Data Analytics Course through IIM SKILLS
- 2. Understanding Data Analytics Basics Introduction to Data Analytics Data Analytics Process Tools and Techniques Case Studies and Applications Challenges in Data Analytics Future Trends in Data Analytics Conclusion
- 3. • Data analytics is the science of examining raw data to discover patterns and answer questions. • Importance of Data-Driven Decisions: Utilizing data analytics enables organizations to make informed decisions, optimizing efficiency and effectiveness. • Industry Applications: Data analytics finds applications in healthcare, finance, marketing, and sports, enhancing diverse decision-making processes. Introduction to Data Analytics
- 4. • Data Collection: This step involves gathering relevant data from varied sources, ensuring breadth and accuracy for analysis. • Data Cleaning: Cleaning data entails correcting inaccuracies and eliminating outliers to enhance the quality of the dataset. • Data Analysis: Analysis employs statistical methods to extract meaningful insights, helping drive informed decision-making processes. Data Analytics Process
- 5. • Popular Tools Overview: Key tools include Excel for basic analysis, Python for programming, R for statistics, and Tableau for visualization. • Descriptive Analytics Technique: Descriptive analytics summarizes past data, aiding understanding by providing insights into historical performance trends. • Predictive and Prescriptive Analytics: Predictive analytics forecasts future outcomes, while prescriptive analytics recommends actions based on data-driven decisions. Tools and Techniques
- 6. • Netflix Content Recommendations: Netflix utilizes viewer data algorithms to personalize content, significantly improving user engagement and retention rates. • Amazon Inventory Optimization: Amazon analyzes purchase patterns to optimize inventory management, enhancing operational efficiency and reducing costs effectively. • Healthcare Readmission Reduction: Hospitals employ patient data analytics to identify trends and reduce readmission rates, improving patient care outcomes significantly Case Studies and Applications
- 7. • Data Quality Challenges: Inaccurate or incomplete data severely undermines analytics validity, leading to misleading insights and poor decisions. • Ethical Considerations: Misuse of data raises ethical concerns, necessitating guidelines to ensure responsible handling and analysis of information. • Privacy and Security: Protecting sensitive data is vital; breaches can compromise individual privacy, leading to trust erosion and legal repercussions. Challenges in Data Analytics
- 8. • AI and Machine Learning: Artificial Intelligence enhances data analytics by automating processes and uncovering patterns previously unnoticed through algorithms. • Big Data Impact: The influx of Big Data amplifies analytic capabilities, enabling organizations to derive actionable insights from vast information sources. • Cloud Computing Adoption: Migrating analytics to cloud platforms fosters collaboration, scalability, and cost-effectiveness, transforming how organizations manage data resources Future Trends in Data Analytics
- 9. • Recap of Key Points: The presentation summarized data analytics' definition, processes, tools, applications, challenges, and future trends comprehensively. • Encouraging Data Literacy: Emphasizing the importance of data literacy enables participants to engage effectively with analytical tools across industries. • Business Decision Empowerment: Data analytics drives informed business decisions, facilitating adaptability and competitiveness in rapidly changing environments. Conclusion