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What you'll learn?
- 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
Course Overview
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.
Pre-requisites
- 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)
Target Audience
- 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.
Curriculum 47 Lectures 08:23:01
-
Section 1 : Course Introduction and Structure
-
Section 2 : Python Installation
- Lecture 1 :
- Installation Introduction
- Lecture 2 :
- Python Installation Via Anaconda
- Lecture 3 :
- Python Via Google Colab
-
Section 3 : Introduction to Variables and Strings
- 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
- Lecture 5 :
- Introduction to Strings and Functions
- Lecture 6 :
- Classroom 2: Hands-on Exercise on Strings
- Lecture 7 :
- Homework: Strings and its Functions
-
Section 4 : List, Tuple and Dictionary
- 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
- 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
- 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
- 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
- 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
- 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
- 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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Frequently Asked Questions
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Instructor
672 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.
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