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GROW YOUR SKILLS

Data Analysis Essentials

Description
Curriculum

Data Analysis Essentials is a comprehensive, beginner-friendly yet professionally structured program designed to equip learners with the core skills needed to collect, clean, analyze, visualize, and interpret data for real-world decision-making. This course bridges theoretical understanding and hands-on practice, empowering students to transform raw data into actionable insights that organizations rely on.

Whether you are transitioning into tech, upgrading your career, or enhancing your analytical knowledge, this course gives you the foundational competencies required in business analysis, data science, finance, marketing analytics, research, and process optimization.

Learners will master essential tools used in modern data analysis—including Excel, SQL, and Python—and understand how to communicate findings using dashboards, reports, and data storytelling frameworks. The course blends practical case studies, exercises, quizzes, and real-world projects to build applied expertise.

By the end of this course, you will confidently handle datasets, uncover patterns, build simple predictive insights, and present data-driven recommendations in a professional format.

🎓 What You Will Gain

  • Strong foundation in Excel, SQL, and Python

  • Ability to clean, analyze, and visualize data

  • Practical business-level analytical skills

  • Portfolio-ready projects

  • Confidence to apply for Data Analyst roles

  • Certification of completion

📌 Target Audience

  • Beginners transitioning into data analysis

  • Business professionals needing analytical skills

  • Students and graduates

  • Entrepreneurs and managers

  • Anyone looking to learn data-driven decision making

Curriculum Overview

Module 1: Introduction to Data Analysis

Understanding the data analysis lifecyclernrnTypes of data rn(structured, unstructured, semi-structured)rnrnData types and formatsrnrnThe role of a Data AnalystrnrnData-driven decision makingrnrnOverview of analyticsrn (descriptive, diagnostic, predictive, prescriptive)

Module 2: Data Collection & Data Sources

Primary vs secondary datarnrnAPIs, databases, spreadsheets, and online data sourcesrnrnWeb data extraction basicsrnrnUnderstanding data qualityrnrnData governance, privacy, and ethics

Module 3: Data Cleaning and Preparation

Handling missing, duplicate, and inconsistent datarnrnOutlier detectionrnrnData formatting and transformationrnrnData validation techniquesrnrnBest practices in dataset organizationrnrnPreparing data for analysis (ETL basics)

Module 4: Data Analysis Using Microsoft Excel

Essential Excel formulas for analysisrnrnData cleaning with Excel functionsrnrnPivotTables and PivotChartsrnrnSorting, filtering, and conditional formattingrnrnBasic statistical analysis in ExcelrnrnIntroduction to dashboards in ExcelrnrnCase Study: Analyzing business performance using Excel

Module 5: Introduction to Statistics for Data Analysis

Mean, median, mode, variance, standard deviationrnrnCorrelation and covariancernrnProbability fundamentalsrnrnSampling methodsrnrnHypothesis testing basicsrnrnWhen and how to apply statistical methods to business problems

Module 6: Data Visualization & Storytelling

Principles of effective data visualizationrnrnVisualizing data with charts, graphs, and dashboardsrnrnChoosing the right chart for the right datarnrnBuilding interactive dashboardsrnrnDesigning insights for non-technical stakeholdersrnrnData storytelling framework (Insight u2192 Impact u2192 Action)

Module 7: SQL for Data Extraction & Analysis

Introduction to databases (RDBMS concepts)rnrnSQL SELECT statementsrnrnFiltering, sorting, and complex conditionsrnrnJOINs (INNER, LEFT, RIGHT, FULL)rnrnAggregations and Group ByrnrnSubqueries and data manipulation (INSERT, UPDATE, DELETE)rnrnReal-world SQL queries for analysisrnrnMini Project: Building analytical queries from a real dataset

Module 8: Python for Data Analysis (Beginner)

Introduction to Python for analyticsrnrnInstalling and setting up Python/AnacondarnrnJupyter Notebook workflowrnrnKey libraries: Pandas, NumPy, MatplotlibrnrnData loading, cleaning, and transformationrnrnVisualizing data using PythonrnrnSimple predictive modeling introductionrnrnPython Project: Analyzing trends from a public dataset

Module 9: Business Intelligence (BI) Concepts

What is BI?rnrnOverview of Power BI / TableaurnrnBuilding dashboards and reportsrnrnConnecting data sources to BI toolsrnrnVisual best practices for stakeholdersrnrnAutomating data refresh and reportingrnrnBI Project: Build a Sales or Finance dashboard

Module 10: Capstone Project

Learners complete a full end-to-end analysis project:rnrnProject Steps:rnrnCollect and clean raw datarnrnPerform descriptive and diagnostic analysisrnrnVisualize and interpret insightsrnrnCreate a report or dashboardrnrnPresent findings professionallyrnrnExamples:rnrnSales performance analysisrnrnCustomer segmentationrnrnFinancial trend analysisrnrnMarketing campaign performance