Modern Data Analysis Masterclass in Pandas and Python

Modern Data Analysis Masterclass in Pandas and Python, available at $84.99, has an average rating of 4.5, with 179 lectures, based on 269 reviews, and has 2873 subscribers.

You will learn about Master advanced Python tools to manage, sort, and visualize data. Learn how to use key Python Libraries such as NumPy for scientific computing and Pandas for Data Analysis. Master Matplotlib and Seaborn libraries to visualize data, gain valuable insights, and make informed decisions. Master strategies on how to manage large datasets, perform featureengineering and data cleaning for machine learning and data science applications. Create heatmaps, correlation plots, scatterplots, pie charts, pair plots, Venn diagrams, 3D plots, histograms, word cloud and swarm plots. This course is ideal for individuals who are Beginner and experienced Python programmers and data scientists wanting to gain a fundamental understanding of data manipulation and analysis tools and their application in the Finance, Banking, healthcare, and technology sectors. or Data analysts who want to harness the power of Python to visualize key metrics, optimize business processes, maximize revenue, and reduce costs. or Data analysts wanting to advance their careers, build their data science portfolio, and gain real-world practical experience. or Tech enthusiasts who are passionate about data and want to gain real-world practical experience. or Visionary business owners who want to harness the power of data to maximize revenue, reduce costs and optimize their business. It is particularly useful for Beginner and experienced Python programmers and data scientists wanting to gain a fundamental understanding of data manipulation and analysis tools and their application in the Finance, Banking, healthcare, and technology sectors. or Data analysts who want to harness the power of Python to visualize key metrics, optimize business processes, maximize revenue, and reduce costs. or Data analysts wanting to advance their careers, build their data science portfolio, and gain real-world practical experience. or Tech enthusiasts who are passionate about data and want to gain real-world practical experience. or Visionary business owners who want to harness the power of data to maximize revenue, reduce costs and optimize their business.

Enroll now: Modern Data Analysis Masterclass in Pandas and Python

Summary

Title: Modern Data Analysis Masterclass in Pandas and Python

Price: $84.99

Average Rating: 4.5

Number of Lectures: 179

Number of Published Lectures: 177

Number of Curriculum Items: 179

Number of Published Curriculum Objects: 177

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Master advanced Python tools to manage, sort, and visualize data.

Learn how to use key Python Libraries such as NumPy for scientific computing and Pandas for Data Analysis.

Master Matplotlib and Seaborn libraries to visualize data, gain valuable insights, and make informed decisions.

Master strategies on how to manage large datasets, perform featureengineering and data cleaning for machine learning and data science applications.

Create heatmaps, correlation plots, scatterplots, pie charts, pair plots, Venn diagrams, 3D plots, histograms, word cloud and swarm plots.

Who Should Attend

Beginner and experienced Python programmers and data scientists wanting to gain a fundamental understanding of data manipulation and analysis tools and their application in the Finance, Banking, healthcare, and technology sectors.

Data analysts who want to harness the power of Python to visualize key metrics, optimize business processes, maximize revenue, and reduce costs.

Data analysts wanting to advance their careers, build their data science portfolio, and gain real-world practical experience.

Tech enthusiasts who are passionate about data and want to gain real-world practical experience.

Visionary business owners who want to harness the power of data to maximize revenue, reduce costs and optimize their business.

Target Audiences

Beginner and experienced Python programmers and data scientists wanting to gain a fundamental understanding of data manipulation and analysis tools and their application in the Finance, Banking, healthcare, and technology sectors.

Data analysts who want to harness the power of Python to visualize key metrics, optimize business processes, maximize revenue, and reduce costs.

Data analysts wanting to advance their careers, build their data science portfolio, and gain real-world practical experience.

Tech enthusiasts who are passionate about data and want to gain real-world practical experience.

Visionary business owners who want to harness the power of data to maximize revenue, reduce costs and optimize their business.

The data revolution is here! Data is the new gold of the 21st Century.

