The Complete Exploratory Analysis Course With Pandas [2022]

The Complete Exploratory Analysis Course With Pandas [2022], available at $44.99, has an average rating of 4.4, with 31 lectures, based on 18 reviews, and has 122 subscribers.

You will learn about Work with Excel data. Work with CSV datasets. Handling missing data. Reading and Working with JSON format. Reading and Working with HTML files. Reading and Working with PICKLE dataset. Reading and Working with SQL-based database. Selecting data from the dataset. Sorting a pandas DataFrame. Filtering rows of a pandas DataFrame. Applying multiple filter criteria to a pandas DataFrame. Using string methods in pandas. Changing the datatype of a pandas series. Modifying a pandas DataFrame using the inplace parameter. Using the Groupby method. Indexing in pandas DataFrames. Renaming columns, and Removing columns from a pandas DataFrame. Working with date and time series data Applying a function to a pandas series or DataFrame. Merging and concatenating multiple DataFrames into one. Controlling plot aesthetics. Choosing the colours for plots. Plotting categorical data. Plotting with Data-Aware Grids. This course is ideal for individuals who are Anyone who is interested in Deep Learning, Machine Learning and Artificial Intelligence, and Data Science. or Anyone who wants to improve Data Analysis skills. or Any students in college who want to start a career in Data Science. or Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets. It is particularly useful for Anyone who is interested in Deep Learning, Machine Learning and Artificial Intelligence, and Data Science. or Anyone who wants to improve Data Analysis skills. or Any students in college who want to start a career in Data Science. or Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.

Enroll now: The Complete Exploratory Analysis Course With Pandas [2022]

Summary

Title: The Complete Exploratory Analysis Course With Pandas [2022]

Price: $44.99

Average Rating: 4.4

Number of Lectures: 31

Number of Published Lectures: 31

Number of Curriculum Items: 31

Number of Published Curriculum Objects: 31

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Work with Excel data.
Work with CSV datasets.
Handling missing data.
Reading and Working with JSON format.
Reading and Working with HTML files.
Reading and Working with PICKLE dataset.
Reading and Working with SQL-based database.
Selecting data from the dataset.
Sorting a pandas DataFrame.
Filtering rows of a pandas DataFrame.
Applying multiple filter criteria to a pandas DataFrame.
Using string methods in pandas.
Changing the datatype of a pandas series.
Modifying a pandas DataFrame using the inplace parameter.
Using the Groupby method.
Indexing in pandas DataFrames.
Renaming columns, and Removing columns from a pandas DataFrame.
Working with date and time series data
Applying a function to a pandas series or DataFrame.
Merging and concatenating multiple DataFrames into one.
Controlling plot aesthetics.
Choosing the colours for plots.
Plotting categorical data.
Plotting with Data-Aware Grids.

Who Should Attend

Anyone who is interested in Deep Learning, Machine Learning and Artificial Intelligence, and Data Science.
Anyone who wants to improve Data Analysis skills.
Any students in college who want to start a career in Data Science.
Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.

Target Audiences

Anyone who is interested in Deep Learning, Machine Learning and Artificial Intelligence, and Data Science.
Anyone who wants to improve Data Analysis skills.
Any students in college who want to start a career in Data Science.
Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.

In the real-world, data is anything but clean, which is why Python libraries like Pandas are so valuable.

If data manipulation is setting your data analysis workflow behind then this course is the key to taking your power back.

Own your data, don’t let your data own you!

When exploratory analysis accounts for up to 80% of your work as a data scientist, learning data munging techniques that take raw data to a final product for analysis as efficiently as possible is essential for success.

Exploratory analysis with Python library Pandas makes it easier for you to achieve better results, increase your productivity, spend more time problem-solving and less time data-wrangling, and communicate your insights more effectively.

This course prepares you to do just that!

With Pandas DataFrame, prepare to learn advanced data manipulation, preparation, and sorting data approaches to turn chaotic bits of data into a final pre-analysis product. This is exactly why Pandas is the most popular Python library in data science and why data scientists at Google, Facebook, JP Morgan, and nearly every other major company that analyzes data use Pandas.

