Data Analytics Lessons 1-5 PDF

Lesson 1: What is Data Analytics? Data: Raw, unorganized facts that need to be processed to become meaningful. Data Analysis: o Process of collecting, organizing, and interpreting raw data to identify patterns and insights. o Focuses on historical data to answer questio...

Lesson 1: What is Data Analytics? Data: Raw, unorganized facts that need to be processed to become meaningful. Data Analysis: o Process of collecting, organizing, and interpreting raw data to identify patterns and insights. o Focuses on historical data to answer questions like "What happened?" or "Why did it happen?" o Techniques: Descriptive statistics, correlation analysis, hypothesis testing. o Tools: Excel, SPSS, R, Python (Pandas). Data Analytics: o Broader than data analysis, includes descriptive, diagnostic, predictive, and prescriptive analytics. o Uses advanced tools and methods (e.g., machine learning, predictive modeling) to predict future outcomes and guide decision-making. o Tools: Python, R, Tableau, Power BI, SQL, Hadoop, Spark, AWS, Google Cloud. Key Difference: o Data Analysis focuses on interpreting existing data. o Data Analytics encompasses the entire process, including planning, analyzing, and predicting future outcomes. Lesson 2: The Analytics Process 6 Steps of the Analytics Process: 1. Problem Definition: Clearly define the problem and objectives. 2. Data Collection: Gather relevant data from reliable sources. 3. Data Preparation/Cleaning: Clean and organize data for analysis (handle missing data, outliers, etc.). 4. Data Analysis: Analyze data to uncover patterns, trends, and insights. 5. Interpretation of Results: Translate analysis into actionable insights. 6. Implementation and Iteration: Apply findings, monitor results, and iterate for improvement. Life Cycle of Analytics (Based on CRISP-DM): o Business Understanding → Data Understanding → Data Preparation → Modeling → Evaluation → Deployment. Lesson 3: Types of Analytics 4 Types of Analytics: 1. Descriptive Analytics: ▪ Summarizes historical data to identify patterns or trends. ▪ Example: Tracking daily customer visits. ▪ Techniques: Data aggregation, summarization, visualization. 2. Diagnostic Analytics: ▪ Examines historical data to identify root causes of outcomes. ▪ Example: Investigating causes of increased customer complaints. ▪ Techniques: Drill-down analysis, statistical analysis (correlation, regression). 3. Predictive Analytics: ▪ Uses historical data to predict future outcomes or trends. ▪ Example: Forecasting sales for the next quarter. ▪ Techniques: Machine learning, time series analysis, predictive modeling. 4. Prescriptive Analytics: ▪ Recommends actions to optimize outcomes based on predictions. ▪ Example: Optimizing delivery routes to reduce costs. ▪ Techniques: Optimization algorithms, decision trees, simulation models. Lesson 4: Importance of Analytics Importance of Analytics: o Informed Decision-Making: Provides evidence-based insights. o Problem Solving: Identifies trends, patterns, and anomalies. o Efficiency and Optimization: Optimizes resource allocation and improves productivity. o Competitive Advantage: Predicts market trends and understands customer needs. o Risk Management: Anticipates and mitigates potential risks. Applications in Specific Fields: o Education: Improves teaching strategies, tracks student performance, and guides curriculum development. o Healthcare: Enhances patient care, operational efficiency, and public health tracking.  o Business and Marketing: Provides customer insights, improves inventory management, and guides strategic planning. o Government: Supports policy development, resource allocation, and crime prevention. Lesson 5: Overview of Analytics Tools and Technologies Key Analytics Tools: o Microsoft Excel: ▪ Spreadsheet software for data wrangling, reporting, and visualization. ▪ Pros: Widely-used, versatile, many built-in functions. ▪ Cons: Limited in handling big data. o Python: ▪ Programming language for data analysis, machine learning, and web scraping. ▪ Pros: Open-source, extensive libraries (Pandas, NumPy, Matplotlib). ▪ Cons: Requires programming knowledge. o R: ▪ Programming language for statistical analysis and data visualization. ▪ Pros: Strong in statistical computing, open-source. ▪ Cons: Steeper learning curve compared to Python. o Microsoft Power BI: ▪ Business analytics service for data visualization and reporting. ▪ Pros: Integrates with other Microsoft products, user-friendly. ▪ Cons: Limited advanced analytics capabilities. o Tableau: ▪ Data visualization tool for interactive dashboards and data storytelling. ▪ Pros: Intuitive interface, powerful visualization capabilities. ▪ Cons: Can be expensive, requires training. o SPSS: ▪ Statistical software for data management, statistical analysis, and reporting. ▪ Pros: User-friendly, extensive statistical capabilities. ▪ Cons: Can be expensive, less flexible compared to Python and R. o KNIME: ▪ Open-source analytics platform for data integration, reporting, and machine learning. ▪ Pros: No coding required, modular. ▪ Cons: Performance can be slow with large datasets. o Weka: ▪ Machine learning and data mining software for data preprocessing, classification, and clustering. ▪ Pros: Extensive library of machine learning algorithms, user-friendly GUI. ▪ Cons: Less efficient with very large datasets, limited customization. Analytics Tools by Type of Data Analytics: o Descriptive Analytics: Excel, Google Sheets, Tableau, Power BI. o Diagnostic Analytics: SPSS, Python (Pandas, Matplotlib), R (ggplot2), Tableau, Power BI. o Predictive Analytics: Excel (basic forecasting), Scikit-learn (Python), caret (R), Google AutoML, AWS Forecast. o Prescriptive Analytics: Solver in Excel, Salesforce Einstein.