5 Important Data Science Methodologies Used in Projects

Every prospective data scientist asks, “What approach does an experienced data scientist employ to address a range of real-world business problems?” Here, I’ll show you how to approach a problem and apply your newfound knowledge to interesting instances from the real world. You will be guided by the data in the science process as you formulate a business challenge while keeping value addition in mind, gather and analyze the data, build an analytical model, deploy the model, and monitor or analyze input from the model. But before moving forward, do check out the advanced Data Science course in Delhi and get certified by IBM. 

Important Data Science Methodologies are:

  1. Data Collection 

Any random format can be used to access the information acquired. As a result, the output should be accepted, and the data obtained should be validated using the selected technique. As a result, more information may be acquired if necessary or discarded if it is not needed.

Data requirements are examined throughout this phase, and decisions are made regarding whether the collection needs more or fewer data. After acquiring the data components, the data scientist will know what they will be working on during the data collection phase.

Descriptive statistics and visualization tools can be applied to the data collection process to assess the data’s substance, quality, and preliminary insights. Data gaps will be identified, and plans must be made to close them or find solutions.

  1. Data Understanding 

Data comprehension methodology answers the question, “Does the acquired data reflect the problem to be solved?”. In order to ascertain the substance and quality of data, descriptive statistics computes the measurements applied to the data. A revisit to the preceding action may be necessary to adjust this step.

  1. Modeling 

Modeling decides whether the data can be processed as is or whether additional finishing and preprocessing are necessary. The creation of predictive, descriptive, and prescriptive models is the focus of this phase.

A descriptive model could look into issues like what are the 10 best-selling items in a given category? A predictive model is a mathematical technique that uses patterns in a set of input data to predict future events or outcomes, such as yes/no or multi-class outcomes. These models depend on the analytics method, which may be powered by machine learning or statistics.

The data scientist would use a training set for predictive modeling. A training set is a set of data with predetermined results. The data scientist will test out several methods to validate the required variables.

An in-depth understanding of the current situation and an appropriate analytical technique is required for effective data collection, preparation, and modeling.

  1. Data Requirements

Without high-quality data, data science cannot produce good outcomes. Getting the proper data quality from various sources is essential in data science.

The analytical technique collects the appropriate sources, amounts, and data formats. Before beginning the data-gathering approach, the following questions must be addressed to grasp the data requirements fully:

  • What kind of data is needed?

  • How to locate a good source or gather them.

  • How to interact with or examine the data, and

  • How to get the data ready to get the desired results.

Finding the required data content, formats, and sources for the initial data collection is part of the data requirement approach.

  1. Analytics Approach

After you become proficient in business understanding, you will be able to identify the type of issue you are attempting to resolve. In the analytics step, all the questions you became familiar with in the previous step are answered using the data.

Typically, four different types of analytics methodologies can be used, depending on your company’s understanding.

  • Descriptive strategy: employing statistical analysis to illustrate relationships between variables, tracking particular key performance metrics using a business intelligence software.

  • Predictive strategy: If the goal is to estimate future action probability using knowledge from the past.

  • Prescriptive approach: When deciding on the best course of action based on the evidence.


This was all about the data science methodology explained in 5 steps. If you are a data science aspirant looking to upgrade your skills, Learnbay has got the Best Data Analytics course in Delhi, developed in collaboration with IBM. 


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