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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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/xkwgnz/statistics
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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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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.
Read more
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Statistics and Data Analysis with Excel: Intermediate.
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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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
For more career information including salaries, visit:
OpenCourser.com/course/xkwgnz/statistics
Reading list
We haven't picked any books for this reading list yet.
Comprehensive introduction to statistics for psychologists, covering topics such as data collection, analysis, and interpretation. It is written in German and good choice for students who want to learn the basics of statistics in German.
Comprehensive introduction to statistics, covering topics such as data collection, analysis, and interpretation. It is well-written and provides plenty of examples and exercises.
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Concise introduction to statistical inference, covering topics such as point estimation, hypothesis testing, and confidence intervals. It good choice for students who want to learn the basics of statistical inference.
Comprehensive introduction to Bayesian data analysis, covering topics such as Bayesian probability, Bayesian inference, and Bayesian modeling. It good choice for students who want to learn the basics of Bayesian statistics.
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Comprehensive introduction to machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It good choice for students who want to learn the basics of machine learning.
Comprehensive introduction to statistical learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It good choice for students who want to learn the basics of statistical learning.
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Comprehensive handbook of statistical techniques, covering topics such as data collection, analysis, and interpretation. It good choice for students who want to learn how to apply statistical techniques to real-world problems.
Popular introduction to statistics, covering topics such as data collection, analysis, and interpretation. It is written in a clear and concise style, making it accessible to readers of all levels.
Comprehensive introduction to causal inference, covering topics such as causal models, causal effects, and causal inference methods. It good choice for students who want to learn the basics of causal inference.
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Comprehensive introduction to biostatistics, covering topics such as data collection, analysis, and interpretation. It good choice for students who want to learn the basics of biostatistics for health science research.
Is an excellent starting point for anyone looking to gain a broad understanding of statistics without getting bogged down in complex formulas. It uses real-world examples and engaging prose to explain fundamental statistical concepts, making it ideal for high school students and undergraduates. It serves as valuable background reading to demystify statistics and build intuition before tackling more technical material.
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.
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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.
Focuses on the process of learning from data and the importance of statistical thinking in everyday life. It's an excellent resource for gaining a broad understanding of how statistics are used and misused, suitable for a wide audience from high school to professional. It emphasizes critical thinking and the practical application of statistical ideas.
This textbook offers a solid theoretical foundation in mathematical statistics while also illustrating its practical applications. It is typically used in undergraduate programs for students majoring in statistics or related quantitative fields. The book helps solidify understanding of the mathematical underpinnings of statistical methods.
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.
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.
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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.
Provides a comprehensive overview of basic statistical concepts, including data collection, analysis, and interpretation. It is written in a clear and concise style, making it accessible to readers of all levels.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/xkwgnz/statistics
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Similar courses
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Level
Intermediate
Via
Coursera
Institution
Macquarie University
Instructor
Assoc Prof Prashan S. M. Karunaratne
Session
November 3, 2025
- December 15, 2025
Language
English
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