Python Beginner Course for Data Science and Machine Learning

Course Duration - 6 Months.
What will you get :
⇒ Video and notes for every class.
⇒ Doubts will be cleared till the student is fully satisfied.
⇒ Certificate will be offered after clearing of all the given assignments, homework, quizzes, and project.
⇒ After completion of the course you will feel comfortable in Python and it will open gates to machine learning and data science.

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Basic Computer Organization :

  • 1. Introduction to Computer Organization :
    ⇒ Input Unit
    ⇒ Output Unit
    ⇒ The CPU (Central Processing Unit)
    ⇒ The Memory (Main Memory/ Primary memory)
    ⇒ The storage Unit
    ⇒ The system bus
  • 2. Types of Software :
    ⇒ System software
    ⇒ Application Software
    ⇒ Software Libraries
  • 3. Transition from Calculator to Computer and Smart Devices
  • 4. Evolution of Computers
  • 5. Troubleshooting Computer Operations :
    ⇒ General tips
    ⇒ Some troubleshooting techniques involving computer parts or Operating System

Data Representation – Basic Concept :

  • 1. Introduction
  • 2. Digital Number Systems :
    ⇒ Decimal Number Systems
    ⇒ Binary Number Systems
  • 3. Number Conversions :
    ⇒ Decimal-to-Binary Conversion
    ⇒ Binary-to-Decimal Conversion
  • 4. Representing characters in memory
  • 5. ISCII
  • 6. UNICODE

Getting started with PYTHON :

  • 1. Introduction
  • 2. Python – Pluses
  • 3. Python – Some Minuses (So Human Like)
  • 4. Working in Python
    ⇒ Working with default IDE called IDLE
    ⇒ Writing and compiling python programs with command line
    ⇒ Understanding First program script.

Python Fundamentals :

  • 1. Introduction
  • 2. Python Character Set
  • 3. Tokens
    ⇒ Keywords
    ⇒ Identifiers (Names)
    ⇒ Literals / Values
    ⇒ Operators
    ⇒ Punctuators
  • 4. Barebones of a Python Programs
  • 5. Variables and Assignments
    ⇒ Creating variables
    ⇒ Multiple Assignments
    ⇒ Variable definition
    ⇒ Dynamic typing
  • 6. Simple Input and Output
    ⇒ Reading Numbers
    ⇒ Output Through print() Statements

Data Handling :

  • 1. Introduction
  • 2. Data Types
    ⇒ Numbers
    ⇒ Strings
    ⇒ Lists and Tuples
    ⇒ Dictionary
  • 3. Mutable and Immutable Types
    ⇒ Variable Internals
  • 4. Operators
    ⇒ Arithmetic Operators
    ⇒ Relational Operators
    ⇒ Identity Operators
    ⇒ Logical Operators
    ⇒ Bitwise Operators
    ⇒ Operator Precedence
    ⇒ Operator Associativity
  • 5. Expressions
    ⇒ Evaluating expressions
    ⇒ Type Casting
  • 6. Working with Some Standard Library Modules
    ⇒ Working with math Module of Python
    ⇒ Using random Module
    ⇒ Using the statistic Module

Conditional and Iterative Statements :

  • 1. Introduction
  • 2. Types of Statement in Python
  • 3. Statement Flow Control
  • 4. Program Logic Development Tools
    ⇒ Flowchart
    ⇒ Algorithms
  • 5. The if Statements of Python
    ⇒ The if Statement
    ⇒ The if - else Statement
    ⇒ The if - elif Statement
    ⇒ The nested if Statement
  • 6. Repetition of Task – A Necessity
  • 7. The range () function
  • 8. Iteration/Looping Statements
  • 9. The for loop
  • 10. The while loops
  • 11. Jump Statements – break and continue
  • 12. Loop else Statement
  • 13. Nested Loops

Text Handling :

  • 1. Introduction
  • 2. Traversing a String
  • 3. String Operators
    ⇒ Basic Operators
    ⇒ Membership Operators
    ⇒ Comparison Operators
  • 4. String Slices (Substring)

List Manipulation :

  • 1. Introduction
  • 2. Creating and Accessing Lists
  • 3. List Operations
    ⇒ Joining Lists
    ⇒ Repeating or Replicating Lists
    ⇒ Slicing the List
    ⇒ Working with List
    ⇒ List Functions and Methods

Dictionaries :

  • 1. Introduction
  • 2. Dictionary – Key : Value Pairs
    ⇒ Creating a Dictionary
    ⇒ Accessing Elements of a Dictionary
    ⇒ Updating Existing Elements in a Dictionary
    ⇒ Deleting Elements from a Dictionary
    ⇒ Checking for Existence of a Key
    ⇒ Pretty Printing a Dictionary
    ⇒ Counting Frequency of elements in a List using Dictionary
  • 3. Dictionary Functions and Methods

Working with Functions :

  • 1. Introduction
  • 2. Understanding Functions
    ⇒ Calling/Invoking/Using a Function
    ⇒ Python Function Types
  • 3. Defining Functions in Python
    ⇒ Structure of a Python Program
  • 4. Flow of execution in a Function Call
    ⇒ Arguments and Parameters
  • 5. Passing Parameters
    ⇒ Positional/Required Arguments
    ⇒ Default Arguments
    ⇒ Keyword (Normal) Arguments
    ⇒ Using Multiple Argument Types Together
  • 6. Returning Values from Functions
    ⇒ Returning Multiple Values
  • 7. Composition
  • 8. Scope of Variables
    ⇒ Name Resolution(Resolving Scope of a Name)
  • 9. Mutable/ Immutable Properties of Passed Data Objects
    ⇒ Mutability/Immutability of Arguments/Parameters and Function Calls

