12 Sep 2022

Data Science Life Cycle

Ekeeda Moderator
Works at Ekeeda

Data science is a combination of various tools & algorithms used to discover hidden patterns in raw but highly potential data. Data science is different from other techniques and its high in demand among businesses, and managers who work on huge amounts of data. Data Scientists is the hottest career field of the 21st century. As a data analyst, you will focus on the visualizations and data history, and as a data scientist, you will work on exploratory analysis and derive useful insights using several kinds of Machine Learning algorithms.

Why Does Industry Needs Data Science?

In prior days, data was not so fluent and easily available. Also, it was in the much smaller size, that entrepreneurs used to believe in gut instinct, rather than facts and figures. But in modern-day, data is creeping around from many sources and has become unstructured so it’s not so easy to analyze. 

We need advanced data science techniques to gain useful insights so that companies could make a positive impact and take bold steps to achieve more result-oriented business.

What Do Data Scientists Do?

Data scientists use a variety of specialized tools & techniques specifically designed for Data Cleaning, Data Processing, Data Analysis, and Model Structure.

Of the various tools, Python and Java, MATLAB is favourite among data scientists. There are other tools available as well like SQL, NoSQL, Deep Learning, Tableau, etc. It contradicts the conventional learning that data scientist takes years and years of experience. Additional data science skills & knowledge will give them good exposure to programming languages and other advanced technologies.

R and Python are amongst the most popular data science programming languages. R is built for data mining and analysis whilst Python is used for data analysis operations.

Data scientists should skill related to data mining, data processing, predictive analytics, ML, and statistical skills. They should also have coding and algorithms skills. Lastly, they should create good data visualizations, reports, and dashboards to show findings to the clients & Stakeholders.

Are you curious to learn more about data science? Read: Why Is Data Science A Good Career Option

Enroll with top data science course online now!

Data Science Lifecycle

Data Science helps individuals to solve problem statements with a series of well-designed steps and it is as follows: 

Let’s understand them in detail

  1. Data Discovery

We have to identify data sources – such as files, databases, scarper, or real-time streaming. There is BIG Data which focused on Data volume in terabytes, Streaming of data with high throughput, Structured, semi-structured and unstructured data, and lastly Data Quality. 

  1. Data Structuring

Data Scientists can understand the data and get to know if it's the right one that can solve their business problems or not. There are various steps in this phase like data structure and removing unnecessary columns. It’s a time-consuming but important part of the lifecycle.

  1. Model Planning 

Under Model Planning, data scientists will identify the relationship between variables that will be used in the next step to build the algorithm. Data Scientists will use exploratory data analysis to achieve the targets. Data scientists help to gain valuable insights into potential data.

  1. Model Building 

Datasets are prepared for the training & testing phase. There are several techniques in the model building like classification, association & clustering. Several tools are available to build a model: SAS Miner, MATLAB, Statistics, etc.

  1. Communication

Data scientists need to report and document all the findings of the projects through visualizations. The results have to be communicated in a simple and actionable form to the stakeholders to decide whether to take action or not. This step decides if the project gets operational or not.

  1. Operations

Data Scientists will deploy the project for the users, but before this, there will be a pilot project deployment that gives basic insights into the performance and the issues. Once the phase is cleared the project gets ready for full-fledged deployment.

Want To Learn More About Data Science Life Cycle? Watch Video

Source: SciLifeLab

On A Concluding Note - 
‘Data Science’ is the hottest career field with 11.6 million data science jobs by 2026.

To learn data science, you can enroll with top players like Ekeeda who have the best data science course designed by industry-experts. With 100+ assignments and real-world projects, it will help individuals gain industry-level knowledge and experience. Mock interviews and career-building workshops will help aspirants boost their confidence and improve communication skills - the much-needed factors to gain edge over the competiton.

Sign up for Ekeeda data science course and become industry-ready today!

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