This article is about Machine Learning
What are the different types of Data & How to Use them?
By NIIT Editorial
Published on 15/12/2021
The world continues to tread along the data-led digitalized trajectory that 21st-century technologists have paved for businesses. As customers crave personalized products, the need to define, segment, and leverage data grows proportionately. Data is the strongest denominator of a business’ prowess in this day and age. Google, Facebook, Microsoft, and Amazon together store about 1,200 petabytes of information, which goes on to show the precipitous care they accord to data operations. For this reason, online data science courses have begun to incorporate data-centric specializations such as predictive modeling, analysis, and forecasting. In this article, we will explore the two major categories of data and walk around the ways in which organizations utilize them.
Qualitative data brings out the characteristic(s) of the data set. It is neither numerical nor analytical in nature and collected vis-a-vis interviews, focus groups, and inference. Such data types are attributed to properties than volume. Also referred to as categorical data, typical examples of this include demographics. Qualitative data sets have been used by data analysts to the better group, distribute, and define their customers. It allows marketers to answer business problems like, who is your target audience, what is the problem statement, etc. There are 5 steps involved in the data analysis of qualitative data:
- Arranging data
- Organizing data
- Identifying characteristic patterns
- Looking for inconsistencies
- Carrying out the analysis
While the above are the steps, there are 2 main approaches involved in the data analysis:
Analysts use this approach to comprehend the data at their disposal, with a preset protocol. It works best in conditions where the researchers have an estimate of the kind of answers they’ll receive through data collection.
Such a framework of rules is adopted when the research team has limited or no knowledge of the data at hand. As a result, it consumes more time than the deductive approach.
Quantitative data is numerical in nature and as the name suggests can be accounted for by means of calculation. The derivations made through such data are used to ascribe neatly defined attributes to the data set. In simple words, quantitative data is helpful in answering questions dealing in volume such as how much, how often and how many. In terms of problem statements, such data sets can elaborate on key performance indicators such as what is the user acquisition cost, what should be the optimum price of products, and what should be the projections for the financial year. Data collection is carried out through surveys, polls, and questionnaires. Based on use, the data can be classified into 5 categories:
Count - Attributed to a collective unit of measurement such as. For instance, “what is the total number of app downloads from the App Store?”
Physical Objects - Used for measuring objects such as calculating the size of cubicles for new employees.
Projection of Data - Data forecasting can be carried out using the information collected.
Quantitative data can be collected in a number of ways. Below we share the 2 most prominent methods deployed by data collectors:
Survey - Data Analysts initiate the process by putting together a checklist of questions that they believe to be pertinent to the product. The key objective is to be as specific as possible. The questionnaires are generalized to maximize the sample size coverage for the survey. As you would guess, surveys can take a bit of time to collate. Therefore, based on this turn-around-time, they are divided further into longitudinal studies, and cross-sectional studies.
One-on-One Interviews - This is an enhanced way to first, connect with your target audience, and second, gather extensive information. Interviews can be conducted either telephonically, through video conferencing apps, or in person.
Common methods involved in analyzing the collected data range from tabulation, and trend analysis, to differential analysis, gap analysis, SWOT analysis, and textual analysis. The crude data collected as part of the process is subsequently structured in a manner that can be understood by key decision-makers. Due to which a lot of online data science courses are covering end-to-end data operation techniques through their curriculum. For instance, NIIT’s Advanced PGP Data Science and Machine Learning (Full Time) is an online placement-assured course that enables learners to be day-1 job-ready. In what is emerging as one of the most sought after courses on data analytics, this certification places eligible program-passouts into the following job roles:
- Jr. Data Scientist
- Data Analyst
- Business Analyst
Being a household skill development conglomerate, NIIT nourishes two ties with 800+ high-end recruiters across India. Such industry collaborations assist NIIT in not only creating a state-of-the-art curriculum but also setting up interviews for relevant positions within these corporate entities. Apply now for a future in data science.
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