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What is Data Science?

• Data Science is a field of computer science that explicitly deals with turning data into information and extracting meaningful insights out of it. Data Science is a broad technology that includes various subsets such as Data Analytics, Data Mining, Data Visualization, etc.

What is Data Analytics?

• Data Analytics is a subset of Data Science. The goal of data analytics is to illustrate the precise details of retrieved insights.

What is Sampling?

• Sampling is defined as the process of selecting a sample from a group of people or from any particular kind for research purposes. It is one of the most important factors which decides the accuracy of a research/survey result.

  Mainly, there are two types of sampling techniques:
    

  Probability sampling

  It involves random selection which makes every element get a chance to be selected. Probability sampling has various subtypes in it, as mentioned below:

  • Simple Random Sampling
  • Stratified sampling
  • Systematic sampling
  • Cluster Sampling
  • Multi-stage Sampling

   

  Non- Probability Sampling

  Non-probability sampling follows non-random selection which means the selection is done based on your ease or any other required criteria. This helps to collect the data easily.

  • Convenience Sampling
  • Purposive Sampling
  • Quota Sampling
  • Referral /Snowball Sampling

What Is Logistic Regression?

• Logistic regression is a form of predictive analysis. It is used to find the relationships that exist between a dependent binary variable and one or more independent variables by employing a logistic regression equation. 

What is Supervised and Unsupervised Learning?

• Supervised learning is a machine learning method in which models are trained using labeled data. In supervised learning, models need to find the mapping function to map the input variable (X) with the output variable (Y).

  Example: Suppose we have an image of different types of fruits. The task of our supervised learning model is to identify the fruits and classify them accordingly. So to identify the image in supervised learning, we will give the input data as well as output for that, which means we will train the model by the shape, size, color, and taste of each fruit. Once the training is completed, we will test the model by giving the new set of fruit. The model will identify the fruit and predict the output using a suitable algorithm.

  Unsupervised learning is another machine learning method in which patterns inferred from the unlabeled input data. The goal of unsupervised learning is to find the structure and patterns from the input data. Unsupervised learning does not need any supervision. Instead, it finds patterns from the data by its own.

  Example: To understand the unsupervised learning, we will use the example given above. So unlike supervised learning, here we will not provide any supervision to the model. We will just provide the input dataset to the model and allow the model to find the patterns from the data. With the help of a suitable algorithm, the model will train itself and divide the fruits into different groups according to the most similar features between them.

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