Introduction to Business Analytics and RStudio

Course Introduction

• Analytical skills are developed for understanding trends in data.
• Topics include exploratory data analysis (EDA), data visualization, handling missing data, statistics, and linear regression.
• R is an integrated suite of software packages for data handling, data manipulation, statistical analysis, and graphical analysis.
• It's open-source and free on all platforms (macOS, Windows, Linux).
• RStudio provides an integrated development environment (IDE) for R.
• RStudio's GUI displays in four windows: script (code), console (output), environment (variables), and plots (graphics).

Overview of RStudio

• R is a programming language for data analysis.
• RStudio is its IDE.
• RStudio allows code writing and result viewing.

Introduction to Business Analytics

• Business analytics is data collection, inspection, cleaning, transformation, and modeling to extract meaningful insights.
• 2021 data volume: 80ZB, projected 2025 volume: 175.2ZB
• Data sources include documents, apps, social media, sensors, logs, and archives.
• Business analytics improves efficiency, productivity, decision-making, financial performance, customer experience, and revenue streams.
• Roles include understanding problems, collecting data, cleaning data, analyzing data, identifying trends, and generating reports.

Business Analyst Skills

• Data cleaning involves detecting and correcting inaccurate data (e.g., removing retweets, handling missing data).
• Exploratory data analysis (EDA) is a process of organizing and understanding data; examples include date formatting adjustments.

Types of Statistics

• Descriptive statistics describe data features (mean, median, mode, range, variance, standard deviation, IQR).
• Inferential statistics uses sample data to make inferences about a population.

Data Visualization

• Data visualization uses charts, graphs, and dashboards to represent data visually.
• Techniques include scatter plots, histograms, line charts, and bar charts to show relationships, distributions, and trends.

Data Visualization in R (ggplot2)

• ggplot2 is an R package used for data visualization using a grammar of graphics approach.
• It allows building plots by combining various components, including data, aesthetics, geometry (visual representation), coordinate system, and facets.

Data Importing and Wording

• R can read different data formats, like CSV, using the data() function.
• Functions like head(), tail(), and str() are helpful with exploring initial data.
• Packages like readr can be used to handle different data formats.

Business Analytics Types

• Descriptive: understanding what happened.
• Predictive: predicting what will happen.
• Prescriptive: determining best actions to take.

Business Analytics Lifecycle

• Defining business requirements,
• Data collection, data cleaning, and exploration,
• Data analysis,
• Developing insights,
• Creating reports and visuals.

Business Analytics Tools

• Python, Tableau, Spark, RapidMiner, Excel, and Google Developers Tools.

Business Analytics Domains

• Travel, marketing, healthcare, social media, sales, automation, and credit/insurance.

R Commands and Data Structures

• Basic R commands and creating different data structures (vectors, data frames).
• Working with data frames (displaying columns, rows, or specific values).

This quiz covers the fundamentals of business analytics and the R programming language. Topics include data analysis techniques, exploratory data analysis (EDA), and the RStudio IDE. Gain insights into handling large data volumes and effective data visualization.