Introduction to Data Science process

You may have seen the two-sentence summaries of what a data scientist performs on a daily basis, which go something like this:

Data science is a multifaceted field that draws data and insights from both structured and unstructured data using scientific methods, tools, and algorithms. However, a data scientist actually does much more than analyze the data. I believe that all of his work is data-related, but it also uses various data-based methods.

The study of data is interdisciplinary. In order to extract useful information from data entails the methodical blending of scientific and statistical techniques, procedures, algorithm development, and technology.

But how are these disparate places integrated? To understand this, you must be familiar with the practice of data science and the day-to-day activities of a data scientist.

Steps involved in Data Science Process

Ask Questions to Frame the Business Problem

Try to obtain a sense of the demands of a company in the first stage and then gather data based on those needs. The first step in the data science approach is asking the correct questions to identify the problem. Let's look at a bag company's most prevalent issue: the sales issue.

Asking a lot of questions is the first step in any problem analysis:

• Who are the clients and the target market?
• How do you reach your intended audience?
• What does the existing sales process look like?
• What details do you know about the intended audience?
• How can we recognize clients who are more inclined to purchase our goods?
• You decide to concentrate on the "How can we identify potential clients who are more likely to buy our product" following a meeting with the marketing team.

The next step is to determine what information you have on hand to address the questions above. If you need profound information about the data science process, you can visit the data scientist course in Bangalore, designed for aspiring professionals. 

Get Relevant Data for Analysis of the Business Problem

Now that you know the issue affecting your firm, it is crucial to gather the information necessary to address it. Check to see if the business already has the required data before gathering it.

In many circumstances, you may receive datasets that have already been gathered for prior research. Age, gender, previous client transaction history, and other information are all needed.

You discover that the company's Customer Relationship Management (CRM) software, 

controlled by the sales team, contains most customer-related data. The back-end tool for CRM software is a SQL database with several tables. When you look through the SQL database, you see that the system contains comprehensive identity, contact, and demographic information about the clients (that they provided to the business) and comprehensive sales process information.

You must set up new data collection if you believe the current data is insufficient. You can even collect feedback from your visitors and customers by displaying or distributing a feedback form. It takes a lot of engineering work and is time-consuming.

Your data is truly "raw data," meaning it has errors and missing numbers. Therefore, you must clean (wrangle) the data before evaluating it.

Explore the Data to Make Error Corrections

Exploration is the process of organizing and cleaning up the data. This method takes up more than 70% of the data scientist's work. Even though you have gathered all the data, you are not yet prepared to use it since, more often than not, the raw data you have gathered probably contains anomalies.

Ensuring the data is accurate and clean must come first. This is the most important step in the process, requiring patience and focus.

For this, various tools and methods are used, including Python, R, SQL, etc.

You then begin responding to the following queries:

• Does the data contain missing values, such as clients without contact information?
• Exist any unreliable values? How can we resolve them?
• Does more than one dataset exist? Is it a wise decision to merge datasets? If so, how should they be combined?
• Your data is prepared for analysis once you have found any missing values and erroneous values in it. Keep in mind that having no insight is preferable to receiving incorrect insights from the data.

Model the Data for In-depth Analysis

How can we identify prospective customers more likely to buy our product? Can be addressed by using a model. after exploring the data.

You evaluate the data in this step to extract information from it. It is necessary to apply multiple algorithms to the data analysis process to extract meaning from it.

• Create a model using the data to provide an answer.
• Compare the model's predictions against the obtained data.
• The use of numerous data visualization technologies.
• Run the relevant statistical analyses and algorithms.
• Compare results with those from different methods and sources.

However, answering these questions will only provide you with hints and theories. Data modeling is a straightforward method for approximating data in an equation that a machine can understand. Based on the model, you ought to be able to forecast the future. You might have to experiment with a few different models to obtain the greatest fit.

Returning to the sales issue, you can estimate which clients are more likely to make a purchase using this model. The prediction may include details such as a female residing in India, 16 to 36 years old.

Communicate the Results of the Analysis

Although they are undervalued, communication skills are crucial to a data scientist's profession. This is a very difficult aspect of your employment because it entails communicating your findings in a way that the general public and other team members can understand.

You must clearly convey the findings of the problem mentioned earlier:

• Graph or chart the data using tools like R, Python, Tableau, or Excel for presentation.
• Apply "storytelling" to the outcomes.
• Respond to different follow-up inquiries.
• Present data in various formats, such as reports and websites.
• I assure you that every time you find an answer, new questions will arise.

Key Takeaway

By now, you fully understand how data science works. This was a peek at a data scientist's day in life and what he did. Particular tasks consist of:

• Finding the analytical data problems that present a fantastic opportunity for an organization.
• Gathering massive amounts of structured and unstructured data from various sources.
• Choosing the appropriate variables and data sets.
• In order to ensure correctness and completeness, the data must be cleaned and errors removed. 
• Mining massive data sets by developing and using models, algorithms, and techniques.
• Data analysis to find hidden patterns and trends.
• Analyzing the data to find answers and opportunities, then making choices based on it.
• Using visualization and other techniques to present findings to managers and other individuals.

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