Advanced Data Analytics and Data Visualisation

• Develop foundational skills in Tableau for data visualization, including data import, integration, and exploration.
• Understand the concepts of dimensions, measures, and aggregations to create meaningful insights in Tableau.
• Learn to differentiate between discrete and continuous data and apply them effectively in visualizations.
• Gain proficiency in creating various types of charts, graphs, and dashboards for data storytelling in Tableau.
• Understand and implement different types of filters in Tableau to refine data analysis and reporting.
• Develop a strong foundation in Python, including data types, control structures, and functions.
• Apply Python for data manipulation, file handling, and error handling in real-world scenarios.
• Gain hands-on experience in SQL database management, including data retrieval, modification, and transactions.
• Explore Google Sheets for data processing, analysis, and automation using built-in functions and scripts.
• Create interactive dashboards and reports in Looker Studio for business intelligence and decision-making.
• Develop statistical computing and data analysis skills using R for various datasets and visualization techniques.
• Understand and apply exploratory data analysis (EDA) techniques for identifying patterns and anomalies.
• Leverage ChatGPT-4 for automated data cleaning, transformation, and predictive analytics.
• Integrate multiple tools, including Tableau, Python, R, SQL, and Google Sheets, for end-to-end data analysis.
• Apply advanced data visualization techniques to enhance data interpretation and storytelling.
• Use automation tools and scripting to optimize repetitive data processing tasks for efficiency.
• Analyse market trends, financial reports, and customer behaviours using advanced data analytics techniques.
• Develop hands-on experience in real-time dashboards, anomaly detection, and sentiment analysis using ChatGPT-4.

• Demonstrate competence in Tableau by creating insightful visualizations and dashboards for data-driven decision-making.
• Use Python for data processing, analytics, and visualization, incorporating real-world datasets.
• Perform SQL database operations for structured data management and retrieval.
• Apply advanced Google Sheets techniques for sorting, filtering, and automating data workflows.
• Leverage Looker Studio to build interactive reports and share insights effectively.
• Utilize R for statistical computing, data manipulation, and visualization.
• Employ ChatGPT-4 for exploratory data analysis (EDA), anomaly detection, and predictive modeling.
• Integrate multiple data tools and platforms to solve business and analytical problems.
• Analyze and interpret data to generate meaningful insights for decision-making.
• Develop automation workflows for repetitive data tasks, improving efficiency and productivity.

The academic approach of the course focuses on ‘work-centric’ education. With this hands-on approach, derive knowledge from and while working to make it more wholesome, delightful and useful. The ultimate objective is to empower learners to also engage in socially useful and productive work. It aims at bringing learners closer to their rewarding careers as well as to the development of the community.

• Step 1: Learners are given an overview of the course and its connection to life and work
• Step 2: Learners are exposed to the specific tool(s) used in the course through the various real-life applications of the tool(s).
• Step 3: Learners are acquainted with the careers and the hierarchy of roles they can perform at workplaces after attaining increasing levels of mastery over the tool(s).
• Step 4: Learners are acquainted with the architecture of the tool or tool map so as to appreciate various parts of the tool, their functions, utility and inter-relations.
• Step 5: Learners are exposed to simple application development methodology by using the tool at the beginner’s level.
• Step 6: Learners perform the differential skills related to the use of the tool to improve the given ready-made industry-standard outputs.
• Step 7: Learners are engaged in appreciation of real-life case studies developed by the experts.
• Step 8: Learners are encouraged to proceed from appreciation to imitation of the experts.
• Step 9: After the imitation experience, they are required to improve the expert’s outputs so that they proceed from mere imitation to emulation.
• Step 10: Emulation is taken a level further from working with differential skills towards the visualization and creation of a complete output according to the requirements provided. (Long Assignments)
• Step 11: Understanding the requirements, communicating one’s own thoughts and presenting are important skills required in facing an interview for securing a work order/job. For instilling these skills, learners are presented with various subject-specific technical as well as HR-oriented questions and encouraged to answer them.
• Step 12: Finally, they develop the integral skills involving optimal methods and best practices to produce useful outputs right from scratch, publish them in their ePortfolio and thereby proceed from emulation to self-expression, from self-expression to self-confidence and from self-confidence to self-reliance and self-esteem!