. Un installing Python packages
OOPs
  1. Procedural v/s Object oriented programming
  2. Principles of OOP – Encapsulation , Abstraction (Data Hiding)
  3. Classes and Objects
  4. How to define class in python
  5. Types of variables – instance variables, class variables.
  6. Types of methods – instance methods, class method, static method
  7. Object initialization
  8. ‘self’ reference variable
  9. ‘cls’ reference variable
  10. Access modifiers – private(__) , protected(_), public
  11. AT property class
  12. Property() object
  13. Creating object properties using setaltr, getaltr functions
  14. Encapsulation(Data Binding)
  15. What is polymorphism?
  16. Overriding
    1. Method overriding
    2. Constructor overriding
  17. Overloading
    1. Method Overloading
    2. Constructor Overloading
  18. Operator Overloading
    1. Class re-usability
    2. Composition
    3. Aggregation
    4. Inheritance – single , multi level, multiple, hierarchical and hybrid inheritance and Diamond inheritance
    5. Constructors in inheritance
    6. Object class
    7. super()
    8. Runtime polymorphism
    9. Method overriding
    10. Method resolution order(MRO)
    11. Method overriding in Multiple inheritance and Hybrid Inheritance
    12. Duck typing
    13. Concrete Methods in Abstract Base Classes
    14. Difference between Abstraction & Encapsulation
    15. Inner classes
    16. Inner classes
    17. Introduction
    18. Writing inner class
    19. Accessing class level members of inner class
    20. Accessing object level members of inner class
    21. Local inner classes
    22. Complex inner classes
    23. Case studies
Exception Handling & Types of Errors
  1. What is Exception?
  2. Why exception handling?
  3. Syntax error v/s Runtime error
  4. Exception codes – AttributeError, ValueError, IndexError, TypeError…
  5. Handling exception – try except block
  6. Try with multi except
  7. Handling multiple exceptions with single except block
  8. Finally block
  9. Try-except-finally
  10. Try with finally
  11. Case study of finally block
  12. Raise keyword
  13. Custom exceptions / User defined exceptions
  14. Need to Custom exceptions
  15. Case studies
Regular expressions
  1. Understanding regular expressions
  2. String v/s Regular expression string
  3. “re” module functions
  4. Match()
  5. Search()
  6. Split()
  7. Findall()
  8. Compile()
  9. Sub()
  10. Subn()
  11. Expressions using operators and symbols
  12. Simple character matches
  13. Special characters
  14. Character classes
  15. Mobile number extraction
  16. Mail extraction
  17. Different Mail ID patterns
  18. Data extraction
  19. Password extraction
  20. URL extraction
  21. Vehicle number extraction
  22. Case study
File &Directory handling
  1. Introduction to files
  2. Opening file
  3. File modes
  4. Reading data from file
  5. Writing data into file
  6. Appending data into file
  7. Line count in File
  8. CSV module
  9. Creating CSV file
  10. Reading from CSV file
  11. Writing into CSV file
  12. Object serialization – pickle module
  13. XML parsing
  14. JSON parsing
Python Logging
  1. Logging Levels
  2. implement Logging
  3. Configure Log File in over writing Mode
  4. Timestamp in the Log Messages
  5. Python Program Exceptions to the Log File
  6. Requirement of Our Own Customized Logger
  7. Features of Customized Logger
Date & Time module
  1. How to use Date & Date Time class
  2. How to use Time Delta object
  3. Formatting Date and Time
  4. Calendar module
  5. Text calendar
  6. HTML calendar
OS module
  1. Shell script commands
  2. Various OS operations in Python
  3. Python file system shell methods
  4. Creating files and directories
  5. Removing files and directories
  6. Shutdown and Restart system
  7. Renaming files and directories
  8. Executing system commands
Multi-threading & Multi Processing
  1. Introduction
  2. Multi tasking v/s Multi threading
  3. Threading module
  4. Creating thread – inheriting Thread class , Using callable object
  5. Life cycle of thread
  6. Single threaded application
  7. Multi threaded application
  8. Can we call run() directly?
  9. Need to start() method
  10. Sleep()
  11. Join()
  12. Synchronization – Lock class – acquire(), release() functions
  13. Case studies
Garbage collection
  1. Introduction
  2. Importance of Manual garbage collection
  3. Self reference objects garbage collection
  4. ‘gc’ module
  5. Collect() method
  6. Threshold function
  7. Case studies
Python Data Base Communications(PDBC)
  1. Introduction to DBMS applications
  2. File system v/s DBMS
  3. Communicating with MySQL
  4. Python – MySQL connector
  5. connector module
  6. connect() method
  7. Oracle Database
  8. Install cx_Oracle
  9. Cursor Object methods
  10. execute() method
  11. executeMany() method
  12. fetchone()
  13. fetchmany()
  14. fetchall()
  15. Static queries v/s Dynamic queries
  16. Transaction management
  17. Case studies
Python – Network Programming
  1. What is Sockets?
  2. What is Socket Programming?
  3. The socket Module
  4. Server Socket Methods
  5. Connecting to a server
  6. A simple server-client program
  7. Server
  8. Client
Tkinter & Turtle
  1. Introduction to GUI programming
  2. Tkinter module
  3. Tk class
  4. Components / Widgets
  5. Label , Entry , Button , Combo, Radio
  6. Types of Layouts
  7. Handling events
  8. Widgets properties
  9. Case studies
Data analytics modules
  1. Numpy
  2. Introduction
  3. Scipy
  4. Introduction
  5. Arrays
  6. Datatypes
  7. Matrices
  8. N dimension arrays
  9. Indexing and Slicing
  10. Pandas
  11. Introduction
  12. Data Frames
  13. Merge , Join, Concat
  14. MatPlotLib introduction
  15. Drawing plots
  16. Introduction to Machine learning
  17. Types of Machine Learning?
  18. Introduction to Data science

