There exist several data exchange and knowledge representation standards. ![]() The topic teaches (a) classification, (b) clustering, and (c) association rule mining. Mocanu)ĭata mining is about discovering patterns in large data sets involving methods from artificial intelligence, machine learning, statistics, and database systems. The topic teaches (a) data warehousing techniques for extracting and transforming data (ETL), (b) modeling data for analytic purposes using the multidimensional modeling approach of OLAP, and (c) data visualization techniques. They are, however, also effective for data science. The skills for Data Preparation and Data Visualization taught are in essence drawn from technologies developed for Business Intelligence. Topic DPV: Data Preparation and Visualization.Projects come from a variety of domains: health, logistics, business intelligence, transport, security, social media, etc. The list of projects and topics will be revised every year. The project grade is the grade for the course. The project is assessed by the project owner and a topic teacher. Supervision is provided during practical sessions twice per week shared with all topics and projects. The practical and project are done in pairs. The projects indicate which technical topics provide the necessary skills for doing the project, so the choice for project and technical topics should be coherent.Įach topic consists of one lecture and a practical for learning the basic skills. The data science skills are offered as technical topics from which the student has to choose two. A project is composed of a real-world data set and a challenge, i.e., what knowledge can potentially be extracted from the data or what the project owner wants to do with the data. There are several projects offered from which the student can choose. The course is assessed with a project that takes about half of the course. The course concept is geared towards self study in an assignment & project-driven manner, i.e., it is designed to offer a rich environment for flexible, effective, and efficient self study with ample guidance and supervision. The goal of the course Data Science is to teach several data science skills needed in various phases of data analysis projects. The need for data scientists and big data analysts is apparent in almost every aspect of our society, including computer science, medicine, physics, and the humanities. There is an increasing need for data scientists and big data engineers seen in job advertisements. They are the driving force behind the successful innovation of Internet companies like Google, Twitter, and Yahoo. ![]() Data scientists dig for value in data by analyzing for instance texts, application usage logs, and sensory data. Scientific and economic progress is increasingly powered by our capabilities to explore big data sets. Ready to take the next step in your Data Science career? Let's get started!Īn entry-level salary for the technologies covered in this track is about $95,000 / yr on average.Data Science is the emerging interdisciplinary field that lies at the intersection of computer science, statistics, visualization and the social sciences. To wrap up this Track, you'll take our Introduction to Big Data course and then our Machine Learning Basics course. ![]() We'll then cover some best practices for cleaning and preparing data, data visualization, and an introduction to scraping data from the Web. You'll get a basic introduction to NumPy, the fundamental package for scientific computing, and then pandas, which provides fast, flexible, and expressive data structures for your Python data work. Matplotlib provides a way to easily generate a wide variety of plots and charts in a few lines of Python code. Additionally, you'll start creating charts with the Python library matplotlib, an industry standard data visualization library. Next we'll cover how to install and use Anaconda, as well as Jupyter Notebooks, two useful tools for your Python work. You'll establish a firm foundation in Python lists, dictionaries, sequences, tuples, and more. Next, we'll cover some Python topics, as it's the language data scientists use the most. The first course you'll take is Data Analysis Basics, where you'll establish some language and definitions as well as how to think about data. You'll pick up the basic building blocks of how to analyze and communicate data findings. In this track, we'll be exploring the tools and techniques to get you started on your journey. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science. Data science unifies statistics, data analysis, machine learning and their related methods in order to understand and analyze actual phenomena with data.
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