Course Summary
Learn how to use Python for data analysis with this comprehensive course. Gain skills in pandas, NumPy, data visualization, and more.Key Learning Points
- Use Python to perform data analysis tasks
- Explore data visualization techniques with matplotlib and seaborn
- Learn how to use pandas and NumPy to manipulate and analyze data
Related Topics for further study
Learning Outcomes
- Perform data analysis tasks using Python
- Manipulate and analyze data with pandas and NumPy
- Create effective visualizations using matplotlib and seaborn
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Familiarity with data analysis concepts
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
- Hands-on projects
Similar Courses
- Data Science Essentials
- Applied Data Science with Python
- Data Analysis and Interpretation
Related Education Paths
Related Books
Description
This course provides an introduction to basic data science techniques using Python. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. This course provides an overview of loading, inspecting, and querying real-world data, and how to answer basic questions about that data. Students will gain skills in data aggregation and summarization, as well as basic data visualization.
Knowledge
- Apply basic data science techniques using Python
- Understand and apply core concepts like Data Frames and joining data, and use data analysis libraries like pandas, numpy, and matplotlib
- Demonstrate how to load, inspect, and query real-world data, and answer basic questions about that data
- Analyze data further by applying learned skills in data aggregation and summarization, as well as basic data visualization
Outline
- Module 1 : Loading, Querying, & Filtering Data Using the csv Module
- Course Introduction
- About the Instructor : Brandon Krakowsky
- Downloading & Installing Jupyter Notebook
- Using Jupyter Notebook
- Importing and reading a file using the csv module
- Coding demonstration : Analyzing the 500 Greatest Albums of All Time
- Coding demonstration : Catching data errors and sorting
- Coding demonstration : Calculating max and min
- Course Layout & Syllabus
- Tips to succeed in this course
- Module 1 Resources (DOWNLOAD RELEVANT CODE AND/OR DATA FILES FOR THIS MODULE HERE)
- Review of dictionaries
- Review of lists
- Review of loops
- Review of functions
- Lambda with max and min
- Homework 1 - Instructions
- Quiz 1 - Loading, Querying, & Filtering Data
- Quiz 2 - Catching Errors & Sorting
- Module 2 : Loading, Querying, Joining & Filtering Data Using pandas
- The pandas module
- Loading data
- Inspecting data
- Querying data
- Joining data
- Code Along Exercise : Join data
- Slicing rows
- Querying data using boolean indexing
- Code Along Exercise : Dive bar recommendation in Las Vegas
- Computations - sum()
- Computations - mean()
- Other methods
- Updating & creating data
- Code Along Exercise : Add rating column
- Module 2 Resources (DOWNLOAD RELEVANT CODE AND/OR DATA FILES FOR THIS MODULE HERE)
- Homework 2 - Instructions
- Casting Data
- Cleaning data & dealing with missing values
- Homework 3 - Instructions
- Quiz 3 - Loading, Inspecting, & Querying Data
- Quiz 4 - Joining & Filtering Data
- Module 3 : Summarizing & Visualizing Data
- Summarizing groups
- The numpy library
- Pivot tables
- Using an index
- Code Along Exercise : Average review count and rating
- Aggregate functions
- Jupyter notebook magic functions
- The matplotlib library
- Histograms
- Histograms Coding Demonstration : To show distribution of data
- Histograms Coding Demonstration : Preparing data
- Histograms Coding Demonstration : Setting options for PyPlot
- Histograms Coding Demonstration : Displaying the visualization
- Scatterplots
- Scatterplots Coding Demonstration : To compare data points on different dimensions
- Scatterplots Coding Demonstration : Preparing data
- Scatterplots Coding Demonstration : Setting options for PyPlot
- Scatterplots Coding Demonstration : Displaying the visualization
- Module 3 Resources (DOWNLOAD RELEVANT CODE AND/OR DATA FILES FOR THIS MODULE HERE)
- Homework 4 - Instructions
- Bar charts and plotting pivot tables
- For reference: Seaborn
- Homework 5 - Instructions
- Quiz 5 - Summarizing Data
- Quiz 6 - Visualizing Data
Summary of User Reviews
Discover the power of data analysis with Python! This course received high praise from learners who found it informative and engaging. One standout aspect of the course is its relevance to real-world applications of data analysis.Pros from User Reviews
- Informative and engaging content
- Relevance to real-world applications
- Good quality supplementary materials
- Excellent instruction and engaging lectures
- Helpful assignments and quizzes
Cons from User Reviews
- Some parts of the course are challenging
- Lack of personal interaction with instructors
- Course may move too quickly for beginners
- A few technical issues reported
- Some learners would have preferred more advanced topics
Brandon Krakowsky
4.5 Raiting
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