Core Python

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Course Description

At Peopleclick Learning our Core Python course is designed to help you build a strong foundation in programming. Python is one of the most beginner friendly and in demand programming languages used across web development data science automation and more. This course is ideal for students career starters and working professionals who want to begin or shift to a tech career.

Learn Python from Scratch

You will start with the basics of Python including how to write simple programs work with variables use data types and control the flow of logic using if else and loops. The course is designed to be easy to follow even if you have no prior coding experience.

Work with Functions Data Structures and OOP

As you move ahead you will learn how to organize your code using functions and work with Pythons built in data structures like lists dictionaries tuples and sets. You will also explore the concepts of Object Oriented Programming OOP including classes and objects which are essential for building scalable applications.

Handle Files Exceptions and Modules

This module teaches you how to read from and write to files using Python. You will also learn how to handle errors with exception handling and how to use and create your own modules for reusable code. These are important steps in writing clean and efficient Python programs.

Practice with Real World Mini Projects

Throughout the course you will work on small real life projects that reinforce your learning. Projects could include a calculator a simple quiz app a file organizer or a text based game. These projects help you build confidence and create a portfolio to showcase your skills.

Get Ready for Jobs and Interviews

We also help you get job ready. The course includes resume tips interview preparation and one on one support from our trainers. You will not only learn Python but also understand how to apply it in real jobs across development automation testing and more.

Course Curriculam

Python introduction

• GETTING STARTED
• History & need of Python
• Application of Python
• Advantages of Python
• Disadvantages of Python
• Installing Python
• Program structure
• Interactive Shell
• Executable or script files
• User Interface or IDE

Python fundamentals

• Working with Interactive mode
• Working with Script mode
• Python Character Set
• Python Tokens, Keywords, Identifiers, Literals, Operators Variables and Assignments
• Input and Output in Python

Data Handling

• Data Types
• Numbers
• Strings
• Lists
• Tuples
• Dictionary
• Set
• Frozenset
• Bool
• Mutable and Immutable

String Manipulation

• Introduction to Python String
• Accessing Individual Elements
• String Operators
• String Slices
• String Functions and Methods

List Manipulation

• Introduction to Python List
• Creating List
• Accessing List
• Joining List
• Replicating List
• List Slicing

Tuples

• Introduction to Tuple
• Creating Tuples
• Accessing Tuples
• Joining Tuples
• Replicating Tuples
• Tuple Slicing

Dictionaries

• Introduction to Dictionary
• Accessing values in dictionaries
• working with dictionaries
• Properties

set

• Introduction to Set
• Accessing values in Set
• working with Set
• Properties

Operators

• Arithmetic Operators
• Relational Operators
• Logical Operators
• Membership Operators
• Identity Operators
• Bitwise Operators
• Assignment Operators
• Operators Precedence
• Evaluating Expression
• Type Casting

Program Control Flow

• Conditional Statements
• The if Statement
• The if-else Statement
• The if-elif Statement
• Nested if Statements
• Python Indentation
• Looping and Iteration
• The For Loop
• The While Loop
• Loop else Statement
• Nested Loops
• Break and Continue
• The Range Function
• Introduction to range()
• Types of range() function
• Use of range() function

Introduction to Functions

• Built-In Functions
• Introduction to Functions
• Using a Functions
• Python Function Types
• Structure of Python Functions E.g. - map, zip, reduce, filter, any, chr, ord, sorted, globals locals, all, etc.
• User Defined Functions
• Structure of a Python Program w.r.t. UDF
• Types of Functions
• Invoking UDF
• Flow of Execution
• Arguments and Parameters
• Default Arguments, Named Arguments
• Scope of Variables
• Lambda function
• Recursion Function
• Use of recursion function

Date & Time

• Creating date objects
• the strftime() method

Modules

• Import statements
• Locating modules
• current directory
• Python path ,sys, OS module
• Packages in Python

Files

• Files in Python- Reading keyboard input, input function
• Opening and closing files
• Syntax and list of modes
• Files object attribute- open, close.
• Reading and writing files, file Position.
• Renaming and deleting files
• CSV and JSON files handling.

Directories

• mkdir method, chdir () method, getcwd method, rm dir

Exceptional Handling

• Exception handling - List of exceptions - Try and exception
• Try- finally clause and user defined exceptions

OOPS

• Class
• Object
• Inheritance
• Abstraction
• Polymorphism
• Encapsulation
• Method overloading
• Method overriding

PYTHON LIBRARIES - NUMPY

Array Creation

• Create an array from lists or tuples
• Create an array of zeros
• Create an array of ones
• Generate a range of values (like Python’s range)
• Generate evenly spaced numbers between a start and end value

Array Manipulation

• Change the shape of an array
• Transpose (flip) the array dimensions
• Flatten a multi-dimensional array into a 1D array
• Return a flattened version of the array (does not create a copy)
• Combine multiple arrays into one

Basic Array Operations

• Arithmetic Operations
• Comparison Operations
• Sum the elements of an array
• Calculate the mean (average) of an array
• Find the minimum and maximum values in an array

Array Indexing & Slicing

• Access elements of an array by position
• Extract parts of an array
• Indexing 2D arrays with multiple indices

Random Number Generation

• Generate random numbers from a uniform distribution
• Generate random integers
• Generate random numbers from a standard normal distribution