DJANGO

DJANGO
  1. Introduction to PYTHON Django
  2. What is Web framework?
  3. Why Frameworks?
  4. Define MVT Design Pattern
  5. Difference between MVC and MVT

PANDAS

Pandas – Introduction
Pandas – Environment Setup
Pandas – Introduction to Data Structures
  1. Dimension & Description
  2. Series
  3. DataFrame
  4. Data Type of Columns
  5. Panel
Pandas — Series
  1. Series
  2. Create an Empty Series
  3. Create a Series f
  4. rom ndarray
  5. rom dict
  6. rom Scalar
  7. Accessing Data from Series with Position
  8. Retrieve Data Using Label (Index)
Pandas – DataFrame
  1. DataFrame
  2. Create DataFrame
  3. Create an Empty DataFrame
  4. Create a DataFrame from Lists
  5. Create a DataFrame from Dict of ndarrays / Lists
  6. Create a DataFrame from List of Dicts
  7. Create a DataFrame from Dict of Series
  8. Column Selection
  9. Column Addition
  10. Column Deletion
  11. Row Selection, Addition, and Deletion
Pandas – Panel
  1. Panel()
  2. Create Panel
  3. Selecting the Data from Panel
Pandas – Basic Functionality
  1. DataFrame Basic Functionality
Pandas – Descriptive Statistics
  1. Functions & Description
  2. Summarizing Data
Pandas – Function Application
  1. Table-wise Function Application
  2. Row or Column Wise Function Application
  3. Element Wise Function Application
Pandas – Reindexing
  1. Reindex to Align with Other Objects
  2. Filling while ReIndexing
  3. Limits on Filling while Reindexing
  4. Renaming
Pandas – Iteration
  1. Iterating a DataFrame
  2. iteritems()
  3. iterrows()
  4. itertuples()
Pandas – Sorting
  1. By Label
  2. Sorting Algorithm
Pandas – Working with Text Data
Pandas – Options and Customization
  1. get_option(param)
  2. set_option(param,value)
  3. reset_option(param)
  4. describe_option(param)
  5. option_context()
Pandas – Indexing and Selecting Data
  1. .loc()
  2. .iloc()
  3. .ix()
  4. Use of Notation
Pandas – Statistical Functions
  1. Percent_change
  2. Covariance
  3. Correlation
  4. Data Ranking
Pandas – Window Functions
  1. .rolling() Function
  2. .expanding() Function
  3. .ewm() Function
Pandas – Aggregations
  1. Applying Aggregations on DataFrame
Pandas – Missing Data
  1. Cleaning / Filling Missing Data
  2. Replace NaN with a Scalar Value
  3. Fill NA Forward and Backward
  4. Drop Missing Values
  5. Replace Missing (or) Generic Values
Pandas – GroupBy
  1. Split Data into Groups
  2. View Groups
  3. Iterating through Groups
  4. Select a Group
  5. Aggregations
  6. Transformations
  7. Filtration
Pandas – Merging/Joining
  1. Merge Using ‘how’ Argument
Pandas – Concatenation
  1. Concatenating Objects
  2. Time Series
Pandas – Date Functionality
Pandas – Timedelta
Pandas – Categorical Data
  1. Object Creation
Pandas – Visualization
  1. Bar Plot
  2. Histograms
  3. Box Plots
  4. Area Plot
  5. Scatter Plot
  6. Pie Chart
Pandas – IO Tools
  1. csv
Pandas – Sparse Data
Pandas – Caveats & Gotchas
Pandas – Comparison with SQL

