Data Analysis

6 months duration

2 modules

Updated Jun 8, 2025

Data & Analytics

Course Overview

Get to know what this course is all about and what you'll learn

Course Description

Transform raw data into actionable business insights with this comprehensive data analysis program. Master the complete data analysis workflow using industry-standard tools including Excel, SQL, Python, Power BI, and essential mathematical and statistical concepts. This hands-on course takes you from data collection and cleaning through advanced visualization and statistical modeling, preparing you for roles as a data analyst, business intelligence specialist, or data-driven decision maker. Whether you're starting your analytics journey or advancing your existing skills, this program provides practical experience with real-world datasets and projects that mirror actual business scenarios. By completion, you'll confidently extract meaningful insights from complex data and communicate findings effectively to stakeholders.

What You'll Learn

This comprehensive program builds your expertise across five core areas that form the foundation of modern data analysis. You'll begin with Excel for data manipulation and visualization, developing advanced skills in formulas, pivot tables, statistical functions, and dashboard creation for immediate business impact. The course then introduces essential mathematical and statistical concepts, covering descriptive and inferential statistics, probability distributions, hypothesis testing, and regression analysis that underpin all analytical work.

Database skills form the next pillar, with SQL training that covers complex queries, table joins, data aggregation, and optimization techniques for working efficiently with large datasets. Power BI training follows, enabling you to create interactive dashboards, perform advanced data modeling with DAX calculations, and share insights across organizations. Finally, Python programming rounds out your technical toolkit, teaching data manipulation with Pandas and NumPy, statistical analysis with SciPy, and compelling visualizations with Matplotlib and Seaborn. The learning approach emphasizes project-based work using real business cases from finance, marketing, healthcare, and retail sectors.

Each module builds progressively on previous knowledge while demonstrating how different tools integrate within the complete data analysis workflow. You'll understand not just how to use each tool, but when and why to apply specific techniques for maximum impact. This course serves career changers transitioning into data roles, business professionals enhancing their analytical capabilities, recent graduates preparing for analyst positions, entrepreneurs seeking to leverage data for growth, and current analysts expanding their technical skills. The only prerequisites are basic computer literacy, high school level mathematics, and curiosity about problem-solving with data.

Upon completion, graduates are prepared for roles including data analyst, business intelligence analyst, reporting specialist, market research analyst, operations analyst, and junior data scientist positions. The 12-16 week program combines video lessons, hands-on exercises, and capstone projects, with flexible pacing options and comprehensive support including instructor guidance and career services. Students earn a professional certificate demonstrating mastery across the complete data analysis spectrum, setting them apart with a comprehensive toolkit rather than single-tool expertise.

Course Curriculum

2 modules • Learn at your own pace • Hands-on experience

Data Analysis Curriculum

Prerequisites

Be ready to learn

Learning Objectives

Create advanced formulas and functions for complex data calculations and analysis in Excel
Build dynamic pivot tables and pivot charts to summarize and explore large datasets
Design interactive dashboards with slicers, timelines, and conditional formatting
Perform statistical analysis using Excel's built-in tools and add-ins
Clean and transform messy data using Excel's data preparation features
Apply descriptive statistics to summarize and interpret dataset characteristics
Conduct hypothesis testing and interpret p-values and confidence intervals
Perform correlation and regression analysis to identify relationships between variables
Select appropriate statistical tests based on data types and research questions
Communicate statistical findings clearly to non-technical stakeholders

Course Modules

Excel serves as the foundation of data analysis, providing immediate access to powerful analytical capabilities that every data professional must master. This module transforms you from a basic Excel user into an advanced analyst capable of handling complex datasets and creating sophisticated analytical solutions.

You'll master advanced formulas, statistical functions, and lookup operations to automate analytical processes and merge data from multiple sources. Pivot tables and pivot charts become your primary tools for summarizing large datasets and revealing hidden patterns through dynamic, interactive analysis. Data visualization extends beyond basic charts to include advanced chart types, interactive dashboards, and professional reporting tools with slicers and conditional formatting.

The module covers essential data cleaning techniques using text functions, Power Query, and data validation to ensure analytical accuracy. Real-world projects in financial analysis, sales tracking, and customer segmentation provide hands-on experience applying Excel's full analytical capabilities to solve business problems and present findings through professional reports and dashboards.

Module Objectives

Create advanced formulas and functions for complex data calculations and analysis
Build dynamic pivot tables and pivot charts to summarize and explore large datasets
Design interactive dashboards with slicers, timelines, and conditional formatting
Perform statistical analysis using Excel's built-in tools and add-ins
Clean and transform messy data using Excel's data preparation features
Use lookup functions (VLOOKUP, INDEX-MATCH, XLOOKUP) to merge data from multiple sources
Apply data validation and error checking techniques to ensure data quality
Create professional charts and visualizations that highlight key insights
Import and transform data using Power Query
Build automated reporting solutions with interactive elements

Statistics and mathematics form the analytical backbone that transforms raw data into reliable insights and evidence-based decisions. This module builds your statistical literacy from fundamental concepts through advanced analytical techniques, ensuring you can confidently interpret data patterns, test hypotheses, and quantify uncertainty in your findings.

You'll master descriptive statistics to summarize and characterize datasets, learning to calculate and interpret measures of central tendency, variability, and distribution shape. Understanding probability distributions becomes essential as you explore normal, binomial, and other key distributions that model real-world phenomena. These foundations enable you to recognize when data follows expected patterns and identify meaningful deviations. Inferential statistics takes center stage as you learn to draw conclusions about populations from sample data. Hypothesis testing becomes a systematic process for validating assumptions, comparing groups, and measuring the statistical significance of your findings. You'll master confidence intervals, p-values, and effect sizes to communicate the reliability and practical importance of your results. Correlation and regression analysis reveal relationships between variables, enabling predictive modeling and causal inference.

The module emphasizes practical application through business scenarios in marketing effectiveness, quality control, financial risk assessment, and operational optimization. You'll learn to select appropriate statistical tests based on data types and research questions, interpret results correctly, and communicate statistical findings clearly to non-technical stakeholders while avoiding common pitfalls and misinterpretations.

Module Objectives

Apply descriptive and inferential statistics to summarize data and draw meaningful conclusions from samples
Conduct hypothesis testing, interpret p-values and confidence intervals, and assess statistical significance
Perform correlation and regression analysis to identify and quantify relationships between variables
Select appropriate statistical tests based on data types and research questions
Communicate statistical findings clearly to non-technical stakeholders while avoiding common misinterpretations