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Modern Data Analysis Masterclass in Pandas and Python
Dr. Ryan Ahmed, Ph.D., MBA,
Ligency Team,
Mitchell Bouchard,
and
SuperDataScience Team
Dr. Ryan Ahmed, Ph.D., MBA,
Ligency Team,
Mitchell Bouchard,
and
SuperDataScience Team
The data revolution is here. Data is the new gold of the 21st Century.
Read more
The data revolution is here. Data is the new gold of the 21st Century.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/tym3kt/modern
What's inside
Learning objectives
- 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.
- 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.
Syllabus
Course Introduction, Success Tips and Key Learning Outcomes
Course Introduction and Welcome Message
Introduction, Key Tips for Success, Getting Help and Course Certification
Read more
Syllabus
Course Introduction, Success Tips and Key Learning Outcomes
Course Introduction and Welcome Message
Introduction, Key Tips for Success, Getting Help and Course Certification
Read more
Why data is considered the new gold of the 21st Century?
Data Sources, Types and Course Outline
Pandas Series Fundamentals
Pandas Series Fundamentals Google Colab Notebooks
Introduction to Pandas Series Notebook
Define a Pandas Series with default index
Define a Pandas Series with default index: Mini Challenge Solution
Define a Pandas Series with custom index
Define a Pandas Series with custom index: Mini Challenge Solution
Define a Pandas Series from a Python dictionary
Define a Pandas Series from a Python dictionary: Mini Challenge Solution
Pandas Series Attributes
Pandas Series Attributes: Mini Challenge Solution
Pandas Methods
Pandas Methods: Mini Challenge Solution
1-D CSV Import Using Pandas
1-D CSV Import Using Pandas: Mini Challenge Solution
Pandas Series and Built-in Python functions
Pandas Series and Built-in Python functions: Mini Challenge Solution
Pandas Series Sorting and Ordering
Pandas Series Sorting and Ordering: Mini Challenge Solution
Perform Math Operations on Pandas Series
Perform Math Operations on Pandas Series: Mini Challenge Solution
Check if a given element exists in Pandas Series
Check if a given element exists in Pandas Series: Mini Challenge Solution
Pandas Series Indexing
Pandas Series Indexing: Mini Challenge Solution
Pandas Series Slicing
Pandas Series Slicing: Mini Challenge Solution
Pandas Series Recap and Concluding Remarks
Pandas DataFrame Fundamentals
Pandas DataFrame Fundamentals Google Colab Notebooks
Define a Pandas DataFrame
Define a Pandas DataFrame: Mini Challenge Solution
Read 2-D CSV and HTML Data Using Pandas
Read 2-D CSV and HTML Data Using Pandas: Mini Challenge Solution
Write DataFrame into CSV
Write DataFrame into CSV: Mini Challenge Solution
Setting and Resetting Pandas DataFrame Index
Setting and Resetting Pandas DataFrame Index: Mini Challenge Solution
Select a Column from the DataFrame
Select a Column from the DataFrame: Mini Challenge Solution
Add and Delete Column from DataFrame
Add and Delete Column from DataFrame: Mini Challenge Solution
Label-based elements selection Using .loc()
Label-based elements selection Using .loc(): Mini Challenge Solution
Integer-based elements selection .iloc()
Integer-based elements selection .iloc(): Mini Challenge Solution
Pandas Broadcasting Operation
Pandas Broadcasting Operation: Mini Challenge Solution
Pandas DataFrames Sorting and Ordering
Pandas DataFrames Sorting and Ordering: Mini Challenge Solution
Pandas DataFrames with Functions
Pandas DataFrames with Functions: Mini Challenge Solution
Pandas Operations with DataFrames
Pandas Operations with DataFrames: Mini Challenge Solutions
Feature Engineering and handling missing datasets
Feature Engineering and handling missing datasets: Mini Challenge Solution
Change DataFrame Datatypes
Change DataFrame Datatypes: Mini Challenge Solution
Pandas DataFrame Recap and Concluding Remarks
DataFrames Concatenation, Merging and Joining
DataFrames Concatenation, Merging and Joining Google Colab Notebook
Dataframe Concatenation
Dataframe Mini Challenge Solution
Concatenation with multiindexing
Multiindexing Mini Challenge Solution
Dataframe Merging
