Python for Data Science Bootcamp Course:Beginner to Advanced

Python for Data Science Bootcamp Course:Beginner to Advanced, available at $19.99, has an average rating of 3.55, with 82 lectures, based on 12 reviews, and has 53 subscribers.

You will learn about Master Everything you need to know about Python, Pandas and Numpy with Code Implementations, Examples and many more! Learn Advanced Python modules and complex features such as Python Decorators, Generators, Comprehensions, Regular Expressions, Map, Filter functions, collection Build a solid beginner-level understanding of the Python programming language by learning and coding simultaneously with Instructor. Build thorough Python Object-Oriented Programming (OOP) skills. Learn how to give structure to the program with Functions. Learn Constructor, destructor, private variables, Inheritance, Polymorphism, Abstraction with Example Implement and call methods. Understand their purpose within classes. Define instance attributes and class attributes. Define logic using conditional statements, looping. Hands-On Implementations and Exercises( Code with Instructor simultaneously). Gain a deep and hands-on understanding of pandas data structures. Learn Series at a Glance – Series Methods and Handling Implement DataFrames in depth Implement GroupBy, Slicing, Aggregates and Reshaping With Pivots Join, Melt, cut, transform, clean, filter, groupby, pivot, merge and otherwise manipulate any dataset. Practice reading data from the web, pickles, Excel files right within pandas. Implement advance Pandas DataFrame manipulations: multiIndexing, stacking, hierarchical indexing, pivoting, melting and more. Import, clean, and merge messy Data and prepare Data for Machine Learning Merge and Concatenate many Datasets efficiently. Scale and Automate data merging Clean and format data easily. Detect and intelligently fill missing values. Group, aggregate and summarise your data. Implement important methods, attributes, and techniques to manipulate data in pandas and python. Learn to use NumPy for Numerical Data Learn basic and advanced features in NumPy (Numerical Python) Learn and practice all relevant Pandas methods and workflows with Real-World Datasets Understand how to use both the Jupyter Notebook and create .py files. Acquire the pre-requisite Python skills to move into different areas – Machine Learning, Data Science, Backend Development etc. Have the skills and understanding of Python, Pandas and Numpy to confidently apply for Python programming jobs at Tech companies. This course is ideal for individuals who are Beginner Python programmers or Data Science Enthusiasts and professionals or Anyone who wants to get into data science and Machine learning or Everyone who wants to master large, messy and unclean Datasets or Data Scientists and Machine Learning or Anyone interested in mastering data analysis with python or Anyone looking to deeply understand and master python, pandas and Numpy or Data Scientists who want to improve their Data Handling/Data Manipulation skills. It is particularly useful for Beginner Python programmers or Data Science Enthusiasts and professionals or Anyone who wants to get into data science and Machine learning or Everyone who wants to master large, messy and unclean Datasets or Data Scientists and Machine Learning or Anyone interested in mastering data analysis with python or Anyone looking to deeply understand and master python, pandas and Numpy or Data Scientists who want to improve their Data Handling/Data Manipulation skills.

Enroll now: Python for Data Science Bootcamp Course:Beginner to Advanced

Summary

Title: Python for Data Science Bootcamp Course:Beginner to Advanced

Price: $19.99

Average Rating: 3.55

Number of Lectures: 82

Number of Published Lectures: 82

Number of Curriculum Items: 82

Number of Published Curriculum Objects: 82

Original Price: $84.99

Quality Status: approved

Status: Live

What You Will Learn

Master Everything you need to know about Python, Pandas and Numpy with Code Implementations, Examples and many more!
Learn Advanced Python modules and complex features such as Python Decorators, Generators, Comprehensions, Regular Expressions, Map, Filter functions, collection
Build a solid beginner-level understanding of the Python programming language by learning and coding simultaneously with Instructor.
Build thorough Python Object-Oriented Programming (OOP) skills.
Learn how to give structure to the program with Functions.
Learn Constructor, destructor, private variables, Inheritance, Polymorphism, Abstraction with Example
Implement and call methods. Understand their purpose within classes.
Define instance attributes and class attributes.
Define logic using conditional statements, looping.
Hands-On Implementations and Exercises( Code with Instructor simultaneously).
Gain a deep and hands-on understanding of pandas data structures.
Learn Series at a Glance – Series Methods and Handling
Implement DataFrames in depth
Implement GroupBy, Slicing, Aggregates and Reshaping With Pivots
Join, Melt, cut, transform, clean, filter, groupby, pivot, merge and otherwise manipulate any dataset.
Practice reading data from the web, pickles, Excel files right within pandas.
Implement advance Pandas DataFrame manipulations: multiIndexing, stacking, hierarchical indexing, pivoting, melting and more.
Import, clean, and merge messy Data and prepare Data for Machine Learning
Merge and Concatenate many Datasets efficiently.
Scale and Automate data merging
Clean and format data easily.
Detect and intelligently fill missing values.
Group, aggregate and summarise your data.
Implement important methods, attributes, and techniques to manipulate data in pandas and python.
Learn to use NumPy for Numerical Data
Learn basic and advanced features in NumPy (Numerical Python)
Learn and practice all relevant Pandas methods and workflows with Real-World Datasets
Understand how to use both the Jupyter Notebook and create .py files.
Acquire the pre-requisite Python skills to move into different areas – Machine Learning, Data Science, Backend Development etc.
Have the skills and understanding of Python, Pandas and Numpy to confidently apply for Python programming jobs at Tech companies.

