Research Methods in Social Science is an entry-level research method course for subjects in social sciences, such as management and economics. It is a compulsory course for the major of Information Management and Information Systems (Sino-foreign cooperation) offered by Aberdeen Institute of Date Science and Artificial Intelligence, South China Normal University. The course aims to provide students with a systematic introduction to theories and research methods used in social science research.

《社会科学研究方法》是一门针对管理学、经济学等社会科学开设的入门级研究方法课程，是阿伯丁数据科学与人工智能学院为信息管理与信息系统（中外合作办学）专业开设的大类教育必修课程，旨在通过对社会科学研究的基本理论和研究方法进行系统介绍，从基础理论和具体研究方法等层次出发，从定性研究和定量研究两个方面入手，为学生讲授运用于社会研究中的数据收集与分析方法及技术，帮助学生了解与掌握社会调查与研究的科学思维，为学生树立科学的方法认知、较强的分析和解决问题的实践能力给予支持，也为学生撰写毕业（设计）论文提供基础。

This course introduces students to the concept of Information Systems in organizations, and how Information Systems can assist managers in coordinating organizational activities, communicating with internal and external parties and making decisions. Particular emphasis is placed on students developing an appreciation of how new types of information system will be likely to influence and change international business practices.

Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the basics of data analysis with Python to building and evaluating data models. Topics covered include: - collecting and importing data - cleaning, preparing & formatting data - data frame manipulation - summarizing data, - building machine learning regression models - model refinement - creating data pipelines You will learn how to import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations. You will then predict future trends from data by developing linear, multiple, polynomial regression models & pipelines and learn how to evaluate them. In addition to video lectures you will learn and practice using hands-on labs and projects. You will work with several open source Python libraries, including Pandas and Numpy to load, manipulate, analyze, and visualize cool datasets. You will also work with scipy and scikit-learn, to build machine learning models and make predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge.