About Me
My name is Boyina Narendra. I hold an M.Tech degree and seasoned software professional with over 11 years of industry experience, currently employed at a reputed product-based company.
I am a certified quality assurance professional, holding both the ISTQB® Foundation Level and Advanced Level certifications, along with an Agile certification — reflecting my commitment to industry best practices and continuous professional development.
Throughout my career, I have been recognised for my contributions and dedication, receiving several prestigious accolades, including the Best Performer of the Year Award (2019), Spot Awards, Star Awards, and Super Star Awards, among others.
My passion for teaching dates back to my college days, where I simultaneously pursued my studies while working as a tuition teacher. Teaching has not only been a source of livelihood in my early years, but also a deeply fulfilling pursuit that I continue to embrace wholeheartedly.
On the professional training front, I began my journey as a C Language Trainer, later transitioning into the domain of software testing training. For the past six years, I have been actively working as a Python Trainer, helping learners build strong technical foundations.
Through this blog, I aim to bring together all my coding expertise and industry knowledge, making it accessible to everyone — whether you are a beginner taking your first steps or a professional looking to sharpen your skills.
****** PYTHON COURSE CONTENT *********
We provide in-depth knowledge with
real-time exposure and practical examples. It’s more interactive
and hands-on-based, industry-oriented training for true learners (25%
Theory & 75% Practical Programming)
- Introduction
to the Python Language.
- Overview of Python
- Why Python is
the best among multiple Languages
- Usage of
Python in Real-world
- What Can You
Do with Python?
- Features of
Python
- Installation
of Python 3.13 version
- Basic syntax
& Commenting
- IDEs (Integrated
development environments)
- PyCharm Idle
(Real-time tool)
- Jupyter
notebook (web-based interactive computing platform)
- Keywords & Variables
- List of
available keywords in Python
- Each keyword
explanation with a program
- Variables
declaration (Is it required?)
- Variables
Initialization
- Variables
Re-declaration
- Local variables
& global variables with examples
- What is an
identifier
- What are the
rules for an Identifier
- Operators
in python
- Arithmetic
operators
- Comparison
operators
- Logical
operators
- Assignment
operators
- Identity
operators
- Bitwise
operators
- Membership
operators
- Strings
in python
- Different
ways to create a string
- String
indexing and string accessing (3 types)
- Upper(),
lower(), swapcase(), tittle(), capitalize()
- center(),
ljust(), rjust(),
- startswith(),
endswith(), count(), find(), rfind(), index()
- string
concatenation and string multiplication
- splitting the
data into different parts as per user requirement
- split(),
join(), partition()
- min(), max(),
replace() and sort()
- lstrip(),
rstrip(), strip(), zfill(), format()
- isidentifier(),
isalpha(), isalnum(), isdigit()
- isupper(),
islower(), isspace(), istitle()
- Numeric
Values of Characters with the ord() and chr() Functions
- Lists
- Purpose
(Importance & Advantages) of learning list.
- creating and
working with homogeneous lists
- creating and
working with heterogeneous lists
- generating a
list by using a split function
- generating a
list by using the range function
- list indexing
and list slicing
- List slicing
(3 types) &Traversing a list
- creating
nested lists and indexing nested lists
- Mutable
(Modifying list elements)
- index(),
count(), Insert(), append(), extend()
- reverse(),
min(), max()
- remove(),
pop() and clear(),
- Deleting
elements from the list (del)
- list
concatenation and list multiplication
- Membership
functions for list
- Shallow &
Deep Copy
- Difference
between sort() and sorted()
- Tuple
- Purpose
(Importance & Advantages) of learning tuple.
- creating and
working with homogeneous tuple
- creating and
working with heterogeneous tuple
- Converting a
list into a tuple
- Converting a
tuple into a list
- tuple
indexing and tuple slicing
- tuple slicing
(3 types) & Traversing a tuple
- creating
nested tuples and indexing a nested tuple
- index(),
count(), min(), max()
- tuple
concatenation
- Membership
operators for tuple
- Dictionary
- Purpose
(Importance & Advantages) of learning tuple.
- Converting a
list of tuples into a dictionary
- Accessing
values
- Updating
Dictionary with new key-value pair
- copy() &
"dict" constructor
- Delete
Dictionary Elements
- Extend
dictionary with "update"
- Extracting
only keys keys()
- Extracting
only values values ()
- fromkeys(),
items(), get()
- list of
dictionaries & working with them (Accessing)
- dictionary of
dictionaries & working with them (Accessing)
- Sets
- Creating and
working with set in different ways
- Normal sets
and frozen sets
- Set mutable
and unpack a set data structure
- Creating and
working with sets with homogeneous elements
- Creating and
working with sets with heterogeneous elements
- Creating
empty sets and modifying the empty sets
- Why do sets
not support indexing and slicing
- Add, remove,
and discard the elements
- issubset,
issuperset
- Union,
intersection, and its difference.
