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Learn Data Analysis using Microsoft Excel Basics Fast
Rating: 1.4 out of 5(6 ratings)
611 students

Learn Data Analysis using Microsoft Excel Basics Fast

Learn Data Analysis using Microsoft Excel
Created byEGMHAcademy EGMHAcademy
Last updated 3/2019
English

What you'll learn

  • Microsoft Excel and Data ANalysis

Course content

1 section • 28 lectures • 40m total length

  • Getting Started0:49

    Start your data analysis journey with Microsoft Excel basics, exploring core ideas and a new feedback option that helps users analyze and organize data.

  • Downlaod Dataset1:11

    Learn to download and work with datasets for statistical analysis in Excel basics, including the iris dataset, and explore high risk datasets to practice data analysis.

  • Read CSV0:48

    Learn how to read csv files in Microsoft Excel to quickly access and analyze datasets. Excel basics support fast data analysis for learners.

  • Data Mining Process5:37

    Understand the data mining process from business understanding with domain experts to data preparation, modeling, evaluation, and deployment, including regression and classification models.

  • INsert LIne Chart2:54

    Insert a line chart in Microsoft Excel and learn to edit the chart, apply borders, and use format options to customize the chart design.

  • INsert Bar Chart1:33

    Create and customize a bar chart in Microsoft Excel by selecting the bar chart type, editing the chart data, and adjusting colors and styles.

  • INsert Pie CHart1:36

    Learn to create and customize a pie chart in Microsoft Excel, including 3d pie charts, borders, and transparency, with basic formatting for clear data.

  • INsert Scatterplot1:10

    Learn how to insert a scatterplot in Excel, perform basic setup steps, and visualize data quickly using essential Excel techniques.

  • Insert Combo Chart1:07

    Learn how to insert and customize a combo chart in Microsoft Excel, format data series, and tailor visuals to clearly compare multiple data types.

  • Pivot Table3:47

    Master pivot tables in Microsoft Excel to analyze data, dragging fields to rows, columns, values, and filters for dynamic insights.

  • INsert Pivot Table and Pivot CHarts1:23

    Master data analysis using Microsoft Excel basics by inserting pivot tables and pivot charts, selecting ranges and fields, and summarizing values for quick insights.

  • Data ANalysis Plugins0:47

    Learn how to enable and use data analysis plugins in Microsoft Excel to perform quick analyses.

  • Data Analysis PLugin 20:50

    Learn to perform data analysis in Microsoft Excel by working with untidy data and preparing the iris dataset for analysis.

  • Descriptive Statistics1:17

    Explore data analysis in Excel and apply descriptive statistics using data analysis tools. Compute means, medians, standard deviations, and confidence intervals to summarize data in the course.

  • HIstogram1:22

    Learn to create a histogram in Microsoft Excel, using frequency and cumulative percentage options to visualize data distributions and adjust settings for the iris data example.

  • Correlation1:20

    Learn to perform correlation in Microsoft Excel using the data analysis tool with iris data as an example, illustrating inferential statistics in data analysis.

  • Covariance0:53

    Explore covariance using Microsoft Excel to analyze iris data and build fundamental data analysis skills.

  • TTest Two Samples Paired for Means1:31

    Explore how to perform a paired two-sample t-test in Excel using data analysis tools, generate p-values and degrees of freedom, and read the t-statistic from the output.

  • TTest Two Samples Equal Variances1:13

    Apply a two-sample t-test with equal variances in Excel to compare mean differences between two variables, understanding degrees of freedom and the t statistic.

  • TTest Two Samples Unequal Variances1:12

    Learn to perform a two-sample t-test for unequal variances in Excel, interpret p-values and degrees of freedom, and compare sample differences.

  • F Test Two Samples for Variances1:05

    Learn to perform an F test for two samples to compare variances in Excel's data analysis tool, interpreting the F statistic, degrees of freedom, and p value.

  • ANOVA SIngle Factor1:09

    Explore how to perform a single-factor anova in Excel using the iris dataset, selecting input ranges and interpreting degrees of freedom and p-values.

  • ANOVA Two Factor without Replication1:15

    Learn to run a two-factor anova without replication in excel, using the iris dataset, and interpret degrees of freedom, mean squares, f statistics, and p values.

  • Regression Analysis2:03

    Explore regression analysis in Microsoft Excel, using the iris dataset to interpret outputs like degrees of freedom, sum of squares, t statistics, p values, and 95 percent confidence intervals.

  • Remove Duplicates0:47

    Learn how to remove duplicates in data preparation with Excel, select a range, and produce clean data by keeping only unique values.

