Python for Data Science and Machine Learning

Kick Start Your Data Science Career from Beginner to Master

Created by : Rated :4 692 views

Last Updated: 2023-03-20 06:03:20 English Course Type: Self Learning

This course covers all the important Python Fundamentals and Data Science Concepts requires to succeed in Academics and Corporate Industry
Opportunity to Apply Data Science Concepts into 3 Real World Case Studies and 2 Real World Projects

This course is meant for beginners and intermediates who wants to expert on Python programming concepts and Data Science libraries for analysis, machine Learning models etc.

They can be students, professionals, Data Scientist, Business Analyst, Data Engineer, Machine Learning Engineer, Project Manager, Leads, business reports etc.

The course have been divided into 6 parts - Chapters, Quizzes, Classroom Hands-on Exercises, Homework Hands-on Exercises, Case Studies and Projects.

Practice and Hands-on concepts through Classroom, Homework Assignments, Case Studies and Projects

This Course is ideal for anyone who is starting their Data Science Journey and building ML models and Analytics in future.

This course covers all the important Python Fundamentals and Data Science Concepts requires to succeed in Academics and Corporate Industry.

Opportunity to Apply Data Science Concepts in 3 Real World Case Studies and 2 Real World Projects.

The 3 Case Studies are on Loan Risk Analysis, Churn Prediction and Customer Segmentation.

The 2 Projects are on Titanic Dataset and NYC Taxi Trip Duration.

The recommended approach for this course - Follow the chapters in their order, Do Yourself all the Hands-on Exercises. Finally, Consistency, discipline and practice is paramount.

This course will not teach you how to build and develop ML models. But, make you expert at python programming language which is needed to build ML models.

There is no technical or prior programming knowledge is required. This course only requires your commitment and practice to become expert at Python.
It requires your local desktop/Laptop (Windows or Mac) or Google Account (Optional)

This course is meant for anyone who wants to get expert on Python programming concepts and Data Science libraries for analysis, machine Learning models etc.
They can be students, professionals, Data Scientist, Business Analyst, Data Engineer, Machine Learning Engineer, Project Manager, Leads, business reports etc.

Section 1 : Course Introduction and Structure 1 Lectures 00:08:05
Lecture 1 :
Introduction Preview
Section 2 : Python Installation 3 Lectures 00:27:33
Lecture 1 :
Installation Introduction
Lecture 2 :
Python Installation Via Anaconda
Lecture 3 :
Python Via Google Colab
Section 3 : Introduction to Variables and Strings 7 Lectures 00:39:35
Lecture 1 :
Introduction to Variables
Lecture 2 :
Variables, Datatypes and Expressions
Lecture 3 :
Classroom 1: Hands-on Exercise on Variables and Expressions
Lecture 4 :
Homework 1: Hands-on Exercise on Variables and Expressions
Ques 1: Write a Program, to find the value of the following expression z = x*y**10 Where x and y are input variables. The output of the expression store in variable z and print its value and datatype.
Lecture 5 :
Introduction to Strings and Functions
Lecture 6 :
Classroom 2: Hands-on Exercise on Strings
Lecture 7 :
Homework: Strings and its Functions
Questions for this assignment Q1. Write a Program to count the number of vowels in a string. e.g. "Hello World" has 3 Vowels Q2. Write a Program to find words from String which are greater than given length k. e.g. String is “My name is Python and learning PowerBI“. Let’s say K =3. The words which are greater than length (K) = 3 are name, Python, learning, PowerBI. Q3. Write a Program to split the string into tokens and then convert each token into both lower case and upper case.
Section 4 : List, Tuple and Dictionary 5 Lectures 01:02:26
Lecture 1 :
Introduction to List
Lecture 2 :
List and its Functions
Lecture 3 :
Introduction to Tuples and Dictionary
Lecture 4 :
Classroom 3: Hands-on Exercise on List and Tuples
Lecture 5 :
Classroom 4: Hands-on Exercise on Dictionary
Section 5 : Conditions and Loops 5 Lectures 01:04:46
Lecture 1 :
If-Else Conditions and Logical Operators
Lecture 2 :
Classroom 5: Hands-on Exercise on Conditions
Lecture 3 :
Loops (For and While)
Lecture 4 :
List Comprehension
Lecture 5 :
Classroom 6: Hands-on Exercise on Loops
Section 6 : Functions and Case Study 1 4 Lectures 00:33:54
Lecture 1 :
Introduction to Functions and Return Statement
Lecture 2 :
Classroom 7: Hands-on Functions
Lecture 3 :
Homework: List, Tuples, Dictionary, Loops, Conditions, Functions
Lecture 4 :
Case Study 1: Loan Risk Analysis
Section 7 : Data Science: Numpy and Functions 5 Lectures 00:56:43
Lecture 1 :
Introduction to Numpy Package
Lecture 2 :
Part 1: Numpy Functions
Lecture 3 :
Part 2: Numpy Functions
Lecture 4 :
Part 3: Numpy Functions
Lecture 5 :
Classroom 8: Hands-on Exercise on Numpy Package
Section 8 : Data Science: Pandas Package and Case Study 2 9 Lectures 02:14:43
Lecture 1 :
Introduction to Pandas Series
Lecture 2 :
Introduction to Pandas DataFrame
Lecture 3 :
Access Pandas DataFrame
Lecture 4 :
Part 1: Pandas Functions
Lecture 5 :
Part 2: Pandas Functions
Lecture 6 :
Part 1: Classroom 9: Hands-on Exercise on Numpy and Pandas
Lecture 7 :
Part 2: Classroom 9: Hands-on Exercise on Numpy and Pandas
Lecture 8 :
Homework 4: Numpy and Pandas Libraries
Lecture 9 :
Case Study 2: Churn Prediction
Section 9 : Data Science: Data Visualization 5 Lectures 01:04:23
Lecture 1 :
Introduction to Visualization
Lecture 2 :
Visualization : Matplotlib and Seaborn Packages
Lecture 3 :
Part 1: Classroom 10: Hands-on Exercise on Visualization
Lecture 4 :
Part 2: Classroom 10: Hands-on Exercise on Visualization
Lecture 5 :
Homework 5: Data Visualization
Section 10 : Case Study 3 and Projects 3 Lectures 00:10:53
Lecture 1 :
Project 2: Titanic Disaster
Lecture 2 :
Project 1: New York Trip Duration
Lecture 3 :
Case Study 3: Customer Segmentation

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Mohit Jain,

692 Course Views

1 Courses

I am passionate data enthusiast and loved to mentor people. My ultimate goal is to bring value in people life. I have 10+ years of professional experience and worked 7 years at American Express at different levels of leadership. Currently, I am working at Fintech company and leading the Credit Risk team and objective is to build model to predict the credit worthiness of customer and help the company to find quality customer that in turn generate profits and minimized the loses and write-offs.