Who Should Attend

Beginner Python programmers
Data Science Enthusiasts and professionals
Anyone who wants to get into data science and Machine learning
Everyone who wants to master large, messy and unclean Datasets
Data Scientists and Machine Learning
Anyone interested in mastering data analysis with python
Anyone looking to deeply understand and master python, pandas and Numpy
Data Scientists who want to improve their Data Handling/Data Manipulation skills.

Target Audiences

Beginner Python programmers
Data Science Enthusiasts and professionals
Anyone who wants to get into data science and Machine learning
Everyone who wants to master large, messy and unclean Datasets
Data Scientists and Machine Learning
Anyone interested in mastering data analysis with python
Anyone looking to deeply understand and master python, pandas and Numpy
Data Scientists who want to improve their Data Handling/Data Manipulation skills.

Harvard University has named a data scientist as the ‘sexiest job title of the 21st century’. For the last 5 years, data science has been featured as a top career by Glassdoor. Data scientists are responsible for finding, filtering, and organizing data for companies. They explore through large piles of data generated every single day to find patterns that will benefit an organization, and at the same time, help to fulfill their strategic goals. This course covers everything you need to know in order to become a brilliant data scientist.

Topics Covered in this course (in depth):

Build a solid beginner-level understanding of the Python programming language by learning and coding simultaneously with Instructor.
Learn Advanced Python modules and complex features such as Python Decorators, Generators, Comprehensions, Regular Expressions, Map, Filter functions, collections etc.
Build thorough Python Object-Oriented Programming (OOP) skills.
Learn how to give structure to the program with Functions.
Learn Constructor, destructor, private variables, Inheritance, Polymorphism, Abstraction with Example.
Implement and call methods. Understand their purpose within classes.
Define instance attributes and class attributes.
Define logic using conditional statements, looping.
Hands-On Implementations and Exercises( Code with Instructor simultaneously).
Gain a deep and hands-on understanding of pandas data structures.
Learn Series at a Glance – Series Methods and Handling
Implement DataFrames in depth
Implement GroupBy, Slicing, Aggregates and Reshaping With Pivots
Join, Melt, cut, transform, clean, filter, groupby, pivot, merge and otherwise manipulate any dataset.
Practice reading data from the web, pickles, Excel files right within pandas.
Implement advance Pandas DataFrame manipulations: multiIndexing, stacking, hierarchical indexing, pivoting, melting and more.
Import, clean, and merge messy Data and prepare Data for Machine Learning
Merge and Concatenate many Datasets efficiently.
Scale and Automate data merging
Clean and format data easily.
Detect and intelligently fill missing values.
Group, aggregate and summarize your data.
Implement important methods, attributes, and techniques to manipulate data in pandas and python.
Learn to use NumPy for Numerical Data
You will learn basic and advanced features in NumPy (Numerical Python)
Learn and practice all relevant Pandas methods and workflows with Real-World Datasets
Understand how to use both the Jupyter Notebook and create .py files.
Have the skills and understanding of Python to confidently apply for Python programming jobs at Tech companies.
Acquire the pre-requisite Python skills to move into different areas – Machine Learning, Data Science, Backend Development etc.
Understand how to create your own Python programs.
Learn Python from experienced professional software developers.
This course teaches you Python Programming Language Fast And Easy Way With Examples From Scratch.
Many Quizzes to master the concepts and test your understanding.
Grasp ESSENTIAL concepts of Python programming.
Start your journey as a developer and data scientist/Machine Learning Engineer with Big Tech Companies.

