# data[].attributes.body
Some notable companies investing in data science:
- Google
- Facebook
- Amazon
- Netflix
- Tesla
- Uber
- Microsoft
- IMB
- Apple
- Airbnb

- Amanda, Suzana & Henrique

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Here are some benefits to being a data scientist:
- High demand
- Versatility
- Innovation
- Competitive advantage
- Data-based decision making

- Amanda, Suzana & Henrique

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To improve your skills and learn programming, you should do some projects. Here are some tips for projects to carry out:
- Chat Bots
- Predictability of Credit Card Fraud
- Gender and age detection

- Amanda, Suzana & Henrique

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You should:

📈 Spot Trends Early: Stay updated on emerging technologies for an edge.

📚 Keep Learning: Stay current with trending areas like AI ethics or quantum computing.

🔄 Adapt Wisely: Choose trends aligned with your goals.

💬 Network: Connect with experts for insights.

🔮 Predict Trends: Analyze data patterns to foresee future trends.

Now, ride those data waves with confidence! 🏄‍♂️🌊

- Matheus Viana e Webert

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What is the relationship between Data Science and Analytics?

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What is the relationship between Data Science and Analytics?

# data[].attributes.body
The relationship between Data Science and Data Analytics is less close than most people realize. It is common to hear the two terms used interchangeably, but they carry different implications for the businesses they are applied to.

When talking about Data Science we are not talking about just one thing. The term encompasses a number of specialties, methods and models that are used to understand data. It is the ability to connect the dots and connect information that is useful for a company.

Analytics is a very specific piece of Data Science. It has a clearer focus and more limited applications than those that fall under the Data Science term.

In Analytics, the goal is to filter data so that it provides insights into particular areas of the enterprise. It's as if its function were to collect data that a company knows it has and knowledge it already has, and point out how they can be used to interpret events.

If Data Analytics is part of Data Science, understanding the distinction between both is identifying which one should be the focus of a business's investments. Those who need more limited analyzes that respond to the aforementioned points do not have a reason to invest in a Data Science framework right away. With the help of a data analyst, you can get all the information you need.
Lucas
Adilson

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Data science, machine learning, artificial intelligence. These are all buzz words that have emerged in our society. We have grown increasingly reliant on these technologies, but this growing reliance also raises questions about how justified we are in giving our complete trust to these technologies. Machine learning and deep learning are known famously to be a black box — we feed data into the model and come up with some results that we just take for granted, without really questioning how these results were obtained, and whether the process is justified. This issue formed the backdrop of the keynote presentation by Professor Lise Getoor at the 2019 IEEE Big Data Conference held in Los Angeles on 10 December 2019, and I will now give an overview of the enlightening talk she gave.

More on:
https://towardsdatascience.com/responsible-data-science-part-1-1906062fe486

Thales e Vinícius Viana

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Hey there, data adventurers! 🌐 The data science community is your treasure trove of knowledge and support. You should definitely dive into it!

You should consider:

🌟 Join Online Communities: Platforms like Reddit's r/datascience and Stack Overflow are great places to ask questions and connect with fellow data enthusiasts.

📅 Attend Meetups: Find local data science meetups or webinars to network and learn from others in the field.

- Matheus Viana e Webert

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Reach Out to Companies for Entry-Level Jobs

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Reach Out to Companies for Entry-Level Jobs

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In the field of data science, prior knowledge is likely to be retained, as it is highly interdisciplinary. The business impact must be linked to data scientist models. MS Excel experience and other transferable skills such as business, communication, and collaboration should be part of your resume and cover letter, even if your resume and cover letter emphasize your data science experience.

Applicants seeking to get a data scientist job without experience should include a brief description of their shift on their resume, which includes keywords, courses they have taken, technical languages they have learned, and any project work they have completed to highlight their growing data science skills.

