Data Science
Data Science - Data Analysis
Data Analysis overview
Data Analysis is a process of collecting, transforming, cleaning, and modelling data.
The goal is discovering the required information.
The terms Data Modeling and Data Analysis mean the same.
Analysis refers to breaking a whole model into its separate components for individual examination.
Data analysis is a process for obtaining raw data and converting it into information useful for decision-making by users.
Data is collected and analyzed to answer questions, test hypotheses or disprove theories.
Several data analysis techniques exist encompassing various domains such as business, science, social science, etc.
The major data analysis approaches are:
- Data Mining
- Business Intelligence
- Statistical Analysis
- Predictive Analytics
- Text Analytics
Data mining
Data Mining is the analysis of large quantities of data.
This is done to extract previously unknown and interesting patterns of data.
Note that the goal is the extraction of patterns and knowledge from large amount of data.
It’s analysis involves computer science methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.
The patterns obtained can be considered as a summary of the input data that can be used in further analysis
Business Intelligence
Business Intelligence techniques and tools are for acquisition and transformation of large amount of unstructured data to help identify, develop and create new strategic business opportunities.
The goal of business intelligence is to allow easy interpretation of large volumes of data to identify new opportunities.
It helps in implementing an effective strategy based on insights that can provide businesses with competitive market-advantage and long-term stability.
Statistical Analysis
Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data.
In data analysis, two main statistical methodologies are used −
Descriptive statistics − data from the entire population or a sample is summarized with numerical descriptors such as
- Mean, Standard Deviation for Continuous Data
- Frequency, Percentage for Categorical Data
Inferential statistics − It uses patterns in the sample data for the accounting of randomness. These inferences can be:
- answering yes/no questions about the data (hypothesis testing)
- estimating numerical characteristics of the data (estimation)
- describing associations within the data (correlation)
- modelling relationships within the data (E.g. regression analysis)
Predictive Analytics
Predictive Analytics use statistical models.
It analyzes current and historical data for forecasting (predictions) about future or otherwise unknown events.
In business, predictive analytics is used to identify risks and opportunities that aid in decision-making.
Text Analytics
Text Analytics is also referred to as Text Mining or Text Data Mining.
It is the process of deriving high-quality information from text.
Text mining usually involves the process of structuring the input text,
deriving patterns within the structured data,
using means such as statistical pattern learning,
and finally evaluation and interpretation of the output.
Let's recap what we have learned about data analysis.
Which of the following must be the approach to ask a question in data analysis.
Select the right answer
A. data replication
B. data cleansing
C. data iteration
D. none of the above
Answer : B.
Pick out the odd one:
Select the right answer
A. data mining
B. text mining
C. predictive analysis
D. big data
Answer : D.
Data Science