Data Science is becoming a popular buzzword in the current market. As new industries and businesses are getting established, they seek data science assistance to learn about their customers and increase profitability. Data science even helps in AI (artificial intelligence) and ML (machine learning) technologies. With time and technology taking over, data science techniques are also growing. But everyone cannot be a day genius or tech-savvy, therefore, to stay competitive in the business, one needs a data scientist who can handle their data process easily.

What is the data science process? 

Data Science is often used in the systemizing process by data scientists to analyze and visualize a large amount of data. The data science process will allow data scientists to extract information and unseen patterns, and convert them into actionable insights. This information helps businesses in making decisions that help customer retention and profits. Further, a data scientist discovers the hidden pattern of structured and unstructured raw data.

The whole data science process solves every problem and gives answers to all business-related issues.

There are six steps of the data science process:

• State the problem

• Accumulate the raw data required for your problem

• Process the data for analysis

• Analyze the data

• Conduct in-depth analysis

• Review the analysis of the results

The data science process helps in converting raw data into structured data which results in monetary profits. A data scientist is skilful in this process and can ease these steps.

What are the steps of data science? 

Below are the steps of data science that can help you get familiar with the process.


Step 1: Crafting the Problem

When you step into solving a problem, the first step is to know the exact problem in detail. All data related questions must be converted into actionable business questions. People will give their input on problems which is further translated into actionable outputs.

A data scientist may ask a question to a business such as:

• Who are your customers?

• What are their demographic and geographic details?

• How to search for them?

• What is the sale process right now?

• Why are they interested in your products?

• Why do they need your product?

• What products they are interested in?

These questions will help you gain more insight and helpful information.


Step 2: Organizing the Raw Data for the Problem

After the problem has been defined, you will have to collect the data to derive insight and turn your business problem into a probable solution. This process involves thinking through data and finding ways to collect data. You can get data by scanning internal databases or can purchase a database from external sources.

Many companies store their data in their CRM (customer relationship management) systems. The data stored in CRM can be easily analyzed by exporting it to more advanced tools.


Step 3: Processing the Data to Analyze

After the second step, you will have all the useful data which you can process before analyzing.

You must maintain the data otherwise it can lead to errors that can corrupt your analysis. Often these issues can be values set to null when they must be set to zero or vice versa. You will have to do an analysis of the data and check if you can get more accurate insights into these data.

Some of the common errors may include:

• Missing values

• Corrupted values like null entries

• Time zone dissimilarities

• Date range errors

After you have clean the data, your data will be ready for further exploratory data analysis (EDA).


Step 4: Analyzing the Data

It is one of the crucial steps of the data science process. Here you will have to think of ideas and identify hidden patterns and insights. Also, you will be able to find out why the sales of a product or service have gone down or up. By analyzing data carefully, you will be able to make better decisions for your business growth. 


Step 5: Conducting In-depth Analysis

In this step, you can analyze your data further such as on basis of age, and social media activity in predicting your customers.

This will also help you to look into several aspects of customers like what number of people prefer using their phones over desktops while purchasing your product or what is the most popular time of highest traffic on your website. These findings will make your marketing simpler and you will be able to target your aimed customers only.


Step 6: Communicating Results of this Analysis

After you have performed these steps and processed the data, you must convey these findings further to the sales head and help them to understand. This communication will lead to action toward business betterment.

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