Mateus Custodio e Marco Antônio

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For data science, postgraduate studies are the best, and there are some platforms with affordable prices and certificates. A recommended platform is Descomplica
Amanda, Suzana and Henrique

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Pós ou MBA com 25% OFF + combo de ofertas: use o cupom MAIS25

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FORMATO ATUALIZADO

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Yan Werneck

Coordenador de Experimentação e Recomendação no PicPay, atua com Estatística e Data Science.

Marcio Santos

Graduado em Análise de Sistemas e Matemática, atua desde 1996 como programador.

INÍCIO IMEDIATO DAS AULAS

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Programa de Curso

Pré-requisito
Graduação em qualquer área.

1 ano

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360 h

de carga horária entre vídeos e materiais complementares

Módulo 1

4 disciplinas ∙ 32 aulas

Statistics for Data Science

8 aulas

Learning Programming

8 aulas

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8 aulas

Módulo 2

4 disciplinas ∙ 32 aulas

Disponível no programa completo

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8 aulas

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Certificações

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# data[].attributes.attachment_link.title
Pós-graduação em Data Science EAD - Descomplica

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A nossa pós-graduação em Data Science EAD é destaque no MEC. O curso tem preço acessível e é 100% online. Inscreva-se!

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You should always:

🚀 Embrace Bold Experiments: Be a fearless pioneer! Dive into uncharted territories, test out novel tools, and embark on unique projects. Innovation often sprouts from these audacious leaps.

💡 Cultivate a Learning Garden: Your mind is the fertile soil for knowledge. Plant the seeds of new skills, water them with education, and watch the vibrant blooms of expertise flourish.

🌐 Connect the Dots: Data isn't just numbers; it's a language. As you learn, strive to weave connections across different data domains. The more languages you understand, the richer your conversations become.

- Matheus Viana e Webert

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Did you think all the points on this list would be technical? The truth is that many soft skills are essential to build a good data scientist. One is curiosity. When working with technology, you have to accept that it evolves constantly and at an accelerated pace. The desire to acquire knowledge is what differentiates good professionals.

Studying new techniques, software, programming languages, mathematical models and visualization tools will be very important for career advancement. Courses and other refresher programs will help you keep up with the news in the field.
Adilson
Lucas
Blasck

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3.000+ Ciência De Dados Ilustrações fotos de stock, imagens e fotos royalty-free - iStock

# data[].attributes.attachment_link.description
Busque em fotos de stock e imagens royalty-free de Ciência De Dados Ilustrações da iStock. Encontre fotos de stock de alta qualidade que você não acha em nenhum outro lugar.

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The data landscape evolves rapidly. To stay at the forefront, commit to continuous learning. Attend conferences, take online courses, and engage with the Data Science community. Adaptability is key in this dynamic field.
Thales e Vinícius Viana

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Data Science is the disciplined study of data and information inherent to the business and all the visions that can surround a certain subject. It is a science that studies information, its capture process, transformation, generation and, later, data analysis. Data science involves several disciplines:

- Computing;
- Statistics;
- Mathematics;
- Business Knowledge

LucasTadala
ViktorFerrarez
Dirso Silva

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Get experience, practice, and meet fellow data scientists

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Get experience, practice, and meet fellow data scientists

# data[].attributes.body
As with any career, to become a data scientist, you’ll need as much experience and practice as possible. Luckily, there are many ways that you can get involved with communities, work on projects, and develop your data science skills.
You had better, for example, apply your knowledge to various data science projects, allowing you to solve real-world problems from your browser.

Mateus Custodio e Marco Antônio

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Which Python libs to start with in data science?

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Which Python libs to start with in data science?

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You should use the following Python libraries to start studying data science, remembering that libraries make development easier.
- SciPy
- Scikit-Learn
- Pandas
- Numpy
Amanda, Suzana e Henrique

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Hey, future data heroes! 🦸‍♂️ Ready to unleash your data magic on the job market? Here's what you should do to secure that dream data role:

You should definitely:

📚 Keep Learning: The data world changes rapidly. Platforms like DataCamp offer courses to keep you up-to-date.

📌 Network: Attend data meetups and conferences. Mingling with fellow data enthusiasts can lead to amazing opportunities.

