Analysis of Data Science by an aspiring Data Scientist

Would it be possible for LinkedIn to become the ultimate career networking site if its co-founder Reid Hoffman had not had the faith in the power of analytics to incorporate recommendations for the people to network? Inclusion of recommendation paved the way for this company to attract and retain billions of users across the globe.

If Alice manages both Bob and Charlie, it is highly likely that they both know each other.

According to HBR, Data Science is the sexiest job of the 21st century. No wonder it is. Data has the power to transform the business of any organization by listening to the voice of the customer, by visualizing the trend in the industry, by providing the customer what he/she wants without asking. The power of data is invaluable that it is believed to create the next world war after the water crisis. Data Scientists play a crucial role in mining insights from the data.

The everyday life of a data scientist:

Roles and responsibilities-

1. Working closely with the business in identifying and formulating business problems.

2. Using machine learning and statistical techniques to develop solutions to create actionable and scalable solutions for business problems.

3. Building and deploying the model from inception to the end and reading data from different data sources.

4. Communicate with the stakeholders in resolving the business problem and providing business feedback.

The impact made by data scientist in organizational decisions

As mentioned before, data helps in driving strategic organizational decisions. With data and trends, it becomes easier for the leaders to persuade stakeholders in making key business decisions. Insights derived from data facilitates an organization to conquer the competition in the market.

The transition from software engineering

For any software engineer who is detail-oriented and who is passionate about deriving actionable insights from data, is it recommendable to move to a data science role?

I sought industry specialists to get the answer before I decided to pursue my career in Data Analytics.

Below are the answers I got:

1. Experience with programming

Certainly, hands-on experience on any programming language especially Python, Ruby, Java is desired by recruiters.

Python scripting is a basic requirement in data cleaning and aggregation. Python is also considered as “the programming language” in the Data Science world (Based on the requirements for Data Scientist/Data Analyst/Business Intelligence Engineer)

The programming skill is also desirable when certain data science task has to be automated to make it repeatable.

2. Handling the massive amount of data

“Big data is like teenage sex. Everyone talks about it, but nobody does it.”

What exactly is Big Data? Any data that has high volume, generated at a high velocity, with a variety of information can be called as Big Data. And how huge is that huge?

An answer given by Andy Barkett, a lecturer at UC Davis, “Any data that cannot be fitted in a computer.”

Big Data, undoubtedly, is a buzz word in Silicon Valley. Though several organizations claim that they play with big data, only a very few actually play with big data.

This big data is the mother of data science. Any software engineer who has performed ETL on big data in their work using big data tools and technologies such as Hadoop, Apache Spark, Tera data has already check marked a requirement.

3. Data design and modeling

As data is extracted from different data sources, it is essential to model and design the data which provides easy and efficient access. Hence, the knowledge and experience of data design and modeling.

Note: This data modeling is not to be confused with the data modeling such as regressing or classification.

Apart from the ones mentioned above, the below skills are crucial for any aspiring data scientist.

Communicating insights

Starting from understanding the stakeholder pain points to communicating the feedback back to stakeholders, data scientists are required to possess clear and efficient communication.

Asking the right question is more important than the answers

Data has most of the answers that we need. It is important to ask the right question to derive the insights from the data.

Customer obsession

Most of the data science challenges revolve around customers. Being customer obsessed is preferred by several organizations (It is one of the Leadership Principles in Amazon)

As an aspiring data scientist, this is written based on my research and the conversation I had with senior managers from different organizations. Hoping to provide more insights into the future as I transition to my new dream role in Data Science.