Views: 788 Author: Site Editor Publish Time: 2023-02-20 Origin: Site
Python data analysis and office applications
1. Data analysis and office applications
| Python basics | content | class |
| Python basic syntax | ▫ Get to know Python ▫ The first Python program ▫ Comments ▫ Identifiers and keywords ▫ Input and output ▫ Operator ▫ Data type conversion ▫ PEP8 encoding specification | 1 |
| if judgment statement | ▫ Comparison relational operators ▫ if statement syntax format ▫ if case-guessing game ▫ Ternary operator | 1 |
| while loop statement | ▫ while statement syntax format ▫ while application case ▫ while loop nesting ▫ break and continue ▫ while loop case | 1 |
| Python commonly used containers | ▫ string ▫ List ▫ Tuple ▫ Dictionary ▫ Ordered dictionary (OrderDict) ▫ Public operations | 1 |
| Function (1) | ▫ Function concepts and functions ▫ Function definition and call ▫ Function parameters (1) ▫ Return value of function (1) ▫ Function documentation ▫ Nested calls of functions ▫ Function application: printing graphics and mathematical calculations | 1 |
| Function (2) | ▫ Local variables ▫ Global variables ▫ Issues to note when executing multi-function programs ▫ Return value of function (2) ▫ Function parameters (2) ▫ Packing and unpacking ▫ Quote ▫ Mutable and immutable types | 1 |
| Function (3) | ▫ Recursive function ▫ Anonymous function ▫ Higher-order functions | 1 |
| File reading and writing | ▫ File and directory operations related operations ▫ Opening and closing files ▫ Reading and writing files | 1 |
| Object-oriented (1) | ▫ Class and object concepts ▫ Class definition ▫ Create objects ▫ Add and get object properties ▫ self parameter ▫ _init_ method ▫ _str_method ▫ Private properties and private methods ▫ Object-oriented case | 1 |
| Object-oriented (2) | ▫ The concept of inheritance ▫ The subclass and parent class methods have the same name ▫ The subclass calls the method with the same name of the parent class ▫ Multiple inheritance ▫ Polymorphism ▫ Class attributes and instance attributes ▫ Class methods and static methods | 1 |
| abnormal | ▫ Abnormal concept ▫ Catch exceptions ▫ Exception delivery ▫ Custom exceptions | 1 |
| module | ▫ Module introduction ▫ Import of modules ▫ _all_ in the module ▫ The role of _name_ in the module | 1 |
| 包 | ▫ The concept of bag ▫ Package import | 1 |
| Python-based project development | ▫ Complete some Python development practices | 1 |
| data analysis | content | class |
| data analysis | ▫ What is data analysis and its importance ▫ Application areas of data analysis ▫ Basic concepts of data analysis: data, variables, observations, etc. ▫ Basic steps of data analysis ▫ Common tools and techniques in data analysis | 1 |
| Probabilistic Basics in Data Analysis | ▫ Overview of the basics of probability ▫ Random variables and probability distributions ▫ Common probability distributions: normal distribution, binomial distribution, Poisson distribution loose distribution etc. ▫ Statistical properties of probability ▫ Basic principles of hypothesis testing and confidence intervals | 1 |
| Use of Anaconda | ▫ Introduction and installation of Anaconda ▫ Anaconda Environmental Management ▫ Use Anaconda’s Jupyter Notebook to line data analysis ▫ Installation of commonly used Python libraries and packages in Anaconda Installation and management | 1 |
| Data preprocessing practice | ▫ Data cleaning: dealing with missing values, outliers, etc. ▫ Data transformation: feature engineering, data standardization, encoding, etc. ▫ Data sampling and segmentation ▫ Data merging and joining | 1 |
| Data visualization | ▫ The importance of data visualization ▫ Commonly used data visualization tools and libraries ▫ Basic visualization charts: scatter plots, histograms, box plots, etc. ▫ Principles and best practices of data visualization | 1 |
| Data Analysis Practice | ▫ Principles of Exploratory Data Analysis (EDA) ▫ Data modeling and forecasting ▫ Decision support in data analysis ▫ Data Analysis Case Studies ▫ Business data analysis case practice in different industries | 1 |
2. Efficient automated office based on Python and AIGC
| serial number | Based on Python and AIGC Efficient automated office | content | class (10) |
