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Modern Data Analysis Masterclass in Pandas and Python

4.4 Filled star Filled star Filled star Filled star Empty star
Based on 281 ratings
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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.

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.

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.

Enroll now

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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.

Syllabus

Course Introduction, Success Tips and Key Learning Outcomes
Course Introduction and Welcome Message
Introduction, Key Tips for Success, Getting Help and Course Certification
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Traffic lights

Read about what's good
what should give you pause
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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Reviews summary

Practical pandas and python for data analysis

According to students, this course offers a practical and hands-on approach to data analysis using Pandas and Python, making it accessible for those just starting out while also boosting the skills of career-focused learners. Reviewers frequently praise the course for its clear instruction and comprehensive coverage of Pandas fundamentals, including DataFrame operations, merging, and grouping. The use of real-world datasets and mini-challenges is highlighted as particularly effective for learning and application. While many find it a strong foundation, some note the "Masterclass" title might suggest more advanced depth than is provided, particularly for experienced data professionals. Recent reviews indicate the course benefits from regular updates, ensuring the content and code remain current.
Regularly updated with current library versions and practices.
"I appreciate that the course has been updated to reflect newer Pandas versions, which is crucial for real-world applications."
"The instructor keeps the material fresh and relevant, addressing changes in libraries, which is a major plus for a tech course."
"Previous issues with outdated code or methods seem to have been resolved in recent updates, making it highly current."
Instructor explains complex topics clearly and concisely.
"The instructor's explanations were very clear and easy to follow, even for someone relatively new to data analysis."
"I found the pace perfect, and the way concepts were broken down into digestible parts really aided my understanding."
"The instructor simplifies complex topics effectively, making the learning process engaging and less intimidating."
Thoroughly covers Pandas fundamentals and advanced operations.
"I now feel confident working with Pandas Series and DataFrames after completing this course."
"The depth on merging, concatenation, and grouping data was particularly helpful for various data manipulation tasks."
"This course provided me with a solid foundation in data manipulation using Pandas, covering all the essential topics."
Focuses on real-world applications and coding exercises.
"The hands-on coding and projects are the strongest part of the course for me; I could immediately apply the concepts."
"I appreciated the use of real-world datasets; it made the concepts much more tangible and relevant to my work."
"The practical exercises truly helped me solidify my understanding and gain confidence in data analysis tasks."
Serves as a strong foundation but lacks advanced depth.
"While excellent for beginners, I expected more truly advanced topics and complex case studies for a 'masterclass' designation."
"I already knew some Python, and parts felt a bit slow for me; it could use more challenging scenarios for intermediates."
"It’s a strong introduction to data analysis with Pandas, but perhaps not a full 'masterclass' for experienced data analysts."

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
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
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
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
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
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
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
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
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

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:

Reading list

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Course image
Rating
4.4 Filled star Filled star Filled star Filled star Empty star
Effort
21 total hours
Via
Udemy
Instructors
Dr. Ryan Ahmed, Ph.D., MBA
Ligency Team
Mitchell Bouchard
SuperDataScience Team
Language
English
Traffic lights
Read about what's good
what should give you pause
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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