- Matheus Viana e Webert

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Being a Data Scientist also involves storytelling. You need to communicate your findings clearly and persuasively so that others can understand and make decisions based on your insights.

Thales e Vinícius Viana

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From the huge data sets, a skilled data scientist should extract new and detailed insights. So, learning to use cloud platforms such as AWS, Microsoft Azure, and Google Cloud can all benefit your career as a data scientis. Similarly, tools like Apache Spark can help with big data processing, analysis and machine learning.

Mateus Custodio e Marco Antônio

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After developing some essential skills you should apply for every job vacancy you can find. Then you should look for learning with people that have more experience than you.
Samuel-ViniciusRamos

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If you don't have a schedule it's about time to have one. Being organized is vital to not only your productivity, but to the success of a business. imagine working on complex lines of code and you can’t keep your work organized, you could make very costly mistakes that could cause systems to stop functioning.

João Luiz and Victor Ramos

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 About Data Science wasn't exactly invented. It emerged from the combination of many disciplines that previously reigned in isolation and is one of the newest professions to appear on the market. The term itself was coined in 2001, but it was from 2010 that Data Science teams were formed in companies to analyze Big Data data using cutting-edge resources, Machine Learning and Artificial Intelligence. That's why, in most universities, we don't see Data Science courses. It is the Mathematics, Statistics, Computer Science and Data Engineering professionals who most commonly exercise the data scientist profession.
Adilson
Lucas
B l a s c k

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3.000+ Ciência De Dados Ilustrações fotos de stock, imagens e fotos royalty-free - iStock

# data[].attributes.attachment_link.description
Busque em fotos de stock e imagens royalty-free de Ciência De Dados Ilustrações da iStock. Encontre fotos de stock de alta qualidade que você não acha em nenhum outro lugar.

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What are the data scientist qualifications?

# data[].attributes.subject
What are the data scientist qualifications?

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Data scientists should have a strong command of the relevant technical skills, which will include programming in Python or R, writing queries in SQL, building and optimizing machine learning models, and often some "workflow" skills like Git and the command line.

Data scientists also need strong problem-solving, data visualization, and communication skills. Whereas a data analyst will often be given a question to answer, a data scientist is expected to explore the data and find relevant questions and business opportunities that others may have missed.

- Bianca e Maju

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As more personal data is collected and used for analysis, ethical and privacy concerns should be addressed. Data Science practitioners should navigate the responsible and ethical use of data to maintain trust and safeguard individuals' privacy.

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Your portfolio should definitely shout, "I'm a data genius!"

You should think about these:

📝 Blogging: Share your insights, tips, and tutorials about data. It's educational and also fantastic for showcasing your skills.

📈 Side Projects: Create small data projects to demonstrate your problem-solving abilities. Think about predicting movie ratings or analyzing social media trends.

- Matheus Viana e Webert

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You ought to work on your statistics, math, and machine learning skills

# data[].attributes.subject
You ought to work on your statistics, math, and machine learning skills

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While you don’t necessarily need a degree that covers these fields, you do need a functional knowledge of these areas. Generally, you should cover areas such as calculus, linear algebra, and statistics. However, it’s worth assessing your intent behind learning these things.

Mateus Custodio e Marco Antônio

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If you want to become a skilled data scientist you had better study big data since huge data sets are normal nowadays. You should also learn to use cloud plataforms such as AWS or Microsoft azure.
Samuel-ViniciusRamos

# data[].attributes.body
Start with a solid base. Dedicate time to learning mathematics, statistics, and programming. This will help you grasp the language of data and construct complex models.
Thales e Vinícius Viana

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Games and Realistic Graphics: The Role of Computer Science in the Digital Entertainment Industry

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Games and Realistic Graphics: The Role of Computer Science in the Digital Entertainment Industry

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Advancements in computer science have played a crucial role in creating immersive virtual worlds and high-quality graphics in games. In this post, we should dive into the technology behind games, covering topics like real-time rendering, physics simulations, and artificial intelligence in gaming. We will discuss how computer science enables increasingly realistic and immersive digital entertainment experiences.

