The key concepts covered in each SDS course are as follows:
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6.431x Probability — The Science of Uncertainty and Data: This course provides an introduction to probabilistic models, including random processes, and the basic elements of statistical inference. It covers the foundations of data science, emphasizing the principles of uncertainty and data analysis.
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14.310x Data Analysis For Social Scientists: This course focuses on the methods used to harness and analyze data in order to answer questions of cultural, social, economic, and policy interest. It covers techniques for data exploration, visualization, and statistical modeling.
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14.310Fx Data Analysis in Social Science: Assessing your Knowledge: This exam-only course tests learners on the concepts and skills they acquired in the content course, 14.310x Data Analysis for Social Scientists. It assesses their understanding and application of data analysis methods in social science research.
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18.6501x Fundamentals of Statistics: This course aims to develop a deep understanding of the principles underlying statistical inference. It covers topics such as estimation, hypothesis testing, and prediction. Learners gain the necessary statistical foundations to analyze and interpret data.
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6.86x Machine Learning with Python: This course offers an in-depth introduction to the field of machine learning. It covers various machine learning algorithms, from linear models to deep learning and reinforcement learning. Learners gain hands-on experience through Python projects.
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6.419x Data Analysis: Statistical Modeling and Computation in Applications: This hands-on data analysis course explores the interplay between statistics and computation in the analysis of real-world data. Learners work on practical projects that involve statistical modeling and computation techniques.
Each course in the SDS program covers specific concepts and skills related to probability, data analysis, statistics, machine learning, and their applications. Together, these courses provide a comprehensive foundation in statistics and data science.
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