Data Science Process – Day-to-Day Tasks of a Data Scientist

Step 1: Ask Questions to Define the Business Problem

Everything begins with identifying what the business needs. You start by asking meaningful questions to frame the problem.

For example, if a bag company is struggling with sales, you might ask:

Who are our target customers?

How do we currently reach them?

What’s our current sales process?

What existing data do we have about them?

How can we find potential buyers?

Once the problem is defined, the next move is identifying the relevant data that could help solve it.

Step 2: Gather Relevant Data for the Problem

Now that the problem is clear, you’ll gather the necessary data. First, check whether the data is already available—often it is, stored in systems like CRM databases.

For instance, you may find tables in a SQL database containing customer demographics, contact details, and past purchase history.

If you don’t find enough data, you’ll need to collect more—this might involve customer feedback forms or surveys. Yes, it’s a lot of effort, but necessary.

This raw data usually contains missing values or errors, so the next task is to clean it.

Step 3: Clean and Explore the Data

Data cleaning, also known as data wrangling, is crucial. It takes up over 70% of a data scientist’s time.

This process involves checking for:

Missing values (e.g., missing contact details).

Incorrect or inconsistent entries.

Merging different datasets logically and meaningfully.

Tools like Python, R, and SQL are often used in this step. Remember: bad data leads to bad results. So before doing any analysis, clean data is a must.

Step 4: Build Models to Analyze the Data

Once the data is clean, it’s time to build models to solve the defined problem.

This step includes:

Choosing the right model.

Validating its accuracy.

Using tools and algorithms to extract patterns.

Testing different models to find the best fit.

For the bag company example, your model might predict that customers who are women aged 16–36 from India are more likely to buy. That insight comes from analyzing customer behavior data.

Step 5: Communicate Results Clearly

One of the most overlooked—but vital—skills for a data scientist is communication.

You need to present your findings in a clear and engaging way using:

Graphs, charts, or dashboards (with Python, R, Tableau, Excel, etc.).

Storytelling to explain your insights.

Answering follow-up questions from stakeholders.

And yes, your answers often lead to new questions—so the cycle continues!

Summary

Let’s recap what a data scientist typically does in a day:

• Identify key business problems related to data.
• Collect large volumes of structured and unstructured data from various sources.
• Choose the right datasets and variables for analysis.
• Clean and process the data for accuracy.
• Apply models and algorithms to extract patterns.
• Analyze the data to uncover trends and insights.
• Communicate findings to decision-makers in a clear, visual format.

The job of a data scientist is dynamic, challenging, and impactful. Mastering this process can unlock a powerful career path in today’s data-driven world.