Data Analytics with Excel

Enhance your data analysis skills with Excel. This course will teach you how to effectively utilize Excel for analyzing and interpreting data. G

Category Data Analytics

What you'll learn

Data Cleaning and Preparation: Master techniques to organize and clean raw data, including handling missing values, removing duplicates, and validating data accuracy.
Statistical Analysis: Learn to calculate descriptive statistics, perform regression analysis, and explore relationships between variables using Excel's Analysis ToolPak.
Data Visualization: Create impactful charts, graphs, and dashboards to communicate insights effectively.
3. Data Visualization: Create impactful charts, graphs, and dashboards to communicate insights effectively.
Interactive Reporting: Develop dynamic Pivot Tables and Pivot Charts to analyze and summarize data efficiently.

Course Syllabus

Module 1 : Data Cleaning

a:4:{i:0;s:50:"Text to Columns: Split data into multiple columns.";i:1;s:54:"Remove Duplicates: Identify and remove duplicate rows.";i:2;s:54:"Find & Replace: Quickly correct errors or modify data.";i:3;s:61:"Data Validation: Set rules for input data to ensure accuracy.";}

Data Analysis

a:4:{i:0;s:61:"Sorting & Filtering: Organize data based on various criteria.";i:1;s:64:"Conditional Formatting: Highlight specific patterns or outliers.";i:2;s:55:"Pivot Tables: Summarize and aggregate data dynamically.";i:3;s:80:"What-If Analysis: Use tools like Goal Seek and Scenario Manager for simulations.";}

Statistical Analysis

a:3:{i:0;s:88:"Descriptive Statistics: Calculate measures like mean median mode standard deviation etc.";i:1;s:103:"Regression Analysis: Perform linear regression and other statistical models using the Analysis ToolPak.";i:2;s:53:"Correlation: Measure relationships between variables.";}

Data Visualization

a:3:{i:0;s:78:"Charts and Graphs: Create bar charts line graphs scatter plots pie charts etc.";i:1;s:64:"Slicers: Enhance interactivity in Pivot Tables and Pivot Charts.";i:2;s:69:"Dashboards: Combine multiple visualizations for a comprehensive view.";}

Applications in Data Analytics

a:6:{i:0;s:124:"Sales Analysis: Monitor sales trends and forecast future performance. o Identify best-performing products or regions.";i:1;s:84:"Customer Segmentation: Group customers based on purchasing patterns or demographics.";i:2;s:71:"Financial Analysis: Track expenses revenue and profitability over time.";i:3;s:33:"Perform budget variance analysis.";i:4;s:138:"Operational Metrics: Analyze productivity and efficiency metrics. o Optimize inventory management using trends and demand patterns.";i:5;s:83:"Market Research: Survey data analysis using pivot tables and statistical functions.";}

Course Syllabus

Text to Columns: Split data into multiple columns.
Remove Duplicates: Identify and remove duplicate rows.
Find & Replace: Quickly correct errors or modify data.
Data Validation: Set rules for input data to ensure accuracy.

Sorting & Filtering: Organize data based on various criteria.
Conditional Formatting: Highlight specific patterns or outliers.
Pivot Tables: Summarize and aggregate data dynamically.
What-If Analysis: Use tools like Goal Seek and Scenario Manager for simulations.

Descriptive Statistics: Calculate measures like mean median mode standard deviation etc.
Regression Analysis: Perform linear regression and other statistical models using the Analysis ToolPak.
Correlation: Measure relationships between variables.

Charts and Graphs: Create bar charts line graphs scatter plots pie charts etc.
Slicers: Enhance interactivity in Pivot Tables and Pivot Charts.
Dashboards: Combine multiple visualizations for a comprehensive view.

Sales Analysis: Monitor sales trends and forecast future performance. o Identify best-performing products or regions.
Customer Segmentation: Group customers based on purchasing patterns or demographics.
Financial Analysis: Track expenses revenue and profitability over time.
Perform budget variance analysis.
Operational Metrics: Analyze productivity and efficiency metrics. o Optimize inventory management using trends and demand patterns.
Market Research: Survey data analysis using pivot tables and statistical functions.

Requirements

Laptop
Good wifi

Description

Data Analytics with Excel

This course on Data Analytics with Excel will teach you how to efficiently organize, clean, and analyze data using a range of Excel's powerful tools. You will learn how to perform statistical analysis, create dynamic visualizations, and build interactive reports with Pivot Tables and Charts. The course covers advanced Excel functions like VLOOKUP, logical formulas, and array functions to help you solve complex data problems. Whether you're working with sales data, financial metrics, or market research, this course will provide the skills needed to uncover insights and make data-driven decisions.

Who this course is for:

Aspiring Data Analysts: Individuals looking to build a strong foundation in data analysis using Excel for business decision-making.
Professionals Seeking to Upskill: Analysts, managers, or business professionals who want to enhance their data analysis and reporting skills.
Small Business Owners: Entrepreneurs who want to leverage Excel to analyze sales, operations, and financial data.
Excel Enthusiasts: Those who are already familiar with basic Excel functions and want to expand their knowledge to advanced data analysis techniques.
Job Seekers: Individuals who have completed basic courses but need practical, actionable skills to improve their employability in data-related roles.

Meet your instructors

Rashmi Ranjan Mangaraj

AI & Data Sceince

View Profile

Rashmi Ranjan Mangaraj is a highly skilled AI/ML Engineer with 6 years of practical, hands-on experience in developing and deploying artificial intelligence and machine learning solutions. His expertise lies in creating data-driven models and AI-powered solutions tailored to solve complex business c...

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Course Syllabus

Module 1 : Data Cleaning

a:4:{i:0;s:50:"Text to Columns: Split data into multiple columns.";i:1;s:54:"Remove Duplicates: Identify and remove duplicate rows.";i:2;s:54:"Find & Replace: Quickly correct errors or modify data.";i:3;s:61:"Data Validation: Set rules for input data to ensure accuracy.";}

Data Analysis

a:4:{i:0;s:61:"Sorting & Filtering: Organize data based on various criteria.";i:1;s:64:"Conditional Formatting: Highlight specific patterns or outliers.";i:2;s:55:"Pivot Tables: Summarize and aggregate data dynamically.";i:3;s:80:"What-If Analysis: Use tools like Goal Seek and Scenario Manager for simulations.";}

Statistical Analysis

a:3:{i:0;s:88:"Descriptive Statistics: Calculate measures like mean median mode standard deviation etc.";i:1;s:103:"Regression Analysis: Perform linear regression and other statistical models using the Analysis ToolPak.";i:2;s:53:"Correlation: Measure relationships between variables.";}

Data Visualization

a:3:{i:0;s:78:"Charts and Graphs: Create bar charts line graphs scatter plots pie charts etc.";i:1;s:64:"Slicers: Enhance interactivity in Pivot Tables and Pivot Charts.";i:2;s:69:"Dashboards: Combine multiple visualizations for a comprehensive view.";}

Applications in Data Analytics

a:6:{i:0;s:124:"Sales Analysis: Monitor sales trends and forecast future performance. o Identify best-performing products or regions.";i:1;s:84:"Customer Segmentation: Group customers based on purchasing patterns or demographics.";i:2;s:71:"Financial Analysis: Track expenses revenue and profitability over time.";i:3;s:33:"Perform budget variance analysis.";i:4;s:138:"Operational Metrics: Analyze productivity and efficiency metrics. o Optimize inventory management using trends and demand patterns.";i:5;s:83:"Market Research: Survey data analysis using pivot tables and statistical functions.";}

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