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Assoc Prof Prashan S. M. Karunaratne

In today's data-driven world, the ability to analyse and interpret complex data is crucial for making informed, strategic decisions. This comprehensive online course will empower you with advanced statistical techniques to transform raw data into actionable insights, using Microsoft Excel.

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In today's data-driven world, the ability to analyse and interpret complex data is crucial for making informed, strategic decisions. This comprehensive online course will empower you with advanced statistical techniques to transform raw data into actionable insights, using Microsoft Excel.

Through hands-on exercises, you will delve into the world of inferential statistics, learning how to draw meaningful conclusions and make accurate predictions based on sample data. Gain proficiency in creating and interpreting pivot tables, pivot charts, and other interactive data visualisations to effectively communicate your findings. Enhance your digital literacy with extensive use of Excel's advanced functions and the Data Analysis ToolPak, streamlining your data analysis workflow.

Join over 1 million professionals who have advanced their careers with our highly-rated Excel courses. Whether you’re looking to boost your employability, enhance your current role, or simply elevate your Excel skills, this course will provide you with the knowledge and tools to stand out in the competitive job market.

Enrol now and become a data-driven leader, driving strategic decisions with confidence and precision.

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What's inside

Syllabus

Pivot Tables and Pivot Charts
In this module, we will cover the Pivot Tables and Pivot Charts. We will be looking at how we can generate useful numerical summaries from large data sets.
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Career center

