How a Data Scientist works?
Today, a huge number of businesses across the globe are striving to make sense of the massive amount of disparate data captured by them through various means. With the help of data scientists, they can transform this inordinate volume of data into actionable insights which can influence their operations heavily. And that’s probably the biggest reason the data scientist career is considered one of the most attractive in the tech field. While data science leaves a profound impact on the tech field, it has also started transforming almost all other sectors – from telecommunications and retail to agriculture and health, just to name a few.
Before we delve deeper into the subject of this post, let’s understand what data science is.
With data at its core, data science works involve multiple disciplines that include technology, algorithm development, and data inference. A data scientist is someone who works with big data extensively utilizing his/her expertise in multiple disciplines and analyzes it to generate business value. And data scientists are often assumed to perform multiple roles – from data miner, data analyst, and software engineer to manager, business communicator, and a key person in any data-driven business that helps the management in decision-making.
However, the responsibilities of a data scientist always understood easily and are often used to describe a broad range of data-related work. If you’re planning to pursue a data scientist career, it’s important that you develop a clear understanding of the working process of a data scientist.
The working process of a data scientist
Though the working methods of data scientists may vary a bit based on their approaches and project goals, they generally follow these steps.
- Acquisition, collection, as well as, storage of data
- Goal identification
- Access, absorb, and integrate data
- Data munging/wrangling (processing and cleaning)
- EDA (exploratory data analysis)
- Choosing algorithms and one or multiple potential models
- Applying data science techniques and methods (artificial intelligence, machine learning, statistical modeling etc)
- Validation and tuning of results
- Presenting and/or communicating final results
Based on the final results, business stakeholders make business decisions and/or implement changes.
Now, let’s have a look at the typical deliverables and goals accomplished by a data scientist using the above process.
- Recommendations (think Netflix and Amazon recommendations)
- Prediction of a value based on provided inputs
- Grouping and pattern detection
- Actionable insights
- Recognition of text, image, audio, video etc
- Segmentation (think demographic-based marketing)
- Forecasts (think revenue and sales)
- Automated decision-making and processes
Each of the above is dedicated toward solving a certain problem and/or addressing a certain goal. While these may not seem like serious issues initially, in reality, these are the pillars of the success of any data-driven business.
By reading till now, if you feel interested to kick-start your data science career and want to take a relatively affordable and quicker pathway, the popular data science bootcamp in Silicon Valley offered by Magnimind Academy is what you should opt for.
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To learn more about data science, click here and read our another article.