Vinícius Pereda - Nicholas

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As you're learning the basics of coding, you should start building projects that answer interesting questions that will showcase your data science skills.

The projects you build don't have to be complex. For example, you could analyze Super Bowl winners to find patterns.
As you're building projects, remember that:

Most data science work is data cleaning.

The most common machine learning technique is linear regression.

Everyone starts somewhere. Even if you feel like what you're doing isn't impressive, it's still worth working on.

- Bianca e Maria Julia

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Machine Learning and Artificial Intelligence

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Machine Learning and Artificial Intelligence

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Data Science should closely intertwine with the fields of Machine Learning and Artificial Intelligence. Machine Learning algorithms should enable automated systems to learn from data and enhance their performance over time.

Vinicius Rodrigues
Otavio Henrique

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Cloud Computing: Demystifying the Technology Behind Convenience

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Cloud Computing: Demystifying the Technology Behind Convenience

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Cloud computing has become an essential part of our digital lives, but how does it really work? In this post, we should explore the fundamentals of cloud computing, from service models (IaaS, PaaS, SaaS) to security and privacy concerns. We will also address common use cases such as data storage, online collaboration, and application development, highlighting how computer science enables this technology.

Vinícius Pereda - Nicholas

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Data Science should guide decision-making across various sectors. Businesses and organizations should utilize data analytics to inform strategic decisions, identify market opportunities, and optimize operational processes.

Vinicius Rodrigues
Otavio Henrique

# data[].attributes.body
You should definitely work on some skills to stand out in the world of data science.

You should focus on these:

💻 Programming: Get familiar with languages like Python and R. They're your magic tools for data manipulation and analysis.

🤖 Machine Learning: Delve into the realm of algorithms and predictive modeling. Resources like fast.ai provide a friendly start.

- Matheus Viana e Webert

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The data scientist is a multidisciplinary professional, responsible for carrying out the process mentioned in the Data Science topic. That is, it is responsible for transforming data into information or information products within a corporation.
LucasTadala
Viktor Ferrarez
Dirso Silva

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In this area you should be able to work in a group and have a great teamwork. Many times, software engineers and data scientists are expected to collaborate with a number of different groups within an organization to ensure timelines are met and expectations are fulfilled.

João Luiz e Victor Ramos

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The Data Era: Computer Science and Big Data Analysis

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The Data Era: Computer Science and Big Data Analysis

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Wee should live in an era flooded with data. This post will explore how computer science empowers big data analysis, allowing companies and researchers to turn raw information into valuable insights. We will cover key concepts like parallel processing, machine learning algorithms, and data visualization, showing how these techniques are shaping sectors like marketing, medicine, and scientific research.

Vinícius Pereda - Nicholas

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Hard Skills

Python

R

Statistics and math

SQL and NoSQL

Data visualization

Machine learning

Deep learning

Natural language processing

Soft Skills

Communication

Storytelling

Critical thinking

Business acumen

Problem-solving

Teamwork

Big data

Cloud computing

Thales e Vinícius Viana

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With the advent of technology and the internet, the amount of generated data has exponentially exploded. Data Science should play a crucial role in handling these large volumes of data and transforming them into meaningful information.

Vinicius Rodrigues
Otavio Henrique

# data[].attributes.body
Here are a few great places to learn:

Dataquest — I started Dataquest to make learning Python for data science or data analysis easier, faster, and more fun. We offer basic Python fundamentals courses, all the way to an all-in-one path consisting of all courses you need to become a data scientist.

Learn Python the Hard Way — a book that teaches Python concepts from the basics to more in-depth programs.

The Python Tutorial — a free tutorial provided by the main Python site.

- Bianca e Maju

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Stay updated with the latest trends in data science, machine learning, and artificial intelligence through online courses, books, and attending conferences or meetups.