Learners who complete Statistics and Data Analysis with Excel: Intermediate will develop knowledge and skills that may be useful to these careers:
Data Analyst
A Data Analyst transforms raw data into meaningful insights, performing statistical analysis and creating visualizations to drive decisions. This course directly equips future and current Data Analysts with essential skills. You will gain proficiency in inferential statistics, categorical data analysis, two-sample tests, correlation, and simple regression. Practical application using pivot tables, pivot charts, and other interactive data visualizations in Microsoft Excel, along with its advanced functions and Data Analysis ToolPak, will enhance your ability to effectively communicate findings. This course is ideal for building a robust foundation in practical data analysis, making you competitive in the job market and confident in strategic decision making.
Reporting Analyst
A Reporting Analyst extracts, transforms, and presents data to create clear, insightful reports and dashboards, crucial for tracking performance and enabling data-driven decisions. The "Statistics and Data Analysis with Excel: Intermediate" course is exceptionally well-suited for a Reporting Analyst. You will gain extensive hands-on experience in generating useful numerical summaries from large data sets through creating and interpreting pivot tables and pivot charts. The course's focus on creating interactive data visualizations in Excel, combined with proficiency in advanced functions and the Data Analysis ToolPak, directly enhances your ability to effectively communicate complex data findings, ensuring reports are not just informative but also actionable.
Statistical Analyst
A Statistical Analyst applies advanced statistical methods to complex data sets to extract insights and support data-driven decision-making. This role involves hypothesis testing and interpreting results. The "Statistics and Data Analysis with Excel: Intermediate" course offers a strong foundation for a Statistical Analyst. You will delve into inferential statistics, learning to draw meaningful conclusions and make predictions. The course specifically covers techniques like analyzing categorical data for patterns, performing two-sample tests, and exploring correlation and simple regression for relationships. Proficiency in using Excel's Data Analysis ToolPak and advanced functions enables efficient execution of analyses, transforming raw data into actionable statistical insights.
Business Analyst
A Business Analyst identifies business problems and opportunities, using data to propose solutions and improve processes. This role requires translating findings into actionable recommendations. The "Statistics and Data Analysis with Excel: Intermediate" course is highly relevant for a Business Analyst, empowering you to transform raw data into insights for strategic decisions. You will learn statistical techniques like categorical data analysis, two-sample comparisons, and correlation and simple regression, vital for understanding business performance. Proficiency in pivot tables, pivot charts, Excel's advanced functions, and Data Analysis ToolPak streamlines analytical workflows, making you adept at data-driven problem-solving and effective communication.
Financial Analyst
A Financial Analyst evaluates financial data, forecasts conditions, and assesses company performance to guide investment decisions. This role demands strong analytical skills to interpret market trends and economic indicators. The "Statistics and Data Analysis with Excel: Intermediate" course provides foundational skills highly applicable to this profession. You will gain proficiency in two-sample tests for comparing financial performance, understanding correlation and simple regression to model relationships between variables, and applying inferential statistics for predictions. Emphasis on utilizing Excel for pivot tables and data visualizations is particularly beneficial for reporting and presenting financial insights with clarity and confidence.
Research Analyst
A Research Analyst collects, interprets, and presents data to inform decision-making across various fields. This role often involves analyzing quantitative data and summarizing findings. The "Statistics and Data Analysis with Excel: Intermediate" course is highly relevant for a Research Analyst, providing robust skills in data interpretation. The course covers crucial statistical techniques such as analyzing categorical data for patterns, conducting two-sample tests for comparing groups, and exploring correlation and simple regression for understanding variable relationships. Proficiency in creating pivot tables and various data visualizations in Excel will greatly assist in summarizing complex research data and effectively communicating insights to stakeholders, making data-driven conclusions.
Marketing Analyst
A Marketing Analyst interprets data to improve campaign effectiveness, understand customer behavior, and identify market trends. This role requires segmenting audiences and measuring performance. The "Statistics and Data Analysis with Excel: Intermediate" course offers highly pertinent skills for a Marketing Analyst. You will utilize statistical techniques, including categorical data analysis for segmentation, two-sample tests for A/B testing, and correlation and simple regression for understanding marketing impact. Proficiency in creating and interpreting pivot tables and pivot charts in Excel allows for robust reporting and visualization, transforming raw information into actionable insights for strategic marketing decisions.
Risk Analyst