Companies nowadays have access to a massive amount of data and their competitive advantage lies in   their ability to gain valuable insights from this data. Not only do they need to analyze all the data, but they need to do it fast!

Data can empower companies to boost their revenues, improve processes and reduce costs.

Data could be leveraged in many industries such as Finance, banking, healthcare, transportation, and technology sectors.

The purpose of this course is to provide you with knowledge of key aspects of data analytics in a practical, easy, and fun way. The courseprovides students with practical hands-on experience using real-world datasets.

We will learn how to analyze data using Pandas Series and DataFrames, how to perform merging, concatenation and joining. We will also learn how to perform data visualization using Matplotlib and Seaborn. Furthermore, we will learn how to deal with datetime and text dataset.

So, whether you’re just getting started with Python and Data Analysis, or you’re well-established in your career and would like to polish your data visualization skills, this course will boost your skillset.

So, are you ready to get your data visualizations up and running? Enroll now!

Course Curriculum

Chapter 1: Course Introduction, Success Tips and Key Learning Outcomes

Lecture 1: Course Introduction and Welcome Message

Lecture 2: Introduction, Key Tips for Success, Getting Help and Course Certification

Lecture 3: Why data is considered the new gold of the 21st Century?

Lecture 4: Data Sources, Types and Course Outline

Chapter 2: Pandas Series Fundamentals

Lecture 1: Pandas Series Fundamentals Google Colab Notebooks

Lecture 2: Introduction to Pandas Series Notebook

Lecture 3: Define a Pandas Series with default index

Lecture 4: Define a Pandas Series with default index: Mini Challenge Solution

Lecture 5: Define a Pandas Series with custom index

Lecture 6: Define a Pandas Series with custom index: Mini Challenge Solution

Lecture 7: Define a Pandas Series from a Python dictionary

Lecture 8: Define a Pandas Series from a Python dictionary: Mini Challenge Solution

Lecture 9: Pandas Series Attributes

Lecture 10: Pandas Series Attributes: Mini Challenge Solution

Lecture 11: Pandas Methods

Lecture 12: Pandas Methods: Mini Challenge Solution

Lecture 13: 1-D CSV Import Using Pandas

Lecture 14: 1-D CSV Import Using Pandas: Mini Challenge Solution

Lecture 15: Pandas Series and Built-in Python functions

Lecture 16: Pandas Series and Built-in Python functions: Mini Challenge Solution

Lecture 17: Pandas Series Sorting and Ordering

Lecture 18: Pandas Series Sorting and Ordering: Mini Challenge Solution

Lecture 19: Perform Math Operations on Pandas Series

Lecture 20: Perform Math Operations on Pandas Series: Mini Challenge Solution

Lecture 21: Check if a given element exists in Pandas Series

Lecture 22: Check if a given element exists in Pandas Series: Mini Challenge Solution

Lecture 23: Pandas Series Indexing

Lecture 24: Pandas Series Indexing: Mini Challenge Solution

Lecture 25: Pandas Series Slicing

Lecture 26: Pandas Series Slicing: Mini Challenge Solution

Lecture 27: Pandas Series Recap and Concluding Remarks

Chapter 3: Pandas DataFrame Fundamentals

Lecture 1: Pandas DataFrame Fundamentals Google Colab Notebooks

Lecture 2: Define a Pandas DataFrame

Lecture 3: Define a Pandas DataFrame: Mini Challenge Solution

Lecture 4: Read 2-D CSV and HTML Data Using Pandas

Lecture 5: Read 2-D CSV and HTML Data Using Pandas: Mini Challenge Solution

Lecture 6: Write DataFrame into CSV

Lecture 7: Write DataFrame into CSV: Mini Challenge Solution

Lecture 8: Setting and Resetting Pandas DataFrame Index

Lecture 9: Setting and Resetting Pandas DataFrame Index: Mini Challenge Solution

Lecture 10: Select a Column from the DataFrame

Lecture 11: Select a Column from the DataFrame: Mini Challenge Solution

Lecture 12: Add and Delete Column from DataFrame

Lecture 13: Add and Delete Column from DataFrame: Mini Challenge Solution

Lecture 14: Label-based elements selection Using .loc()

