What Should You Know About Data Analytics Sampling Techniques

A well-structured data analytics course provides you with everything you need to start a career as a data analyst. As data is becoming more and more significant in the decision-making process of businesses, the demand for data analytics professionals is constantly increasing. A career in data analytics is undoubtedly one of the best career options in the 21st century. To become a data analyst, you must have the required skill set and a thorough knowledge of all the important methods used in analyzing large data sets.

One of the many important techniques used in data analytics is data sampling. It helps you analyze large data sets without actually studying each element of the data. If you are aspiring to make a career in the field of analytics, you should know everything about what data sampling is, what are its different types, and what are the different methods used in sampling.

What is Data Sampling?

Sampling is a technique used by data scientists, which allows them to study the entire population based on a subset of the population. In simple terms, they take a sample and use it to get information about the whole population instead of studying every single individual. The subset should be in such a way that it covers every type of individual present within the population. It should neither be too large nor very small.

Let us make you understand this with the help of an example. Suppose you have to find out the average height of all females in a city. It would be nearly impossible for you to reach out to every female, note her height, and then calculate the average height of all of them. However, what you can do instead is ask random females about their height and take the average of the same.

Now, it would not be a good idea to visit a basketball court and take the entire sample of female heights from there. The average height of basketball players is more than that of other females. So, it would not make an ideal sample. You can note the height of any one basketball-playing female and collect the rest of the sample from different random places. An ideal sample must not be biased in any manner.

Different Types of Sampling Methods in Data Analytics

There are majorly two types of sampling techniques in data analytics, Probability Sampling, and Non-Probability Sampling. Different methods used under both these types of sampling as discussed below:

Probability Sampling

Probability sampling techniques are very important in data analytics. In this type of sampling, every participant in a population has an equal chance of getting selected for the sample. Some most popular probability sampling methods are as follows:

1. Simple Random Sampling
Simple random sampling refers to the process of taking random participants from a set of people and analyzing their information to understand the whole data set. In this, you assign numerical values to all the individuals in a population and then use data analytics tools like random number generators to create a sample of your desired size.

It is one of the easiest and most direct methods for sampling. The biggest advantage of this technique is that it gives every member of the population an equal chance of selection. However, it may not select enough participants with different characteristics.

2. Systematic Sampling
In systematic sampling, the first participant is chosen randomly and others are selected systematically using fixed sampling intervals. Just like simple random sampling, you have to assign numbers to every population, but the participants are not chosen randomly. Suppose the total number of participants is ‘m’ and you have to select ‘n’ participants for your sample. Now, the interval size for selection would be m/n (total population/required number of participants for sample).

Let us make it easier for you with the help of an example. Suppose you have to select 10 participants from a population of 1,000 individuals. Now, the interval size would be 1,000/10 = 100 (m/n) and hence, you will need to choose every 100th individual from the population. You have to select the first participants randomly and let’s say you choose 21. The next participant would be 121 (21+100), then 221 (121+100), and so on.

3. Cluster Sampling
Cluster sampling is a bit different from the above two techniques. In this, you first divide the whole population into different groups known as clusters and then select an entire cluster as your sample. Instead of selecting individuals, the method allows you to randomly choose an already-formed cluster as the sample.

This technique is helpful when you have to deal with large data sets or focus on specific regions. For example, suppose a company has offices in 5 different cities with an equal number of employees in each office. To get information about the employees of this company, you can select any of the 5 offices randomly as your sample.

4. Stratified Sampling
In stratified sampling, the entire population is divided into different subgroups on the basis of different characteristics, such as gender, age, category, etc. These subgroups are known as strata. After forming the strata, you can use random or systematic sampling techniques to choose samples from every subgroup or strata. Stratified sampling is considered one of the most accurate techniques as it allows you to choose participants from each subgroup with different characteristics.

Non-Probability Sampling

Non-probability sampling doesn’t give every participant an equal chance of being selected for the sample. Therefore, the chances of bias are higher in these techniques. Some most popular non-probability sampling methods are as follows:

1. Convenience Sampling
This is one of the easiest methods used for sampling. In this, the analysts choose participants, who are the closest or most easily accessible to them. The only requirement is that the participants must be willing to get selected and should be available. Though the technique is easy, it doesn’t guarantee an unbiased selection for the sample. The sample may not include enough representatives with specific traits.

2. Voluntary Response Sampling

In this type of sampling, the analysts do not have to choose participants, but the participants themselves show the willingness to volunteer. Just like convenience sampling, there is a high chance of bias in this case as well. There is no guarantee that individuals from each required trait will be willing to participate.

3. Purposive Sampling
Purposive sampling is quite different from the other two non-probability sampling methods mentioned above. In this case, the analyst or the researcher uses his/her field expertise or judgment skills to identify which participants would be the best fit for their sample. Therefore, it is also known as Judgement sampling.

4. Snowball Sampling
In snowball sampling, the first participant is chosen at random and then he/she is asked to choose the next participant for the sample. The technique is named snowball because it works exactly like a rolling snowball that keeps adding snow and gets bigger as it rolls. Suppose you choose person ‘x’ randomly, he/she recommends person ‘y,’ then y recommends person ‘z,’ and so on.

However, there is a significant space for bias as the participants may not represent the entire population. This technique is suitable for sampling when you need to gather information about people sharing common traits.

Data Analytics Courses at Edvancer

The most appropriate way to start a career in data analytics is to enroll in a data analytics course offered by a reputed institute. Edvancer is one of the most trusted online platforms for career-oriented education. You can find the following two courses for data analytics at Edvancer:

These courses cover all the important topics of analytics, including data sampling. You can understand every aspect of data analytics along with developing your practical skills by working on real industry projects. Moreover, Edvancer allows you to select a learning style that suits you. You can either choose to learn at your own pace or via the live online classes.

FAQs

1. How are the sampling techniques relevant in data analytics?
Ans. Sampling techniques allow data analysts to deal with larger data sets by choosing small samples and analysing them to get information about the whole data. It makes their task easier and less time consuming.

2. Why are correct sampling techniques required in any analytical analysis?
Ans. It is important to choose the correct sampling technique in order to ensure that the chosen sample is unbiased and represents the entire population properly.

3. What is the main purpose of the sampling technique?
Ans. The main purpose of using sampling techniques in data analytics is to make the massive amounts of data manageable for the professionals working on it.

4. Why is sampling important in data collection?
Ans. Sampling becomes important when the data sets are too large to be analysed as a whole. Sometimes it is not possible to study every single element of a data set and that is where sampling techniques come into play.