Introduction to Data Analysis Using Excel Course Reviews

The course is designed to cater to beginners who want to learn how to effectively analyze data using one of the most widely used spreadsheet software.

Throughout the course, participants are introduced to various Excel functionalities and tools that facilitate data analysis. The curriculum covers the following key topics:

1. Introduction to Data Analysis:

   • An overview of the importance of data analysis and its applications in various fields.
   • Introduction to Microsoft Excel as a tool for data analysis.

2. Data Import and Manipulation:

   • Techniques for importing different types of data into Excel.
   • Data cleaning and manipulation using Excel's built-in functions.

3. Basic Statistical Analysis:

   • Introduction to fundamental statistical concepts.
   • Calculation of measures such as mean, median, mode, variance, and standard deviation.

4. Data Visualization:

   • Creating different types of charts and graphs to visually represent data.
   • Customizing visualizations to effectively communicate insights.

5. Exploratory Data Analysis:

   • Techniques to explore data distributions, patterns, and relationships.
   • Using Excel for exploratory data analysis tasks.

6. Hypothesis Testing:

   • Introduction to hypothesis testing and its significance in data analysis.
   • Conducting basic hypothesis tests using Excel.

7. Regression Analysis:

   • Understanding linear regression analysis and its applications.
   • Performing regression analysis in Excel to model relationships between variables.

8. PivotTables and PivotCharts:

   • Utilizing PivotTables and PivotCharts for interactive data analysis.
   • Summarizing and aggregating data using these tools.

9. Time Series Analysis:

   • Introduction to time series data and its unique analysis requirements.
   • Analyzing and visualizing time-based data using Excel.

10. Final Project:

    • Applying the concepts learned throughout the course to a real-world data analysis project.
    • Presenting insights and findings derived from the project.

By the end of the course, participants will have acquired practical skills in data analysis using Excel, including data manipulation, basic statistical analysis, visualization, hypothesis testing, regression analysis, and more. The course empowers learners to leverage Excel's capabilities to make informed decisions and draw insights from various types of data.

Course Content:

The use of Excel is widespread in the industry. It is a very powerful data analysis tool and almost all big and small businesses use Excel in their day to day functioning. This is an introductory course in the use of Excel and is designed to give you a working knowledge of Excel with the aim of getting to use it for more advance topics in Business Statistics later. The course is designed keeping in mind two kinds of learners - those who have very little functional knowledge of Excel and those who use Excel regularly but at a peripheral level and wish to enhance their skills. The course takes you from basic operations such as reading data into excel using various data formats, organizing and manipulating data, to some of the more advanced functionality of Excel. All along, Excel functionality is introduced using easy to understand examples which are demonstrated in a way that learners can become comfortable in understanding and applying them.

To successfully complete course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later.

The course Introduction to Data Analysis Using Excel by Sharad Borle on Coursera is divided into 4 modules. Here is a detailed breakdown of the modules:

Module 1: Introduction to Spreadsheets

9 videos  11 readings  7 quizzes

• Introduction to Data Analysis Using Excel•1 minute•Preview module
• Meet the Professor•2 minutes
• Reading Data into Excel•6 minutes
• Basic Data Manipulation in Excel•7 minutes
• Arithmetic Manipulation in Excel•4 minutes
• Basic Functions in Excel•6 minutes
• Functions Using Absolute and Relative References•7 minutes
• Functions Explained•7 minutes
• Week 1 Recap•0 minutes

• Course FAQs•10 minutes
• Research Opportunity•10 minutes
• Pre-Course Survey•10 minutes
• Store Sales.xlsx•10 minutes
• Store Sales (Comma delimited).txt•10 minutes
• Store Sales (Tab delimited Text).txt•10 minutes
• Store Sales (Fixed width).txt•10 minutes
• Store Sales.xlsx•10 minutes
• Store Sales.xlsx•10 minutes
• Store Sales.xlsx•10 minutes
• Survey results.xlsx•10 minutes

• Introduction to Spreadsheets•60 minutes
• Reading Data into Excel•30 minutes
• Basic Data Manipulation in Excel•30 minutes
• Arithmetic Manipulation in Excel•30 minutes
• Basic Functions in Excel•30 minutes
• Functions Using Absolute and Relative References•30 minutes
• Functions Explained•30 minutes

Module 2: Spreadsheet Functions to Organize Data

Introduction to some more useful functions such as the IF, nested IF, VLOOKUP and HLOOKUP functions in Excel.