  • Remove Missing Values1:10

    Select a range in Microsoft Excel to remove missing values, then use filtering to identify and delete rows with missing data.

  • Sort Data0:38

    Sort data in microsoft excel by slotting values and organizing columns. Highlight cells, expand selections, and arrange days from largest to smallest for quicker analysis.

  • Filer Data0:29

    Learn to filter data in Excel by using the Data tab, work with columns, and fill in missing values to prepare clean data for analysis.

Requirements

  • Basic COmputer Knowledge

Description

Data Analysis using Microsoft Excel: Fast Track with CRISP-DM

​Master essential data analysis, statistical testing, and data preparation techniques in Microsoft Excel using the industry-standard CRISP-DM framework.

​What You'll Learn

  • ​Apply the CRISP-DM methodology to real-world datasets for structured data analysis.

  • ​Build expressive visual charts, including Line, Bar, Pie, Scatter, and Combo Charts.

  • ​Leverage Pivot Tables and Pivot Charts to summarize and slice raw data quickly.

  • ​Perform advanced statistical analysis using the Excel Analysis ToolPak (Descriptive Statistics, Correlation, Covariance, Regression).

  • ​Conduct rigorous hypothesis testing using t-Tests, F-Tests, and ANOVA (Single Factor & Two-Factor).

  • ​Clean datasets by filtering, sorting, removing duplicates, and handling missing values.

​Why Learn Data Analysis & Data Science?

​According to SAS, here are 5 key reasons why analytics skills are essential in today's workforce:

  1. ​Sharpen Problem-Solving Skills: Build structured analytical thinking to solve complex challenges in professional and everyday life.

  2. ​High Market Demand: A growing global skill shortage means data analysts and data scientists command significant value across industries.

  3. ​Analytics Is Everywhere: Companies across all sectors need actionable insights to optimize processes and guide strategic decisions.

  4. ​Growing Importance: With the massive growth of enterprise data, skilled analysts enjoy expanding career opportunities.

  5. ​Versatile & Cross-Disciplinary: Data analysis bridges computer science, business strategy, and mathematics—plus applications in IoT and Smart Cities.

​Course Overview

​This bite-sized, practical course focuses on conducting data analysis fast using Microsoft Excel. Following the CRISP-DM framework, you will cover:

  • ​Data Understanding: Visualizations, summary statistics, and trend analysis.

  • ​Data Preparation: Data cleaning, removing duplicates, and handling missing values.

  • ​Statistical Modeling & Inference: Regression analysis and hypothesis testing with Excel plugins.

​Certification Pathway

​This course is part of the SVBook Advanced Certificate in Microsoft Office and Data Analysis with Excel. Complete the full track to prepare for the certificate exam at EGMHAcademy:

  • ​Learn Microsoft Word Basics Fast

  • ​Learn Microsoft PowerPoint Basics Fast

  • ​Learn Microsoft Excel Basics Fast

  • ​Learn Data Analysis using Microsoft Excel Basics Fast

​Course Content

​1. Getting Started & Setup

  • ​Introduction & Overview

  • ​Downloading Datasets & Importing CSV Files

  • ​The CRISP-DM Data Mining Process

​2. Visual Data Understanding & Pivot Tables

  • ​Inserting Line, Bar, & Pie Charts

  • ​Building Scatterplots & Combo Charts

  • ​Creating Interactive Pivot Tables & Pivot Charts

​3. Statistical Data Analysis

  • ​Enabling & Using Data Analysis Plugins (Analysis ToolPak)

  • ​Generating Descriptive Statistics & Histograms

  • ​Evaluating Correlation & Covariance

​4. Inferential Statistics & Modeling

  • ​t-Tests: Paired Two-Sample for Means, Equal Variances, & Unequal Variances

  • ​F-Test: Two-Sample for Variances

  • ​ANOVA: Single-Factor & Two-Factor Without Replication

  • ​Regression: Building & Interpreting Regression Models

​5. Data Preparation & Cleaning

  • ​Removing Duplicates & Handling Missing Data

  • ​Advanced Data Sorting & Filtering Techniques

​Prerequisites

  • ​Basic computer literacy.

  • ​Familiarity with Microsoft Excel operations (or completion of Learn Microsoft Excel Basics Fast).

​Who This Course Is For

  • ​Professionals and business analysts looking to harness Excel's built-in statistical suite for fast data analysis.

  • ​Students building practical analytics foundations before moving into advanced data science.

  • ​Anyone pursuing the SVBook Advanced Certificate in Microsoft Office and Data Analysis with Excel at EGMHAcademy.

Who this course is for:

  • Beginner Computer Passion People a nd analysts who want to learn Microsoft Excel and Data Analysis

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