A Risk Analyst identifies, assesses, and quantifies potential risks impacting an organization, building models and forecasting scenarios to mitigate adverse outcomes. The "Statistics and Data Analysis with Excel: Intermediate" course provides highly relevant analytical capabilities for a Risk Analyst. You will gain proficiency in inferential statistics, crucial for making predictions from risk-related sample data. Statistical techniques such as two-sample tests for comparing risk profiles and correlation and simple regression for modeling risk factors are directly applicable. Utilizing Excel's advanced functions and Data Analysis ToolPak, coupled with data visualization skills, helps in effectively analyzing and communicating complex risk assessments.
Operations Analyst
An Operations Analyst improves efficiency and effectiveness of processes by analyzing data to identify bottlenecks and optimize workflows. The "Statistics and Data Analysis with Excel: Intermediate" course provides valuable analytical tools for this role. Learners will discover how to use statistical techniques such as two-sample tests to compare process performance, and correlation and simple regression to understand relationships between operational metrics. Developing proficiency in creating numerical summaries from large data sets using pivot tables and other data visualizations will enhance an Operations Analyst's ability to monitor performance, report findings, and drive strategic improvements within supply chains, logistics, or production environments.
Product Analyst
A Product Analyst optimizes product performance and strategy by analyzing user behavior, market trends, and product metrics. This role is crucial for making data-driven decisions about product features and roadmaps. The "Statistics and Data Analysis with Excel: Intermediate" course provides an excellent foundation for a Product Analyst. You will learn to apply statistical techniques such as categorical data analysis for user segmentation, two-sample tests for evaluating A/B tests, and correlation and simple regression for understanding feature impact. The course's emphasis on creating and interpreting pivot tables and various data visualizations in Excel will enable you to effectively communicate product insights and drive strategic decisions with confidence.
Supply Chain Analyst
A Supply Chain Analyst optimizes the flow of goods and services by analyzing data to improve efficiency and reduce costs. This role involves forecasting demand and managing inventory. The "Statistics and Data Analysis with Excel: Intermediate" course provides strong analytical skills for a Supply Chain Analyst. You will learn to apply statistical techniques such as two-sample tests to compare supplier performance or routes, and correlation and simple regression to understand factors influencing demand or delivery times. Proficiency in generating numerical summaries with pivot tables and creating data visualizations in Excel will enable you to identify operational efficiencies and drive strategic improvements within the supply chain.
Healthcare Analyst
A Healthcare Analyst examines clinical, financial, and operational data to improve patient care and enhance efficiency within healthcare systems. This role is vital for informing policy and managing resources. The "Statistics and Data Analysis with Excel: Intermediate" course provides relevant analytical capabilities for a Healthcare Analyst. You will gain proficiency in applying statistical techniques, including categorical data analysis for patient demographics, two-sample tests for comparing treatment groups, and correlation and simple regression for understanding factors affecting health. The ability to create and interpret pivot tables and other data visualizations in Excel will be instrumental in transforming complex healthcare data into actionable insights for strategic decisions.
Junior Data Scientist
A Junior Data Scientist often focuses on data cleaning, exploratory analysis, and basic statistical modeling, serving as a stepping stone to advanced roles which frequently require an advanced degree. This role requires understanding statistical principles and practical data manipulation. The "Statistics and Data Analysis with Excel: Intermediate" course provides a foundational entry point for a Junior Data Scientist. You will learn inferential statistics, categorical data analysis, two-sample tests, and correlation and simple regression, all crucial for understanding data. Proficiency in Excel's advanced functions and the Data Analysis ToolPak, coupled with data visualization, aids in transforming raw data into actionable insights, preparing you for sophisticated data projects.
Management Consultant
A Management Consultant advises organizations on strategies to improve performance and solve problems, often requiring rigorous data analysis. The "Statistics and Data Analysis with Excel: Intermediate" course is relevant for a Management Consultant, developing the analytical rigor needed to transform raw data into actionable insights for strategic decisions. Learners will develop skills in statistical techniques such as categorical data analysis, two-sample tests for comparing scenarios, and correlation and simple regression for understanding business drivers. Proficiency in Excel, especially with pivot tables and data visualizations, enhances the ability to quickly summarize complex client data and effectively communicate compelling, data-driven recommendations during engagements.
Economist
An Economist studies economic trends and market behavior, often requiring an advanced degree like a master's or PhD. Foundational data analysis skills remain critical. The "Statistics and Data Analysis with Excel: Intermediate" course may be useful for an Economist, providing a practical foundation in applying statistical techniques. You will explore concepts like correlation and simple regression, vital for understanding economic relationships, and apply inferential statistics to interpret complex data. Proficiency in Excel, including pivot tables and data visualizations, may help in quickly summarizing and presenting economic data and making data-driven observations and predictions within economic contexts.

