Data Science Process – What daily tasks are performed by a Data Scientist?

The Data Science Process

You might have read the two-sentence definitions of a data scientist that briefly explains what he does on a day-to-day basis like:

Data Science is a multidimensional field that uses scientific methods, tools, and algorithms to extract knowledge and insights from structured and unstructured data.

But in reality, he does so much more than just studying the data. I agree that all his work is related to data but it involves a number of other processes based on data.

Data Science is a multidisciplinary field. It involves the systematic blend of scientific and statistical methods, processes, algorithm development and technologies to extract meaningful information from data.

But how are all these areas worked together? To understand this, you need to know the process of data science/the work of a data scientist on a day-to-day basis.

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Data Science Process – Daily Tasks of Data Scientist

The steps involved in the complete data science process are:

 The Data Science Process - Data Scientist Daily Tasks

Step 1. Ask Questions to Frame the Business Problem

In the first step, try to get an idea of what are the needs of a company and extract data based on it. You begin the process of data science by asking the right questions to find what the problem is. Let’s take a very common problem of a bag company – The sales problem.

For analysis of the problem, you need to start by asking a lot of questions:

  • Who are the target market and the customers?
  • How do you approach the target market?
  • How does the sales process look currently?
  • What information do you have about the target market?
  • How can we identify customers who are more likely to buy our product?

After a discussion with the marketing team, you decide to focus on the problem: “How can we identify potential customers who are more likely to buy our product?”

The next step for you is to figure out what all data you have available with you to answer the above questions.

Step 2. Get Relevant Data for Analysis of the Problem

Now that you know about your business problem, it is time to collect the data that will help you solve the problem. Before gathering the data, you should ask if the data required is already available with the company?

In many cases, you might get the datasets previously collected in other investigations. Data related to following is required: age, gender, previous customers transaction history, etc.
You find that most of the customer-related data is available in the company’s Customer Relationship Management (CRM) software, managed by the sales team.

SQL database is the rear tool for CRM software with several tables. When you go through the SQL database, you find out that the system stores detailed identity, contact, and demographic information about the customers (that they gave the company) and also their detailed sales process.

If you think the data available is not sufficient, then you must make arrangements to collect new data. You can even take feedback from your visitors and customers by displaying or distributing a feedback form. I agree, that is a lot of engineering work and requires time and effort.

The data you have collected is actually ‘raw data’ that contains errors and missing values. So before you analyze the data, you need to clean (wrangle) the data.

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Step 3. Explore the Data to Make Error Corrections

Exploring the data is actually cleaning and organizing it. More than 70% of the data scientist’s time is spent on this process. Despite collecting all the data, you are not ready to use it, because more often the raw data you have collected likely contains oddities.

First, you need to make sure the data is clean and free from errors. This is the most important step in the process which requires patience and focus.

Various tools and techniques are put to use for this purpose like Python, R, SQL, etc.

Then, you start answering these questions:

  • Are there missing values in the data i.e. are there customers without their contact numbers?
  • Are there any invalid values? If there are, how can you fix it?
  • Are there multiple datasets? Is merging datasets a good choice? If yes, then how should you merge them?

Once you have uncovered missing and false values in your data, it is ready for analysis. Remember that getting the wrong insights from the data is worse than having no insight at all.

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Step 4. Model the Data for In-depth Analysis

After exploring the data, you have enough information to create a model to answer the question: “How can we identify potential customers who are more likely to buy our product?”

In this step, you analyze the data to get information from it. Analyzing the data requires applying various algorithms that will draw out meaning from it:

  • Build a model of the data to answer the question.
  • Validate the model against the data collected.
  • Usage of various visualization tools to present data.
  • Perform the necessary algorithms and statistical analysis.
  • Compare results against other techniques and sources.

However, answering these questions will only give you hints and hypotheses. Data modeling is a simple way to approximate data in a proper equation that the machine understands. You should be able to make predictions based on the model. You might have to try several models in order to find the best fit.

Going back to the sales problem, this model will be able to help you predict which customers are more likely to buy. The prediction can be specific, like Female, 16-36 age group living in India.

Step 5. Communicate the Results of the Analysis

Communication skills are an important part of a data scientist job but also very underrated. This will actually be a very challenging part of your job as it involves presenting your findings to the public and other members of the team in a way that is easily understood by them.

You need to effectively communicate the results of the problem specified previously:

  • Graph or chart the information for presentation with tools – R, Python, Tableau, Excel.
  • Use “storytelling” to fit the results.
  • Answer the various follow-up questions.
  • Present data in different formats- reports, websites.
  • Believe me, answers will always spark more questions, and the process begins again.

Must Check – How to get your first Data Science Job

Summary

I hope you have understood the process of data science. This was a look at a day in data scientist job and his tasks. Specific tasks include:

  • Identifying the analytical problems related to data that offer great opportunities to an organization.
  • Collecting large sets of structured and unstructured data from all different kinds of sources.
  • Determining the correct data sets and variables.
  • Cleaning and eliminating errors from the data to ensure accuracy and completeness.
  • Coming up with and applying models, algorithms, and techniques to mine the stores of big data.
  • Analyzing the data to uncover hidden patterns and trends.
  • Interpreting the data to discover solutions and opportunities and making decisions based on it.
  • Communicating findings to managers and other people using visualization and other means.

Any other step that you would like to add in the data science process? Share your ideas in the comments.

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