INTRODUCTION TO DATA ANALYTICS USING MICROSOFT EXCEL
Data Analytics educates participants with little to no statistical or software expertise in fundamental statistical and visualization tools that make use of Microsoft Excel’s intuitive and popular capabilities. Participants will master the numerous strong capabilities of spreadsheets before utilizing them in conjunction with statistical techniques to investigate and identify relationships in their data using Microsoft Excel.
This course is typically delivered through a combination of lectures, case studies, hands-on exercises, and interactive discussions. Participants will have opportunities to work with REAL-TIME data sets of their own, engage in group activities, and learn from industry examples and best practices.
COURSE OBJECTIVES
- Develop critical thinking skills: Enhance your ability to think critically, analyze information objectively, and evaluate
alternative solutions. - Understand problem-solving methodologies: Learn different problem solving frameworks and methodologies that will
guide you through the process of identifying, defining, and solving problems effectively. - Utilize analytics for problem-solving: Discover how to collect and analyze data using various analytical tools and techniques to gain insights and support decision-making.
- Apply data-driven decision making: Learn how to integrate data analysis into the decision-making process to make informed and evidence-based choices.
- Enhance problem-solving creativity: Explore methods to stimulate creative thinking and generate innovative solutions to complex problems
- Develop effective communication skills: Learn how to present your analysis, findings, and recommendations in a clear and compelling manner to stakeholders.
LEARNING OUTCOMES
Upon completion, participants will able to:
- Use Pivot Tables and functions effectively to summaries and augment Data in Microsoft Excel.
- Visualize data & create analytical dashboards for reporting with Microsoft Excel.
- Assess data distributions and statistical relationships with Microsoft Excel.
- Utilize Microsoft Excel for basic data management and analysis.
- Optimize their operational workflow/schedule by incorporating their analytical skills of
- Microsoft Excel.
- Be able to present key findings within their visualization tool that is easily understood by
- their audience.
- Participants will learn how to narrate and evaluate insights.
COURSE CONTENT
DAY 1 : Introduction to Data Analysis with Excel
Module 1: Classic data analysis with excel
- Types of Data Analysis
- Data Analysis with Excel
- Copying Name using Formula Autocomplete
- Creating Range Names
Module 2: Spreadsheet fundamental
- Editing Names
- Using Names in a formula
- Difference between Tables and Ranges
Module 3: Predefined functions
- Quick analysis with TOTALS
- Aggregates values using Sum
- Average of data
- Count of cells
- Running Total of table data
Module 4: Application of Data Validation
- Prepare the Structure for the Worksheet
- Format Serial Number Values
Module 5: Data Normalization
- Date Formats
- Converting Dates in Serial Format to MonthDay-Year Format
- Obtaining Today’s Date
- Customizing the Definition of a Weekend
- Extracting Year, Month, Day from Date
- Obtaining Date from Year, Month and Day
- Combining texts
Module 6: Pivot Tables
- Creating Pivot Table
- Recommended Pivot Tables
- Pivot Table Fields
- Pivot Table Areas
- Nesting in the Pivot Table
- Filters
- Slicers
- Summarizing Values by other Calculations
- Pivot Table Tools
- Expanding and Collapsing Field
- Report Presentation Styles
Module 7: Data Visualization in Excel
- Creating Combination Charts
- Creating a Combo Chart with Secondary Axis.
- Discriminating Series and Category Axis
- Chart Elements and Chart Styles
DAY 2 : Advanced Techniques of Data Analytics
Module 8: Module Build Interactive Excel Dashboards
- Report Layout Finalization
- Selecting the Background
- Changing the Font
- Slicers
- Drill-down analysis
Module 9: Examining Distribution
- Descriptive Statistics
- Constructing advanced graphs and charts
- Introduction to Statistical Data Analysis
- One categorical variable
Module 10: Descriptive Statistics
- Arithmetic mean of data
- Most common value in data
- Standard deviation of data
- Variance of data
- Range of data
- Maximum value of data
- Minimum value of data
- Summation of values
- Counts of number values
Module 11: Introduction to Statistical Data Analysis
- Correlation coefficient between two sets of numbers
- Coefficient of determination between two sets of numbers
- Slope of a regression line through two sets of numbers
Module 12: Regression Analysis
- Simple Linear Regression
- Regression Function
- Exploring Regression
- Performing a Regression Analysis
- Checking the Regression model
Module 13: Forecast Analysis
- Forecast using historical data
- Create Forecast Chart
- Create Forecast Data
Module 14: Case Studies and Practical Exercises (REAL TIME DATA)
- Applying problem-solving techniques to realtime scenarios
- Collaborative exercises and group discussions
- Analyzing and presenting solutions to case studies
- Peer feedback and learning from best practices