Using Python Libraries :

  • 1. Introduction
  • 2. What is a Library?
    ⇒ What is a Module?
  • 3. Importing Modules in a Python Program
    ⇒ Importing Entire Module
    ⇒ Importing Selected Objects from a Module
    ⇒ Python’s Processing of import Command
  • 4. Using Python Standard Library’s Functions and Modules
    ⇒ Using Python’s Built-in Functions
    ⇒ Working with some standard Library Modules
  • 5. Creating a Python Library
    ⇒ Structure of a Package
    ⇒ Procedure for Creating Packages
    ⇒ Using/Importing Python Libraries

File Handling :

  • 1. Introduction
  • 2. Data Files
  • 3. Opening and Closing Files
  • 4. Working with Text Files
    ⇒ Reading from Text Files
    ⇒ Writing onto Text Files
    ⇒ The flush () Function
    ⇒ Removing whitespaces after reading from file
    ⇒ Significance of File Pointer in File Handling
  • 5. Standard Input, Output and Error Streams
  • 6. Working with Binary Files
    ⇒ Creating/Opening/Closing Binary Files
    ⇒ Writing onto a Binary File – Pickling
    ⇒ Reading from a Binary File – Unpickling
    ⇒ Searching in a File
    ⇒ Updating in a Binary File
  • 7. Working with CSV Files
    ⇒ Opening/Closing CSV Files
    ⇒ Writing in CSV Files
    ⇒ Reading in CSV Files

Working with NumPy :

  • 1. Introduction
  • 2. What are NumPy Arrays?
    ⇒ Anatomy of NumPy Arrays
    ⇒ NumPy Arrays vs Python Lists
  • 3. NumPy Data Types
  • 4. Creating NumPy Arrays
    ⇒ Creating NumPy 1D Arrays
    ⇒ Creating 2 - Dimensional NumPy Arrays
    ⇒ Some Alternative Array Creation Methods
  • 5. Working with NumPy Arrays
    ⇒ Accessing Individual Elements Using Array Indexing
    ⇒ Array Slices
    ⇒ Joining or Concatenating NumPy Arrays
    ⇒ Obtaining Subsets of Arrays
    ⇒ Arithmetic Operations on 2D Arrays

Concept of Relational Database :

  • 1. Introduction
  • 2. Purpose of DBMS
  • 3. Relational Database Model
  • 4. The Relational Model Terminology
    ⇒ Views
    ⇒ Structure of Relational Databases
  • 5. Brief History of MySQL
  • 6. MySQL Database System
  • 7. Starting MySQL
  • 8. MySQL and SQL
    ⇒ Processing Capabilities of SQL
    ⇒ Data Definition Language
    ⇒ Classification of SQL Statements

Simple Queries in SQL :

  • 1. Introduction
  • 2. Some MySQL SQL Elements
    ⇒ Literals
    ⇒ Data Types
    ⇒ Null Values
    ⇒ Comments
  • 3. SQL Command Syntax
  • 4. Sample Database
  • 5. Making Simple Queries
    ⇒ Accessing Database
    ⇒ The SELECT Command
    ⇒ Selecting all columns
    ⇒ Reordering Columns in Query Results
    ⇒ Eliminating Redundant Data( with keyword DISTINCT)
    ⇒ Selecting from all the Rows – ALL Keyword
    ⇒ Viewing structure of Table
    ⇒ How to perform simple calculations?
    ⇒ Scalar expressions with Selected Fields
    ⇒ Using column Aliases
    ⇒ Handling Nulls
    ⇒ Putting Text in the Query Output
    ⇒ Selecting Specific Rows – WHERE clause
    ⇒ Relational Operators
    ⇒ Logical Operators
    ⇒ Conditional Based on a Range
    ⇒ Conditional Based on a List
    ⇒ Conditional Based on Pattern Matches
    ⇒ Searching for NULL
    ⇒ Operator Precedence
    ⇒ Sorting Results – ORDER BY clause
  • 6. MySQL Functions
    ⇒ String Functions
    ⇒ Numeric Functions
    ⇒ Date and Time Functions
  • 7. Aggregate Functions

Table Creation and Data Manipulation Commands :

  • 1. Introduction
  • 2. Database in MySQL
    ⇒ Creating Databases
    ⇒ Opening Databases
    ⇒ Removing Databases
  • 3. Creating Tables
    ⇒ Data Integrity Through Constraints
  • 4. Changing Data with DML Commands
    ⇒ Inserting data into table
    ⇒ Modifying data with UPDATE Command
    ⇒ Deleting Data with DELETE Command
  • 5. More DDL Commands
    ⇒ ALTER TABLE Command
    ⇒ The DROP TABLE Command

Grouping Records, Joins in SQL :

  • 1. Introduction
  • 2. Types of SQL Functions
  • 3. Grouping Result – GROUP BY
    ⇒ Nested Groups – Grouping on Multiple Columns
    ⇒ Placing Conditions on Groups – HAVING Clause
    ⇒ Non-Group Expression with GROUP BY
  • 4. JOINS
    ⇒ Cartesian Product
    ⇒ Table Aliases
    ⇒ Equi - Join and Natural Join
    ⇒ Additional Search Conditions in Joins

Table Creation and Data Manipulation Commands :

  • 1. Introduction
  • 2. Installing MySQL Python Connector
  • 3. Connecting to MySQL from Python
    ⇒ Steps for Creating Database Connectivity Applications
    ⇒ Connecting with MySQL Database using pymysql
  • 4. Parameterized Queries
  • 5. Performing insert and Update Queries

Project: Application Development Using Python and MySQL Database.