Data Mining

Question 1

What is Data Mining?

Answer 1

Is the process of extracting knowledge from data. It aims to identify correlations in data, find patterns and variations, understand trends, and predict probabilities.

Question 2

Patterns

Answer 2

Pattern are a variable that changes in a repeating or predictable way.

Question 3

Trends

Answer 3

Trends is a general change in one variable compared to another over time.

Question 4

Data Mining Techniques

Data Mining Techniques

Answer 4

• Classification.
• Clustering.
• Anomalies.
• Association Rule Mining.
• Sequential Patterns.
• Affinity grouping.
• Decision Trees.
• Regression.

Question 5

Commonly use software’s tools for data mining:

Answer 5

• Spreadsheets.
• R-Language.
• Python.
• IBM SPSS Statistics.
• IBM Watson Studio.
• SAS.

Question 6

Spreadsheets (Excel and Google Sheets)

Answer 6

Are used for hosting data that has been exported from other systems, so they can be accessible, easy-to-read, and use to draw comparations between sets of data.

Excel add-ins: Data mining Client, XLMiner, and KnowledgeMiner.
GoogleSheets add-ins: Text Analysis, Text Mining, and Google Analytics.

Question 7

R-Language packages:

Answer 7

• tm: a framework for text mining applications within R.
• twitteR: a framework for mining tweets.

Question 8

R-Language

Answer 8

commonly use for statistical modeling and computations by statisticians and data miners. With R Libraries we can perform data mining operations such as:
- Regression.
- Classification.
- Data Clustering.
- Association Rule Mining.
- Text Mining.
- Outlier Detection.
- Social Network Analysis.

Question 9

Python libraries:

Answer 9

• Pandas.
• NumPy.
• Jupyter.

Question 10

Pandas

Answer 10

Any type of data format can be uploaded and organize, sort, and manipulate. We can perform:

• Basic numerical computations such as mean, median, mode, and range.
• Calculate statistics and make correlations between data and distribution of data.

Question 11

NumPy

Answer 11

is a tool for mathematical computing and data preparation, that offers a host of built-in functions and capabilities for data mining.

Question 12

Jupyter

Answer 12

Data Scientist and Data Analysis use this tool to perform data mining and statistical analysis.

Question 13

IBM SPSS(Statistical Package for Social Sciences) Statistics

Answer 13

Popularly used for advanced analytics, text analytics, trend analysis, validation of assumptions, and translation of business problems into data science solutions

Question 14

IBM Watson Studio

Answer 14

Leverages a collection of open source tools such as Jupyter notebooks, and extends them with closed source IBM tools that make it a powerful environment for data analysis and data science.

Question 15

SAS Enterprise Miner

Answer 15

Is a powerful graphical workbench that enables the capabilities for interactive data exploration, mine, transform, identify anomalies, analyze big data, identify patterns, and identity relationships within data.