What is Data Science? Here's How to Become a Data Scientist

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Do you have any idea about what data science is and what a data scientist does? In this post, we will explain everything you need to know about the profession!

After all, this is one of the most sought-after areas of technology today. But future experts in the sector must invariably go through a practical question and know how to answer what data science is.

Learn everything about the subject below and discover the importance of the profession, its characteristics and, especially, where to study data science. Enjoy your reading!

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If you want to go from zero to an advanced level and learn absolutely everything you need to use Data Science to stand out in the Job Market and be able to enter the most promising and desired careers in companies, this course is for you.

Data science involves using methods to extract insights from the data available in a company. Its main goal is to find out what happened, why it happened, predict what will happen next, and suggest actions to help the company make decisions.

For example, you can predict how much a soft drink student data factory needs to buy based on the amount of soft drinks produced and sold.

Read also: Introduction to Data Science

Therefore, it is safe to say that data science is present in practically everything in your life — from when you make a purchase and swipe your card on the machine: there, it is data science that decides whether your transaction will be approved or not.

Or when you request a credit limit increase in a banking application, your information is sent to a model, built by a data scientist, and this model will evaluate your information in seconds and decide, for your case, how much your limit can be.



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How does data science work?
Data science follows a structured process that involves several steps from data collection to final analysis. Let’s break down each of these steps.

Data collection
Data collection is the first step in the data science process. In this phase, relevant data is gathered from a variety of sources, such as databases, APIs, sensors, CSV files, and server logs, among others.

The quality of the data collected is crucial, as incorrect or incomplete data can compromise subsequent analysis. Collection methods can include web scraping, surveys, downloads from public databases, and more.

Data storage
After collection, data needs to be stored in an organized and accessible manner.

This can be done using relational databases (such as MySQL and PostgreSQL), NoSQL databases (such as MongoDB and Cassandra), or cloud storage systems (such as Amazon S3 and Google Cloud Storage).

The choice of storage solution depends on the volume of data, the frequency of access and the specific needs of the project.

Data processing
Data processing involves cleaning and transforming raw data into a format suitable for analysis.

This includes removing duplicates, handling missing values, normalizing data, and applying preprocessing techniques such as standardization or data aggregation.

Tools and languages ​​such as Python , R, and SQL are commonly used at this stage to prepare the data.

Data analysis
Data analysis is the step where processed data is examined to extract insights and patterns.

Analysis techniques can range from simple statistical analysis to more complex methods such as machine learning and data mining.

Additionally, tools such as Pandas, NumPy, Scikit-learn, and data visualization libraries such as Matplotlib and Seaborn are often used for this purpose.

The goal here is to transform data into useful information that can guide decision-making.

What are the pillars of data science?
Data science is supported by three main pillars: business, technology and statistics.

Each of these pillars plays a crucial role in structuring and executing data science projects.

Business
The business pillar focuses on understanding the context and business objectives of data analysis . It involves translating business needs into data problems that can be solved using data science techniques.

This pillar is essential to ensure that the insights generated are aligned with the organization's strategic objectives and can generate real value.

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