Course Code: DAP103

Course Overview

This comprehensive Data Analytics with Microsoft Excel course is intended to provide learners with the fundamental skills required to effectively analyze and interpret data using Excel. Participants will receive hands-on experience deriving meaningful insights from varied datasets, from data cleaning and modification through visualization and basic statistical analysis. The course stresses practical application through real-world examples and exercises, helping learners to make data-driven decisions.

While Excel is a powerful tool, it’s crucial to remember that for more advanced data analysis and larger datasets, more specialist tools such as Python, R, or dedicated data analytics software may be required. This course is designed to provide a good foundation in data analysis using Excel.

There is no prior data analysis or Excel experience required. It is recommended that you are familiar with basic computer operations.

Learning Outcome

Overall, understanding data analytics using Excel equips you with a broad set of abilities that can be useful in a variety of areas where data-driven decision-making is critical.

Data Manipulation: You will learn how to organize, clean, and transform raw data into a usable format in a timely and effective manner. Sorting, filtering, and text operations in Excel can help you manage and preprocess data effectively.

Excel can do basic statistical analysis on data, such as measures of central tendency, variability, correlations, and more. You’ll learn more about the distribution and qualities of your data.

Excel provides a variety of charting and graphing capabilities for visualizing data trends, patterns, and relationships. Learning to make various sorts of charts can allow you to communicate your findings more intuitively.

Excel allows you to study data by using pivot tables, which assist summarize and aggregate enormous datasets. You will learn how to swiftly extract useful insights from complex data.

Basic hypothesis testing may be performed in Excel, allowing you to make data-driven decisions with confidence. You will be able to judge the significance of observed data differences.

Excel has regression features that allow you to study correlations between variables and generate predictions based on data. This can be used to forecast and discover causal links.

Data Cleaning and Transformation: You’ll learn how to deal with missing data, outliers, and inconsistencies so that your analysis is accurate and dependable.

Scenario Analysis: Using Excel’s “What-If” analysis capabilities, you can experiment with alternative scenarios by changing the input variables. This is useful for forecasting and comprehending the probable consequences of decisions.

Dashboard Design: You may construct engaging and informative dashboards by combining several Excel capabilities. These dashboards can consolidate important metrics and KPIs for easier decision-making.

Time Series Analysis: Excel allows you to analyze time-based data, such as trends and seasonal patterns. You can make dynamic visualizations and learn about temporal trends.

Data Interpretation: You will learn how to interpret results and make meaningful inferences from your data in addition to completing analysis. This ability is essential for making sound commercial or research judgments.

Applications in the Real World: Excel is widely used in business, finance, marketing, and research. Learning data analytics using Excel provides you with practical skills that you may apply in a variety of professional settings.

Problem-Solving Ability: Data analytics in Excel frequently entails breaking difficult problems down into smaller steps. This improves critical thinking and problem-solving skills.

Communication: You will learn how to successfully convey your findings through visualizations, charts, and reports.

Target Audience

Individuals who are new to data analysis and wish to learn how to manipulate and visualize data in Excel.
Professionals that work with data and want to improve their Excel skills for faster analysis.
Business analysts, marketing experts, financial analysts, and anybody else who wants to present data in a relevant and powerful way would benefit from this course.
Students and instructors who want to learn about data analysis ideas using a technology that is freely available.

Toolkit

Microsoft Excel: The most often used tool for learning data analysis techniques, including functions, formulas, and visualization tools.
Real-world datasets for hands-on experience and experimentation.
Video Lectures: In-depth video content that explains key concepts and practices.
Hands-on tasks: These are practical tasks that are used to reinforce learning and build confidence.
Periodic quizzes and assessments are used to assess understanding and development.
Online discussion forums where individuals can ask questions, share insights, and collaborate.
Case Studies: Real-world case studies in which taught concepts are applied in practical situations.
Project Work: Culminating in a data analysis project to demonstrate learned skills.
Certificate of Completion: Recognizes successful completion of a course.

Course Content

Module 1: Data Analysis Fundamentals

Understanding the process of data analysis and its value.
Examining Excel’s function in data analysis.
A summary of the course’s structure and objectives.

Module 2: Data Preparation and Cleaning

Importing data from numerous sources into Excel.
Identifying and dealing with missing data.
Getting rid of duplicates and outliers.

Module 3: Data Transformation and Manipulation

Using Excel functions to transform data.
Filters and sorting are used to obtain relevant insights.
Using pivot tables to group and summarize data.

Module 4: Techniques for Data Visualization,

Making good charts, graphs, and visualizations.
Changing aesthetic elements for more clarity and impact.
Using visualizations to draw conclusions.

Module 5: Excel Basic Statistical Analysis

Understanding central tendency and variability measures.
Correlations between variables are calculated.
An overview of hypothesis testing.

Module 6: Data Analysis Case Studies

Putting newly acquired skills to use in real-world circumstances.
Analyzing sales data, customer behavior, and other information.

Module 7: Building Data Reports and Dashboards

Creating detailed data reports.
Creating interactive data presentation dashboards.

Module 8: Project for Final Data Analysis

Participants complete a full data analysis project.
Disclosing results and insights gained.

Course Duration

The course is designed to be completed in approximately 2Days, with flexibility for self-paced learning.

Course Details

Duration: 2 days