NUMPY

NUMPY − INTRODUCTION
NUMPY − ENVIRONMENT
NUMPY − NDARRAY OBJECT
NUMPY − DATA TYPES
  1. Data Type Objects (dtype)
NUMPY − ARRAY ATTRIBUTES
  1. shape
  2. ndim
  3. itemsize
  4. flags
NUMPY − ARRAY CREATION ROUTINES
  1. empty
  2. zeros
  3. ones
NUMPY − ARRAY FROM EXISTING DATA
  1. asarray
  2. frombuffer
  3. fromiter
NUMPY − ARRAY FROM NUMERICAL RANGES
  1. arange
  2. linspace
  3. logspace
NUMPY − INDEXING & SLICING
NUMPY − ADVANCED INDEXING
  1. Integer Indexing
  2. Boolean Array Indexing
NUMPY − BROADCASTING
NUMPY − ITERATING OVER ARRAY
  1. Iteration
  2. Order
  3. Modifying Array Values
  4. External Loop
  5. Broadcasting Iteration
NUMPY – ARRAY MANIPULATION
  1. reshape
  2. ndarray.flat
  3. ndarray.flatten
  4. ravel
  5. transpose
  6. ndarray.T
  7. swapaxes
  8. rollaxis
  9. broadcast
  10. broadcast_to
  11. expand_dims
  12. squeeze
  13. concatenate
  14. stack
  15. hstack and numpy.vstack
  16. split
  17. hsplit and numpy.vsplit
  18. resize
  19. append
  20. insert
  21. delete
  22. delete
NUMPY – BINARY OPERATORS
  1. bitwise_and
  2. bitwise_or
  3. invert()
  4. left_shift
  5. right_shift
NUMPY − STRING FUNCTIONS
NUMPY − MATHEMATICAL FUNCTIONS
  1. Trigonometric Functions
  2. Functions for Rounding
NUMPY − ARITHMETIC OPERATIONS
  1. reciprocal()
  2. power()
  3. mod()
NUMPY − STATISTICAL FUNCTIONS
  1. amin() and numpy.amax()
  2. ptp()
  3. percentile()
  4. median()
  5. mean()
  6. average()
  7. Standard Deviation
  8. Variance
NUMPY − SORT, SEARCH & COUNTING FUNCTIONS
  1. sort()
  2. argsort()
  3. lexsort()
  4. argmax() and numpy.argmin()
  5. nonzero()
  6. where()
  7. extract()
NUMPY − BYTE SWAPPING
  1. ndarray.byteswap()
NUMPY − COPIES & VIEWS
  1. No Copy
  2. View or Shallow Copy
  3. Deep Copy
NUMPY − MATRIX LIBRARY
  1. empty()
  2. matlib.zeros()
  3. matlib.ones()
  4. matlib.eye()
  5. matlib.identity()
  6. matlib.rand()
NUMPY − LINEAR ALGEBRA
  1. dot()
  2. vdot()
  3. inner()
  4. matmul()
  5. Determinant
  6. linalg.solve()
NUMPY − MATPLOTLIB
  1. Sine Wave Plot
  2. subplot()
  3. bar()
NUMPY – HISTOGRAM USING MATPLOTLIB
  1. histogram()
  2. plt()
NUMPY − I/O WITH NUMPY
  1. save()
  2. savetxt()

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