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Statistics and Data Analysis with Excel: Intermediate.
This comprehensive guide covers everything from creating basic pivot tables and charts to using advanced features like calculated fields and slicers. It's an excellent resource for anyone who wants to learn more about pivot tables and pivot charts.
This classic book by Edward Tufte must-read for anyone interested in data visualization. It covers a wide range of topics, from the principles of visual perception to the design of effective charts and graphs.
Seminal work on the grammar of graphics, a formal language for describing data visualizations. It covers a wide range of topics, from the basics of the grammar of graphics to the use of the grammar of graphics to create a variety of charts and graphs.
The third edition of the essential guide to data wrangling with pandas, NumPy, and Jupyter in Python. Updated for newer versions of the libraries, this book remains a core resource for anyone performing data analysis in Python. It widely used textbook and reference for its clear explanations and practical examples.
Provides a comprehensive and current introduction to the foundations of linear regression analysis. It is widely used as a textbook in academic institutions and is suitable for gaining a broad understanding. The latest edition includes new material on generalized regression techniques and updated examples.
A more recent iteration of the comprehensive 'Bible' series, this book covers the features of Excel 2021. It serves as an excellent reference and learning tool for users who want a deep understanding of the software, from basic to advanced functionalities. It's suitable for a wide range of users.
A seminal work in the field of statistical learning, this book covers advanced topics at the intersection of statistics and machine learning. It key reference for graduate students, researchers, and professionals working in data science and related areas. While mathematically rigorous, it provides in-depth coverage of modern statistical techniques.
Is specifically geared towards data scientists and provides a practical guide to statistical concepts relevant in the field. It focuses on the statistical methods most useful in a data science context and includes code examples in R and Python. It's highly relevant for professionals and graduate students applying statistics in data-driven roles.
Dives into contemporary Excel topics like Power Pivot and Power Query, which are crucial for handling and analyzing large datasets. It's a key resource for users looking to move beyond basic data analysis and leverage Excel's business intelligence capabilities. Recommended for intermediate to advanced users and professionals.
A recent edition of a popular series, this book covers pivot tables with an emphasis on new features like Dynamic Arrays, Power Query, and Copilot. It provides updated techniques for data crunching and reporting, making it highly relevant for users of the latest Excel versions. valuable resource for staying current with contemporary pivot table capabilities.
Provides an accessible introduction to causal inference, a crucial topic in contemporary statistics and data science for determining cause-and-effect relationships. It's suitable for graduate students and researchers in social sciences, economics, and data science. The book uses a modern approach and includes coding examples, making it highly practical for applied work.
Considered a classic in the field, this comprehensive textbook covers both probability and statistics in depth. It's often used in graduate-level courses and provides a rigorous treatment of the subject matter. is excellent for deepening one's theoretical understanding and valuable reference for researchers and professionals.
Specifically targets financial modeling using Excel, a key application in finance and accounting. is essential for students and professionals in these fields who need to build and analyze financial models. It covers relevant formulas, functions, and best practices.
Provides a practical and engaging introduction to regression analysis and other statistical methods, focusing on real-world data and research questions. It's suitable for a broad audience and emphasizes the importance of data visualization and model checking.
Provides a foundational understanding of the fundamental principles of data science and the data-analytic thinking necessary for extracting value from data in a business context. It is highly relevant for undergraduate business analytics programs and working professionals. It serves as a useful reference for understanding the business applications of data analysis and is commonly used as a textbook.
A focused guide on Power Query, a powerful tool for data extraction, transformation, and loading within Excel and Power BI. is crucial for anyone dealing with data from multiple sources and needing to clean and prepare it for analysis. It's a valuable resource for data professionals.
Focuses on creating effective and advanced data visualizations using Excel, going beyond the default chart types. It's highly relevant to contemporary data analysis and communication. It provides step-by-step guides for creating various chart types and is valuable for users at all levels looking to improve their data storytelling.
Is written by John Walkenbach, a world-renowned Excel expert. It's a comprehensive guide to pivot tables and pivot charts, covering everything from the basics to advanced techniques. It valuable resource for anyone who wants to learn everything there is to know about pivot tables and pivot charts.
This open-source textbook provides a solid foundation in introductory statistics, covering a wide range of topics with clear explanations and real-world examples. It's suitable for high school and undergraduate students and is often used as a primary textbook in introductory courses. The book includes numerous datasets and exercises, making it a practical resource for learning by doing.
Practical guide to creating dynamic and interactive dashboards and reports using pivot tables and charts. It covers a wide range of topics, from creating basic pivot tables and charts to using advanced features like PowerPivot and Power BI. It is an excellent resource for anyone who wants to learn how to use pivot tables and charts to create effective data visualizations.
Provides a comprehensive overview of data visualization, covering the basics of visual perception, data types, and chart types. It great resource for beginners who want to learn the fundamentals of data visualization.
