Display working knowledge of Excel for Data Analysis.

Perform basic spreadsheet tasks including navigation, data entry, and using formulas.

Employ data quality techniques to import and clean data in Excel.

Analyze data in spreadsheets by using filter, sort, look-up functions, as well as pivot tables.

Create basic visualizations such as line graphs, bar graphs, and pie charts using Excel spreadsheets.

Explain the important role charts play in telling a data-driven story. 

Construct advanced charts and visualizations such as Treemaps, Sparklines, Histogram, Scatter Plots, and Filled Map Charts.

Build and share interactive dashboards using Excel and Cognos Analytics.

Describe data, databases, relational databases, and cloud databases.

Describe information and data models, relational databases, and relational model concepts (including schemas and tables). 

Explain an Entity Relationship Diagram and design a relational database for a specific use case.

Develop a working knowledge of popular DBMSes including MySQL, PostgreSQL, and IBM DB2

Analyze data within a database using SQL.

Create a relational database on Cloud and work with tables.

Write SQL statements including SELECT, INSERT, UPDATE, and DELETE.

Build more powerful queries with advanced SQL techniques like views, transactions, stored procedures and joins.

Job-ready data warehousing skills in just 6 weeks, supported by practical experience and an IBM credential.

Design and populate a data warehouse, and model and query data using CUBE, ROLLUP, and materialized views.

Identify popular data analytics and business intelligence tools and vendors and create data visualizations using IBM Cognos Analytics.

How to design and load data into a data warehouse, write aggregation queries, create materialized query tables, and create an analytics dashboard.

Describe the fundamental concepts of statistics and apply them to business and data analytics settings.

Apply data collection, analysis, and interpretation techniques to derive actionable insights for making informed business decisions.

Apply descriptive and inferential analysis methods to derive insights and actionable recommendations from data.

Apply hypothesis testing, regression analysis, and forecasting to support business decision-making processes.

Understand Tableau's fundamental concepts, significance in data visualization, diverse product range and key features crucial for data professionals.

Analyze and evaluate the capabilities of Tableau Public as a powerful Business Intelligence (BI) tool for data visualization and analysis.

Implement relationships between data tables, use Tableau calculations for analytics, empower user interaction, and apply filters and highlighting.

Develop expertise in Tableau dashboard design principles, organize visual elements effectively, and apply best practices for interactive dashboards.

Apply advanced techniques to create interactive and dynamic visualizations in Tableau, demonstrating high proficiency in data presentation

Create interactive dashboards in Tableau, incorporating navigation, links between story points, and advanced features such as actions and tooltips

Demonstrate a comprehensive understanding of Tableau's capabilities and features such as advanced formatting, dashboard design and web objects

Effectively use Tableau’s features to deliver insightful dashboards and stories for data exploration and monitoring

Apply data analysis techniques to derive meaningful insights and uncover patterns in the data.

Use the knowledge and skills acquired throughout the program to design and execute a practical BI project.

Present and showcase the BI project outcomes, highlighting its impact on decision-making and business performance.

Demonstrate proficiency in using BI tools and technologies, such as data visualization tools and database management systems.

Explain the role of generative AI in every stage of the BI lifecycle.

Use generative AI to improve data handling and analysis in BI.

Leverage generative AI-powered tools for enhanced data visualization and reporting in BI.

Discuss ethical considerations and challenges in implementing generative AI in BI.