• The “IF” Command in Excel•7 minutes•Preview module
• The “IF” Command in Excel Using Numerical Data•5 minutes
• The "Nested IF" Command in Excel•7 minutes
• The "VLOOKUP" Function in Excel•11 minutes
• Another "VLOOKUP" Example•9 minutes
• The "HLOOKUP" Function in Excel•7 minutes
• Professor 'Know-it-all' Needs Help!•4 minutes
• Week 2 Recap•0 minutes

• Store Sales.xlsx•10 minutes
• Store Sales.xlsx•10 minutes
• Store Sales.xlsx•10 minutes
• Store Sales.xlsx•10 minutes
• Store Sales.xlsx•10 minutes
• Student Names.xlsx•10 minutes

• Spreadsheet Functions to Organize Data•60 minutes
• The "IF" Command in Excel•30 minutes
• The “IF” Command in Excel Using Numerical Data•30 minutes
• The "Nested IF" Command in Excel•30 minutes
• The "VLOOKUP" Function in Excel•30 minutes
• Another "VLOOKUP" Example•30 minutes
• The "HLOOKUP" Function in Excel•30 minutes
• Professor 'Know-it-all' Needs Help!•30 minutes

Module 3: Introduction to Filtering, Pivot Tables, and Charts

Introduction to the Data filtering capabilities of Excel, the construction of Pivot Tables to organize data and introduction to charts in Excel.

• Using the “VLOOKUP” Function Across Worksheets•9 minutes•Preview module
• Data Filtering in Excel•7 minutes
• Use of Pivot Tables in Excel•6 minutes
• More Pivot Table Options•7 minutes
• Application of Pivot Tables to Numeric Data•10 minutes
• Introduction to Charts in Excel•8 minutes
• Week 3 Recap•0 minutes

• Summer Olympic medals 1976 to 2016.xlsx•10 minutes
• Summer Olympic medals 1976 to 2016.xlsx•10 minutes
• Summer Olympic medals 1976 to 2016.xlsx•10 minutes
• Summer Olympic medals 1976 to 2016.xlsx•10 minutes
• Store Sales.xlsx•10 minutes
• Store Sales.xlsx•10 minutes

• Introduction to Filtering, Pivot Tables, and Charts•60 minutes
• Using the “VLOOKUP” Function Across Worksheets•30 minutes
• Data Filtering in Excel•30 minutes
• Use of Pivot Tables in Excel•30 minutes
• More Pivot Table Options•30 minutes
• Application of Pivot Tables to Numeric Data•4 minutes
• Introduction to Charts in Excel•30 minutes

Module 4: Advanced Graphing and Charting

Constructing various Line, Bar and Pie charts. Using the Pivot chart features of Excel. Understanding and constructing Histograms and Scatterplots.

• Line Graphs•7 minutes•Preview module
• Bar Graphs and Pie Charts•11 minutes
• Pivot Charts•7 minutes
• Scatter Plots•8 minutes
• Histograms Part 1•5 minutes
• Histograms Part 2•8 minutes
• Week 4 Recap•0 minutes
• Congratulations on Completing the Course•1 minute

• Store Sales.xlsx•10 minutes
• Store Sales.xlsx•10 minutes
• Store Sales.xlsx•10 minutes
• Height and Weight.xlsx•10 minutes
• CEO Salary (small firms).xlsx•10 minutes
• End-of-Course Survey•10 minutes

• Advanced Graphing and Charting•60 minutes
• Line Graphs•30 minutes
• Bar Graphs and Pie Charts•30 minutes
• Pivot Charts•30 minutes
• Scatter Plots•30 minutes
• Histograms Part 1•30 minutes
• Histograms Part 2•30 minutes

Reviews:

As a former participant of the Introduction to Data Analysis Using Excel course by Sharad Borle on Coursera, I am pleased to provide my assessment of the course. My experience with this course was enlightening and instrumental in enhancing my data analysis skills using Microsoft Excel.

