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[Big Data Technology] 1.Python Data Analysis Application
#ICT# #IOT#

Lesson Code: TCEN2026I020

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Academic Hours
4.10hours
Publish Date
Aug 2026

Lecturer

1. Lecturer ZHOU JieGuangxi Polytechnic University

General Introduction
This course focuses on developing students’ ability to process and analyze data using Python. It introduces the fundamental approaches and methods of data processing and analysis, emphasizing practical competencies in data preprocessing,
statistical analysis, and data visualization for large-scale datasets. The course aims to lay a solid foundation for students to pursue careers in data analysis and related fields.

This Course is for
1. Helping trainees master basic Python syntax and the core methods of numerical computation with NumPy, statistical analysis with pandas, and data preprocessing.
2. Equipping trainees to use tools such as Matplotlib and Seaborn to perform data visualization.
3. Enabling trainees to develop practical skills in data cleaning, data aggregation and group operations, and time series analysis.
4. Laying the necessary foundation for trainees to pursue advanced integrated courses in Big Data Technology and careers in data analysis.

Learning Materials

1. Corresponding PPT
2. Online Course Video
3. Simulation Question Bank

Recognized By

Benefits of Learning

1. Understanding basic Python syntax and being able to define variables, use operators, and apply commonly used statement structures proficiently.
2. Being able to use NumPy proficiently for array creation, numerical computation, and universal function operations.
3. Understanding pandas data structures and being able to perform data preprocessing operations, including data reading, cleaning, merging, and reshaping.
4. Being able to perform data aggregation and group operations and use methods such as groupby for statistical analysis.
5. Being able to use tools such as Matplotlib and Seaborn for data visualization and create commonly used charts.
6. Understanding the basic operations of time series analysis and being able to apply acquired knowledge comprehensively to solve practical data-related problems.

Courses Videos
01 Getting to Know the NumPy Array Object.mp4play02 Creating NumPy Arrays.mp4play03 Data Types of Ndarray Objects.mp4play04 Array Operations.mp4play05 Basic Usage of Ndarray Indexing and Slicing.mp4play06 Ndarray Indexing and Slicing-Basic Usage of Fancy Indexing.mp4play07 Array Transposition and Axis Symmetry.mp4play08 Data Processing Using NumPy Arrays.mp4play09 Array Statistical Operations.mp4play10 Array Sorting.mp4play11 Retrieving Array Elements.mp4play12 Linear Algebra Module.mp4play13 Random Module.mp4play14 Spacewalk Case.mp4play15 Pandas Data Structure Analysis Series.mp4play16 Pandas Data Structure Analysis DataFrame.mp4play17 Pandas Indexing Operations and Advanced Indexing-Index Object.mp4play18 Pandas Indexing Operations and Advanced Indexing Resetting the Index.mp4play19 Indexing Operations.mp4play20 Arithmetic Operations and Data Alignment.mp4play21 Statistical Computation and Statistical Description.mp4play22 Understanding Hierarchical Indexing.mp4play23 Hierarchical Indexing Operations.mp4play24 Reading and Writing Excel Files.mp4play25 Reading and Writing CSV Files.mp4play26 Reading HTML Table Data.mp4play27 Pandas Read and Write Operations for Databases.mp4play28 Exam Case.mp4play29 Handling Null Values, Missing Values, and Duplicate Values.mp4play30 Outlier Handling.mp4play31 Changing Data Types and Stacking Data Axially.mp4play32 Merging Data by Primary Key.mp4play33 Combining Data by Row Index and Merging Overlapping Data.mp4play34 Splitting Data into Groups Using the groupby() Method.mp4play35 Aggregating Data Using Built-in Statistical Methods.mp4play36 Data Transformation and Application.mp4play37 Data Visualization Overview and Basic Usage of Matplotlib.mp4play38 Creating Subplots.mp4play39 Adding Subplots and Labels.mp4play40 Plotting and Saving Figures.mp4play

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