Data Analysis with R Course Syllabus

About the course:

Data analysis is the process of collecting, cleaning, and interpreting data. The insights gleaned from data analysis help businesses make more informed decisions. Data analysis can sound a lot like data science. R analytics is a free, open-source software used for all kinds of data science, statistics, and visualization projects. R programming language is powerful, versatile, AND able to be integrated into BI platforms like Sisense, to help you get the most out of business-critical data.

Course Certification:

we take pride in acknowledging the achievement of individuals who have successfully completed our Data Analysis with R online course. This comprehensive program is designed to empower participants with the knowledge and skills necessary to navigate the intricate landscape of data analysis using the R programming language. Participants who earn this certification have demonstrated a solid understanding of data manipulation, statistical analysis, and data visualization using R. This recognition is not only a validation of their newfound expertise but also a valuable asset as they navigate the dynamic field of data science.

Introduction to Statistical Analysis

Counting, Probability, and Probability Distributions - Sampling Distributions - Estimation and Hypothesis Testing - Scatter Diagram - Anova and Chisquare - Imputation Techniques - Data Cleaning - Correlation and Regression.

Data Analytics Overview

Importance of Data Analytics - Types of Data Analytics - Descriptive Analytics - Diagnostic Analytics - Predictive Analytics - Prescriptive Analytics - Benefits of Data Analytics - Data Visualization for Decision Making - Data Types, Measure of Central Tendency, Measures of Dispersion - Graphical Techniques, Skewness & Kurtosis, Box Plot - Descriptive Stats - Sampling Funnel, Sampling Variation, Central Limit Theorem, Confidence Interval

Excel: Basics to Advanced

• Excel tutorial - Text to ColumnConcatenate - The Concatenate Function - The Right Function with Concatenation - Absolute Cell References - Data Validation - Time and Date Calculations - Conditional Formatting - Exploring Styles and Clearing Formatting - Using Conditional Formatting to Hide Cells - Using the IF Function - Changing the “Value if false” Condition to Text - Pivot Tables - Creating a Pivot Table - Specifying PivotTable Data - Changing a PivotTables Calculation - Filtering and Sorting a PivotTable - Creating a PivotChart - Grouping Items -Updating a PivotTable - Formatting a PivotTable - Using Slicers - Charts - Creating a Simple Chart - Charting Non-Adjacent Cells - Creating a Chart Using the Chart Wizard - Modifying Charts - Moving an Embedded Chart - Sizing an Embedded Chart -Changing the Chart Type -Chart Types -Changing the Way Data is Displayed - Moving the Legend - Formatting Charts - Adding Chart Items - Formatting All Text - Formatting and Aligning Numbers - Formatting the Plot Area - Formatting Data Markers - Pie Charts - Creating a Pie Chart - Moving the Pie Chart to its Own Sheet - Adding Data Labels - Exploding a Slice of a Pie Chart - Data Analysis Overview - Types of Data Analysis - Data Analysis Process - Working with Range Names - Copying Name using Formula Autocomplete - Range Name Syntax Rules - Creating Range Names - Creating Names for Constants - Managing Names - Scope of a Name - Editing Names - Applying Names - Using Names in a Formula - Viewing Names in a Workbook - Copying Formulas with Names - Difference between Tables and Ranges - Create Table - Table Name - Managing Names in a Table - Table Headers replacing Column Letters - Propagation of a Formula in a Table - Resize Table - Remove Duplicates - Convert to Range - Table Style Options - Table Styles - Cleaning Data with Text Functions - Removing Unwanted Characters from Text - Extracting Data Values from Text - Formatting Data with Text Functions - Date Formats - Conditional Formatting - Sorting - Filtering - Lookup Functions – Pivoting

SQL

Introduction to Oracle Database - Retrieve Data using the SQL SELECT Statement - Learn to Restrict and Sort Data - Usage of Single-Row Functions to Customize Output - Invoke Conversion Functions and Conditional Expressions - Aggregate Data Using the Group Functions - Display Data from Multiple Tables Using Joins - Use Sub-Queries to Solve Queries - The SET Operators - Data Manipulation Statements - Use of DDL Statements to Create and Manage Tables - Other Schema Objects - Control User Access - Management of Schema Objects - Manage Objects with Data Dictionary Views - Manipulate Large Data Sets - Data Management in Different Time Zones - Retrieve Data Using Sub-queries - Regular Expression Support Tableau

Module 1: Tableau Course Material

Start Page - Show Me - Connecting to Excel Files - Connecting to Text Files - Connect to Microsoft SQL Server - Connecting to Microsoft Analysis Services - Creating and Removing Hierarchies - Bins - Joining Tables - Data Blending

Module 2: Learn Tableau Basic Reports

Parameters - Grouping Example 1 - Grouping Example 2 - Edit Groups - Set - Combined Sets - Creating a First Report -Data Labels -Create Folders - Sorting Data - Add Totals, Sub Totals and Grand Totals to Report

