PYTHON
Superior Training Methodology
ABOUT THE COURSE
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.
There are no hard pre-requisites. Basic understanding of Computer Programming terminologies is sufficient. Also, basic concepts related to Programming and Database is beneficial but not mandatory.
QUALIFICATIONS
Aggregate 50% marks or above in a Graduate degree (BE/B.Tech. or M.Sc) in Electronics Engineering & Telecommunication/ Electrical engineering/ Computer Science & Engineering/Instrumentation or Master of Computer Applications (MCA). (Students of 4th year engineering are also eligibile).
OBJECTIVE 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
SELECTION
INTERNSHIP
Python internships are designed for final year electronics / electrical engineering students of B.Tech/M.Tech/Phd (INDIA) and M.S/Phd (USA) and it starts with learning of concepts on Python software, fundamentals, variables & operators, Data Structures and Functions which will be highly required to start an industry standard project. Doing this internship will make you a hands-on Python developer.
RESEARCH
Python research projects are designed for final year electronics / electrical engineering students of B.Tech/M.Tech/Phd (INDIA) and M.S/Phd (USA) and it starts with learning of concepts on Python software, fundamentals, variables & operators, Data Structures and Functions which will be highly required to start an industry standard project. Doing these research projects will make you a hands-on Python experience suitable for industry and MS/Phd studies.
KEY FEATURES
1. 24×7 Support on exercises.
2. Case studies
3. 4.7/5 rating
4. Industry standard tools
5. Two decade of experience
6. World class course structure
7. Expert mentorship on Python career
8. 100% Placement Support
9. Lifelong membership
1. Scholarship will be provided based on online test and technical interview performance.
2. Candidates with score 80% in Engineering and 90% above in online test will be selected.
3. Candidates with good GATE score can avail additional scholarship.T&C Apply
COURSE CURRICULUM
CORE PYTHON
- What is Language?
- Types of languages
- Introduction to Translators
- Compiler
- Interpreter
- What is Scripting Language?
- Types of Script
- Programming Languages v/s Scripting Languages
- Difference between Scripting and Programming languages
- What is programming paradigm?
- Procedural programming paradigm
- Object Oriented Programming paradigm
- What is Python?
- WHY PYTHON?
- History
- Features – Dynamic, Interpreted, Object oriented, Embeddable, Extensible, Large standard libraries, Free and Open source
- Why Python is General Language?
- Limitations of Python
- What is PSF?
- Python implementations
- Python applications
- Python versions
- PYTHON IN REALTIME INDUSTRY
- Difference between Python 2.x and 3.x
- Difference between Python 3.7 and 3.8
- Software Development Architectures
- Python Distributions
- Download &Python Installation Process in Windows, Unix, Linux and Mac
- Online Python IDLE
- Python Real-time IDEs like Spyder, Jupyter Note Book, PyCharm, Rodeo, Visual Studio Code, ATOM, PyDevetc
- Python Implementation Alternatives/Flavors
- Keywords
- Identifiers
- Constants / Literals
- Data types
- Python VS JAVA
- Python Syntax
- Interactive Mode
- Scripting Mode
- Programming Elements
- Structure of Python program
- First Python Application
- Comments in Python
- Python file extensions
- Setting Path in Windows
- Edit and Run python program without IDE
- Edit and Run python program using IDEs
- INSIDE PYTHON
- Programmers View of Interpreter
- Inside INTERPRETER
- What is Byte Code in PYTHON?
- Python Debugger
- bytes Data Type
- byte array
- String Formatting in Python
- Math, Random, Secrets Modules
- Introduction
- Initialization of variables
- Local variables
- Global variables
- ‘global’ keyword
- Input and Output operations
- Data conversion functions – int(), float(), complex(), str(), chr(), ord()
- Arithmetic Operators
- Comparison Operators
- Python Assignment Operators
- Logical Operators
- Bitwise Operators
- Shift operators
- Membership Operators
- Identity Operators
- Ternary Operator
- Operator precedence
- Difference between “is” vs “==”
- Input
- Command-line arguments
- Introduction
- Importance of Data structures
- Applications of Data structures
- Types of Collections
- Sequence
- Strings, List, Tuple, range
- Non sequence
- Set, Frozen set, Dictionary
- Strings
- What is string
- Representation of Strings
- Processing elements using indexing
- Processing elements using Iterators
- Manipulation of String using Indexing and Slicing
- String operators
- Methods of String object
- String Formatting
- String functions
- String Immutability
- Case studies
- What is List
- Need of List collection
- Different ways of creating List
- List comprehension
- List indices
- Processing elements of List through Indexing and Slicing
- List object methods
- List is Mutable
- Mutable and Immutable elements of List
- Nested Lists
- List_of_lists
- Hardcopy, shallowCopy and DeepCopy
- zip() in Python
- How to unzip?
