Learn Data Wrangling with Python
Learn Data Wrangling with Python, available at $19.99, has an average rating of 4.3, with 18 lectures, based on 61 reviews, and has 6696 subscribers.
You will learn about To load a local dataset from CSV and Excel files. To import a dataset from CSV and Excel files via a URL. To determine the size of a dataset. To explore the first and last records of a dataset. To explore the datatypes of the features of a dataset. To check for missing data in a dataset. To deal with missing data in a dataset. To filter for records with certain values from a dataset. To filter records with multiple filters from a dataset. To filter for records from a dataset through the use of conditions. To perform sorting in ascending and descending order. To split a column in a dataset. To merge data frames to form a dataset. To concatenate two columns to one column in a dataset. To export a dataset in CSV and Excel formats. This course is ideal for individuals who are This course is designed for professionals with an interest in getting hands-on experience with the respective data science techniques and tools. It is particularly useful for This course is designed for professionals with an interest in getting hands-on experience with the respective data science techniques and tools.
Enroll now: Learn Data Wrangling with Python
Summary
Title: Learn Data Wrangling with Python
Price: $19.99
Average Rating: 4.3
Number of Lectures: 18
Number of Published Lectures: 18
Number of Curriculum Items: 18
Number of Published Curriculum Objects: 18
Original Price: $29.99
Quality Status: approved
Status: Live
What You Will Learn
- To load a local dataset from CSV and Excel files.
- To import a dataset from CSV and Excel files via a URL.
- To determine the size of a dataset.
- To explore the first and last records of a dataset.
- To explore the datatypes of the features of a dataset.
- To check for missing data in a dataset.
- To deal with missing data in a dataset.
- To filter for records with certain values from a dataset.
- To filter records with multiple filters from a dataset.
- To filter for records from a dataset through the use of conditions.
- To perform sorting in ascending and descending order.
- To split a column in a dataset.
- To merge data frames to form a dataset.
- To concatenate two columns to one column in a dataset.
- To export a dataset in CSV and Excel formats.
Who Should Attend
- This course is designed for professionals with an interest in getting hands-on experience with the respective data science techniques and tools.
Target Audiences
- This course is designed for professionals with an interest in getting hands-on experience with the respective data science techniques and tools.
By the end of this course, you will be able to:
-
Load a local dataset from CSV and Excel files.
-
Import a dataset from CSV and Excel files via a URL.
-
Determine the size of a dataset.
-
Explore the first and last records of a dataset.
-
Explore the datatypes of the features of a dataset.
-
Check for missing data in a dataset.
-
Deal with missing data in a dataset.
-
Filter for records with certain values from a dataset.
-
Filter records with multiple filters from a dataset.
-
Filter for records from a dataset through the use of conditions.
-
Perform sorting in ascending and descending order.
-
Split a column in a dataset.
-
Merge data frames to form a dataset.
-
Concatenate two columns to one column in a dataset.
-
Export a dataset in CSV and Excel formats.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Chapter 2: Learning Outcomes
Lecture 1: Learning Outcomes
Chapter 3: Overview of Data Wrangling
Lecture 1: Overview of Data Wrangling
Chapter 4: Notebook Introduction
Lecture 1: Notebook Introduction
Chapter 5: Prerequisites
Lecture 1: Prerequisites
Chapter 6: Reading Data
Lecture 1: Reading Data
Chapter 7: Data Exploration
Lecture 1: Data Exploration
Chapter 8: Standardisation
Lecture 1: Standardisation
Chapter 9: Syntax Errors
Lecture 1: Syntax Errors
Chapter 10: Irrelevant Data
Lecture 1: Irrelevant Data
Chapter 11: Duplicates
Lecture 1: Duplicates
Chapter 12: Missing Data
Lecture 1: Missing Data
Chapter 13: Filtering
Lecture 1: Filtering
Chapter 14: Sorting
Lecture 1: Sorting
Chapter 15: Splitting, Merging and Concatenation
Lecture 1: Splitting, Merging and Concatenation
Chapter 16: Outliers
Lecture 1: Outliers
Chapter 17: Exporting Data
Lecture 1: Exporting Data
Chapter 18: Next Steps
Lecture 1: Next Steps
Instructors
-
Valentine Mwangi
Data Science Curriculum Designer
Rating Distribution
- 1 stars: 3 votes
- 2 stars: 1 votes
- 3 stars: 11 votes
- 4 stars: 22 votes
- 5 stars: 24 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!
You may also like
- Machine Learning, Deep Learning & Neural Networks in Matlab
- Power Apps for Beginners
- Gulp 4: Automate your development workflow
- Web Design FORMS Layouts A Must to Learn For All Levels
- Practical guide to AI in Unity
- Hyperledger Fabric 2.x Basic & Advanced Network Operations
- Jupyter x Docker
- JavaScript For Complete Beginners – Go from Zero to Hero
- Comprehensive Python Programming Course with Hands-on Coding
- Scratch Programming – Basic to Expert
- NLP Course for Beginner
- JUnit 5, Mockito, PowerMock, TDD, BDD & ATTD
- Chatbot Development Full-Cycle: From Concept to Growth
- Learn Data Wrangling with Python
- Create a basic website with links using Python and Django
- Building a Crafting Game, with GameMaker
- Learn Creating Premium WordPress Website with Elementor
- API Manual/Automation testing using PYTHON/ PYTEST Framework
- ROS2 Nav2 [Navigation 2 Stack] – with SLAM and Navigation
- Fuzzy Logic and Fuzzy Artificial Intelligence Tutorial