If you want to learn how to efficiently utilize Pandas to manipulate, transform, and merge your data for preparation of visualization, statistical analysis, or machine learning, then this course is for you.

Here’s what you can expect when you enrolled in the course:

Learn how to Work with Excel data, CSV datasets.
Learn how to Handling missing data.
Learn how to read and work with JSON format, HTML files, PICKLE dataset, and SQL-based database.
Learn how to select data from the dataset.
Learn how to sort a pandas DataFrame and filtering rows of a pandas DataFrame.
Learn how to apply multiple filter criteria to a pandas DataFrame.
Learn how to using string methods in pandas.
Learn how to change the datatype of a pandas series.
Learn how to modifying a pandas DataFrame.
Learn how to indexing and renaming columns, and removing columns in and from pandas DataFrame.
Learn how to working with date and time series data.
Learn how to applying a function to a pandas series or DataFrame.
Learn how to merging and concatenating multiple DataFrames into one.
Learn how to control plot aesthetics.
Learn how to choose the colours for plots.
Learn how to plot categorical data.
Learn how to plot with Data-Aware Grids.

Performing exploratory analysis with Python’s Pandas library can help you do a lot, but it does have its downsides. And this course helps you beat them head-on:

1. Pandas has a steep learning curve:As you dive deeper into the Pandas library, the learning slope becomes steeper and steeper. This course guides beginners and intermediate users smoothly into every aspect of Pandas.

2. Inadequate documentation: Without proper documentation, it’s difficult to learn a new library. When it comes to advanced functions, Pandas documentation is rarely helpful. This course helps you grasp advanced Pandas techniques easily and saves you time in searching for help.

After this course, you will feel comfortable delving into complex and heterogeneous datasets knowing with absolute confidence that you can produce a useful result for the next stage of Exploratory analysis.

Here’s a closer look at the curriculum:

Loading and creating Pandas DataFrames
Displaying your data with basic plots, and 1D, 2D and multidimensional visualizations.
Working with Different Kinds of Datasets
Data Selection
Manipulating, Transforming, and Reshaping Data.
Visualizing Data Like a Pro
Merging Pandas DataFrames

Lastly, this course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice with Pandas too.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course structure

Lecture 2: What is the prerequisite of this course

Lecture 3: How To Make The Most Out Of This Course

Lecture 4: Important note about tools in this course

Chapter 2: Working with Different Kinds of Datasets

Lecture 1: Using advanced options while reading data from CSV files

Lecture 2: Reading data from Excel files

Lecture 3: Reading data from other popular formats

Chapter 3: Data Selection

Lecture 1: Introduction to datasets

Lecture 2: Sorting a pandas DataFrame

Lecture 3: Filtering rows of a pandas DataFrame

Lecture 4: Applying multiple filter criteria to a pandas DataFrame

Lecture 5: Using the axis parameter in pandas

Lecture 6: Using string methods in pandas

Lecture 7: Changing the datatype of a pandas series

Lecture 8: Summary

Chapter 4: Manipulating, Transforming, and Reshaping Data

Lecture 1: Modifying a pandas DataFrame using the inplace parameter

Lecture 2: Using the groupby method

Lecture 3: Handling missing values in pandas

Lecture 4: Indexing in pandas DataFrames

Lecture 5: Renaming columns in a pandas DataFrame

Lecture 6: Removing columns from a pandas DataFrame

Lecture 7: Working with date and time series data

Lecture 8: Applying a function to a pandas series or DataFrame

Lecture 9: Merging and concatenating multiple DataFrames into one

Lecture 10: Summary

Chapter 5: Visualizing Data Like a Pro

Lecture 1: Controlling plot aesthetics

Lecture 2: Choosing the colors for plots

Lecture 3: Plotting categorical data

Lecture 4: Plotting with Data-Aware Grids

Lecture 5: Summary

Chapter 6: Thank you

Lecture 1: Thank you

Instructors

Hoang Quy La
Electrical Engineer

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1 stars: 0 votes
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3 stars: 3 votes
4 stars: 0 votes
5 stars: 15 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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