- Python Arrays:
- Case studies
- What is tuple?
- Different ways of creating Tuple
- Method of Tuple object
- Tuple is Immutable
- Mutable and Immutable elements of Tuple
- Process tuple through Indexing and Slicing
- List v/s Tuple
- Case studies
- What is set?
- Different ways of creating set
- Difference between list and set
- Iteration Over Sets
- Accessing elements of set
- Python Set Methods
- Python Set Operations
- Union of sets
- functions and methods of set
- Python Frozen set
- Difference between set and frozenset ?
- Case study
- What is dictionary?
- Difference between list, set and dictionary
- How to create a dictionary?
- PYTHON HASHING?
- Accessing values of dictionary
- Python Dictionary Methods
- Copying dictionary
- Updating Dictionary
- Reading keys from Dictionary
- Reading values from Dictionary
- Reading items from Dictionary
- Delete Keys from the dictionary
- Sorting the Dictionary
- Python Dictionary Functions and methods
- Dictionary comprehension
- What is Function?
- Advantages of functions
- Syntax and Writing function
- Calling or Invoking function
- Classification of Functions
- No arguments and No return values
- With arguments and No return values
- With arguments and With return values
- No arguments and With return values
- Recursion
- Python argument type functions :
- Default argument functions
- Required(Positional) arguments function
- Keyword arguments function
- Variable arguments functions
- ‘pass’ keyword in functions
- Lambda functions/Anonymous functions
- map()
- filter()
- reduce()
- Nested functions
- Non local variables, global variables
- Closures
- Decorators
- Generators
- Iterators
- Monkey patching
ADVANCED PYTHON
- Importance of modular programming
- What is module
- Types of Modules – Pre defined, User defined.
- User defined modules creation
- Functions based modules
- Class based modules
- Connecting modules
- Import module
- From … import
- Module alias / Renaming module
- Built In properties of module
- Organizing python project into packages
- Types of packages – pre defined, user defined.
- Package v/s Folder
- py file
- Importing package
- PIP
- Introduction to PIP
- Installing PIP
- Installing Python packages
- Un installing Python packages
- Procedural v/s Object oriented programming
- Principles of OOP – Encapsulation , Abstraction (Data Hiding)
- Classes and Objects
- How to define class in python
- Types of variables – instance variables, class variables.
- Types of methods – instance methods, class method, static method
- Object initialization
- ‘self’ reference variable
- ‘cls’ reference variable
- Access modifiers – private(__) , protected(_), public
- AT property class
- Property() object
- Creating object properties using setaltr, getaltr functions
- Encapsulation(Data Binding)
- What is polymorphism?
- Overriding
- i) Method overriding
- ii) Constructor overriding
- Overloading
- i) Method Overloading
- ii) Constructor Overloading
iii) Operator Overloading
- Class re-usability
- Composition
- Aggregation
- Inheritance – single , multi level, multiple, hierarchical and hybrid inheritance and Diamond inheritance
- Constructors in inheritance
- Object class
- super()
- Runtime polymorphism
- Method overriding
- Method resolution order(MRO)
- Method overriding in Multiple inheritance and Hybrid Inheritance
- Duck typing
- Concrete Methods in Abstract Base Classes
- Difference between Abstraction & Encapsulation
- Inner classes
- Introduction
- Writing inner class
- Accessing class level members of inner class
- Accessing object level members of inner class
- Local inner classes
- Complex inner classes
- Case studies
- What is Exception?
- Why exception handling?
- Syntax error v/s Runtime error
- Exception codes – AttributeError, ValueError, IndexError, TypeError…
- Handling exception – try except block
- Try with multi except
- Handling multiple exceptions with single except block
- Finally block
- Try-except-finally
- Try with finally
- Case study of finally block
- Raise keyword
- Custom exceptions / User defined exceptions
- Need to Custom exceptions
- Case studies
- Understanding regular expressions
- String v/s Regular expression string
- “re” module functions
- Match()
- Search()
- Split()
- Findall()
- Compile()
- Sub()
- Subn()
- Expressions using operators and symbols
- Simple character matches
- Special characters
- Character classes
- Mobile number extraction
- Mail extraction
- Different Mail ID patterns
- Data extraction
- Password extraction
- URL extraction
- Vehicle number extraction
- Case study
- Introduction to files
- Opening file
- File modes
- Reading data from file
- Writing data into file
- Appending data into file
- Line count in File
- CSV module
- Creating CSV file
- Reading from CSV file
- Writing into CSV file
- Object serialization – pickle module
- XML parsing
- JSON parsing
- Logging Levels
- implement Logging
- Configure Log File in over writing Mode
- Timestamp in the Log Messages
- Python Program Exceptions to the Log File
- Requirement of Our Own Customized Logger
- Features of Customized Logger
- How to use Date & Date Time class
- How to use Time Delta object
- Formatting Date and Time
- Calendar module
- Text calendar
- HTML calendar
- Shell script commands
- Various OS operations in Python
- Python file system shell methods
- Creating files and directories
- Removing files and directories
- Shutdown and Restart system
- Renaming files and directories
- Executing system commands
- Introduction
- Multi tasking v/s Multi threading
- Threading module
- Creating thread – inheriting Thread class , Using callable object
- Life cycle of thread
- Single threaded application
- Multi threaded application
- Can we call run() directly?