Dataframe Merging Mini Challenge Solution
Pandas Multi-indexing and Groupby
Pandas Multi-indexing and Groupby Google Colab Notebooks
Introduction to Multi-Indexing and Group by
Import and Explore e-Commerce Dataset
Import and Explore e-Commerce Dataset Mini Challenge Solution
Groupby Operation
Groupby Operation Mini Challenge Solution
Create Multi-Indexed DataFrame
Create Multi-Indexed DataFrame Mini Challenge Solution
Multi-indexing Operations Part 1
Multi-indexing Operations Part 1 Mini Challenge Solution
Multi-indexing Operations Part 2
Multi-indexing Operations Part 2 Mini Challenge Solution
Recap and Concluding Remarks
Data Visualization with Pandas and Matplotlib
Data Visualization with Pandas and Matplotlib Google Colab Notebooks
Introduction to Data Visualization with Matplotlib
Basic Line Plot
Basic Line Plot: Mini Challenge Solution
Download data directly from Yahoo Finance
Download data directly from Yahoo Finance: Mini Challenge Solution
Multiple Plots
Multiple Plots: Mini Challenge Solution
Subplots
Subplots: Mini Challenge Solution
Scatterplot
Scatterplot: Mini Challenge Solution
Pie Charts
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches how to perform data mining, which is necessary for modern business operations in multiple sectors
Develops Python and Data Analysis skills, which are foundational for working with data science and machine learning
Uses NumPy and Pandas, which are industry-standard Python libraries for computational science and data manipulation
Advanced course, suitable for learners with some prior exposure to Python and data analysis
Instructed by Dr. Ryan Ahmed, Ph.D., MBA, Ligency Team, and Mitchell Bouchard, SuperDataScience Team, who are recognized for their work in the industry
Covers data visualization using Matplotlib and Seaborn, which are industry-standard libraries for data visualization
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Modern Data Analysis Masterclass in Pandas and Python with these
activities:
Review Python programming basics
Show steps
Ensure a strong foundation by reviewing the fundamentals of Python programming.
Browse courses on
Python Basics
Show steps
-
Go through online tutorials or documentation on Python
-
Practice writing Python code to solve simple problems
Read 'Python for Data Analysis' by Wes McKinney
Show steps
Supplement your learning by reviewing a comprehensive book on Python for data analysis, providing a deeper understanding of the concepts covered in the course.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
-
Read the book thoroughly, taking notes and highlighting important concepts
-
Complete the practice exercises provided in the book
Join a study group or online forum for peer support
Show steps
Engage with peers to discuss course material, share insights, and reinforce your understanding.
Browse courses on
Peer Support
Show steps
-
Join an existing study group or create your own
-
Participate in discussions, ask questions, and provide answers
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete exercises on Pandas Series and DataFrames
Show steps
Sharpen your skills in manipulating data using Pandas Series and DataFrames.
Show steps
-
Work through examples provided in the course materials
-
Attempt practice exercises on Udemy or Coursera.
-
Create your own datasets and perform operations on them
Attend a workshop on advanced data visualization techniques
Show steps
Enhance your data visualization skills through hands-on training in a workshop setting.
Show steps
-
Identify and register for a relevant workshop
-
Attend the workshop, actively participate, and take notes
-
Practice implementing the techniques learned in your own projects
Explore advanced Matplotlib and Seaborn tutorials
Show steps
Enhance your data visualization skills by delving into advanced tutorials on Matplotlib and Seaborn.
Browse courses on
Data Visualization
Show steps
-
Follow tutorials on external platforms such as DataCamp or Kaggle
-
Experiment with different visualization techniques
Develop a data visualization dashboard
Show steps
Solidify your understanding of data visualization by creating an interactive dashboard to represent real-world data.
