Introduction to Data_Science and fundamentals
- 1. Data Science Data science is a collection of techniques used to extract value from data. Data science is also commonly referred to as knowledge discovery, machine learning, predictive analytics, and data mining. An essential tool for any organization that collects, stores, and processes data as part of its operations. Finds useful patterns, connections, and relationships within data. Multidisciplinary approach that combines principles and practices from Mathematics, Statistics, AI and Machine learning to analyze large amounts of data.
- 2. AI, MACHINE LEARNING, AND DATA SCIENCE AI - Giving machines the ability to mimic human behavior, particularly cognitive functions. ML - subfield of AI that makes the machines learn from their experience (data). Training data - data used to train the machine. Input(Features)+Output(ClassLabels) → RepresentativeModel(MLClassifier ) Data Science - business application of ML + AI + Statistics + Visualization + Mathematics. Interdisciplinary fields that extract value from data. DS relies heavily on ML and sometimes data mining techniques. Ex - Movie/ product recommendation.
- 3. AI, MACHINE LEARNING, AND DATA SCIENCE
- 4. AI, MACHINE LEARNING, AND DATA SCIENCE
- 5. WHAT IS DATA SCIENCE?
- 6. WHAT IS DATA SCIENCE? Data science starts with data, which can range from a simple array of a few numeric observations to a complex matrix of millions of observations with thousands of variables. Data science utilizes certain specialized computational methods in order to discover meaningful and useful structures within a dataset.
- 7. WHAT IS DATA SCIENCE? Data science starts with data, which can range from a simple array of a few numeric observations to a complex matrix of millions of observations with thousands of variables. Data science utilizes certain specialized computational methods in order to discover meaningful and useful structures within a dataset.
- 8. Data Analysis vs data science
- 9. Data Analysis vs data science
- 10. Associated field of data science
- 11. Drill Down The Drill Down OLAP operation allows users to view more detailed data by expanding a particular dimension in a multidimensional cube. For example, a user can drill down into the product dimension to view data for individual products, or a user can expand quarterly sales data into monthly sales figures. This operation is important in data mining as it enables users to explore data at lower levels of detail and gain insights into specific aspects of the data. By drilling down into the data, users can identify trends and patterns that may not be apparent at higher levels of aggregation, allowing for more targeted analysis and decision-making. Operations in OLAP( Online Analytical Processing)
- 12. The Slice OLAP operation allows users to extract data from a multidimensional cube by selecting a single dimension and a specific value for that dimension.
- 13. The Dice OLAP operation allows users to extract data from a multidimensional cube by selecting multiple dimensions and specific values for each selected dimension.
- 14. Pivot The Pivot OLAP operation allows users to rotate the orientation of a multidimensional cube to view the data from a different perspective. For example, if a user wants to analyze sales data by product category and sales channel, they can pivot the cube to view the sales data by sales channel and product category. This operation is important in data mining as it enables users to view the same data from different angles and gain new insights into the patterns and relationships in the data.
- 17. Data