A Quick Walk With A Data Scientist| A Zestful Day!

a day in the life of a Data Scientist

“No job would be more sought-after over the next decade than Data Scientist.”

the Harvard Business Review

Stating Data Scientists as the sexiest jobs in the 21st century, the Harvard Business Review predicted an exponential demand for Data Scientists. Over the years since then, Google witnessed around 675% rise in the search volume for the term “Data Scientist”. That is to say, a Data Scientist job is one of the most desired professions as of today. So, what are the interesting tasks that make a day of a Data Scientist so exciting?

If you are one of these aspiring Data Scientists, then aren’t you curious to know what it is that those on the other side keep themselves busy with all the day? What do the Data Scientists do that makes their jobs so much important?

“Data Scientists unleash the power of Data. In the process, they analyze problems, collect adequate data, and uncover secrets in those Data to formulate a great solution to the above big problem. “

its Made up by US

Haha! the above statement is roughly coiled up by us. However, that is the essence of what we know Data Science is all about, isn’t it? You might now jump up and ask, “Is a day in the life of a Data Scientist as simple as that? NO! the answer is a straight BIG NO.

So, let walk along with a Data Scientist and find out how does a day in the life of a Data Scientist looks like?

a Data Scientist

How does a day in the life of a Data Scientist looks like?

8 interesting tasks in a day in the life of a Data Scientist.

Let us walk through the various responsibilities of Data Scientists that usually keep them occupied playing around with DATA.

Before beginning with anything, first and foremost, a Data Scientist has to look in-and-out through the organization and find out what that big problem is, which when solved will add augmented value to the system? So, here goes the number 1 in the list of Data Science responsibilities.

1. Identify the Data Analytics problems

Understanding the Data Analytics related problems is becoming hugely difficult with the tremendous amount of data being curated across the businesses. What do you think? Is storing this data, which is growing with a rate of 40-60% per year, productive at all? The underlining fact to understand here is that the business data analysis issues are not only related to analytics by themselves! Oh really! Then?

Business data analysis problems are also instilled due to deep system or infrastructure problems. Hmmm! That means, we need to put a check on the infrastructure and go right into the system problems and get them sorted first.

“87% of companies have low BI (business intelligence) and analytics maturity, lacking data guidance and support”


What are these issues that need to be addressed first?

 Some of the problems faced by the organizations while using business intelligence analytics on a strategic level are as follows:

Unable to provide new or timely insights

It could be due to lack of data, long data responses, or old approaches applied to a new system. There seems to be more like old

Timely Data
 Inaccurate analytics

Poor quality of source data or system defects related to the data flow can throw inaccurate analytics issues.

What is the Impact of Poor Data Quality in Business?
 Handling data analytics becomes too complicated:

The complexity issue can be either because of the over-engineered system or due to messy data visualization.

Longer system response time

Due to inefficient data organization or problems with the data analytics infrastructure and resource utilization, the system might take an unusually take long response time.

Expensive maintenance

The ongoing investment in maintenance and infrastructure would be high owing to Outdated technologies, Non-optimal infrastructure, or over-engineered systems again.

So, all that said, the very first step of the ladder seems to be involving the analysis and problem-solving part. Interesting? We know, as kids we all have liked ‘The Hardy Boys’ and ‘The Famous Five’ kinds of books. This part reminds me of those books wherein the adventurous lives of the kids will demand them to go beyond their comfort zones and solve the mysteries.

Anyhow, let’s not lose track and quickly step onto the 2nd task in the Data Scientist’s responsibilities. Once the big problem is identified, it’s then time to determine the correct data sets and variables.

2. Determine the correct data sets & variables| a day of a Data Scientist all filled with data

“A data set is an ordered collection of data.”

The data set can be in any form as tables, schema, pie charts, or anything similar. Basically, the data set is nothing but an organized model of data that helps to process the needed information. It can be considered as the arranged collection of information.

After determining the correct data set, a Data Scientist then moves on to collecting large amounts of data.

3. Collect large amounts of data

A day in the life of Data Science

It is a very important step in the life of a Data Scientist. Because a day without Data is not a well-spent day you see. Data Scientists collect huge amounts of structured & unstructured data from disparate sources and store it in various data sets.

4. Clean and validate data

The large data sets are of no significance if they are not cleaned and validated for accuracy, completeness, and uniformity. Data Scientists take the next step in the process of reaching their destination by arranging the essential data and discarding the ones that are not really relevant.

5. Analyze the data

This is probably the popularly known task of a Data Scientist.

The large sets of Data thus collected and sorted now need to be analyzed. Analyzing the data is the next task. Data Scientists analyze the data to identify patterns and trends!

6. Data Mining in a day of a Data Scientist

Data mining is the process of extracting and discovering patterns in large data sets.

Yes, gold mining is what came to our minds too!

Data Scientist

Alike gold mining, data mining involves the extraction of the most valuable element from the data. Data mining methods devise and apply models and algorithms at the intersection of machine learning, statistics, and database systems. 

7. Interpret the data

The process of reviewing data, known as Data Interpretation, is equally important. Predefined Processes assist Data Scientists to come to a definite conclusion from the derived Data. This step helps in assigning some meaning to the data. Data Scientists arrive at a relevant conclusion with Data Interpretation.

8. Communicate findings to stakeholders

Wow, the journey through the tasks in a day of a Data Scientist seems so amazing! We didn’t even realize that we arrived at the 8th task. Last but not least in the list of tasks comes the communication part. The conclusions made on the data are then to be presented to the stakeholders using visualizations and other means.

So, we have come to the end of this beautiful day in the life of a Data Scientist. Data Scientists are in-demand professionals as of the date and make an awesome career with lucrative packages. That makes a career in Data Science so much desired and happening.

Wrapping Up

After that flashback journey with data, would you like to step into a career in Data Science? If it was a huge Yes, that you just nodded to then this IIT Certified Advanced Data Science Program is where you need to register to.

To make a standout career in Data Science, click the below button.

Become an IIT Certified Programming Professional & Master Data Science

So, what do you think about a day in the life of a Data Scientist? Interesting? Do let us know with your comments. We look forward to hearing from you!

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A traveler, and explorer, Archana is an active writer at GUVI. You can usually find her with a book/kindle binge-reading (with an affinity to the mystery/humor).

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[…] as one of the top career options by Glassdoor, LinkedIn, etc, a Data Scientist is responsible for collecting, analyzing, and interpreting huge amounts of data that constitute both structured and unstructured data. Creating useful business solutions and […]

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