- "Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine)."
Basic overview of what is data science, its history, and related fields.
Statistics: The science of collecting, analyzing, and interpreting data. Understanding statistical concepts such as probability, hypothesis testing, and regression analysis is crucial in data science.
Programming: Proficiency in a programming language such as Python or R is essential. Topics such as data structures, algorithms, control flow, and error handling are important for data manipulation and analysis.
Data Mining: Uses machine learning and statistical techniques to extract valuable information from data sets. Topics include data preprocessing, clustering, classification, and association rule mining.
Data Cleansing: The process of detecting and correcting or removing corrupt or inaccurate records from a record set. Topics include outlier detection, missing value imputation, and data normalization.
Data Visualization: The graphical representation of data and information. Topics include chart selection, visualization design principles, and interactive dashboards.
Big Data: The management and analysis of large, complex data sets. Topics include distributed computing, map-reduce, and cloud computing.
Natural Language Processing (NLP): The application of computational techniques to analyze and generate human languages. NLP can be used to analyze social media data sentiment, customer feedback, and legal documents.
Web Scraping: The process of automatically extracting information from websites. Topics include HTML parsing, web requests, and data extraction.
Time Series Analysis: A statistical approach to analyzing data with time-dependent data points. Topics include trend analysis, seasonality, and forecasting.
Feature Engineering: The process of selecting and extracting relevant features from data that can be used to train a model. Topics include feature scaling, feature selection, and feature extraction.
Statistical Introduction to Data Science: This type of introduction to data science focuses on statistical techniques used in data science for statistical analysis, interpretation of data, and making predictions.
Programming Introduction to Data Science: This type of introduction to data science focuses on programming languages and their use in data science. It covers programming tools, data mining techniques, data analysis, and visualization.
Machine Learning Introduction to Data Science: This type of introduction to data science focuses on the application of machine learning algorithms and techniques to solve real-world problems. It covers supervised and unsupervised learning, regression, clustering, classification, and deep learning.
Database Introduction to Data Science: This type of introduction to data science focuses on databases and their importance in data science. It covers SQL, NoSQL, and other related concepts.
Data Visualization Introduction to Data Science: This type of introduction to data science focuses on data visualization techniques and tools. It covers various visualization approaches such as graphs, charts, tables, and dashboards.
Big Data Introduction to Data Science: This type of introduction to data science focuses on big data and its challenges. It covers distributed computing, data management, data processing, and data storage.
Web Scraping Introduction to Data Science: This type of introduction to data science focuses on web scraping and data extraction techniques from websites. It covers web crawling, data parsing, and data cleaning.
Natural Language Processing Introduction to Data Science: This type of introduction to data science focuses on natural language processing (NLP) techniques used to analyze text data. It covers text classification, sentiment analysis, information extraction, and machine translation.
Data Mining Introduction to Data Science: This type of introduction to data science focuses on data mining techniques used to extract useful information from large datasets. It covers association rule mining, cluster analysis, decision trees, and frequent pattern mining.
Data Ethics Introduction to Data Science: This type of introduction to data science focuses on ethical concerns associated with data science. It covers privacy, security, bias, and fairness in data science.
- "Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession."
- "Data science is a 'concept to unify statistics, data analysis, informatics, and their related methods' to 'understand and analyze actual phenomena' with data."
- "It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge."
- "However, data science is different from computer science and information science."
- "Turing Award winner Jim Gray imagined data science as a 'fourth paradigm' of science (empirical, theoretical, computational, and now data-driven)."
- "Everything about science is changing because of the impact of information technology."
- "A data scientist is a professional who creates programming code and combines it with statistical knowledge to create insights from data."
- "Data science uses statistics, scientific computing, scientific methods, processes, algorithms, and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data."
- "Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession... from noisy, structured, and unstructured data."
- "Data science is a 'concept to unify statistics, data analysis, informatics, and their related methods' to 'understand and analyze actual phenomena' with data."
- "Data science also integrates domain knowledge from the underlying application domain."
- "Everything about science is changing because of the impact of information technology" and the data deluge.
- "Data science is a 'concept to unify statistics, data analysis, informatics, and their related methods' to 'understand and analyze actual phenomena' with data."
- "A data scientist is a professional who creates programming code and combines it with statistical knowledge to create insights from data."
- "It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge."
- "Jim Gray imagined data science as a 'fourth paradigm' of science."
- "Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine)."
- "Data science uses statistics, scientific computing, scientific methods, processes, algorithms, and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data."
- "Everything about science is changing because of the impact of information technology."