Chapter-03-Introduction-to-Data-Science.pptx.pdf

App Development and Emerging Technologies Chapter 3 : Introduction to Data Science Learning Objective By the end of this lesson, students should have a solid understanding of what Data Science is, why it is important, and how it is applied in various industries. They will also gain a basic a...

App Development and Emerging Technologies Chapter 3 : Introduction to Data Science Learning Objective By the end of this lesson, students should have a solid understanding of what Data Science is, why it is important, and how it is applied in various industries. They will also gain a basic awareness of the tools and technologies used in Data Science. Understand and define the business or research problem. Set goals and formulate a hypothesis that data can test. What is Data Science? Definition: Data Science is an interdisciplinary field that uses scientific methods, algorithms, processes, and systems to extract knowledge and insights from structured and unstructured data. Interdisciplinary Nature: It combines elements from statistics, computer science, mathematics, and domain knowledge to analyze and interpret complex data. Role of a Data Scientist: A Data Scientist is someone who can extract meaningful insights from data, which can be used to make informed decisions. Data Science vs. Traditional Analysis Traditional Data Analysis: Often focuses on describing data, identifying patterns, and making predictions using structured data. Data Science: Goes beyond traditional analysis by handling large volumes of data (big data), using advanced machine learning algorithms, and dealing with unstructured data (e.g., text, images). Unstructured data Unstructured data refers to information that doesn't have a predefined format or organization. Unstructured data is rich in information but requires specialized tools like natural language processing (NLP), machine learning algorithms, or text mining techniques to extract meaningful insights. Text documents (e.g., emails, reports), Audio and video files, Social media posts, Webpages, Images, PDFs Structured data Structured data is organized in a defined, predictable format, making it easier to search, process, and analyze. It typically resides in relational databases or spreadsheets, where information is stored in rows and columns with clear labels (e.g., tables). Key characteristics of structured data: Organized: Schema-based Easily searchable Example sources: Relational databases (like MySQL, PostgreSQL), spreadsheets (Excel, Google Sheets). Big Data Big Data is any data that is expensive to manage and hard to extract value from Volume - The size of the data Velocity - The latency of data processing relative to the growing demand for interactivity Variety and Complexity - the diversity of sources, formats, quality, structures. The Evolution of Data Science Roots in Statistics: Data Science originated from statistical methods used for analyzing and interpreting data. Computer Science Integration: The advent of computers in the mid-20th century enabled large-scale data processing and storage. Data Mining Era: In the 1980s-1990s, data mining emerged to extract patterns and insights from growing datasets. Modern Data Science: The 2000s saw the rise of "Data Science" as a distinct field, blending statistics, computer science, and domain expertise. AI and Big Data: Today, Data Science incorporates advanced AI, machine learning, and big data technologies, driving innovation across industries. Why Data Science? Importance in Various Industries Healthcare: Predictive models for disease outbreaks, personalized medicine. Finance: Fraud detection, risk management, algorithmic trading. E-commerce: Customer behavior analysis, recommendation systems, inventory management. Marketing: Targeted advertising, customer segmentation, sentiment analysis. Search Algorithm using Data Science Google Search Algorithm: How Data Science improves search results. Netflix Recommendation System: How Netflix uses data science to suggest content. Amazon's Product Recommendations: Analyzing customer behavior to improve sales. The Data-Driven Decision Making Process Data as a Strategic Asset: How companies leverage data to gain a competitive edge. Examples of Data-Driven Companies: Google, Facebook, Amazon, and how they use data to drive their business. Brief Evolution of Data Science Early Foundations: Statistics Origins: Data Science has its roots in statistics, which has been used for centuries to collect, analyze, and interpret data. Pioneers like Carl Friedrich Gauss and Sir Francis Galton developed foundational concepts such as the Gaussian distribution and correlation. Applications: Early applications were primarily in fields like economics, astronomy, and social sciences. The Rise of Computer Science Mid-20th Century: The development of computers in the 1940s and 1950s transformed data analysis, allowing for large-scale data storage and processing. Programming Languages: The creation of languages like FORTRAN and COBOL in the 1950s and 1960s enabled more sophisticated data processing techniques. Database Systems: The 1970s saw the development of database management systems (DBMS), which organized data storage and retrieval, laying the groundwork for modern data handling. The Emergence of Data Mining 1980s-1990s: The explosion of data, driven by the advent of the internet and digital technologies, led to the development of data mining—a process of discovering patterns and knowledge from large datasets. Algorithms and Tools: This era saw the development of algorithms and tools to extract