Data Science is the disciplined study of the data and information inherent to the business and all the views that can surround a given subject. It is a science that studies information, its process of capturing, transforming, generating and subsequently analyzing data. Data science involves several disciplines:
- Computing;
- Statistic;
- Math;
- Business Knowledge.
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Data Science or Data Scientist
Since we define Data Science /Data Scientist, why not define the professional Data Science /Data Scientist?
The data scientist is a multidisciplinary professional, responsible for carrying out the process mentioned in the Data Science topic above. That is, it is responsible for transforming data into information or information products within a corporation.
In addition, he should also be responsible for formulating the problems, choosing simulation and statistical models, and delivering the data products.
Well, now that we understand a little bit what Data Science and Data Scientist are , it is now convenient for us to understand the difference between:
Data Scientist x Business Analyst x Data Analyst
Following the same simplicity in demystifying what is Data Science and Data Scientist , let’s define the three items that give the topic its name. Come on!
Data Scientist:
Participates in problem formulation, resolution hypotheses and results analysis.
Business Analyst:
Analyzes the data generated in relation to the business or company evaluated.
Data Analyst:
Analyzes the available data in search of a solution to the problems faced.
Simple, no? Initially we can describe the trio in this way. And ‘walking’ forward, we arrive at Big Data . But what is Big Data ?
What is Big Data
Big Data , in Information technology, refers to a large set of stored data. And it can be based on 5V’s: Velocity, Volume, Variety, Veracity and Value.
Big Data is a term widely used today to name very large or complex data sets that traditional data processing applications cannot handle. To work with Big Data, one must understand the challenges of working in the area, which include: Analysis, Capture, Data Curation, Research, Sharing, Storage, Transfer, Views and information about data privacy.
I want to work with Big Data
To work with Big Data, it is believed that the best way is:
- Know the tools used (which we will cover in another article soon);
- Possess a mixed profile: technical and business;
- Know Business Intelligence and Data Warehouse ;
- Understand the company’s processes;
- And know statistics and mathematics.
We can divide professionals who work with Big Data into three profiles:
1. DATA ANALYST
DUTIES AND TASKS:
- Responsible for meeting the demands of the business or planning areas of the company;
- Participates in the formulation of problems and answers;
- Closest level to the business;
- Must know the tools for querying and accessing data;
- You should know statistics.
2. DEVELOPER
DUTIES AND TASKS:
- Responsible for Develop the necessary processes for data generation;
- Data Capture, Transformation and Loading Processes;
- Must technically know the tools involved;
- Must know about programming;
- Will be responsible for the development of new routines and processes.
3. ADMINISTRATOR
DUTIES AND TASKS:
- Responsible for keeping the environments and tools working in the best way;
- Must know about the operating systems used, especially Linux;
- Must know about hardware architecture and networks to ensure the best performance;
- You must know about the Tunning processes of the tools.
What do you need to know to work with Big Data?
There’s not much of a secret, below you can check out important technical points for working with Big Data.
- Programming – tools are still poorly automated in code generation;
- Linux Operating System – Several software runs on Linux. It is necessary to know basic commands to execute processes;
- Data Modeling
- Know about the business or company processes;
- Know or have minimal notions of statistics and mathematics applied to data.
Specializations/Courses
Below are links to courses on the topics we cover here in this article.
That’s it for now. You can also supplement our article with more useful information. Send us an email with suggestions for improvements and/or new articles.
Until the next article.
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