Statistical Operations

• Find the median of an array
• Calculate the standard deviation
• Calculate the variance
• Sum of array elements

Array Concatenation and Stacking

• Stack arrays vertically (row-wise)
• Stack arrays horizontally (column-wise)
• Join arrays along a specified axis

Array Reshaping

• Reshape an array to have a different shape
• Flatten a multi-dimensional array to a 1D array

Array Operations with Scalars

• Scalar Operations Broadcasting

Saving and Loading Data

• Save and load arrays in binary format for quick reading and writing.
• Save and load arrays in text format

Python Libraries - Pandas

Introduction to Pandas

• What is Pandas?
• Pandas Data Structures
• Series
• DataFrame

Creating DataFrames and Series

• pandas.Series(): Create a Pandas Series from a list or dictionary
• pandas.DataFrame(): Create a DataFrame from lists, dictionaries, or other data structures
• Loading Data:
• pandas.read_csv(): Load data from a CSV file
• pandas.read_excel(): Load data from an Excel file
• pandas.read_json(): Load data from a JSON file

Exploring Data

• View the first few rows of a DataFrame. View the last few rows of a DataFrame. Get a concise summary of the DataFrame (e.g., data types, number of non-null values)
• Get statistical summaries (e.g., mean, std, min, max) for numeric columns
• List all column names
• Access row indices

Selecting Data

• Selecting Columns
• Selecting Rows
• Conditional Selection

Manipulating Data

• Renaming Columns
• Changing Data Types
• Handling Missing Data
• Adding New Columns

Sorting and Ordering

• Sort rows by column values
• Sort by row index

Grouping and Aggregating

• df.groupby(): Group data by one or more columns
• Aggregation Functions: sum(), mean(), count(), min(), max(): Apply aggregation functions after grouping
• Apply multiple aggregation functions on grouped data

Merging and Joining DataFrames

• Merge two DataFrames on a common column
• Join two DataFrames on their index

Concatenating DataFrames

• Concatenate two or more DataFrames along a particular axis (rows or columns)

Saving and Exporting Data

• Save a DataFrame to a CSV file
• Save a DataFrame to an Excel file
• Save a DataFrame to a JSON file

Data Visualization

Introduction to Data Visualization

• Introduction to Data Visualization

Popular Libraries for Dv

a. Matplotlib (Basic visualization)

• matplotlib.pyplot: The primary module for creating static, interactive, and animated plots
• Basic Plots:
• Line plot: Shows trends over time or continuous data. Use plt.plot()
• Bar plot: Compares quantities across categories. Use plt.bar()
• Histogram: Displays the distribution of numerical data. Use plt.hist()
• Scatter plot: Displays relationships between two variables. Use plt.scatter()
• Pie chart: Displays proportions. Use plt.pie()
• Customization:
• Title, Labels, and Legends
• Gridlines: Use plt.grid() to add gridlines for better readability.

b. Seaborn (Higher-level API)

• Why Seaborn?
• Key Plots:
• sns.lineplot(): Line plot with better styling
• sns.barplot(): Bar plot with error bars
• sns.histplot(): Histogram with better customization and density estimates
• sns.scatterplot(): Scatter plot with enhanced visual features
• sns.boxplot(): Visualizes the distribution of numerical data and outliers
• sns.heatmap(): Used for correlation matrices or heatmaps
• Themes: Customize the look of plots with sns.set_theme() for more polished, visually appealing charts

c. Plotly (Interactive visualization)

• Why Plotly?: Allows you to create interactive plots that can be embedded into websites or notebooks
• Key Plots:
• plotly.express.scatter(): Interactive scatter plot
• plotly.express.line(): Interactive line chart
• plotly.express.bar(): Interactive bar chart
• plotly.express.pie(): Interactive pie chart
• Interactivity: Hover over points, zoom in/out

Database MySQL

Introduction

• What Is Database?
• What is Database Management System (DBMS)?
• What is Relational Model?
• Introduction to RDBMS.
• Brief on E.F CODD

Datatypes and Constraints

• What are Datatypes?
• What are Constraints?
• Types and Examples
• How to use?

Statements in SQL

• Data Definition Language (DDL)
• Data Manipulation Language (DML)
• Transaction Control Language (TCL)
• Data Control Language (DCL)
• Data Control Language (DCL)
• Data Query Language (DQL)

Software Installation

• Installing and set up of software Working on Mysql workbench

Data Query Language

• SELECT
• FROM
• WHERE
• GROUPBY
• HAVING CLAUSE
• ORDER BY CLAUSE

Functions in SQL

• Single Row Function
• Multi Row Function MAX(), MIN() SUM(), AVG() COUNT()

Sub Query

• Introduction
• Working with Sub Query
• Query Writing and Execution
• Types of Sub Query
• 1.Single Row Sub Query
• 2.Multi Row Sub Query

JOINS

• What Is Join? Types of Joins Cartesian Join Inner Join Outer Join Self-Join Queries and Examples

Data Definition Language (DDL)

• Create Alter Rename Truncate & Drop

Data Manipulation Language (DML)

• Insert Update Delete

Transaction Control Language (TCL)

• Commit Save-point Rollback

Data Control Language (DCL)

• Grant Revoke

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