LIS 208 Data Analytics - Unit 1 PDF

2nd Semester UNIT 1: INTRODUCTION TO DATA ANALYTICS |LIS 208: Data Analytics - which are synthesized into actionable conclusions DATA th...

2nd Semester UNIT 1: INTRODUCTION TO DATA ANALYTICS |LIS 208: Data Analytics - which are synthesized into actionable conclusions DATA that drive decisions. - The progression emphasizes how data transitions - Raw facts and figures (e.g., numbers, text, from raw form to meaningful insights, ultimately measurements enabling informed actions. - Data is much more than a collection of numbers. DATA ANALYSIS VS DATA ANALYTICS - Raw, unorganized that need to be processed. (wala pang nabubuo or naiinterpret) DEFINITION Information DATA ANALYSIS - The process of collecting, organizing, - Data is processed, organized, structured or and interpreting raw data to identify patterns, presented on a given context. relationships, and trends. It focuses on extracting - Processed data that has meaning or context (e.g., a meaningful insights from data for specific purposes. summary, report, or chart). - Data itself 2 types of data DATA ANALYTICS - A broader field that encompasses data analysis but also includes the use of tools, Quantitative Data technologies, and methodologies to collect, clean, Numerical data that can be measured or process, and analyze data. It often involves predictive counted. modeling, machine learning, and data visualization to Used to answer questions like "How many?", derive actionable insights and support decision-making. "How much?", or "How often?“ - The result of the analysis Examples: - To make decisions and predictions to the future Number of books borrowed in a library per day. Average time a library user spends reading. SCOPE Total revenue generated from late return fees. DATA ANALYSIS - More focused and specific. Qualitative Data Typically deals with historical data to answer questions Descriptive data that provides insights into the like "What happened?" or "Why did it happen?“ qualities or characteristics of something. Example: Used to answer questions like "Why?" or "What Analyzing past borrowing data kind?" DATA ANALYTICS - Broader in scope, encompassing Examples: descriptive, diagnostic, predictive, and prescriptive Comments from library users: analytics. Answers questions like "What will happen?" Feedback about library services: and "What should we do?" using advanced methods. Observations on user behavior: Example: Using library data to predict which books will be popular in the next few months GOALS DATA ANALYSIS - To understand and summarize the current or past state of data, helping to make immediate or retrospective decisions. DATA ANALYTICS - To generate insights and forecasts that can guide future strategies and long-term decision- making. APPLICATION What is Data Analytics? DATA ANALYSIS - Used in research, academic studies, and one-time reporting tasks. - is a systematic approach that transforms raw data Example: book borrowing patterns in the library into valuable insights. DATA ANALYTICS - To generate insights and forecasts - also known as data analysis, is a crucial component that can guide future strategies and long-term decision- of modern business operations. It involves making. examining datasets to uncover useful information Example: predicting which books will be borrowed next that can be used to make informed decisions. This month process is used across industries to optimize performance, improve decision-making, and gain a TECHNIQUES AND TOOLS competitive edge. DATA ANALYSIS Techniques: Descriptive statistics, correlation analysis, DATA ANALYSIS hypothesis testing. - Data Analysis is a process that begins with the Tools: Excel, SPSS, R (basic statistical functions), Python collection of raw data. (e.g., Pandas). - This data is then organized and summarized to DATA ANALYTICS extract meaningful information. Techniques: Advanced statistical methods, machine - Through detailed analysis, patterns and insights are learning, data mining, predictive modeling, and derived, optimization.  2nd Semester UNIT 1: INTRODUCTION TO DATA ANALYTICS |LIS 208: Data Analytics Tools: Python, R, Tableau, Power BI, SQL, Hadoop, Spark, and cloud platforms like AWS or Google Cloud. CONCLUSION Data Analysis is a subset of Data Analytics. While data analysis focuses on examining and interpreting data, data analytics takes a broader approach by using advanced tools and methods to process data, uncover trends, and predict future outcomes. DATA ANALYSIS “Insight” Focuses on interpreting existing data to uncover patterns, trends, and insights about what happened. DATA ANALYTICS “Strategy” Encompasses the entire process, including planning, analyzing, and predicting outcomes, to guide future actions and decision-making.