Course Details

This course is appropriate for a wide range of professionals but not limited to:

• Business analysts and data analysts
• Engineers and technical staff involved in reporting or KPIs
• Operations, finance, HR, and marketing professionals
• Project managers and decision-makers
• Anyone interested in learning how to extract insights from data
• Beginners to intermediate-level learners in data analysis

Prerequisites: Basic computer literacy. No prior programming experience required, but helpful.

• Expert-led sessions with dynamic visual aids
• Comprehensive course manual to support practical application and reinforcement
• Interactive discussions addressing participants’ real-world projects and challenges
• Insightful case studies and proven best practices to enhance learning

By the end of this course, participants should be able to:

• Understand core concepts and workflow of data analysis.
• Perform data cleaning and preprocessing on raw datasets.
• Apply descriptive and inferential statistical techniques.
• Use data visualization to communicate patterns, trends, and outliers.
• Utilize tools such as Excel, Python (pandas, matplotlib), or Power BI/Tableau for analysis.
• Perform exploratory data analysis (EDA) to derive insights.
• Understand basics of predictive modeling (regression and classification).
• Make data-driven decisions and communicate findings effectively.

DAY 1

Introduction to Data Analysis and Basic Tools

• Welcome and introduction
• Pre-test
• What is Data Analysis? Scope and Importance
• Types of Data (Qualitative vs Quantitative, Structured vs Unstructured)
• Data Analysis Workflow
• Introduction to Microsoft Excel for Data Analysis

• Sorting, Filtering, Conditional Formatting
• Basic functions (SUM, AVERAGE, COUNTIF, VLOOKUP, etc.)

• Introduction to Python for Data Analysis

• Jupyter Notebook, pandas basics

• Hands-on exercise

DAY 2

Data Cleaning and Preprocessing

• Understanding dirty data
• Techniques for handling missing values
• Data transformation and formatting
• Removing duplicates and outliers
• Data types and conversions
• Best practices for clean datasets
• Hands-on exercise

DAY 3

Descriptive Statistics and Exploratory Data Analysis (EDA)

• Measures of Central Tendency (Mean, Median, Mode)
• Measures of Dispersion (Range, Variance, Standard Deviation)
• Frequency distributions and cross-tabulations
• Introduction to correlation and covariance
• Introduction to Data Visualization (Bar, Pie, Histogram, Boxplot)
• Detecting trends, patterns, and outliers
• Hands-on exercise

DAY 4

Inferential Statistics and Basic Predictive Modeling

• Probability concepts and distributions
• Hypothesis Testing (T-test, Chi-square test)
• Confidence Intervals
• Introduction to Linear Regression
• Introduction to Classification (Logistic Regression basics)
• When and how to use predictive models
• Hands-on exercise

DAY 5

Data Visualization, Reporting, and Communication

• Storytelling with Data
• Choosing the right chart for the right data
• Dashboards and Reports in Excel / Power BI / Tableau
• Communicating insights to non-technical audiences
• Final Project
• Hands-on exercise
• Post-test
• Certificate ceremony