Postgraduate Certificate in Data Mining for Logistics Management
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4 Postgraduate Certificate in Data Mining for Logistics Management
3 intake season: [1]
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Course details
• OPERA 2: Hello to Python Programming for Data Analysis - This unit covers the basics of Python programming and data analysis, including data types, data structures, and data manipulation techniques.
• SI4: Introduction to Data Mining and its Applications - This unit provides an overview of data mining, including its applications in logistics management, and introduces students to the various databases and tools used in data mining.
• SI3: Trends in Data Mining and Competing Technologies - This unit covers the current trends in data mining and its competing technologies, such as machine learning and data warehousing.
4. INS3: Data Mining with Python - This unit covers the application of Python programming for data mining, including data wrangling, visualization, and predictive modeling techniques.
4. M3: Data Mining with R - This unit covers the application of R programming for data mining, including data processing, analysis, and visualization techniques.
5. M2: Data Mining with Excel - This unit covers the application of Excel for data mining, including data processing, analysis, and visualization techniques.
4. M4: Data Mining with SQL - This unit covers the application of SQL for data mining, including data processing, analysis, and visualization techniques.
4. del 3: Data Mining with Database Management - This unit covers the use of databases for data mining, including data warehouses, Relational databases, and data mart.
3. M4: Data Mining with R Machine Learning - This unit covers the application of machine learning for data mining, including data mining and machine learning techniques.
4 del3: Data Mining with Machine Learning - This unit covers the application of machine learning for data mining, including data mining and machine learning techniques.
4. M4: Data Mining with Data Visualization - This unit covers the application of data visualization for data mining, including data visualization techniques.
4. M4: Data Mining with SQL - This unit covers the application of SQL for data mining, including data processing, analysis, and visualization techniques.
3. M3: Data Mining with Python - this unit covers the application of Python programming for data mining, including data wrangling, visualization, and predictive modeling techniques.
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