Data Analytics with R, Python and SQL

This course emphasizes the utilization of data analytic techniques and methodologies to address business challenges using R, Python, and SQL. It underscores the integration of business requirements with data analytics, treating the latter as a toolkit for tackling business queries and issues. 

Key objectives of the course are:

- Offering coding demonstrations to explore data from various angles.
- Acquiring proficiency in technologies necessary for analyzing diverse datasets and extracting insights.
- Understanding how to uncover narratives within data and identify potential business opportunities.
- Mastering essential analytical domains to glean meaningful insights from data.

Upon completion of the course, students will be equipped to:

• Code effectively in R and Python for any dataset.
• Delve into data to extract valuable insights.
• Tackle real-world challenges in both research and business contexts.
• Aggregate pertinent information at each stage of analysis.
• Strategize subsequent analysis steps to uncover deeper insights.
• Visualize data effectively and present findings throughout the data analytics project lifecycle.
• Adapt and address evolving issues during and after project completion.
• Iterate steps to enhance visibility and rigorously validate information before dissemination.

What you'll learn

• Develop data analysis methods, approaches and handling business problems using data analysis as a toolset
• Uses R, Python, and SQL languages to implement the required statistical and mathematical methods to analyze practical datasets that are similar to the ones used
• Gain insights, trends, patterns, and predict future course based on the historical data and present the findings
• Work on a project that involves solving a business problem from start to finish; achieve end-to-end solution for the problem.

Who is this course for:

• Intended for beginners and intermediate learners/professionals who want to get into data analytics field and to accelerate their journey

• Introduction
• What is Data Analytics
  • Examples of Data Analytics
  • Starting to interpret the data
  • Using basic stats to interpret the data
  • Using charts to interpret the data
• R and Python
  • Use of R vs Python for Data Analysis
• Working Environment
  •    Getting Ready to Code
  •    Writing Data from R to a File
  •    Preparing Working Environment
  •    Download and get ready with R and RStudio - make sure the environment is working
• Getting Data Summary and Observations
  •    Data Observations
  •    Data Observations - Filtering the Data
  •    Use the R scripts provided to modify; execute them to get the results and verify
• RMarkdown
  •    R Markdwon
  •    Use the RMD file to execute after you update per your environment, and validate.
• Statistical Measures
  •    Stats Measure
• Plots and Charts
  •    Charting and Plotting
  •    Box Plots - five metrics
  •    Update the R scripts per your environment and execute and verify.
• Correlation
  •    Correlation Coefficient
• Mosaic Plots
  •    Mosaic Plot Construction
  •    Trouble shoot the code, so that the chart labels looks legible within the area
• Pie Chart
  •    Pie Charting
  •    Update the code to get the Sales Pie Chart for the Segments within same dataset
• Scatter Plots
  •    Scatter Plotting
  •    Use the R script provided to update and get scatter plot of all variables.
• Line Graph
  •    Line Graph
  •    Consider taking first 20 rows of the dataset and update the R script and execute
• Q-Q Plots
  •    Q-Q Plots - Quantile-Quantile plots
  •    Update the R script to get Q-Q plot for Discounts
• Python Environment
  •    Python Environment
  •    Add comments to the Python code (Data_Sumamry.py)
  •    Use VS Code IDE to run the script
  •    Getting Started with Python
  •    Use the script to run on your RStudio environment; update the script as needed
• Python and Plotting
  •    Working Python code from R Code
  •    Python Nulls and NAs
  •    Plotting in Python
  •    Code in Python for bar and histograms based on R scripts from previous sections
• Project
  •    Analyze the data for the given dataset - Financial Sample.xlsx
  •    Project Work
• Database and SQL
  •    Database and Structured Query Language
  •    Install MySQL database and verify your environment
  •    Getting to work with Python plus SQL
  •    Install MySQL libraries
  •    GUI tool for MySQL database
  •    Install DB Visualizer
  •    Using Python with SQL
  •    Python with MySQL database for running queries