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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)
The trainer adapted the materials and contents to what he thought would be best for us and he succeeded. The quality of the training was excellent.
Jorge Sanchez Hernandez - CSMART - Carnival
Course - QGIS for Geographic Information System
A lot of patience
Mateusz - WestWind Energy Polska Sp. z o.o.
Machine Translated
Professional and very practical, usuefull in a daily work
Jozefin Rékasi - SC Automobile Dacia SA
Course - Advanced Data Analysis with TIBCO Spotfire
It covered the areas i said i was interested in before the course: data relationships, using python script. Connecting to databases will be covered in the advanced module.
Cristian Tudose - SC Automobile Dacia SA
Course - Introduction to Spotfire
I genuinely enjoyed the lots of labs and practices.
