Course Description: This is a course in the application of Python and R for business analysis purpose. The goal of this course is to give you a working knowledge of how to use to extract knowledge and information from data. General topics covered include, but are not limited to: basic programming syntax; web scraping; relational database; data analysis; text mining; machine learning; neural network, and artificial intelligence.

Learning Objectives: Upon completion of this course, students should be able to:

1. Familiarize with the integrated development environment (IDE) of Python and R.
2. Understand the basic programming concepts and logic.
3. Illustrate the process of loading and storing data from/to files (csv, html, json, xml) and employ relational databases (mysql) for data management.
4. Define and explain the process of standardization in data cleaning to reduce redundancy and anomalies.
5. Apply statistical packages for data analysis and data visualization.

Course Description: This is a course in the application of Python and R for business analysis purpose. The goal of this course is to give you a working knowledge of how to use to extract knowledge and information from data. General topics covered include, but are not limited to: basic programming syntax; web scraping; relational database; data analysis; text mining; machine learning; neural network, and artificial intelligence.

Learning Objectives: Upon completion of this course, students should be able to:

1. Familiarize with the integrated development environment (IDE) of Python and R.
2. Understand the basic programming concepts and logic.
3. Illustrate the process of loading and storing data from/to files (csv, html, json, xml) and employ relational databases (mysql) for data management.
4. Define and explain the process of standardization in data cleaning to reduce redundancy and anomalies.
5. Apply statistical packages for data analysis and data visualization.

Course Description: This is a course in the application of Python and R for business analysis purpose. The goal of this course is to give you a working knowledge of how to use to extract knowledge and information from data. General topics covered include, but are not limited to: basic programming syntax; web scraping; relational database; data analysis; text mining; machine learning; neural network, and artificial intelligence.

Learning Objectives: Upon completion of this course, students should be able to:

1. Familiarize with the integrated development environment (IDE) of Python and R.
2. Understand the basic programming concepts and logic.
3. Illustrate the process of loading and storing data from/to files (csv, html, json, xml) and employ relational databases (mysql) for data management.
4. Define and explain the process of standardization in data cleaning to reduce redundancy and anomalies.
5. Apply statistical packages for data analysis and data visualization.

Course Description: This is a course in the application of Python and R for business analysis purpose. The goal of this course is to give you a working knowledge of how to use to extract knowledge and information from data. General topics covered include, but are not limited to: basic programming syntax; web scraping; relational database; data analysis; text mining; machine learning; neural network, and artificial intelligence.

Learning Objectives: Upon completion of this course, students should be able to:

1. Familiarize with the integrated development environment (IDE) of Python and R.
2. Understand the basic programming concepts and logic.
3. Illustrate the process of loading and storing data from/to files (csv, html, json, xml) and employ relational databases (mysql) for data management.
4. Define and explain the process of standardization in data cleaning to reduce redundancy and anomalies.
5. Apply statistical packages for data analysis and data visualization.

Course Description: This is a course in the application of Python and R for business analysis purpose. The goal of this course is to give you a working knowledge of how to use to extract knowledge and information from data. General topics covered include, but are not limited to: basic programming syntax; web scraping; relational database; data analysis; text mining; machine learning; neural network, and artificial intelligence.

Learning Objectives: Upon completion of this course, students should be able to:

1. Familiarize with the integrated development environment (IDE) of Python and R.
2. Understand the basic programming concepts and logic.
3. Illustrate the process of loading and storing data from/to files (csv, html, json, xml) and employ relational databases (mysql) for data management.
4. Define and explain the process of standardization in data cleaning to reduce redundancy and anomalies.
5. Apply statistical packages for data analysis and data visualization.

Course Description: This is a course in the application of Python and R for business analysis purpose. The goal of this course is to give you a working knowledge of how to use to extract knowledge and information from data. General topics covered include, but are not limited to: basic programming syntax; web scraping; relational database; data analysis; text mining; machine learning; neural network, and artificial intelligence.

Learning Objectives: Upon completion of this course, students should be able to:

1. Familiarize with the integrated development environment (IDE) of Python and R.
2. Understand the basic programming concepts and logic.
3. Illustrate the process of loading and storing data from/to files (csv, html, json, xml) and employ relational databases (mysql) for data management.
4. Define and explain the process of standardization in data cleaning to reduce redundancy and anomalies.
5. Apply statistical packages for data analysis and data visualization.

Course Description: This is a course in the application of Python and R for business analysis purpose. The goal of this course is to give you a working knowledge of how to use to extract knowledge and information from data. General topics covered include, but are not limited to: basic programming syntax; web scraping; relational database; data analysis; text mining; machine learning; neural network, and artificial intelligence.

Learning Objectives: Upon completion of this course, students should be able to:

1. Familiarize with the integrated development environment (IDE) of Python and R.
2. Understand the basic programming concepts and logic.
3. Illustrate the process of loading and storing data from/to files (csv, html, json, xml) and employ relational databases (mysql) for data management.
4. Define and explain the process of standardization in data cleaning to reduce redundancy and anomalies.
5. Apply statistical packages for data analysis and data visualization.