Module 3: Learn Tableau Charts

Area Chart - Bar Chart - Box Plot - Bubble Chart - Bump Chart - Bullet Graph - Circle Views - Dual Combination Chart -Dual Lines Chart - Funnel Chart - Traditional Funnel Charts - Gantt Chart - Grouped Bar or Side by Side Bars Chart - Heatmap - Highlight Table - Histogram - Cumulative Histogram - Line Chart - Lollipop Chart - Pareto Chart - Pie Chart - Scatter Plot - Stacked Bar Chart - Text Label - Tree Map - Word Cloud - Waterfall Chart

Module 4: Learn Tableau Advanced Reports

Dual Axis Reports - Blended Axis - Individual Axis - Add Reference Lines - Reference Bands - Reference Distributions - Basic Maps - Symbol Map - Use Google Maps - Mapbox Maps as a Background Map - WMS Server Map as a Background Map

Module 5: Learn Tableau Calculations & Filters

Calculated Fields - Basic Approach to Calculate Rank - Advanced Approach to Calculate Rank - Calculating Running Total - Filters Introduction - Quick Filters - Filters on Dimensions - Conditional Filters - Top and Bottom Filters - Filters on Measures - Context Filters - Slicing Filters - Data Source Filters - Extract Filters

Module 6: Learn Tableau Dashboards

Create a Dashboard - Format Dashboard Layout - Create a Device Preview of a Dashboard - Create Filters on Dashboard - Dashboard Objects - Create a Story

Module 7: Server

Tableau Online - Overview of Tableau Server - Publishing Tableau objects and scheduling/subscription

Power BI

Module 1: Introduction to Power BI

Get Started with Power BI - Overview: Power BI concepts - Sign up for Power BI - Overview: Power BI data sources - Connect to a SaaS solution - Upload a local CSV file - Connect to Excel data that can be refreshed - Connect to a sample - Create a Report with Visualizations - Explore the Power BI portal

Module 2 - Viz and Tiles - Overview:

Visualizations - Using visualizations - Create a new report - Create and arrange visualizations - Format a visualization - Create chart
visualizations - Use text, map, and gauge visualizations and save a report - Use a slicer tofilter visualizations - Sort, copy, and paste visualizations - Download and use a custom visual from the gallery.

Module 3 -

Reports and Dashboards - Modify and Print a Report - Rename and delete report pages - Add a filter to a page or report - Set visualization interactions - Print a report page - Send a report to PowerPoint - Create a Dashboard - Create and manage dashboards - Pin a report tile to a dashboard - Pin a live report page to a dashboard - Pin a tile from another dashboard - Pin an Excel element to a dashboard - Manage pinned elements in Excel - Add a tile to a dashboard - Build a dashboard with Quick Insights - Set a Featured (default) dashboard - Enable Cortana for Power BI.

Module 4 -

Publishing Workbooks and Workspace - Share Data with Colleagues and Others - Publish a report to the web - Manage published reports - Share a dashboard - Create an app workspace and add users - Use an app workspace - Publish an app - Create a QR code to share a tile - Embed a report in SharePoint Online.

Module 5 -

Other Power BI Components and Table Relationship - Use Power BI Mobile Apps - Get Power BI for mobile - View reports and dashboards in the iPad app - Use workspaces in the mobile app - Sharing from Power BI Mobile -Use Power BI Desktop - Install and launch Power BI Desktop - Get data - Reduce data - Transform data - Relate tables - Get Power BI Desktop data with the Power BI service - Export a report from Power BI service to Desktop.

Module 6 -

DAX functions - New Dax functions - Date and time functions - Time intelligence functions - Filter functions - Information functions - Logical functions - Math & trig functions - Parent and child functions - Text functions.

Module 7 -

Introduction to R for Data Analysis - Overview of R and its applications in data analysis - Installing and setting up R and RStudio - R Basics: Syntax, variables, data types, and basic operations - Data Import and Export in R: Reading and writing data in various formats (CSV, Excel, etc.) - Data Cleaning and Preprocessing: Handling missing data, dealing with duplicates, and data transformation - Data Manipulation with dplyr: Filtering, selecting, arranging, and summarizing data - Data Visualization with ggplot2: Creating various types of plots and charts for data exploration - Exploratory Data Analysis (EDA): Statistical summaries, distribution analysis, and correlation exploration - Introduction to Statistics with R: Descriptive statistics, inferential statistics, and hypothesis testing - Linear Regression in R: Understanding and implementing simple and multiple linear regression - Logistic Regression in R: Binary classification using logistic regression - Clustering Algorithms in R: K-Means clustering and hierarchical clustering - Dimensionality Reduction in R: Principal Component Analysis (PCA) - Time Series Analysis in R: Handling time-series data, trends, and seasonality - Introduction to Machine Learning with R: Basic concepts, model training, and evaluation - Integration with Databases in R: Connecting R to databases (e.g., SQLite, MySQL) for data retrieval and storage.