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Complete guide to begin with Python for Data Science

Name: Complete guide to begin with Python for Data Science
Rating: 4.7 (4 reviews)

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

Created byPlay With Data

Last updated 11/2020

English

English [Auto],

What you'll 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

Course content

16 sections • 62 lectures • 9h 48m total length

Course Overview3:17
Learn to set up Python and Anaconda for data science, and begin coding basics. Practice variables, data types, operators, input, conditionals, and core data structures with functions and recursion.
Introduction to Python5:13
Explore how Python, an open source, general-purpose language, powers web development, data analytics, machine learning, and automation with libraries like pandas, SciPy, and Selenium.

Getting Started and Commenting8:23
Begin by writing your first Python program with a simple print statement. Learn to use comments in Python, including hash based notes and multi-line guidance, inside a notebook.
Arithmetic Operations10:05
Arithmetic Operations Assignment
Arithmetic Operations Assignment4:12
Learn how to perform arithmetic operations on vectors in Python, control order with brackets, and compute division, exponentiation, quotient, and remainder for data science tasks.

Variables12:12
In this lecture, we will learn about, what are variables, how to define a variable in python, distinguish between invalid/valid variable names, some arithmetic operations using Variables.
Variables Assignment
Variables Assignment3:28
defines variables X, Y, Z, A, and B, then performs arithmetic in a single line, using addition, subtraction, multiplication, division, exponentiation, and modulus to compute a result.
Data Types19:42
In this lecture, we will learn about type of data. We will also learn about different data types that exists in python. Will have a short discussion on each data type, which will be discussed in details in upcoming lectures. In this lecture, we will also learn about how to find the data type of a variable and how to change the data type.
User Input and Formatting print statements19:48
In this lecture, we will learn about how to take an input from user, how to store it and how to use it.
We will also see about different ways of formatting the print statement.
User Input Assignment
User Input Assignment7:33
Gather user input for name and scores in science, mathematics, English, and history, convert to numbers, and compute the overall percentage with Python formatting.

Assignment Operators7:44
Explore python assignment operators by assigning and updating variables with = and compound forms (+=, -=, *=, /=, **=, %=), demonstrated in practical notebook examples.
Relational Operators8:04
Master Python relational operators: less than, less than equal to, greater than, greater than equal to, equal, and not equal—and learn how they produce boolean results for loops and conditionals.
Boolean values11:05
Explore boolean values, true and false, and see how relational and logical operators yield truth in conditions for data science in Python.

Indentation7:10
From python point of view, this is very important lecture.
As in this lecture, we will learn about indentation that needs to be maintained while writing loops, if else statements, functions etc.
If else statements15:30
Explore how to use if, else, and elif statements in Python to build conditional logic with boolean values and relational operators, including multiple conditions.
If else Assignment
If else Assignment8:28

List18:08
List Assignment
List Assignment4:53
Explore Python list assignment by slicing and indexing a mixed list of animals and numbers, using negative indices and reverse-order selections to practice core list operations.
List Methods14:32
Learn to create and modify lists in python, using append, extend, insert, pop, and remove, then apply index, count, sort, reverse, and len.
List Methods Assignment
List Methods Assignment5:22

String12:49
Learn how to define strings in Python using single, double, and triple quotes, then explore indexing and slicing, including positive and negative indices and step-based selections.
String Assignment
String Assignment6:51
String Methods21:41
Master Python string methods, including strip, lower, upper, replace, and split, and learn in and not in operations; handle backslashes, escape sequences, and newline or tab formatting for text processing.
String Methods Assignment
String Methods Assignment1:54
Learn how to count words in a string with split and length in Python, while exploring string metrics and palindrome concepts for data science.

For Loop13:45
Explore how to use for loops in Python to iterate ranges, lists, sets, and dictionaries, and generate sequences such as factorials with range-based patterns.
For Loop Assignment
For Loop Assignment2:07
While loop5:44
Learn to use a while loop in Python by assigning a value, defining a condition, and executing code until the condition fails, including incrementing i to exit.
While loop Assignment
While loop Assignment3:20
Apply a Python while loop in a Jupyter notebook to compute the product of all numbers in a list, practicing indexing, accumulation, and debugging for data science basics.
Break and Continue10:49
Explore how break and continue control loops in Python, using for and while loops with range and conditional tests to exit or skip iterations and print selective outputs.
Break and Continue Assignment
Break and Continue Assignment2:44

Functions11:24
Learn how to define and call Python functions using def, arguments, and indentation, return values, and test with square, cube, addition, multiplication, and factorial examples in a notebook.
Functions Assignment
Functions Assignment7:05
Recursive Functions13:11
Demonstrate how recursive functions call themselves in Python, using factorial and fibonacci examples, while guiding function definition, calling, and proper indentation in a Jupyter notebook.
Recursive Functions Assignment
Recursive Functions Assignment12:15

Requirements

Simple Mathematics - Addition, Subtraction, Multiplication, Division

Description

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.

Who this course is for:

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

Complete guide to begin with Python for Data Science

What you'll learn

Explore related topics

Course content

Introduction to Python and Course Overview2 lectures • 9min

Setup Installation and Jupyter Interface3 lectures • 49min

Get Started with Simple Operations in Python3 lectures • 23min

Variables and Type of Data5 lectures • 1hr 3min

Assignment, Relational Operators and Boolean Values3 lectures • 27min

Indentation and Control flow statements - if else3 lectures • 31min

Lists4 lectures • 43min

Strings4 lectures • 43min

Loops6 lectures • 38min

Functions4 lectures • 44min

Requirements

Description

Who this course is for: