Rafael Oliveira Bitcoin diz que, na prática, os cientistas de dados trabalham em uma variedade de tarefas, como limpeza de dados, exploração de dados, engenharia de recursos, modelagem e avaliação.

1. Data Science : What Does it
Entail?
By rafaeloliveirabitcoin
Rafael Oliveira Bitcoin pointed out that to discover the hidden actionable
insights in an organization’s data, data scientists mix maths and statistics,
specialized programming, sophisticated analytics, artificial intelligence (AI), and
machine learning with specialized subject matter expertise. Strategic planning
and decision-making can be guided by these findings.
Data science is one of the fields with the quickest growth rates across all
industries as a result of the increasing volume of data sources and data that

2. results from them. As a result, it is not surprising that the Harvard Business
Review named the position of data scientist the “sexiest job of the 21st century”
(link is external to IBM). They are relied upon more and more by organizations
to analyze data and make practical suggestions to enhance business results.
Analysts can gain practical insights from the data science lifecycle, which
includes a variety of roles, tools, and processes. A data science project often
goes through the following phases:
Data Ingestion:
The data collection phase of the lifecycle involves gathering raw, unstructured,
and structured data from all pertinent sources using several techniques. These
techniques can involve data entry by hand, online scraping, and real-time data
streaming from machines and gadgets. Unstructured data sources like log files,
video, music, photos, the Internet of Things (IoT), social media, and more can
also be used to collect structured data, such as consumer data says Rafael
Oliveira Bitcoin.

3. Data Processing and Storage:
Depending on the type of data that needs to be gathered, businesses must take
into account various storage systems. Data can have a variety of formats and
structures. Creating standards for data storage and organization with the aid of
data management teams makes it easier to implement workflows for analytics,
machine learning, and deep learning models.
Using ETL (extract, transform, load) jobs or other data integration tools, this
stage involves cleaning, deduplicating, transforming, and merging the data.
Before being loaded into a data warehouse, data lake, or another repository,
this data preparation is crucial for boosting data quality, says Rafael Oliveira.

4. Data Analysis:
In this case, data scientists perform an exploratory data analysis to look for
biases and trends in the data as well as the ranges and distributions of values.
The generation of hypotheses for a/b testing is driven by this data analytics
exploration. Additionally, it enables analysts to evaluate the data’s applicability
for modelling purposes in predictive analytics, machine learning, and/or deep
learning. According to Rafael Oliveira, organizations may depend on these
insights for corporate decision-making, enabling them to achieve more
scalability, depending on the model’s accuracy.
Communicate:
Finally, insights are presented as reports and other data visualizations to help
business analysts and other decision-makers better understand the insights and
how they will affect the organization, says Rafael Oliveira Bitcoin. In addition
to using specialized visualization tools, data scientists can create visualizations
using components built into programming languages for data science, such as R
or Python.

5. Tools For Data Science
Popular programming languages are used by data scientists to do statistical
regression and exploratory data analysis. These open-source tools include
pre-built machine learning, graphics, and statistical modelling capabilities. You
can learn more about these languages in “Python vs. R: What’s the Difference?”
The following are some of them:
R Studio:
A free and open-source environment and programming language for creating
statistical computing and visuals.

6. Python:
This programming language is dynamic and adaptable. For rapid data analysis,
the Python language comes with several libraries, including NumPy, Pandas, and
Matplotlib.
Data scientists can use GitHub and Jupyter Notebooks to make it easier to share
code and other information.
A user interface may be preferred by certain data scientists, and two popular
enterprise tools for statistical analysis are:
SAS:
A complete set of tools for analysis, reporting, data mining, and predictive
modeling that includes interactive dashboards and visualizations.
IBM SPSS:
Advanced statistical analysis, a sizable collection of machine learning
algorithms, text analysis, open source extensibility, big data integration, and
simple application setup are all features of IBM SPSS.
Additionally, big data processing platforms like Apache Spark, Apache Hadoop,
and NoSQL databases are mastered by data scientists. They are also proficient
with a variety of data visualization tools, including open-source tools like D3.js
(a JavaScript library for creating interactive data visualizations) and RAW

7. Graphs, as well as built-for-purpose commercial tools like Tableau and IBM
Cognos. These tools are simple graphics tools included with business
presentations and spreadsheet applications (like Microsoft Excel).
Data scientists regularly use a variety of frameworks, including PyTorch,
TensorFlow, MXNet, and Spark MLib, to create machine learning models.
Given the steep learning curve in data science, many businesses are looking to
speed up the ROI on AI projects. However, they frequently struggle to find the
talent necessary to fully realize the potential of data science projects. Rafael
Oliveira Bitcoin says they are using multipersona data science and machine
learning (DSML) systems to close this gap, creating the position of “citizen data
scientist.”
Automation, self-service portals, and low-code/no-code user interfaces are used
by multipersona DSML platforms to enable people with little to no experience
with digital technology or expert data science to produce business value using
data science and machine learning. These platforms also provide a more
sophisticated interface to support expert data scientists. A multipersona DSML
platform promotes enterprise-wide cooperation.