Complete guide to begin with Python for Data Science

Complete guide to begin with Python for Data Science, available at $59.99, has an average rating of 4.75, with 62 lectures, based on 4 reviews, and has 17 subscribers.

You will learn about Learn how to use Jupyter Notebook efficiently for Programming Learn fundamentals of Python Programming Language and how to approach Python Assignments and solve them Learn various data structures of Python – List, set, tuple, dictionaries Learn how developers use Exception Handling for handling errors. Learn how to use if else statements, loops with certain illustrations Learn how to use functions and recursion to build a python project Learn advanced functions – map and lambda which will be used in data science frequently Get ready to appear for any interview, assignment, projects related to data science, development Learn Variables and data types in detail. Learn how to format strings and print statements This course is ideal for individuals who are Beginner Python developers or Data science aspirants or web development aspirants or Python for interview preparation or Data Analytics aspirants It is particularly useful for Beginner Python developers or Data science aspirants or web development aspirants or Python for interview preparation or Data Analytics aspirants.

Enroll now: Complete guide to begin with Python for Data Science

Summary

Title: Complete guide to begin with Python for Data Science

Price: $59.99

Average Rating: 4.75

Number of Lectures: 62

Number of Published Lectures: 62

Number of Curriculum Items: 85

Number of Published Curriculum Objects: 85

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will Learn

Learn how to use Jupyter Notebook efficiently for Programming
Learn fundamentals of Python Programming Language and how to approach Python Assignments and solve them
Learn various data structures of Python – List, set, tuple, dictionaries
Learn how developers use Exception Handling for handling errors.
Learn how to use if else statements, loops with certain illustrations
Learn how to use functions and recursion to build a python project
Learn advanced functions – map and lambda which will be used in data science frequently
Get ready to appear for any interview, assignment, projects related to data science, development
Learn Variables and data types in detail.
Learn how to format strings and print statements

Who Should Attend

Beginner Python developers
Data science aspirants
web development aspirants
Python for interview preparation
Data Analytics aspirants

Target Audiences

Beginner Python developers
Data science aspirants
web development aspirants
Python for interview preparation
Data Analytics aspirants

A complete guide to begin your python learning for data science, data analysis and machine learning.

For those, who has never written a single code in entire life and want to move into data science or advanced python, this course provides you a simple approach to learn coding from scratch using python as a tool and master it with illustrations and assignments.

For those, who are already experienced in coding, but want to move into advanced python, this course provides you ample hands-on exercises and assignments for deeply understanding the concept.

In this course, you will be learning from the very basics – which includes basic numbers, arithmetic operations, lists, sets, tuples, dictionaries, loops, if else statements, nested dictionaries, functions, recursive functions etc.

We will be using Jupyter notebook in order to execute all the codes. Jupyter notebook is a tool that is being used by all the multinational organisation, who hire people for analytics and machine learning jobs.

Key features:

# Learn Python from scratch – from installation to writing your first code to understand the basics and finally to reach advance level.

# No prior coding experience required.

# Command yourself in Jupyter Notebook.

# Prepare yourself for Data Analytics, Machine Learning, Python Development.

Have a great learning ahead.

Course Curriculum

Chapter 1: Introduction to Python and Course Overview

Lecture 1: Course Overview

Lecture 2: Introduction to Python

Chapter 2: Setup Installation and Jupyter Interface

Lecture 1: Python Installation

Lecture 2: Anaconda Installation

Lecture 3: Jupyter Interface

Chapter 3: Get Started with Simple Operations in Python

Lecture 1: Getting Started and Commenting

Lecture 2: Arithmetic Operations

Lecture 3: Arithmetic Operations Assignment

Chapter 4: Variables and Type of Data

Lecture 1: Variables

Lecture 2: Variables Assignment

Lecture 3: Data Types

Lecture 4: User Input and Formatting print statements

Lecture 5: User Input Assignment

Chapter 5: Assignment, Relational Operators and Boolean Values

Lecture 1: Assignment Operators

Lecture 2: Relational Operators

Lecture 3: Boolean values

Chapter 6: Indentation and Control flow statements – if else

Lecture 1: Indentation

Lecture 2: If else statements

Lecture 3: If else Assignment

Chapter 7: Lists

Lecture 1: List

Lecture 2: List Assignment

Lecture 3: List Methods

Lecture 4: List Methods Assignment

Chapter 8: Strings

Lecture 1: String

Lecture 2: String Assignment

Lecture 3: String Methods

Lecture 4: String Methods Assignment

Chapter 9: Loops

Lecture 1: For Loop

Lecture 2: For Loop Assignment

Lecture 3: While loop

Lecture 4: While loop Assignment

Lecture 5: Break and Continue

Lecture 6: Break and Continue Assignment

Chapter 10: Functions

Lecture 1: Functions

Lecture 2: Functions Assignment

Lecture 3: Recursive Functions

Lecture 4: Recursive Functions Assignment

Chapter 11: Data Structures – Set, Tuple

Lecture 1: Set

Lecture 2: Set Assignment

Lecture 3: Tuple

Lecture 4: Tuple Assignment

Lecture 5: Tuple Methods

Lecture 6: Tuple Methods Assignment

Chapter 12: Data Structures – Dictionary

Lecture 1: Dictionary

Lecture 2: Nested Dictionary

Lecture 3: Dictionary Assignment

Chapter 13: Advanced Functions

Lecture 1: Lambda Function

Lecture 2: Lambda Function Assignment

Lecture 3: Map Function

Lecture 4: Map Function Assignment

Chapter 14: Exception Handling and concatenation

Lecture 1: Exception Handling

Lecture 2: Concatenation

Chapter 15: Final Assignments

Lecture 1: Dictionary of words assignment

Lecture 2: Remove Duplicates assignment

Lecture 3: Greatest Common Divisor Assignment

Lecture 4: Least Common Multiple Assignment

Chapter 16: Bonus Lectures on Numpy

Lecture 1: Accessing Numpy Library and Creating our first Numpy array using list

Lecture 2: Elements in Numpy Arrays are of same data type

Lecture 3: Creating n-dim array and determining its shape, size and dimension – part 1

Lecture 4: Creating n-dim array and determining its shape, size and dimension – part 2

Lecture 5: Creating n-dim array and determining its shape, size and dimension – part 3

Instructors

Play With Data
Data enthusiast

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Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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