Data Analysis and Visualization
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COURSE DATES AND LOCATIONS
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INTRODUCTION
Data analysis is the process of extracting information from data. It involves multiple stages, including establishing a data set, preparing the data for processing, applying models, identifying key findings and creating reports. Data analysis aims to find actionable insights that can inform decision-making. Data visualization is the graphical representation of information and data in a pictorial or graphical format. Data and visual analytics is an emerging field concerned with analyzing, modeling, and visualizing complex high dimensional data.
Data analysis and visualization program is created for beginners who have never programmed before and want to build a complete understanding of Python from the ground up as well as begin the artificial intelligent, machine learning or data science track
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COURSE OBJECTIVES
By the end of the course, you‘ll be able to:
- Utilize statistical tools for working with data sets
- Use Python to solve tasks
- Explain what Data Analytics is and the key steps in the Data Analytics process.
- Manage pandas in a data analysis context
- Manage data import and cleaning for different types of data
- Practice using different data visualization libraries and extracting insights from them
- Describe the data ecosystem, tasks a Data Analyst performs, as well as skills and tools required for successful data analysis
- Analyze machine learning algorithms for working with datasets
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COURSE AUDIENCE
This course is made for :
- Data analysts
- Data architect
- Data science specialist
- Programmers
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COURSE OUTLINE
Day One
Data analysis software tools
- Introduction to data analysis and visualization
- Excel, R, MINITAB, MATLAB, and Python
- Data Analysis Tools Resources
Day Two
Statistical process control
- Control and Specification Limits
- Variation Analysis
- Process Performance
Day Three
Data visualization
- What is data visualization
- Data visualization vs data mining
- Human cognition
- HMI
- Common pitfalls
Day Four
Data visualization
- Tableau, Excel, and Power BI
- Insight Evaluation
- Visualization Tools Resource
- Testing and Re-Evaluation Resources
Day Five
Data Preparation
- Ingestion, selection, cleansing, and transformation
- Ensuring data quality – correctness, meaningfulness, and security
- Exception reports
