Data Analytics Crash Course For Beginners

Data Analyst, Python, Excel, Power BI, Data Science

What you'll learn

Understand the fundamentals of Data Analytics using Excel, Power BI, and Python.

Create professional dashboards and reports using Power BI.

Perform data cleaning and basic analysis in Microsoft Excel.

Write basic Python scripts for data manipulation using libraries like Pandas.

Connect and analyze data from multiple sources (CSV, Excel, Databases).

Requirements

No prior experience in data analytics is required – this course is for beginners.

Description

Course Title:Power BI, Excel & Python Basics for Data Analytics – Crash Course with ProjectFull Course Description:Are you ready to step into the world of data analytics but don’t know where to begin? This beginner-friendly crash course is designed to introduce you to three of the most powerful tools in data analytics—Power BI, Microsoft Excel, and Python—using real-world examples and a practical project.This course is perfect for students, professionals, and freelancers who want to gain foundational knowledge and hands-on experience in visualizing, analyzing, and interpreting data. Whether you're aiming for a career in data analytics or want to enhance your reporting skills, this course gives you the essential tools to get started.What You'll Learn:Power BI Basics:What is Power BI and how does it workInstalling and setting up Power BI DesktopConnecting to real-world datasets (Excel, CSV, Web, Databases)Cleaning and transforming data using Power QueryCreating interactive dashboards with charts, tables, filters, and slicersManaging relationships between tablesPublishing and sharing reports through Power BI ServiceExcel for Data Analytics:Basics of Microsoft Excel for data entry and formattingApplying formulas and functions (SUM, IF, VLOOKUP, etc.)Creating pivot tables and chartsPreparing structured data for use in Power BIPython Basics:Introduction to Python for data analysisUsing Jupyter Notebook or Google ColabImporting and exploring datasets using pandasWriting basic scripts to clean and analyze dataVisualizing simple trends using matplotlib or seabornFinal Project:You’ll apply what you've learned by working on a small end-to-end data analytics project—from importing raw data to visualizing insights using Power BI. This project will help you build a mini-portfolio piece and gain the confidence to move forward in your analytics journey.By the End of This Course, You Will:Understand the role and workflow of Power BI, Excel, and Python in data analyticsBe able to prepare and clean raw data using Excel and PythonCreate and publish Power BI dashboards using real-world dataGain confidence in building simple analytical reports from scratchRequirements:A laptop or desktop with internet accessPower BI Desktop (free)Microsoft Excel (any version recommended)No prior coding or analytics experience required — this course is beginner-friendlyWho This Course Is For:Beginners interested in data analytics and visualizationStudents and job seekers who want to enhance their resume with in-demand skillsFreelancers and small business owners who want to understand and present data effectivelyAnyone curious about how data can be used to make smart decisionsStart learning today and take your first step into the world of data analytics with tools that power professionals worldwide.

Overview

Section 1: Power BI

Lecture 1 Introduction

Lecture 2 License options

Lecture 3 Data Loading and Visualization

Lecture 4 Touring Views of Power BI

Lecture 5 Accessing Data from Databases and Relations

Lecture 6 Load Data from Web

Lecture 7 Performance Optimization with Excel and Pivot Data Model

Lecture 8 Resolving Different Errors

Lecture 9 Locally and Cloud Based Excel Files

Lecture 10 Publishing Files to the Service and Conclusion

Section 2: Excel - Basics

Lecture 11 Introduction to Excel

Lecture 12 Entering, Editing, and Formatting Data

Lecture 13 Formulas and Basic Functions

Lecture 14 Managing Rows, Columns and Worksheets

Lecture 15 Sorting and Filtering Data

Lecture 16 Creating Charts and Graphs

Lecture 17 Essential Excel Tool for Smart Data Management

Section 3: Python for Data Visualization

Lecture 18 CLASS 1 - Setting up things

Lecture 19 Working with Data in Python using Pandas

Lecture 20 Working with python using Pandas - 2

Lecture 21 Working with Time Series Data

Lecture 22 Data Cleaning Techniques

Lecture 23 Exploratory Data Analysis (EDA)

Lecture 24 Data Visualization with Seaborn and Matplotlib

Lecture 25 Data Visualization with Seaborn and Matplotlib - 2

Lecture 26 Introduction to Data Modeling (Linear Regression)

Lecture 27 Introduction to Data Modeling (K-means)

Lecture 28 Introduction to Data Modeling (Logistic Regression)

Lecture 29 Building a Mini Data Analytics Project

Beginners who want to learn data analytics with no prior experience.