Giovana e Tais

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Blockchain and Security in the Digital Age: A Computer Science Perspective

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Blockchain and Security in the Digital Age: A Computer Science Perspective

# data[].attributes.body
Blockchain technology is not only the foundation of cryptocurrencies but also has significant implications for cybersecurity. In this post, we will delve into the intricacies of blockchain, explaining how it works to ensure data integrity and immutability. We will discuss use cases beyond cryptocurrencies, such as digital medical records and supply chain tracking, highlighting the importance of security in the digital age.

Vinícius Pereda - Nicholas

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To work in the Data Science area, it is important to have some higher education in the area. Luckily, there are several possible majors such as Information Systems, Computer Science and Computer Engineering. - Victor Ramos & João Luiz

# data[].attributes.body
Data Science combines elements of mathematics, statistics, programming, data analysis, and domain-specific knowledge.It involves an interdisciplinary approach to extracting knowledge and insights from datasets.

Vinicius Rodrigues
Otavio Camargo

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you should find your self a good place to keep the study on top, a youtuber, blog and other places can be realy usefull for that one question you might have, I do say w3school is a prety good site to look some answers online.

Nicholas E Vini. Pereda

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Enroll in online data science courses from platforms like Coursera, edX, Udacity, and Khan Academy to learn from experts in the field. Bethany & Luis

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If You Lack Interest, Analyze Things You Enjoy

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If You Lack Interest, Analyze Things You Enjoy

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Perhaps you don't enjoy the process of coming up with questions in the abstract, but maybe you enjoy analyzing health or finance data. Find what you're passionate about, and then start viewing that passion with an analytical mindset.

- Biancão e Majuletes

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Learn about the industry or field you want to work in. Data science is most effective when you understand the context and nuances of the data you're dealing with.

Giovana e Tais

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These are now usually referred to as ‘power skills’ or ‘human skills,’ and they are the cross-functional skills that aren’t job specific but are still necessary. As a data scientist, you had better study about:

Communication

Storytelling

Critical thinking

Business acumen

Problem-solving

Teamwork

Mateus Custodio e Marco Antônio

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Relational databases are a foundation of data science , you should master it before entering the area. You ought to learn Postgre SQL , SQLite or most commonly MySQL.
Samuel-ViniciusRamos

# data[].attributes.body
Work on diverse projects that involve real data. This will help you gain practical experience and showcase your skills to potential employers.

Giovana e Tais

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You Should Study the Data Science Market

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You Should Study the Data Science Market

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To work with Data Science, it is extremely important to know what the market needs, and then, you should specialize yourself in these areas. - Victor Ramos & João Luiz

# data[].attributes.body
Familiarize yourself with data manipulation libraries like Pandas, data visualization libraries like Matplotlib and Seaborn, and machine learning frameworks like scikit-learn.

Giovana e Tais

# data[].attributes.body
Data Science is all about using numbers, facts, and information to understand the world better. Imagine you have a big box of colorful building blocks. Each block has a different shape and color. Data Scientists are like builders who take these blocks, organize them, and build amazing structures of knowledge.
Thales e Vinícius Viana

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First of all , you should start by learning how to code by algoritms and logic . Then you should try python or R two of the most important languages for data science and machine learning.
Samuel-ViniciusRamos

# data[].attributes.body
Start with a solid understanding of programming (Python or R), statistics, and linear algebra. These are essential for data manipulation, analysis, and modeling.

Giovana e Tais

# data[].attributes.body
Not just in Data Science, but in the entire IT field, it is very important to have good logic skills to solve the problems. This will definitely help you a lot. - Victor Ramos & João Luiz

# data[].attributes.headline
Study statistic math and machine learning skills

# data[].attributes.subject
Study statistic math and machine learning skills

# data[].attributes.body
You should learn all of these topics before enter in the data science market, because they are important topics for the area.
Samuel-ViniciusRamos

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The data science and data analytics field is appealing because you get to answer interesting questions using actual data and code. These questions can range from Can I predict whether a flight will be on time? to How much does the U.S. spend per student on education?

- Bianca e Maju

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How do you become a data scientist? What are the must-have abilities? 

# data[].attributes.subject
How do you become a data scientist? What are the must-have abilities? 