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Lesson 1. Data Strategy: History, Objectives and Areas of Application 12 February 2025 17:11 Objectives: 。 appreciate the history and background of the worldwide push towards data analytics 。 understand key definitions associated with big data 。 obtain basic skills of data handling using Excel This lesson introduces the fundamental concepts of big data, its applications, technologies, and basic data analysis techniques. Let's go through each topic in detail. ✰ Topic 1: Introducing Data Analytics What is Data Analytics? Data analytics refers to the systematic computational analysis of data. It is used to extract insights from structured and unstructured data, enabling better decision-making. Key Definitions: Big Data: Extremely large data sets that require advanced methods to process and analyze. . Data Science: A field that uses scientific methods, algorithms, and systems to extract knowledge from data. • • Business Analytics: The use of data, statistical analysis, and modeling to make business decisions. Machine Learning: A subset of Al that enables computers to learn patterns from data. Why is Data Analytics Important? • Helps businesses gain insights from data. Improves efficiency and productivity. • Enhances decision-making with data-driven strategies. Supports predictive analytics for forecasting trends. Skills Required for Data Analytics • Technical Skills: Python, SQL, Excel, R, Hadoop. • Statistical Knowledge: Understanding of probability, regression, clustering, and classification. • Business Understanding: Ability to apply data insights to business problems. ✰ Topic 2: Applicability of Business Analytics Data analytics is used in various industries to optimize processes, improve customer experience, and drive innovation. 1. Retail & Digital Marketing • Uses customer data to personalize recommendations (e.g., Amazon, Netflix). . Tracks buying behaviors and predicts trends. • Optimizes pricing strategies. 2. Risk Management • Identifies fraud patterns in banking and insurance. • Helps companies predict financial risks and market fluctuations. 3. Healthcare & Medicine • Uses Al for medical image analysis and diagnostics. Predicts disease outbreaks and patient trends. 4. Artificial Intelligence & Text Processing . Analyzes customer feedback from social media. Automates customer service using chatbots. 5. Internet of Things (IoT) Connects smart devices to gather real-time data. Enhances automation in industries like logistics, home automation, and healthcare. 6. Public Governance • Uses big data for policy-making and social impact. • Helps governments track and manage resources efficiently. Topic 3: Business Data Technologies Business analytics relies on several core technologies. 1. Data Management • Involves collecting, storing, protecting, and processing data securely. Ensures data integrity and quality for analysis. 2. Data Mining • Finds patterns and relationships in data. • Helps companies predict customer behaviors. 3. Hadoop . An open-source framework used for storing and processing big data. Enables distributed computing across multiple servers. 4. Predictive Analytics • Uses machine learning models to forecast future trends. Commonly applied in finance, healthcare, and marketing. 5. Text Mining Extracts meaningful information from text data. Used for sentiment analysis, fraud detection, and legal document analysis. Topic 4: Evolution of Data Applications Lesson 1. Data Strategy History, Objectives and Areas of Application Page 1 Data applications have evolved over time, enabling more efficient business operations. 1. Enterprise Resource Planning (ERP) • Software that integrates business processes like finance, HR, and supply chain management. • Example: SAP, Oracle. 2. Customer Relationship Management (CRM) • Helps businesses manage customer interactions and sales data. • Example: Salesforce, Microsoft Dynamics. 3. E-Commerce • Platforms that facilitate online buying and selling. . Example: Amazon, Alibaba. 4. Big Data Analytics Helps businesses analyze large datasets for strategic decision-making. Example: Azure, Google Cloud, IBM Watson. ✰ Topic 5: Practical Data Analysis Techniques Even without sophisticated tools, you can start analyzing data using Excel. 1. Basic Excel Functions • Sorting & Filtering: Helps in organizing data. ⚫ Pivot Tables: Summarizes large datasets quickly. • Descriptive Statistics: Calculates mean, median, mode, standard deviation. 2. Hands-On Exercise • Sort Sales Data from February to April. • . • . Filter Data to analyze customer behavior. Calculate Averages using Excel formulas. Lesson Summary Data analytics helps organizations make data-driven decisions. Big data is used in various industries, including retail, healthcare, and lot. • Technologies like Hadoop, predictive analytics, and CRM support data analysis. Excel provides basic but powerful data-handling functions. Next Steps Reflect on how data analytics can be applied in your work. • Try sorting and filtering datasets in Excel. • Explore predictive analytics tools like Power BI or Python. Lesson 1. Data Strategy History, Objectives and Areas of Application Page 2