- Need to start() method
- Sleep()
- Join()
- Synchronization – Lock class – acquire(), release() functions
- Case studies
- Introduction
- Importance of Manual garbage collection
- Self reference objects garbage collection
- ‘gc’ module
- Collect() method
- Threshold function
- Case studies
- Introduction to DBMS applications
- File system v/s DBMS
- Communicating with MySQL
- Python – MySQL connector
- connector module
- connect() method
- Oracle Database
- Install cx_Oracle
- Cursor Object methods
- execute() method
- executeMany() method
- fetchone()
- fetchmany()
- fetchall()
- Static queries v/s Dynamic queries
- Transaction management
- Case studies
- What is Sockets?
- What is Socket Programming?
- The socket Module
- Server Socket Methods
- Connecting to a server
- A simple server-client program
- Server
- Client
- Introduction to GUI programming
- Tkinter module
- Tk class
- Components / Widgets
- Label , Entry , Button , Combo, Radio
- Types of Layouts
- Handling events
- Widgets properties
- Case studies
- Numpy
- Introduction
- Scipy
- Introduction
- Arrays
- Datatypes
- Matrices
- N dimension arrays
- Indexing and Slicing
- Pandas
- Introduction
- Data Frames
- Merge , Join, Concat
- MatPlotLib introduction
- Drawing plots
- Introduction to Machine learning
- Types of Machine Learning?
- Introduction to Data science
- Introduction to PYTHON Django
- What is Web framework?
- Why Frameworks?
- Define MVT Design Pattern
- Difference between MVC and MVT
Pandas – Introduction
Pandas – Environment Setup
Pandas – Introduction to Data Structures
- Dimension & Description
- Series
- DataFrame
- Data Type of Columns
- Panel
Pandas — Series
- Series
- Create an Empty Series
- Create a Series f
- rom ndarray
- rom dict
- rom Scalar
- Accessing Data from Series with Position
- Retrieve Data Using Label (Index)
Pandas – DataFrame
- DataFrame
- Create DataFrame
- Create an Empty DataFrame
- Create a DataFrame from Lists
- Create a DataFrame from Dict of ndarrays / Lists
- Create a DataFrame from List of Dicts
- Create a DataFrame from Dict of Series
- Column Selection
- Column Addition
- Column Deletion
- Row Selection, Addition, and Deletion
Pandas – Panel
- Panel()
- Create Panel
- Selecting the Data from Panel
Pandas – Basic Functionality
- DataFrame Basic Functionality
Pandas – Descriptive Statistics
- Functions & Description
- Summarizing Data
Pandas – Function Application
- Table-wise Function Application
- Row or Column Wise Function Application
- Element Wise Function Application
Pandas – Reindexing
- Reindex to Align with Other Objects
- Filling while ReIndexing
- Limits on Filling while Reindexing
- Renaming
Pandas – Iteration
- Iterating a DataFrame
- iteritems()
- iterrows()
- itertuples()
Pandas – Sorting
- By Label
- Sorting Algorithm
Pandas – Working with Text Data
Pandas – Options and Customization
- get_option(param)
- set_option(param,value)
- reset_option(param)
- describe_option(param)
- option_context()
Pandas – Indexing and Selecting Data
- .loc()
- .iloc()
- .ix()
- Use of Notations
Pandas – Statistical Functions
- Percent_change
- Covariance
- Correlation
- Data Ranking
Pandas – Window Functions
- .rolling() Function
- .expanding() Function
- .ewm() Function
Pandas – Aggregations
- Applying Aggregations on DataFrame
Pandas – Missing Data
- Cleaning / Filling Missing Data
- Replace NaN with a Scalar Value
- Fill NA Forward and Backward
- Drop Missing Values
- Replace Missing (or) Generic Values
Pandas – GroupBy
- Split Data into Groups
- View Groups
- Iterating through Groups
- Select a Group
- Aggregations
- Transformations
- Filtration
Pandas – Merging/Joining
- Merge Using ‘how’ Argument
Pandas – Concatenation
- Concatenating Objects
- Time Series
Pandas – Date Functionality
Pandas – Timedelta
Pandas – Categorical Data
- Object Creation
Pandas – Visualization
- Bar Plot
- Histograms
- Box Plots
- Area Plot
- Scatter Plot
- Pie Chart
