Course Description

Data Science is a rapidly growing field that has gained significant importance in recent years. It involves the analysis of large amounts of data using advanced statistical and computational techniques to extract meaningful insights and make informed decisions. Data Science Fundamentals is an introductory course that provides a comprehensive overview of the key concepts and techniques used in this field. The course covers a wide range of topics, including statistical inference, probability theory, data visualization, machine learning, and big data technologies. Students will gain hands-on experience with popular data analysis tools such as Python and R, as well as learn how to use libraries and frameworks like NumPy, Pandas, and Scikit-learn. They will also develop the skills needed to clean, manipulate, and explore data, as well as apply various statistical techniques to derive meaningful insights. The course begins with an introduction to data science and its applications in different industries. Students will learn about the various types of data, data collection methods, and data pre-processing techniques. They will also explore different data visualization techniques and understand how to create effective visualizations to communicate insights to different stakeholders. Next, the course covers the basics of probability theory and statistical inference, which are essential for making data-driven decisions. Students will learn about probability distributions, hypothesis testing, and regression analysis. They will also understand how to use statistical models to make predictions and estimate uncertainties. The course then moves on to machine learning, which is a critical aspect of data science. Students will learn about supervised and unsupervised learning, as well as different algorithms used for classification, regression, and clustering. They will also gain hands-on experience with popular machine learning libraries and frameworks such as Scikit-learn and TensorFlow. Finally, the course covers big data technologies such as Hadoop and Spark, which are used to process and analyze massive amounts of data. Students will learn about the architecture of these systems and understand how to write MapReduce programs to analyze large datasets. Throughout the course, students will work on real-world data science projects and apply the techniques learned in class to solve practical problems. By the end of the course, students will have gained a solid understanding of the fundamentals of data science and be equipped with the skills needed to pursue more advanced courses in this field. Author: