The document outlines the steps for conducting a data science project, emphasizing the importance of identifying a problem, collecting, and preparing data, exploring it, and communicating results. It defines data as a set of facts with two main types: qualitative and quantitative, and discusses various data formats and categories, including experimental and observational data. The document highlights the significance of data collection for decision-making and improving services based on customer feedback.

1. R programming for Data Science
Part-1
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2. Steps to do any Data
Science project
Identify the problem
(question)
Collect & Prepare the
data
Explore the data
Communicatethe
results
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3. Data Collection
Data collection is the process of gathering information from a
specific source, which can be used to answer relevant questions
and evaluate outcomes.
Data can help us in:
• learning more about customers, items, products, ..etc.
• discovering trends in the current system, organization,
..etc.
• segmenting elements into different groups based on
their individual needs.
• decision making process to improve the quality of the
system.
• improving the quality of the product or service based
on the feedback obtained. www.dataspoof.info

4. Data Sources
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5. Data Format
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6. Define Data
Data is a set of facts such as numbers, words, measurements, observations or descriptions
of things.
There are two types of data are there
• Qualitative data: descriptive information (describes something).
• Quantitative data: numerical information (numbers).
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7. Qualitative vs
Quantitative

8. Types of Data Values
►Numeric:
•Discrete - integer values. Example: number of car in the park.
•Continuous - any value in a pre-defined range (float, double). Example: average mark (e.g., 63.4)
►Categorical: values are selected from a predefined number of categories.
•Ordinal - categories could be meaningfully ordered. Example: grades (A, B, C, D, E, F).
•Nominal - don’t have any order. Example: eye colours (blue, black, honey, etc.)
•Binary - the special case of nominal, with only 2 possible
categories. Example: binary value (1, 0)
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9. Types of Data Values
►Date: datetime, timestamp. Example: 11.10.2018.
►Text: Multidimensional data
►Time series: Data points indexed in the time order

10. Types of Data Category
There are two main categories
• Experimental data: Data collected from strictly controlled/designed experiments with efforts
made to ensure statistical validity.
Examples- Medical clinical trials, Election polls
• Observational data: Data collected from ’real-world’ settings without control over the captured
underlying phenomena. It is easier to collect and obtain, but results and conclusions from such
data may be biased or inconclusive.
Examples- Almost all data used in data mining, bushiness analytic and data science are
observational data.

11. Various Data Types are
• Numbers
• String
• Relational data
• Factors or categorical variables
• Dates and times
• Description