- Conversions:
- Converting
given string data structure into a set
- Converting a
given list data structure into a set
- Converting a
given tuple data structure into a set
- Converting a
given set data structure into a string
- Converting a
given set data structure into a list
- Converting a
given set data structure into a tuple
- Decision-making
/ Conditional statements
- Simple
if
- Nested if
- If-else
Statements
- If-elif-else
Statements
- Nested if-else
- Using Logical
Operators (and, or)
- Control
flow statements (Loops)
- For
Loops (real-time examples)
- With strings
- With list
- With
dictionaries
- With sets
- With
enumerate function
- With Break
statements
- With
Continue statement
- With else
- While
Loops
- Infinite
loops
- Break
statements
- Continue
statement
- walrus operator :=
- Functions
- Non-recursive
functions
- without
arguments
- With
/Required arguments
- Keyword
arguments
- Default
arguments
- Variable-length
arguments
- command-line
arguments
- Recursive
functions
- Directories & Files
- Creating,
Modifying & Deleting directories
- Creating a
file in a directory
- Different
ways to open the file in Python
- Writing data to the file
- Appending data to the existing file
- Modes of
operations
- Seek and tell methods
- Read-line and read-lines
- Organizing files (using OS, pathlib, and shutil
modules)
Advanced Python course
content
- Functionalities
of zip and unzip
- Lambda
functions
- Creating
functions by using the lambda keyword
- Difference
between def and lambda functions
- Working with
filter functions
- Working with
map functions
- Comprehensions
- List
Comprehensions
- Dictionary
comprehensions
- Set
comprehensions
- Exceptions
- What is an
Exception?
- When to use
exceptions?
- How many
ways, can we raise Exceptions?
- Different
types of Exceptions:
- TypeError
- ValueError
- IOError
- KeyError
- Unknown
Exception handling
- Object-oriented
programming concepts.
- What is a
class?
- What is an
Object?
- What is the
difference between
- variables, arguments, attributes
- Class variable & instance variable (attributes)
- What is the
purpose of the __init__() method
- Importance of
self-keyword in class
- Object-oriented
features with examples & execution.
- Inheritance
- Multiple
Inheritance
- Multi-Level
Inheritance
- Hybrid
Inheritance
- Polymorphism
- Encapsulation
- Data Hiding
(methods & variables)
- Modules -– In-depth
- What is a
module and what are the purposes of modules?
- Different
types of modules (3)
- Different
ways to import modules (17)
- Usage of an inbuilt
module with examples
- JSON
- Time &
datetime, Calendar
- Sys, math
- OS, pathlib
- Shutil
module, Subprocess module
- Random
module, string module
- Usage of External
module with examples
- Creating our
modules (User defined modules)
- Generators
(Generator function & generator expression)
- Iterators
- Multi-Threading
Module
- NumPy Module
1. Introduction to NumPy
2. Create Arrays using NumPy
a. Create integer/float/
heterogeneous arrays
b. Create a NumPy array using a
tuple
3. Create arrays in multiple dimensions
a. Create a 0D array, 1D array, 2D
array, 3D arrays
4. Accessing Elements
a. Individual elements accessed
using the Index from 1-D, 2-D, 3-D arrays
b. Range of elements accessed
using the Index from 1-D, 2-D, 3-D arrays
c. Step elements accessed using
the Index from 1-D, 2-D, 3-D arrays
c. Accessing elements with
Omitting Indices from 1-D, 2-D, 3-D arrays
d. Elements accessing using
Negative indexing for(1D,2D,3D arrays)
e. Fancy Indexing
5. Properties of nd Arrays
a.
shape
b.
Data Type
c.
size
d.
Itemsize
6. Array Operations - NumPy
a.
Arithmetic operations
b.
Relational operations
7. Initialization of arrays
np.arange(), np.zeros(),
np.ones() --> 2 dimensions & 3 dimensions
np.full(), np.eye()
8. Array Manipulations
np.resize(), np.reshape(),
ravel() vs flatten(), np.matmul(), np.transpose(),
9. Functions
a. Aggregate Functions
b. Broadcast
c. Exponential and Logarithmic
Functions
10. Arrays Splitting and Joining
a. np.split() Vs np.array_split()
b. np.hstack() Vs np.vstack()
11. Array Adding and Removing Elements
a. np.append(), np.insert().
np.delete()
12. Pseudo-random Number Generation
a. np.random.randint()
b. np.random.normal()
c. np.random.rand()
- Pandas module
(Data Manipulation)
· Introduction of Pandas
· Creating Series
a. Creating a Series with integer values
b. Creating a Series with float values
c. Creating a Series of different data type values
d. Creating a Series by providing different labels for the values
· Built-In Functions
· Aggregation Functions
· DataFrame
a. Creating a DataFrame using a nested list (list of lists)
i. Accessing Single Column data
ii. Accessing Multiple Column data
b. Creating a DataFrame using a Dictionary
c. Create a Custom Row Index to the dataframe
· Accessing required data from Series & DataFrame
a. Accessing data from series using Index (iloc)
b. Accessing range of data from series using Index (iloc)
c. Accessing data from series using label (loc)
d. Accessing range of data from series using label (loc)
c. Accessing data from DataFrame using Index (iloc)
d. Accessing range of data from DataFrame using Index (iloc)
e. Accessing data from DataFrame using label (loc)
f. Accessing range of data from DataFrame using label (loc)
· Handling Missing Data
Apply below methods for series & DataFrame
isnull(), notnull(), fillna(value), dropna() --> for series & DataFrame
· Accessing, and Filtering
· Merging the dataframes
· Importing DataSet
· CSV
· EXCEL
- Modularization
of Python code (PEP-8 standards)
- We will share
the following:
- Topic-wise
multiple-choice IQs (500)
- Telephonic
round /theory-oriented IQs (30)
- Programming-oriented
IQs (40)
Feel
free to ask if you have any questions about IQs.
Author: Boyina Narendra
Supporting Author: M. Meera Sindhu
Request: If you find this information useful, please provide your valuable comments