A solid reference book that covers a wide range of theoretical aspects in regression analysis. While extensive, some users note the code examples might be in a less common dialect. It valuable resource for deepening understanding and is often used at the undergraduate and graduate levels.
A widely used textbook for undergraduate courses, this book covers a broad range of statistical methods, including inferential procedures and data analysis techniques.
This comprehensive 'Bible' covers all aspects of Excel 365, with dedicated sections on pivot tables and related features like Power Query and Power Pivot. It's an excellent all-in-one reference for users of the subscription version of Excel, providing in-depth coverage of both basic and advanced functionalities.
Addresses contemporary data analytics practices within Excel, focusing on modern techniques for cleaning, transforming, and analyzing data. It's highly relevant for those looking to update their skills and leverage Excel for more sophisticated data analysis tasks.
Offers a concise yet comprehensive overview of the essential topics in statistical inference, suitable for students with a strong mathematical background. It's often used in graduate-level statistics and machine learning programs. The book provides a solid theoretical foundation and covers a wide range of modern topics, making it relevant for contemporary applications.
Offers a visual and comprehensive guide specifically focused on pivot tables. It aims to cover all aspects of this feature, making it a deep dive for those who want to master pivot table creation and manipulation. Its visual approach makes it accessible for learners of varying levels.
This well-regarded textbook provides a comprehensive introduction to mathematical statistics with a focus on data analysis. It's suitable for advanced undergraduate and graduate students in statistics and related fields. The book emphasizes the connection between statistical theory and real-world data problems, making it a valuable reference.
True to its title, this book offers a straightforward and accessible introduction to statistical concepts and techniques. It's particularly useful for undergraduate students and those in fields outside of statistics who need to understand and interpret statistical results. Each chapter clearly explains a statistical technique, when to use it, how it works, and provides examples of how to write about the results.
Offers in-depth coverage of regression analysis and generalized linear models with a focus on applications, particularly in the social sciences. It balances theoretical concepts with practical examples and is suitable for both students and researchers. The latest edition includes new chapters on mixed-effects models.
Focuses on the crucial skill of communicating insights from data effectively through compelling visualizations. is highly relevant for all levels, emphasizing the importance of clear and impactful data presentation. It is valuable additional reading that complements technical data analysis skills.
This definitive text on the analysis of categorical data, covering a wide range of models and methods. It standard reference for graduate students and researchers in statistics and various applied fields. The book provides comprehensive coverage of this important area of statistics.
Ideal for beginners, this book provides a solid foundation in Excel basics with step-by-step guides. It covers a wide range of fundamental topics, making it a great starting point for those new to spreadsheets. While suitable for initial learning, it also touches upon more complex areas.
Emphasizes the practical application of regression analysis through numerous examples. It's an excellent resource for understanding how to perform regression and interpret the results, making it suitable for both broad understanding and practical application. The focus is on exploratory data analysis rather than just theory.
Focuses on applying Excel to solve business problems and analyze business data. It's a practical guide for students and professionals in business-related fields who need to use Excel for decision-making and analysis.
Dives deep into array formulas, a powerful technique for complex calculations in Excel. It's for users who want to deepen their understanding of advanced formulas and push the boundaries of what Excel can do. Recommended for intermediate to advanced users.
Focuses on the process of building and evaluating predictive models using a variety of statistical and machine learning techniques. It's a practical guide for students and professionals involved in predictive analytics. The book covers essential concepts and provides detailed examples, making it a valuable resource for applied work.
A focused guide on mastering Excel formulas and functions, which are fundamental to using Excel effectively. is helpful for users looking to deepen their understanding of how to perform calculations and manipulate data. Relevant for all levels beyond the absolute beginner.
Is for users who want to leverage the power of Power BI and Power Pivot for more advanced data analysis and business intelligence. It delves into data modeling and DAX formulas, which are essential for creating sophisticated reports and dashboards using pivot tables and related technologies. It is geared towards users with a solid foundation in Excel and pivot tables.
Offers a new way of thinking about data science and ethics informed by intersectional feminist thought. It challenges existing power structures within data and explores how data can be used to work towards justice. It's highly relevant for contemporary discussions around fairness and bias in data analysis.
Offers a hands-on approach to learning Excel VBA, with step-by-step instructions and exercises designed for quick learning. It's suitable for users who want to rapidly acquire VBA skills for automation and enhancing their Excel capabilities.
Introduces Bayesian inferential methods, providing a coherent framework for incorporating prior knowledge and updating beliefs in light of new data.
This specialized book covers advanced topics in inferential statistics, such as generalized linear models, nonlinear regression, and nonparametric methods.
Provides a practical guide to inferential methods for data science, emphasizing the importance of understanding the underlying assumptions and limitations of statistical models.
Introduces Bayesian methods for social science research, covering topics such as Bayesian modeling, hierarchical models, and Markov chain Monte Carlo (MCMC) methods.

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