R programming for data science
- 1. R programming for Data Science Part-1 w www.dataspoof.info
- 2. Steps to do any Data Science project Identify the problem (question) Collect & Prepare the data Explore the data Communicatethe results www.dataspoof.info
- 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: ā¢ learning more about customers, items, products, ..etc. ā¢ discovering trends in the current system, organization, ..etc. ā¢ segmenting elements into different groups based on their individual needs. ā¢ decision making process to improve the quality of the system. ā¢ improving the quality of the product or service based on the feedback obtained. www.dataspoof.info
- 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 ā¢ Qualitative data: descriptive information (describes something). ā¢ Quantitative data: numerical information (numbers). www.dataspoof.info
- 8. Types of Data Values āŗNumeric: ā¢Discrete - integer values. Example: number of car in the park. ā¢Continuous - any value in a pre-defined range (float, double). Example: average mark (e.g., 63.4) āŗCategorical: values are selected from a predefined number of categories. ā¢Ordinal - categories could be meaningfully ordered. Example: grades (A, B, C, D, E, F). ā¢Nominal - donāt have any order. Example: eye colours (blue, black, honey, etc.) ā¢Binary - the special case of nominal, with only 2 possible categories. Example: binary value (1, 0) www.dataspoof.info
- 9. Types of Data Values āŗDate: datetime, timestamp. Example: 11.10.2018. āŗText: Multidimensional data āŗTime series: Data points indexed in the time order
- 10. Types of Data Category There are two main categories ā¢ Experimental data: Data collected from strictly controlled/designed experiments with efforts made to ensure statistical validity. Examples- Medical clinical trials, Election polls ā¢ Observational data: Data collected from āreal-worldā 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 ā¢ Numbers ā¢ String ā¢ Relational data ā¢ Factors or categorical variables ā¢ Dates and times ā¢ Description