Data Science Life Cycle: Step by Step Explanation [2020]

Data is the present, and it is already creating the future. Many Data Science concepts are clouded by confusion due to a lack of clarity. The general understanding of Data Science projects is usually covered in a haze of vagueness. Most people do not have a concrete comprehension of how the process progresses.

Right from the first step of obtaining data to analysis and result presentation, a Data Science Life Cycle is a definite procedure that has five important steps. Read on to gain a clear understanding of all of them, and the Data Science Life Cycle as a whole.

Data Science Life Cycle

1. Gathering Data

The first thing to be done is to gather information from the data sources available. Technical skills, such as MySQL, are used to query databases. There are special packages to read data from specific sources, such as R or Python, right into the data science programs. You may find numerous kinds of databases, such as Oracle, PostgreSQL, and MongoDB. Yet another alternative is to obtain data through Web APIs and crawling data. Social media sites such as Twitter and Facebook let their users approach data by connecting with web servers.

The most conventional way of gathering data is straight from the files. It can be done by downloading from Kaggle or preexisting information stored in Tab Separated Values (TSV) or Comma Separated Value (CSV) format. Since these are flat text files, a specific Parser format is needed to read them.

2. Cleaning Data

The next step is to clean the data, referring to the scrubbing and filtering of data. This procedure requires the conversion of data into a different format. It is necessary for processing and analyzing of information. If the files are web locked, then it is also needed to filter the lines of these files. Moreover, cleaning data also constitute withdrawing and replacing values. In case of missing data sets, the replacement must be done properly, since they could look like non-values. Additionally, columns are split, merged, and withdrawn as well. 

3. Exploring Data

The data now has to be examined before it is ready for use. In business settings, it is completely up to the Data Scientist to transform the data that is available into something feasible in a corporate setting. This is why the first thing to be done is the exploration of data. The data and its characteristics require inspection. It is due to the fact that different data types, such as nominal and ordinal data, numerical data, and categorical data need different handling. 

After this, the descriptive statistics have to be computed. It is so that features can be extracted and important variables can be tested. The important variables are mostly inspected with correlation. It does not mean causation even if some of these variables are correlated. 

In Machine Learning, Feature is used. This helps the Data scientists pick out the properties that represent the concerned data. These may be things such as ‘name’, ‘gender’, and ‘age’. Furthermore, data visualization is utilized to highlight important trends and patterns in data. The significance of data can be adequately comprehended through simple aids such as bar and line charts.

4. Modeling Data

After the essential stages of cleaning and exploring data, comes the phase of modeling. It is often considered the most interesting part of a Data Science Life Cycle. The first step to take while modeling data is to minimize the dimension of the data set. Every value and feature is not necessary for the prediction of the results. At this stage, the Data Scientist needs to choose the essential properties that will directly aid the prediction of the model.

Modeling comprises of quite a few tasks. For example, models can be trained to differentiate via classification, such as mails received as ‘Primary’ and ‘Promotion’ through logistic regressions. Forecasting is also possible through the use of linear regressions. Grouping data to comprehend the logic backing these sections is also an achievable feat. For instance, E-Commerce customers are grouped so that their behavior on a particular E-Commerce site can be understood. This is made possible with hierarchical clustering or with the aid of K-Means, and such clustering algorithms.

Prediction and regression are the main two devices used for classification and identification, forecasting values, and clustering groups.

Read: Data Scientist Salary in India

5. Interpreting Data

Interpreting data is the final and most important juncture of a Data Science Life Cycle. Interpretation of data and models is the last phase. Generalization ability is the crux of the power of any predictive model. The model explanation is dependent upon its capacity to generalize future data which is vague and unseen.

Data interpretation means the data presentation to the regular layman, someone who has no technical knowledge about data. Business questions posed at the beginning of the life cycle are answered in the form of delivered results. It is coupled along with the actionable insights discovered through the process of the Data Science Life Cycle.

Actionable insight is a crucial part of demonstrating how Data Science can furnish both predictive analytics and even prescriptive analytics. This allows one to know how to replicate a positive result and avoid a negative one. If you learn data science you will be able to understand Data Science Life Cycle properly.

Moreover, these findings need to be visualized appropriately. This is done by making sure the original corporate concerns back them. The biggest aspect of all of this is concisely representing all of this information, so that it is actually productive for the business concerned.

Conclusion

To summarise, these are the five essential steps of a Data Science Life Cycle which every student of Data Science should be familiar with. However, it is not simply the basic data skills that get the job done. One of the most important skill sets to have is the ability to provide a lucid and actionable narrative.

The presentation of the data obtained and transformed must be succinct and clear enough for the audience to comprehend. Communication is the key to success here, as in most places. The heart of the Data Science Life Cycle is the interplay between the existing goals, data content, and analytical method.

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