insights from data, combining elements of statistics, artificial intelligence, and machine learning. The Birth of Modern Data Science 2000s: The term "Data Science" became more popular as the volume, variety, and velocity of data (the "3 Vs" of Big Data) grew exponentially. The role of the "Data Scientist" emerged, combining skills in statistics, computer science, and domain expertise. Technological Advancements: The development of open-source tools (like Python, R, and Hadoop), cloud computing, and big data technologies further expanded the capabilities of data analysis. Current Trends and Future Directions Artificial Intelligence and Machine Learning: Modern Data Science heavily incorporates AI and ML to build predictive models and automate decision-making processes. Deep Learning and Big Data: Advances in deep learning and big data technologies continue to push the boundaries of what can be achieved with data. Interdisciplinary Nature: Data Science today is highly interdisciplinary, integrating knowledge from statistics, computer science, business, and domain-specific areas to solve complex problems. Data Science Workflow Data Collection Gather data from various sources (databases, APIs, web scraping, etc.). Ensure the data is relevant to the problem at hand. Data Cleaning Handle missing data, remove duplicates, and address inconsistencies. Prepare the data by normalizing, scaling, or transforming variables. Data Exploration and Analysis: Perform Exploratory Data Analysis (EDA) to understand data distributions, patterns, and relationships. Use visualization tools to generate insights. Model Building: Select appropriate machine learning models (e.g., regression, classification, clustering). Train the model on the data, and fine-tune the parameters. Model Evaluation: Evaluate model performance using metrics like accuracy, precision, recall, etc. Validate the model through cross-validation or testing on new datasets. Interpretation and Presentation: Interpret the results and draw meaningful conclusions. Present findings using visualizations, reports, or dashboards to stakeholders. Deployment and Maintenance: Deploy the model into a production environment, if applicable. Monitor model performance and retrain as necessary to maintain accuracy. Introduction to Tools and Technologies in Data Science  Programming Languages Python - Widely used in data science due to its simplicity and rich ecosystem of libraries. Key libraries: Pandas for data manipulation, NumPy for numerical computations, Matplotlib/Seaborn for data visualization, and Scikit-learn for machine learning. R - Another popular language, particularly in academia and research for statistical analysis and visualization. R offers packages like ggplot2 for data visualization and dplyr for data manipulation. Development Environments Jupyter Notebooks - A web-based interactive development environment that allows for live code execution, visualizations, and narrative text.Ideal for data exploration, prototyping, and sharing results. Rstudio - An integrated development environment (IDE) for R, providing a powerful platform for statistical computing and graphics. Data Manipulation and Analysis Libraries Pandas (Python) - Essential for data manipulation, cleaning, and transformation. It provides powerful data structures like DataFrames for easy data handling. NumPy (Python) - Provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on them. Dplyr (R) - A powerful tool for data manipulation, offering functions for filtering, selecting, and transforming data efficiently. Data Visualization Tools Matplotlib is a basic plotting library for creating static, animated, and interactive visualizations in Python. Seaborn is built on top of Matplotlib, offering more advanced statistical visualizations like heatmaps and pair plots. ggplot2 (R) - A popular data visualization package in R based on the "grammar of graphics," allowing users to create complex, multi-layered graphics with ease. Machine Learning Libraries and Frameworks Scikit-learn (Python) - A user-friendly library that provides simple and efficient tools for data mining and machine learning tasks, including classification, regression, clustering, and dimensionality reduction. TensorFlow & PyTorch - Both are deep learning frameworks widely used for building neural networks and machine learning models. TensorFlow (by Google) and PyTorch (by Facebook) are particularly popular in research and development of artificial intelligence models. Data Storage and Big Data Technologies SQL - Standard language for managing and querying structured data in relational databases like MySQL, PostgreSQL, and SQLite. Hadoop - A big data platform for processing and storing vast amounts of data across distributed systems. Apache Spark - A fast and general-purpose cluster computing system, used for processing large-scale datasets in a distributed manner, faster than Hadoop’s MapReduce. Cloud Platforms Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP) Cloud services that offer data storage, machine learning tools, and large-scale computational power to perform big data analysis. Tools like AWS S3 for storage and AWS SageMaker or Google AI Platform for building and deploying machine learning models. Version Control and Collaboration Tools Git and GitHub - Version control systems that allow teams to collaborate on code, track changes, and manage multiple versions of a project. Kaggle - A platform for data science competitions, which also provides datasets, tutorials, and collaborative notebooks to practice and improve data science skills. End of Chapter 3