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Course Outline
- 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
Requirements
Working knowledge of computers and software, and basic knowledge of math/statistics. Prior programming knowledge helps. Suitable for both technical and business professionals with interest to learn.
14 Hours
Testimonials (5)
Hands-on examples allowed us to get an actual feel for how the program works. Good explanations and integration of theoretical concepts and how they relate to practical applications.
Ian - Archeoworks Inc.
Course - ArcGIS Fundamentals
All the topics which he covered including examples. And also explained how they are helpful in our daily job.
madduri madduri - Boskalis Singapore Pte Ltd
Course - QGIS for Geographic Information System
I liked Pablo's style, the fact that he covered a lot of subjects from report design , customization with html to implementing simple ML algortithms. Good balance theoretical information / exercices. Pablo really covered all topics i was interested in and gave comprehensive answers to my questions.
Cristian Tudose - SC Automobile Dacia SA
Course - Advanced Data Analysis with TIBCO Spotfire
Actual application of spotfire and all basic functions.
Michael Capili - STMicroelectronics, Inc.
Course - Introduction to Spotfire
The thing I liked the most about the training was the organization and the location