| 1. Automated processing PDF file | Batch process PDF files | ▫ Merge PDF files in batches ▫ Split PDF files in batches ▫ Encrypt PDF files in batches | 1 |
| Batch to PDF files add watermark | ▫ Custom function to create watermark file ▫ Custom function to add watermark ▫ Add watermark to each PDF file using a loop | ||
| Extract PDF documents in batches Table and write to Excel | ▫ Extract text, tables and images from PDF documents ▫ Merge and split PDF documents ▫ Mutual conversion between different document formats | ||
| 2. Automated processing Word document | Generate Word documents in batches | ▫ Read files and perform find and replace ▫ Generate contracts using circular templates | 1 |
| Batch process Word documents | ▫ Batch convert Word documents to PDF files ▫ Mark keywords in batches in Word documents ▫ Batch replace keywords in Word documents | ||
| Using Python—docx library Manipulate Word documents | ▫ Python—docx common objects ▫ Python—docx common methods | ||
| Text with Python Automated processing | ▫ Automated processing of setting headers and footers ▫ Use paragraph-specific style attributes ▫ Batch replace cell data in workbooks | ||
| Building a business using Python Monthly Operation Report Word Version | ▫ Organize and clean store sales data ▫ Visual analysis of store operation data ▫ Visual analysis of regional sales data ▫ Visual analysis of customer purchase data ▫ Prepare store operation analysis report | ||
| Building a business using Python Other data analysis reports | ▫ Visual analysis of regional sales data ▫ Visual analysis of customer purchase data ▫ Prepare regional sales analysis report ▫ Prepare customer consumption analysis report | ||
| File format conversion | ▫ Batch convert .doc files to .docx files ▫ Batch statistics on the number of pages and words in documents ▫ Batch convert Word files to PDF files | ||
| 3. Automated processing Excel workbook | Python for data processing | ▫ Handling of duplicate values, missing values and outliers ▫ Data deletion, sorting and indexing ▫ Batch replace cell data in workbooks | 1 |
| Generate Excel workbooks in batches | ▫ Objects in the xlwings library and their hierarchical relationships ▫ Add, delete, modify and check Excel files ▫ Pandas library operations on CSV files and Excel files | ||
| Openpyxl and Pandas usage of | ▫ Manipulate large workbooks ▫ Use Openpyxl to read and import workbook data ▫ Use Pandas to filter, split and describe Excel data | ||
| Generate Excel workbooks in batches | ▫ Extract data from the shipping statistics table ▫ Use the for statement to create a product shipping list ▫ Batch replace cell data in workbooks | ||
| Merge multiple worksheets | ▫ Use the xlwings module to read data from multiple worksheets ▫ Create a new workbook to store the merged data ▫ Split a worksheet into multiple workbooks ▫ Split column data in batches | ||
| 4. Automated processing Power Point presentation | Python operates PPT files Common applications | ▫ Common operations and objects of Python-pptx ▫ Extract tables in PPT in batches and write them to Excel ▫ Read pictures in PPT presentations ▫ Add text, paragraphs, tables and adjust styles to PPT presentations | 1 |
| Batch Process Power Points presentation | ▫ Extract text content in presentations in batches ▫ Batch export presentations as images and PDF files ▫ Automatically read graphic and text materials to create presentations ▫ Extract picture materials in presentations in batches | ||
| 5. Automated processing | Automatically send emails in batches | ▫ Obtain SMTP authorization code ▫ Automatically send emails in text format ▫ Automatically send emails in HTML format ▫ Automatically send emails with attachments | 1 |
| Common email protocols and remote control | ▫ POP3, SMTP and IMAP ▫ Introduction to using the smtplib module and automatically sending and receiving customized emails ▫ Use imapclient and pyzmail libraries to automatically receive emails ▫ Use Python to remotely control computers | ||
| Python automation Office expansion applications | ▫ Introduction to other commonly used open source modules in Python ▫ Use PySimpleGul to add a graphical user interface to Python programs ▫ Introduction and use of Pylnstaller ▫ Package Python program into executable file | ||