With this just one course you can start your data science journey right away! Go for it.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Installing Python on Jupyter

Lecture 3: Installing and Running Python on Jupyter

Lecture 4: Installing Pandas on Jupyter

Chapter 2: Python Basics

Lecture 1: Introduction to Python Data Types

Lecture 2: Coding : Introduction to Python Data Types

Lecture 3: Introduction to Strings

Lecture 4: Coding : Introduction to Strings

Lecture 5: Indexing and Slicing with Strings

Lecture 6: Coding : Indexing and Slicing with Strings

Lecture 7: String Methods

Lecture 8: Coding : String Methods

Lecture 9: Lists in Python

Lecture 10: Coding : Lists in Python

Lecture 11: Dictionaries in Python

Lecture 12: Coding : Dictionaries in Python

Lecture 13: Tuples in Python

Lecture 14: Coding : Tuples in Python – Part 1

Lecture 15: Coding : Tuples in Python – Part 2

Lecture 16: Sets in Python

Lecture 17: Coding : Sets in Python

Lecture 18: Coding : Type Conversion

Lecture 19: Coding : Booleans in Python

Lecture 20: Input Output in Python

Lecture 21: Coding : Input Output in Python

Lecture 22: Files in Python

Lecture 23: Coding : Files in Python

Lecture 24: Introduction to Functions

Lecture 25: Coding : Constructors

Lecture 26: Tuple Unpacking with Python Functions

Lecture 27: Coding : Tuple Unpacking with Python Functions

Chapter 3: Advanced Python

Lecture 1: Closures in Python

Lecture 2: Coding : Closures in Python

Lecture 3: Packing and Unpacking Arguments

Lecture 4: Coding : Packing and Unpacking Arguments

Lecture 5: Lambda functions in Python

Lecture 6: Coding : Lambda functions in Python

Lecture 7: Map and Filter Functions

Lecture 8: Coding : Map Function

Lecture 9: Coding : Filter Function

Lecture 10: Decorators in Python

Lecture 11: Coding : Decorators in Python

Lecture 12: Memoization using decorators

Lecture 13: Coding : Memoization using decorators

Lecture 14: Generators in Python

Lecture 15: Coding : Generators in Python

Lecture 16: Coding : Generator Expressions

Lecture 17: Coroutine in Python

Lecture 18: Coding : Coroutine in Python

Lecture 19: Filter and Reduce Functions

Lecture 20: Coding : Filter and Reduce Functions

Lecture 21: Coding : Itertools in Python Part 1

Lecture 22: Coding : Itertools in Python Part 2

Lecture 23: Efficient Code and Optimization techniques for Python

Lecture 24: Coding : Efficient Code and Optimization techniques for Python

Chapter 4: Pandas Basics

Lecture 1: Introduction to Pandas

Lecture 2: Pandas Basics

Lecture 3: Pandas Objects

Lecture 4: Hands On : Pandas Objects

Lecture 5: Hands On : Series in Pandas

Lecture 6: Hands On : Data Frame in Pandas

Lecture 7: Hands on : Data Indexing in Pandas

Lecture 8: Hands On : Data Selection Part 1

Lecture 9: Hands On : Data Selection Part 2

Chapter 5: Operations in Pandas

Lecture 1: Hands On : Missing Values

Lecture 2: Hands On : Concatenation and Append in Pandas

Lecture 3: Hands On : Sorting in Pandas

Lecture 4: Hands On : Drop Operations in Pandas

Lecture 5: Hands On : Pandas Window

Lecture 6: Hands On : Hierarchical Indexing Part 1

Lecture 7: Hands On : Hierarchical Indexing Part 2

Lecture 8: Hands On : Reindexing in Pandas

Lecture 9: Hands On : Merging and Joining in Pandas

Lecture 10: Hands on : Aggregation in Pandas

Lecture 11: Hands On : Grouping in Pandas Part 1

Lecture 12: Hands On : Grouping in Pandas Part 2

Lecture 13: Hands On : Pivot Tables in Pandas

Lecture 14: Hands On : Important Operations and Functions in Pandas

Lecture 15: Hands On : Statistical Functions in Pandas

Lecture 16: Hands On : Descriptive Statistics in Pandas

Chapter 6: Numpy

Lecture 1: Hands On : Numpy

Chapter 7: Congratulations

Lecture 1: Congratulations on Course Completion

Instructors

Naina Chaturvedi
Software Engineer

Rating Distribution

1 stars: 0 votes
2 stars: 2 votes
3 stars: 5 votes
4 stars: 1 votes
5 stars: 4 votes

Frequently Asked Questions

How long do I have access to the course materials?

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

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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