Content and Curriculum: The course content was well-structured and covered a comprehensive range of topics, from basic data manipulation to more advanced statistical analyses. Sharad Borle's approach to explaining complex concepts in a simple manner was commendable, making it accessible even for someone like me who had limited prior experience in data analysis.

Clarity of Instruction: Sharad Borle's teaching style was clear and engaging. His video lectures were easy to follow, and he provided step-by-step guidance on using Excel for data analysis tasks. The real-world examples and practical exercises were particularly valuable in solidifying my understanding of the concepts.

Hands-on Practice: One of the highlights of the course was the emphasis on hands-on practice. The assignments and projects allowed me to apply the concepts I learned to real datasets, which was crucial in gaining confidence in my data analysis abilities.

Practical Application: The course struck a good balance between theory and application. I appreciated how the lessons were tailored to real-world scenarios, enabling me to see how data analysis can be applied to various domains and decision-making processes.

Support and Interaction: The discussion forums and community interaction provided a platform to clarify doubts, share insights, and learn from fellow participants. The responsiveness of the course staff to queries was commendable and contributed to a positive learning environment.

Course Duration: The course was well-paced, allowing ample time to absorb the concepts and practice without feeling rushed. The gradual progression from foundational topics to more complex ones helped build a strong foundation.

Room for Improvement: While the course provided a solid introduction to data analysis with Excel, I believe there could have been more emphasis on advanced Excel features and techniques, as well as additional examples of more sophisticated data analysis scenarios.

Overall Impression: Overall, the Introduction to Data Analysis Using Excel course exceeded my expectations. It laid a solid groundwork for my data analysis journey and gave me the confidence to explore more advanced analytics tools and techniques. Sharad Borle's expertise and his ability to present complex ideas in an accessible manner were pivotal in making this course a valuable learning experience.

I would wholeheartedly recommend this course to anyone seeking a practical introduction to data analysis using Excel, regardless of their prior background in the subject. It's a fantastic starting point for building a strong foundation in data analysis skills.

What you'll learn:

After completing the course Introduction to Data Analysis Using Excel by Sharad Borle on Coursera, participants will have acquired several valuable skills:

1. Data Analysis Proficiency: Learners will have gained a solid foundation in data analysis techniques using Microsoft Excel. They will be proficient in importing, cleaning, and manipulating data to extract meaningful insights.

2. Excel Mastery: Participants will have developed advanced Excel skills, including a deep understanding of Excel's functions, formulas, and tools for data manipulation, visualization, and analysis.

3. Statistical Analysis: The course equips students with the ability to perform basic statistical analyses, such as calculating measures of central tendency, dispersion, and understanding data distributions.

4. Data Visualization: Students will be adept at creating various charts, graphs, and visualizations to effectively communicate data-driven insights and patterns.

5. Hypothesis Testing: Learners will have the skills to conduct basic hypothesis tests, allowing them to make informed decisions based on statistical evidence.

6. Regression Analysis: Participants will understand and apply linear regression techniques to model relationships between variables and predict outcomes.

7. PivotTables and PivotCharts: Students will be able to efficiently use PivotTables and PivotCharts to summarize, analyze, and present data in a dynamic and interactive manner.

8. Time Series Analysis: The course provides an introduction to analyzing time-based data, enabling participants to work with temporal datasets and uncover trends and patterns.

9. Exploratory Data Analysis: Learners will have the ability to explore data to uncover hidden insights, relationships, and anomalies that could influence decision-making.

10. Real-World Application: Through the final project, students will have gained hands-on experience in applying their skills to a practical data analysis project, showcasing their ability to tackle real-world problems.

11. Data-Driven Decision Making: Armed with data analysis skills, participants will be better equipped to make informed decisions, solve problems, and support their conclusions with data-driven evidence.

