Level 1
Data Analytics and Visualization using MS Excel
Registrations Open !!
Basic Computer Knowledge; Basic understanding of MS Excel
Registrations Open !!
Basic Computer Knowledge; Basic understanding of MS Excel
Data Analytics and Visualization (Beginner Level) is a skill-oriented course designed to equip students with industry-relevant competencies in data visualization and business intelligence. It introduces students to fundamental concepts of data analytics, data handling, and visual storytelling. The course focuses on understanding data types, performing data cleaning, applying descriptive statistics, and creating meaningful visualizations using MS Excel and introductory Python tools aligns on experiential learning, tool proficiency, and employability.
| Day | Topic | Sub-Topic No. | Sub-Topic Title | Detailed Contents |
| Day 1 | Introduction to Data Visualization | 1.1 | What is Data Analytics | Definition, importance, real-world examples ,Data vs Information vs Insight, Role of Excel in analytics |
| 1.2 | Types of Data | Structured vs Unstructured, Qualitative vs Quantitative Categorical vs Numerical Discrete vs Continuous Structured data (Excel tables) | ||
| 1.3 | Data Analytics Lifecycle | Descriptive, diagnostic, predictive (overview), prescriptive (overview). | ||
| 1.4 | Analytics Lifecycle | Problem definition, data collection, cleaning, analysis, visualization, decision-making. | ||
| Hands-On Activity: | 1.5 | Excel Foundations | Explore a sample CSV file and understand rows, columns, and variables. Import CSV, convert to table, format columns, identify data types. | |
| Day 2 | Data Collection & Cleaning | 2.1 | Data Sources | Surveys, Databases, APIs, CSV Files, Primary and secondary data, CSV files, Excel sheets, databases (conceptual). |
| 2.2 | Handling Missing Data | Identifying missing values, removing duplicates, basic imputation methods. | ||
| 2.3 | Data Formatting | Data type correction, date formatting, text cleaning. | ||
| 2.4 | Excel Functions | Remove duplicates, apply IF formula, use summary functions. Sorting, filtering, SUM, AVERAGE, COUNT, IF function. | ||
| Hands-On Activity: | 2.5 | MS-Excel | Remove duplicates, handle missing values, apply sorting and filtering, use basic formulas. | |
| Day 3 | Descriptive Statistics | 3.1 | Measures of Central Tendency | Mean, median, mode with examples. |
| 3.2 | Measures of Dispersion | Range, variance, standard deviation. | ||
| 3.3 | Correlation | Positive and negative correlation, scatter plot interpretation. | ||
| 3.4 | Data Distribution Basics | Introduction to skewness and boxplot interpretation. | ||
| Hands-On Activity: | 3.5 | Statistical Analysis in Excel | Calculate statistical measures in Excel; create scatter plot and interpret results. | |
| Day 4 | Creating and Formatting Visualizations | 4.1 | Principles of Visualization | Clarity, simplicity, avoiding misleading representations. |
| 4.2 | Types of Charts | Bar chart, line chart, pie chart, histogram, scatter plot. Selecting appropriate chart based on data type | ||
| 4.3 | Formatting and interactive features | colors, data labels, legends, titles, backgrounds, and tooltips for effective presentation, filters, slicers, cross-highlighting, and drill-down | ||
| 4.4 | Accessibility , Visualization selection and design best practices ,Design Rules. | Create bar, line, histogram, scatter charts; improve poorly designed chart. | ||
| Hands-On Activity: | 4.5 | Charts & Formatting | Create bar, line, histogram, scatter charts; labels, legends, colors, axis formatting. improve poorly designed chart. | |
| Day 5 | Dashboard Development | 5.1 | Introduction to Dashboards | Purpose, components, and layout design. KPIs. |
| 5.2 | Pivot Tables | Data aggregation and summarization. | ||
| 5.3 | Pivot Charts and Slicers | Interactive filtering, slicers, and visualization. | ||
| 5.4 | Intro to Python Visualization | Basic Matplotlib plotting commands (overview). | ||
| Hands-On Activity: | 5.5 | Dashboard Development | Build interactive dashboard using Pivot Tables and Slicers. | |
| Day 6 | Mini Project and Presentation | 6.1 | Problem Understanding | Define objective and identify variables. |
| 6.2 | Data Cleaning and Analysis | Apply cleaning techniques and descriptive statistics. | ||
| 6.3 | Visualization and Dashboard | presenting insights using narratives, visuals, and contextual explanations | ||
| 6.4 | Presentation of Insights | End-to-end BI workflow recap Real-world reporting scenarios Project evaluation criteria | ||
| 6.5 | Mini Project & Presentation (End-to-end Excel analytics project) | Build a complete dashboard using a new dataset Presentation and peer feedback. |
| Know your Mentor | Contact Number | Email Id | Teaching Experience (in Yrs.) |
| Dr. Prabhjot Kaur | 8968797787 | Prabhjot.e16646@cumail.in | 21 Years |
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