Browse courses on
Data Visualization
Show steps
-
Identify a suitable dataset and prepare it for visualization
-
Choose appropriate visualization techniques for different data types
-
Use a dashboarding tool to create an interactive interface
-
Present your dashboard to peers or instructors for feedback
Work on a data analysis project using real-world data
Show steps
Apply your newly acquired skills to a practical project, solidifying your understanding and building your portfolio.
Show steps
-
Identify a problem or question that can be addressed through data analysis
-
Collect and prepare the necessary data
-
Perform data analysis and visualization
-
Interpret the results and draw conclusions
-
Present your findings in a clear and concise manner
Review Python programming basics
Show steps
Ensure a strong foundation by reviewing the fundamentals of Python programming.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Go through online tutorials or documentation on Python
- Practice writing Python code to solve simple problems
Read 'Python for Data Analysis' by Wes McKinney
Show steps
Supplement your learning by reviewing a comprehensive book on Python for data analysis, providing a deeper understanding of the concepts covered in the course.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly, taking notes and highlighting important concepts
- Complete the practice exercises provided in the book
Join a study group or online forum for peer support
Show steps
Engage with peers to discuss course material, share insights, and reinforce your understanding.
Browse courses on
Peer Support
Show steps
Show steps
Show steps
- Join an existing study group or create your own
- Participate in discussions, ask questions, and provide answers
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete exercises on Pandas Series and DataFrames
Show steps
Sharpen your skills in manipulating data using Pandas Series and DataFrames.
Show steps
Show steps
Show steps
- Work through examples provided in the course materials
- Attempt practice exercises on Udemy or Coursera.
- Create your own datasets and perform operations on them
Attend a workshop on advanced data visualization techniques
Show steps
Enhance your data visualization skills through hands-on training in a workshop setting.
Show steps
Show steps
Show steps
- Identify and register for a relevant workshop
- Attend the workshop, actively participate, and take notes
- Practice implementing the techniques learned in your own projects
Explore advanced Matplotlib and Seaborn tutorials
Show steps
Enhance your data visualization skills by delving into advanced tutorials on Matplotlib and Seaborn.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Follow tutorials on external platforms such as DataCamp or Kaggle
- Experiment with different visualization techniques
Develop a data visualization dashboard
Show steps
Solidify your understanding of data visualization by creating an interactive dashboard to represent real-world data.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify a suitable dataset and prepare it for visualization
- Choose appropriate visualization techniques for different data types
- Use a dashboarding tool to create an interactive interface
- Present your dashboard to peers or instructors for feedback
Work on a data analysis project using real-world data
Show steps
Apply your newly acquired skills to a practical project, solidifying your understanding and building your portfolio.
Show steps
Show steps
Show steps
- Identify a problem or question that can be addressed through data analysis
- Collect and prepare the necessary data
- Perform data analysis and visualization
- Interpret the results and draw conclusions
- Present your findings in a clear and concise manner
Career center
Learners who complete Modern Data Analysis Masterclass in Pandas and Python will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/tym3kt/modern
Reading list
We haven't picked any books for this reading list yet.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/tym3kt/modern
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Effort
21 total hours
Via
Udemy
Instructors
Dr. Ryan Ahmed, Ph.D., MBA
Ligency Team
Mitchell Bouchard
SuperDataScience Team
Language
English
Good to know
Teaches how to perform data mining, which is necessary for modern business operations in multiple sectors
Develops Python and Data Analysis skills, which are foundational for working with data science and machine learning
Uses NumPy and Pandas, which are industry-standard Python libraries for computational science and data manipulation
Advanced course, suitable for learners with some prior exposure to Python and data analysis
Instructed by Dr. Ryan Ahmed, Ph.D., MBA, Ligency Team, and Mitchell Bouchard, SuperDataScience Team, who are recognized for their work in the industry
Covers data visualization using Matplotlib and Seaborn, which are industry-standard libraries for data visualization
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