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Data Analysis Using Python: Practical Skills and Projects
Python for Data Analysis: Mastering Essential Techniques - Learn Pandas, NumPy, Matplotlib, and More
This Course Includes
udemy
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6 total hours 
english
Online - Self Paced
course
Udemy
About Data Analysis Using Python: Practical Skills and Projects
Course Description:
This course provides a comprehensive introduction to data analysis using Python. It covers essential libraries like NumPy and pandas, and explores data visualization with Plotly. The course culminates in an end-to-end project analyzing the Google Playstore dataset, providing practical experience in applying the learned techniques to real-world data.
Modules and Key Features:
Module 1: Introduction to Data Analysis with Python
- Overview of data analysis and its importance
- Introduction to the concept and significance of data analysis in various fields.
- Understanding the benefits of data-driven decision making.
- Introduction to Python for data analysis course content
Module 2: Introduction to NumPy
- Introduction to NumPy arrays
- Understanding the structure and benefits of NumPy arrays over Python lists.
- Creat ing NumPy arrays from lists and using built-in functions.
- Basic array operations (creation, manipulation)
- Performing fundamental array operations such as reshaping, slicing, and element-wise operations.
- Array indexing and slicing
- Mastering techniques to access and modify specific parts of an array.
- Universal functions (ufuncs)
- Utilizing universal functions for element-wise operations and mathematical computations.
- Array broadcasting
- Understanding the concept of broadcasting and how it enables operations on arrays of different shapes.
- Mathematical functions
- Using built-in mathematical functions to perform calculations on arrays.
Module 3: Introduction to pandas
- Introduction to pandas Series and DataFrame
- Learning the fundamentals of Series and DataFrame, the core data structures in pandas.
- Reading and writing data with pandas (CSV, Excel, SQL)
- Techniques to read data from and write data to different file formats.
- Data manipulation and cleaning techniques
- Methods for cleaning and preparing data, including handling missing values and duplicates.
- Indexing, selection, and filtering data
- Techniques for selecting and filtering data in DataFrames using various methods.
- Handling missing data
- Approaches to detect, fill, and drop missing data to maintain data quality.
- Grouping and aggregating data
- Using groupby operations to aggregate and summarize data.
Module 4: Data Visualization with Plotly
- Introduction to Plotly and its architecture
- Overview of Plotly for interactive data visualization and its underlying architecture.
- Basic plots (line plots, scatter plots, bar plots) with Plotly Graph Objects
- Creating basic plots to visualize data using Plotly Graph Objects.
- Customizing plots (labels, titles, colors, styles) using Plotly
- Techniques for customizing plots to enhance readability and aesthetics.
- Subplots and multiple axes
- Creating complex visualizations with subplots and multiple axes.
Module 5: End-to-End Data Analysis Project – Google Playstore Analysis
- Installs Analysis
- Determining the most installed category of apps.
- Identifying the top 5 apps in the top 5 installed categories.
- Rating Analysis
- Determining the most rated category of apps.
- Identifying the top 5 apps in the top 5 rated categories.
- Free vs. Paid Apps Analysis
- Analyzing the distribution of paid and free apps in each category.
- Comparing the average ratings of paid vs. free apps.
- Price Analysis:
- Distribution of average price in each category.
Project Title: Google Playstore Dataset Analysis
Project Description:
Analyze the Google Playstore dataset to derive meaningful insights. The project will involve data preprocessing, analysis, and visualization to understand trends and patterns in app installations and ratings.
Key Features:
a. Reading and preprocessing the Google Playstore dataset using pandas.
b. Analyzing the most installed and rated categories of apps.
c. Identifying the top 5 apps in the top 5 installed and rated categories.
d. Visualizing the distribution of paid and free apps in each category.
e. Comparing the average ratings of paid and free apps.
f.Visualizing average price distribution in each category.
g. Utilizing Plotly for interactive data visualizations.
Who Should Enroll:
- Aspiring Data Analysts and Data Scientists
- Professionals looking to enhance their data analysis skills
- Individuals interested in leveraging Python for data-driven decision making
Prerequisites:
- Basic understanding of Python programming
- Familiarity with fundamental programming concepts
What You'll Gain:
- A solid foundation in data analysis using Python
- Practical experience with essential Python libraries: NumPy, pandas, and Plotly
- Ability to clean, manipulate, and visualize data effectively
- Hands-on experience with an end-to-end data analysis project
- Skills to derive actionable insights from real-world datasets
What You Will Learn?
- A solid foundation in data analysis using Python.
- Practical experience with essential Python libraries: NumPy, pandas, and Plotly.
- Ability to clean, manipulate, and visualize data effectively.
- Hands-on experience with an end-to-end data analysis project.
- Skills to derive actionable insights from real-world datasets.