Lecture 15: Label-based elements selection Using .loc(): Mini Challenge Solution

Lecture 16: Integer-based elements selection .iloc()

Lecture 17: Integer-based elements selection .iloc(): Mini Challenge Solution

Lecture 18: Pandas Broadcasting Operation

Lecture 19: Pandas Broadcasting Operation: Mini Challenge Solution

Lecture 20: Pandas DataFrames Sorting and Ordering

Lecture 21: Pandas DataFrames Sorting and Ordering: Mini Challenge Solution

Lecture 22: Pandas DataFrames with Functions

Lecture 23: Pandas DataFrames with Functions: Mini Challenge Solution

Lecture 24: Pandas Operations with DataFrames

Lecture 25: Pandas Operations with DataFrames: Mini Challenge Solutions

Lecture 26: Feature Engineering and handling missing datasets

Lecture 27: Feature Engineering and handling missing datasets: Mini Challenge Solution

Lecture 28: Change DataFrame Datatypes

Lecture 29: Change DataFrame Datatypes: Mini Challenge Solution

Lecture 30: Pandas DataFrame Recap and Concluding Remarks

Chapter 4: DataFrames Concatenation, Merging and Joining

Lecture 1: DataFrames Concatenation, Merging and Joining Google Colab Notebook

Lecture 2: Dataframe Concatenation

Lecture 3: Dataframe Mini Challenge Solution

Lecture 4: Concatenation with multiindexing

Lecture 5: Multiindexing Mini Challenge Solution

Lecture 6: Dataframe Merging

Lecture 7: Dataframe Merging Mini Challenge Solution

Chapter 5: Pandas Multi-indexing and Groupby

Lecture 1: Pandas Multi-indexing and Groupby Google Colab Notebooks

Lecture 2: Introduction to Multi-Indexing and Group by

Lecture 3: Import and Explore e-Commerce Dataset

Lecture 4: Import and Explore e-Commerce Dataset Mini Challenge Solution

Lecture 5: Groupby Operation

Lecture 6: Groupby Operation Mini Challenge Solution

Lecture 7: Create Multi-Indexed DataFrame

Lecture 8: Create Multi-Indexed DataFrame Mini Challenge Solution

Lecture 9: Multi-indexing Operations Part 1

Lecture 10: Multi-indexing Operations Part 1 Mini Challenge Solution

Lecture 11: Multi-indexing Operations Part 2

Lecture 12: Multi-indexing Operations Part 2 Mini Challenge Solution

Lecture 13: Recap and Concluding Remarks

Chapter 6: Data Visualization with Pandas and Matplotlib

Lecture 1: Data Visualization with Pandas and Matplotlib Google Colab Notebooks

Lecture 2: Introduction to Data Visualization with Matplotlib

Lecture 3: Basic Line Plot

Lecture 4: Basic Line Plot: Mini Challenge Solution

Lecture 5: Download data directly from Yahoo Finance

Lecture 6: Download data directly from Yahoo Finance: Mini Challenge Solution

Lecture 7: Multiple Plots

Lecture 8: Multiple Plots: Mini Challenge Solution

Lecture 9: Subplots

Lecture 10: Subplots: Mini Challenge Solution

Lecture 11: Scatterplot

Lecture 12: Scatterplot: Mini Challenge Solution

Lecture 13: Pie Charts

Instructors

Dr. Ryan Ahmed, Ph.D., MBA
Best-Selling Professor, 400K+ students, 250K+ YT Subs

Ligency Team
Helping Data Scientists Succeed

Mitchell Bouchard
B.S, Host @RedCapeLearning 540,000 + Students

SuperDataScience Team
Helping Data Scientists Succeed

Rating Distribution

1 stars: 1 votes

2 stars: 2 votes

3 stars: 17 votes

4 stars: 92 votes

5 stars: 157 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!