| 6. Automated office Comprehensive Practice | Comprehensive Practice of HR Automated Office | ▫ Use Python to send salary slips or payment orders in batches ▫ Use Python to batch screen job resumes | 1 |
| Comprehensive Practice of Financial Automation Office | ▫ Use Python to intelligently identify financial invoice information ▫ Use Python to extract contract data in batches | ||
| Comprehensive Practice of Administrative Automation Office | ▫ Use Python to batch produce electronic business cards ▫ Use Python to create DingTalk and Feishu robots to automatically send text and information ▫ Implement automated operation of enterprise WeChat robots based on Python ▫ Use Python to automatically send e-commerce member emails | ||
| 7. AIGC is highly efficient Office applications | AIGC Technical Overview | ▫ Basic concepts of artificial intelligence generated content (AIGC) ▫ Commonly used AIGC tool experience ▫ Text generators, image creation tools, and automated data analysis systems ▫ Automate content creation and workflow optimization | 1 |
| Application of AIGC in efficient office | ▫ Use AIGC to simplify daily tasks and improve efficiency ▫ How to choose the appropriate AIGC tool according to different work needs ▫ Application of AIGC technology in creative and design work ▫ Data security and privacy protection during use | ||
| 8. AIGC has different Industry practice cases | Administrative field | ▫ Automated document processing: Use AIGC to generate and edit official documents, reports and submission materials ▫ Meeting management: Use AIGC to record meetings and automatically generate meeting minutes and action items ▫ Schedule optimization: Use AI-assisted tools for efficient schedule planning and management | 1 |
| Procurement area | ▫ Market analysis: Use AIGC for market trend analysis and price prediction ▫ Supplier evaluation: Using AI tools for supplier evaluation and selection ▫ Contract management: Application of AIGC technology in contract drafting and review | ||
| Operational areas | ▫ Process optimization: Use AIGC to analyze and optimize operating processes ▫ Inventory management: Using AI for inventory forecasting and automatic replenishment ▫ Customer Service: Provide instant customer support using AI-powered chatbots | ||
| Engineering field | ▫ Design assistance: Use AIGC tools for engineering design and simulation ▫ Project management: Use AI for project progress prediction and resource optimization ▫ Maintenance and fault prediction: Use AI for equipment maintenance planning and fault prediction | ||
| The financial sector | ▫ Market analysis and forecasting: Use AIGC tools to conduct trend analysis and forecasting of financial markets ▫ Risk Management: Using AI for credit risk assessment, fraud detection and compliance monitoring ▫ Personalized financial advice: Provide customized investment advice and financial planning services through AI ▫ Automated report generation: Use AIGC technology to automatically generate reports and market analysis ▫ Customer service optimization: Deploy AI-driven chatbots and customer service tools |
course features
Multi-field coverage: Covers multiple fields such as Python programming, data analysis, artificial intelligence, automated office, algorithms and data structures, providing students with comprehensive and practical skills.
Cutting-edge technology learning: Focus on cutting-edge fields such as artificial intelligence, virtual character design, and AI generation technology to cultivate students' innovative abilities in the digital age.
Improvement of practical skills: Through actual project practice, case analysis, etc., the emphasis is on practical operation and problem solving, so that students can skillfully apply the knowledge they have learned.
Cultivation of problem-solving thinking: Focus on the teaching of algorithms and data structures, cultivate students' thinking methods of analyzing and solving problems, and improve programming abilities.
Comprehensive ability improvement: The knowledge system learned is interdisciplinary, so that students have the ability to comprehensively and comprehensively respond to complex technical and business challenges.
