This document provides an overview of data analysis, including the definition, main methods, typical process, common techniques, and popular tools. It defines data analysis as the systematic application of statistical and logical techniques to describe data, condense its representation, evaluate trends, and derive meaningful conclusions. The two main methods are quantitative and qualitative analysis. The standard process involves specifying requirements, collecting/processing data, analyzing results, and interpreting inferences. Common techniques include descriptive statistics, regression analysis, decision trees, and various visualization methods. Popular tools for data analysis include Excel, Power BI, R, Python, Tableau, and SAS.

1. DATA ANALYSIS
Method of Research

2. Presenters
Joy Mae Sinlao Jamillah Banda Jasmin Todio Veronica Jayme
Sarah Mae Espino
Cam Aquino Rosalyn Nacion Cindy Tandoc

3. Topic Outline
01 | Icebreaker 02 | Definition of
Data Analysis
04 | Data Analysis
Process
05 | Data Analysis
Techniques
03 | Data Analysis
Methods
06 | Data Analysis
Tools
07 | Conclusion

5. What is Data
Analysis?

6. Data Analysis
The systematic application of statistical and logical techniques
to describe the data scope, modularize the data structure,
condense the data representation, illustrate via images, tables, and
graphs, and evaluate statistical inclinations, probability data, and
derive meaningful conclusions known as Data Analysis.

7. Data Analysis Methods

8. Data Analysis Methods
Qualitative
Analysis
Quantitative
Analysis
There are two main methods of Data Analysis:

9. Content
Analysis
Grounded
Analysis
Discourse
Analysis
Narrative
Analysis
Data Analysis Methods

10. Data Analysis Process

11. Data Analysis Process
2. Data Collection
1. Data Requirement
Specification - define
your scope:
3. Data Processing
4. Data Analysis
5. Infer and Interpret
Results
1. Data Mining
2. Data
Modelling

12. Data Analysis
Techniques

13. Data Analysis Techniques
1. Techniques based on Mathematics and Statistics
● Descriptive Analysis
● Dispersion Analysis
● Regression Analysis
● Factor Analysis
● Discriminant Analysis
● Time Series Analysis

14. Data Analysis Techniques
2. Techniques based on Artificial Intelligence and
Machine Learning
● Artificial Neural Networks
● Decision Trees
● Evolutionary Programming
● Fuzzy Logic

15. Data Analysis Techniques
3. Techniques based on Visualization and Graphs
● Column Chart, Bar Chart
● Line Chart
● Area Chart
● Pie Chart
● Funnel Chart
● Word Cloud Chart
● Gantt Chart

16. Data Analysis Techniques
3. Techniques based on Visualization and Graphs
● Radar Chart
● Scatter Plot
● Bubble Chart
● Gauge
● Frame Diagram
● Rectangular Tree Diagram

17. Data Analysis Techniques
3. Techniques based on Visualization and Graphs
● Map
- Regional Map
- Point Map
- Flow Map
- Heat Map

18. Data Analysis Tools

19. 1. Excel
4. Tableau
2. Power BI
5. Fine Report
3. R & Python
6. SAS
Data Analysis Tools

20. Conclusion
Data Analysis is the key to any business, whether starting
up a new venture, making marketing decisions, continuing
with a particular course of action, or going for a complete
shut-down. The inferences and the statistical probabilities
calculated from data analysis help base the most critical
decisions by ruling out all human bias. Different analytical
tools have overlapping functions and different limitations,
but they are also complementary tools. Before choosing a
data analytical tool, it is essential to consider the scope of
work, infrastructure limitations, economic feasibility, and
the final report to be prepared.

21. Thank you!