As the World Wide Web and social media platforms continue expanding, they generate and make accessible enormous volumes of data requiring meaningful analysis. To apply this information effectively across diverse fields, including business, scientific research, and social science, the data must be thoroughly analyzed. This comprehensive course teaches you to apply your Python programming expertise to solve complex data analysis challenges. You will master Pandas for powerful data analysis, Seaborn for creating professional data visualizations, and JupyterLab as your integrated development environment. Additionally, you will develop competency in acquiring, cleaning, preparing, and analyzing data, including time-series information, and you will learn to apply linear regression models for predicting unknown values and forecasting future trends.
Ideal Audience:
Professionals with previous experience in Python programming who want to expand their technical skills into data analysis.
Course Prerequisites:
You should have basic Python programming experience and be comfortable working with strings, lists, tuples, dictionaries, loops, conditional statements, and writing your own functions.
Course Content and Topics
Introduction to Python for Data Analysis
- Defining data analysis and its applications
- Identifying Python skills essential for effective data analysis
- Using JupyterLab as your primary integrated development environment
- Working with split-screen notebook displays for comparison and reference
- Leveraging magic commands to enhance productivity
Pandas Essentials for Data Analysis
- Introduction to the Pandas DataFrame and its structure
- Examining and understanding your data
- Accessing data through columns and rows
- Working effectively with data
- Reshaping data for analysis
- Analyzing data to uncover patterns and insights
Pandas Essentials for Data Visualization
- Foundational concepts in data visualization
- Creating eight different types of plots
- Enhancing plots with styling and annotation
Seaborn Essentials for Data Visualization
- Introduction to Seaborn and its capabilities
- Enhancing and saving your visualizations
- Creating relational plots showing relationships between variables
- Creating categorical plots for categorical data analysis
- Creating distribution plots for understanding data patterns
- Additional visualization techniques and enhancement methods
Acquiring Data
- Finding and locating the data you need to analyze
- Importing data into a Pandas DataFrame
- Accessing data from databases and transforming into DataFrames
- Working with Stata statistical files
- Working with JSON formatted data
Data Cleaning and Preparation
- Understanding data cleaning fundamentals and importance
- Simplifying and reducing data complexity
- Finding and correcting missing values systematically
- Fixing data type issues and inconsistencies
- Identifying and addressing outliers and anomalies
Advanced Data Preparation
- Adding and modifying columns
- Applying functions and lambda expressions to data
- Working with and manipulating indexes
- Combining multiple DataFrames
- Handling the SettingWithCopyWarning appropriately
Data Analysis Techniques
- Creating and plotting long-format data
- Grouping and aggregating data for summary analysis
- Creating and utilizing pivot tables
- Using binning for categorical analysis
- Advanced data analysis skills and techniques
Time-Series Data Analysis
- Reindexing time-series data
- Resampling time-series data at different frequencies
- Working with rolling window calculations
- Working with running totals and cumulative calculations
Predictive Analysis with Linear Regression
- Introduction to predictive analysis and its applications
- Finding correlations between variables
- Using Scikit-learn for linear regression modeling
- Plotting regression models with Seaborn visualizations
Multiple Regression Modeling
- Building a simple regression model using sample datasets
- Working with multiple regression models
- Handling categorical variables in regression
- Improving regression model performance and accuracy