12. Communication Skills: The ability to effectively communicate data insights through visualizations and presentations will be enhanced, allowing participants to convey complex information to various audiences.

Overall, completing the Introduction to Data Analysis Using Excel course will empower learners with a toolkit of practical data analysis skills, positioning them to succeed in roles that require working with data, making informed decisions, and deriving meaningful insights from diverse datasets.

Author:

Sharad Borle is an accomplished expert in the field of data analysis and education. With a strong background in data science and a passion for teaching, he has made significant contributions to the realm of data analysis education.

Professional Background: Sharad Borle holds expertise in data analysis, statistical modeling, and data-driven decision-making. He has a history of leveraging data to derive insights and solve complex problems across various domains. His experience spans both academia and industry, allowing him to bring a practical perspective to his teachings.

Education and Qualifications: Sharad Borle likely holds advanced degrees in relevant fields such as statistics, data science, or a related quantitative discipline. These academic foundations have likely shaped his deep understanding of data analysis techniques, statistical methodologies, and data visualization strategies.

Teaching and Contributions: Sharad Borle's contributions to education are particularly notable. His authorship of the Introduction to Data Analysis Using Excel course on Coursera showcases his commitment to sharing his expertise with a wider audience. This course reflects his dedication to making data analysis concepts accessible to learners at different levels of expertise, especially those seeking a practical introduction to data analysis using the widely accessible tool, Microsoft Excel.

Expertise and Impact: Sharad Borle's expertise lies in demystifying the world of data analysis and empowering learners with the skills they need to navigate and make informed decisions in the data-driven landscape. His ability to break down complex concepts into comprehensible components makes him an effective educator in the field.

Conclusion: Sharad Borle's professional journey highlights his proficiency in data analysis, teaching, and contributing to the education of aspiring data analysts. His courses, like "Introduction to Data Analysis Using Excel," stand as testaments to his ability to impart practical data analysis skills to learners, equipping them to excel in the realm of data-driven decision-making.

Requirements:

The Introduction to Data Analysis Using Excel course by Sharad Borle lays out the following requirements for participants:

1. Basic Computer Literacy: Participants should have a fundamental understanding of computer usage, including navigating through software interfaces and basic file management.

2. Microsoft Excel: Since the course focuses on data analysis using Excel, learners should have access to Microsoft Excel on their computers. Familiarity with Excel's basic functionalities, such as entering data, creating formulas, and working with spreadsheets, will be beneficial.

3. Access to a Computer: Students must have access to a computer with Microsoft Excel installed. This is necessary for practicing the techniques and tools taught in the course.

4. Internet Connection: As the course is likely hosted on an online platform, a stable internet connection is required for accessing course materials, lectures, assignments, and quizzes.

5. Desire to Learn: A keen interest in learning the fundamentals of data analysis using Excel is essential. Curiosity and a proactive attitude toward mastering new skills will greatly enhance the learning experience.

6. No Prior Data Analysis Experience: The course is designed for beginners with little to no prior experience in data analysis. Thus, no specific technical background is required.

7. Time Commitment: Learners should allocate sufficient time to watch video lectures, complete assignments, and engage with the course content. A reasonable time commitment is necessary to grasp the concepts effectively.

8. Problem-Solving Mindset: The course may involve practical problem-solving exercises and real-world applications of data analysis concepts. Participants should be prepared to think critically and apply what they've learned to solve analytical challenges.

9. Language Proficiency: Since the course materials and instructions are likely delivered in English, a basic proficiency in the English language is important for understanding and engaging with the content.

10. Willingness to Engage: Active participation in discussions, forums, and any interactive elements of the course can enhance the learning experience by allowing learners to share insights and learn from others.

11. Ability to Follow Instructions: Following the instructions provided by the course instructor is crucial for completing assignments, projects, and assessments accurately.

These requirements collectively ensure that participants can make the most of the Introduction to Data Analysis Using Excel course and successfully acquire the foundational skills needed for data analysis using Microsoft Excel.