If you follow the digital world, you may have heard of the term Data Science – as well as the terms Machine Learning, Artificial Intelligence, Big Data, or any other similar term.
These keywords have gained a lot of shine in our daily news, every day technology companies launch new developments in the market.
What does the concept data science mean?
It is an English term meaning data science. It is a field of study that uses scientific methods to obtain knowledge through data, to provide support for decision-making.
Companies generally use data science techniques to analyze data and make decisions that help expand businesses.
In this post we will provide some practical examples explaining how people are using these technologies to make better decisions, increase sales and expand businesses.
But, how does Data Science work in practice?
There is no consensus that there is a specific and appropriate way to work with data science, but the process is broadly divided into 7 phases:
1. Ask questions
The Data Science process usually begins with a question that must be answered. Then we organize a list of 10 most common questions that are directed to those who deal with digital products:
1- What will be the sales numbers in the coming months?
2- What will happen if you completely change the design of the product page?
3- When is the appropriate time to go to the customer and present the product?
4- What are the characteristics that determine the success of the affiliate marketer in selling the product?
5- What is the amount of money that should be invested in the field of network traffic to obtain the specified results?
6- What is the ideal price for the product?
7- When can a new product be introduced to the customer?
8- How many emails should be given to the lead before introducing the product to him?
9- What is the best format for content marketing? Articles in a blog or via videos?
10- What’s the best month to do product launch events? What is the ideal time to start selling?
2. Collecting data
After that, it is necessary to search for data that can help answer these questions. This data can come from various sources, for example:
Systems and applications.
Internet searches.
Data of organizations and companies.
Research.
If the question is which content will produce the best results – whether it is posts or videos – you will need to compare the engagement data of the leads who read your blog articles with the engagement data of the leads who viewed the videos. This data is provided to you by Youtube Analytics
3. Processing and organizing data
With the data collected, it is very important to carry out the process of cleaning, coordinating, processing and organizing the information. This happens because the resulting data often suffers from a mismatch that may negatively affect the wrong analysis and decision-making.
When the data is organized and processed, the analysis process can begin.
4. Data analysis
There are several types of analyzes. They vary from simple to extremely complex operations. But it is important to remember that in most cases analyzing basic data leads to a very valuable result for your business.
5. Development of models and algorithms
In cases where analyzing data becomes a complex matter, or raises more new questions than it provides in terms of answers to the main question, it may be time to prepare statistical models and algorithms to find the solution that will bring the most value to your business.
In the example below, we can better understand how this works.
But how do I discover which indicators are important to my audience?
If I want to test design details on my page that increase the chances of visitors buying my product, I will need:
Generate many different pages.
Separate the groups of users who will access each of these pages.
6. Data preview
After using the models and algorithms, it will be necessary to visualize the results to ensure that the analytical results are consistent with the objective of the study.
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