Mtech First_Year Data Analytics in Industry with power bI
- 3. Unit 1 : Introduction to Data Analytics •Introduction to Business Intelligence •History on Methodologies for Data Analytics •Types of Data Analytics •Big Data and Data Science •State of the Practice in Analytics •Characteristics of Data Analysis •Applications of Data Analysis •Difference between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data Syllabus
- 4. Definition of Business Intelligence (BI): Business Intelligence (BI) refers to the technology-driven process for analyzing data and presenting actionable information to help executives, managers, and other corporate end users make informed business decisions. BI encompasses a variety of tools, applications, and methodologies that enable organizations to collect data from internal and external sources, prepare it for analysis, develop and run queries against the data, and create reports, dashboards, and data visualizations. Key Concepts of BI: Data Warehousing: Centralized storage of large amounts of data collected from various sources for easy access and analysis. Data Visualization: The graphical representation of data to help users quickly understand trends, patterns, and insights. Reporting: The process of organizing data into summaries and distributing them to decision- makers for use in strategy formulation.
- 5. Importance of BI in Organizations: Improved Decision Making: 1. BI tools provide real-time data, allowing organizations to make more informed decisions based on evidence rather than intuition or guesswork. 2. It offers insights into customer behavior, market trends, and internal performance metrics. Operational Efficiency: 1. BI helps to streamline operations by identifying inefficiencies, tracking performance, and monitoring progress in real- time. 2. Automated reporting and alerts can help businesses save time and reduce manual effort. Competitive Advantage: 1. By using BI to analyze market trends, competitors, and customer needs, organizations can identify opportunities to differentiate themselves from their competitors. 2. Predictive analytics through BI can help anticipate changes and position the organization strategically. Cost Management: 1. BI enables organizations to track spending, evaluate return on investment (ROI), and identify areas where they can cut costs or improve margins. Customer Insights and Personalization: 1. BI tools help in segmenting customers based on behavior, preferences, and demographics, which leads to more personalized services. 2. This can enhance customer satisfaction and retention rates.
- 6. Examples of BI Tools: Power BI (Microsoft) Tableau Looker (Google Cloud)
- 7. Date/Year Milestone 1950s-1960s Emergence of Data Processing: Mainframe computers used for processing large volumes of data (e.g., US Census Bureau's automation for census tabulation). 1970s-1980s Rise of Business Intelligence (BI): Development of decision support systems (DSS) and relational database management systems (RDBMS). IBM introduces first RDBMS. 1990s Era of Data Warehousing and OLAP: Widespread adoption of data warehouses and Online Analytical Processing (OLAP). Ralph Kimball's “The Data Warehouse Toolkit” published. 2000s Big Data and Advanced Analytics: Advent of big data technologies. Google’s MapReduce paper leads to frameworks like Apache Hadoop for distributed data processing. 2010s Democratization of Data Analytics: Cloud computing, open-source tools, and self-service analytics platforms (e.g., Tableau) make data analytics more accessible. 2020s Integration of AI and Machine Learning: AI-driven analytics, automation, and innovations such as GPT-3 expand possibilities for text analytics and predictive modeling. History on Methodologies for Data Analytics
- 11. What is Big Data? Big Data refers to the vast volumes of data generated at high velocity from a variety of sources. This data is characterized by the three V’s: Volume, Velocity, and Variety. 1.Volume: Big Data involves large datasets that are too complex for traditional data processing tools to handle. These datasets can range from terabytes to petabytes of information. 2.Velocity: Big Data is generated in real-time or near real-time, requiring fast processing to extract meaningful insights. 3.Variety: The data comes in multiple forms, including structured data (like databases), semi-structured data (like XML files), and unstructured data (like text, images, and videos).
- 14. What is Data Science? Data Science is an interdisciplinary field that utilizes scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It encompasses a variety of techniques from statistics, machine learning, data mining, and big data analytics. Data Scientists use their expertise to: 1.Analyze: They examine complex datasets to identify patterns, trends, and correlations. 2.Model: Using statistical models and machine learning algorithms, they create predictive models that can forecast future trends or behaviors. 3.Interpret: They translate data findings into actionable business strategies and decisions. Data Science involves a broad skill set, including proficiency in programming languages like Python and R, knowledge of databases, and expertise in machine learning frameworks such
- 17. Data Analysis vs Data Analytics
- 21. Data Analytics The process of inspecting, cleaning, transforming, and modeling data to discover useful information for decision- making. Descriptive statistics, Data visualization, BI tools (Tableau, Power BI) Provide actionable business insights and support decision- making. Analyzing sales data to identify market trends. Data Analysis The process of systematically applying statistical and logical techniques to describe and evaluate data. Excel, SQL, R, Python (Pandas, Numpy), Statistical tests Understand specific questions and extract relevant information from raw data. Finding correlations between different sales factors. Data Mining The process of discovering patterns, correlations, and anomalies in large datasets, using techniques at the intersection of machine learning, statistics, and database systems. Algorithms (Apriori, Decision Trees, K-Means), Python, R Extract patterns and knowledge from large datasets to predict future trends. Analyzing customer purchase behavior for segmentation. Data Science A multidisciplinary field that uses scientific methods, processes, and systems to extract insights from structured and unstructured data. Machine Learning (scikit-learn, TensorFlow), Data Engineering (SQL, Hadoop), Statistics Derive insights, create predictive models, and guide strategic decisions using data. Building a recommendation engine for an e-commerce platform. Machine Learning A subset of AI, involving systems that learn from data and improve performance over time without being explicitly programmed for every task. Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, PCA), Deep Learning Automate decision- making, build predictive models, and enable AI. Predicting loan defaults based on customer profiles. Big Data Refers to extremely large and complex datasets that cannot be processed with traditional data-processing techniques. Hadoop, Spark, NoSQL databases, Distributed computing Manage, process, and analyze massive amounts of data efficiently. Analysing social media data for sentiment analysis Term Definition Key Techniques/Tools Focus/ Objective Examples of Usage