Pandas – IO Tools
- csv
Pandas – Sparse Data
Pandas – Caveats & Gotchas
Pandas – Comparison with SQL
NUMPY − INTRODUCTION
NUMPY − ENVIRONMENT
NUMPY − NDARRAY OBJECT
NUMPY − DATA TYPES
- Data Type Objects (dtype)
NUMPY − ARRAY ATTRIBUTES
- shape
- ndim
- itemsize
- flags
NUMPY − ARRAY CREATION ROUTINES
- empty
- zeros
- ones
NUMPY − ARRAY FROM EXISTING DATA
- asarray
- frombuffer
- fromiter
NUMPY − ARRAY FROM NUMERICAL RANGES
- arange
- linspace
- logspace
NUMPY − INDEXING & SLICING
NUMPY − ADVANCED INDEXING
- Integer Indexing
- Boolean Array Indexing
NUMPY − BROADCASTING
NUMPY − ITERATING OVER ARRAY
- Iteration
- Order
- Modifying Array Values
- External Loop
- Broadcasting Iteration
NUMPY – ARRAY MANIPULATION
- reshape
- ndarray.flat
- ndarray.flatten
- ravel
- transpose
- ndarray.T
- swapaxes
- rollaxis
- broadcast
- broadcast_to
- expand_dims
- squeeze
- concatenate
- stack
- hstack and numpy.vstack
- split
- hsplit and numpy.vsplit
- resize
- append
- insert
- delete
- unique
NUMPY – BINARY OPERATORS
- bitwise_and
- bitwise_or
- invert()
- left_shift
- right_shift
NUMPY − STRING FUNCTIONS
NUMPY − MATHEMATICAL FUNCTIONS
- Trigonometric Functions
- Functions for Rounding
NUMPY − ARITHMETIC OPERATIONS
- reciprocal()
- power()
- mod()
NUMPY − STATISTICAL FUNCTIONS
- amin() and numpy.amax()
- ptp()
- percentile()
- median()
- mean()
- average()
- Standard Deviation
- Variance
NUMPY − SORT, SEARCH & COUNTING FUNCTIONS
- sort()
- argsort()
- lexsort()
- argmax() and numpy.argmin()
- nonzero()
- where()
- extract()
NUMPY − BYTE SWAPPING
- ndarray.byteswap()
NUMPY − COPIES & VIEWS
- No Copy
- View or Shallow Copy
- Deep Copy
NUMPY − MATRIX LIBRARY
- empty()
- matlib.zeros()
- matlib.ones()
- matlib.eye()
- matlib.identity()
- matlib.rand()
NUMPY − LINEAR ALGEBRA
- dot()
- vdot()
- inner()
- matmul()
- Determinant
- linalg.solve()
NUMPY − MATPLOTLIB
- Sine Wave Plot
- subplot()
- bar()
NUMPY – HISTOGRAM USING MATPLOTLIB
- histogram()
- plt()
NUMPY − I/O WITH NUMPY
- save()
- savetxt()
Frequently Asked Questions
Most frequent questions and answers
Can I get a job into Python Industry, as I am fresh college graduate?
Yes, industry is hiring trained fresh college graduates for entry level jobs. Many of our students have got placed in top product and services companies. Along, startups are relying on new college grads for fresh ideas and out of box thinking.
Do you have a free demo session, to get a feel of the trainer and understand my choice of field better, before payment?
Yes. You are always welcome! Send us a query or call us. We will arrange a 1 to 1 meeting with the trainer and counselor. They explain you course content, job opportunities and prerequisites.
Can I get an internship, after the coursework? What do I need to ensure?
We are connected with companies focused on IT, Analytics, IoT, VLSI and Embedded. After every training session, we send our candidate profiles to these companies based on their interest. Companies interview and select the candidates of their choice. However, we try our level best to get you an entry into your dream job.
I am not from electronics, neither do I have a engineering degree. Can I join?
At industry, degree is no constrain, but Skill is. At design nation, qualification is not prerequisite, but passion is. If you are passionate to shine in the area of interest, come and talk to us. We are here to help you!
Do you provide a certificate after completion of the course?
Yes. We provide a certificate after the course completion. You can add it to LinkedIn profile, resume and mention during the interviews. Companies prefer trained resources than untrained candidates.
Can I avail the scholarship at Design Nation?
Yes, our scholarships are for people like you, with great talent and financial needs. We are more than happy to help you, with the process. Please check the cutoffs for scholarships in above section. We helped many, and still counting!!
