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Career Switch: How to Become a Financial Engineer as a Data Scientist

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Career Switch: How to Become a Financial Engineer as a Data Scientist

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Perhaps a career as a data scientist was a perfect fit from the start. It’s like looking for a needle in a haystack: this is an incredibly sought-after and competitive position, especially at the higher pay levels.

Now, you are open to possibilities at the moment. If you wish to switch careers, how can you put your existing skill set to use?

- Felipe de Andrade Nascimento

https://medium.datadriveninvestor.com/career-switch-how-to-become-a-financial-engineer-as-a-data-scientist-cbbc42df269b

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Learn about how to make the switch and the key requirements

Perhaps a career as a data scientist was a perfect fit from the start. It's like looking for a needle in a haystack: this is an incredibly sought-after and competitive position, especially at the higher pay levels.

Now, you are open to possibilities at the moment. If you wish to switch careers, how can you put your existing skill set to use?

Instead of starting with a list of different sorts of positions and cross-referencing which sectors need them, I have chosen to only focus on finance, and financial engineering. In future posts, I will decompose other industries for data scientists.

Previously, I've expounded at length on the topic of using python or data science in a variety of financial contexts. When it comes to putting into action machine learning and deep learning models, financial engineers are usually the ones in charge. As part of a normal day, you could determine which models most effectively address the use cases. Common applications include AI models to guide existing or prospective clients toward their ideal financial portfolio composition.

Not every business has such a high demand for financial engineers, but those who excel in this field are highly sought after. Companies and financial institutions can afford to pay these employees' wages because of their exceptional skills. Contrary to other fields where top talent may have trouble finding work due to high salary expectations, financial engineers seldom have trouble finding work.

A lot of lessons may be learned by financial engineers from machine learning engineers, data scientists, and data engineers. The work of a financial engineer often overlaps with that of a data scientist or machine learning engineer in several ways, including understanding the ModelOps and MLOps lifecycle; identifying machine learning models; optimizing or fine-tuning such models; and deploying them (that a data scientist is highly accustomed to completing on a daily basis).

Computer science, financial engineering, mathematics, and statistics are common undergraduate majors for financial engineers. Thus, there is no one set way to get to the position of financial engineer. Of course, having a degree in financial engineering is helpful. Still, anybody with a strong quantitative background in a related field (math, computer science, data science, etc.) should be able to fulfill the basic and any extra criteria for these positions.

If you're set on a career in finance (and will likely work with a group of financial engineers), one piece of advice is to educate yourself as much as possible in data engineering. Data scientists frequently triangulate on the best preferences for which models to use, deploy, and move on to the next problem domain. Expertise in developing (custom, from the ground up) and tuning distinct machine learning models are needed. After more than a decade of collaborating with leading data scientists, researchers, engineers, and architects, I can attest to the significant difference in influence between the two professions (data scientists vs. data engineers).

I've watched them instruct, mentor, and otherwise prime data scientists on the basics of those models countless times (before data scientists consumed them for the next steps). The lesson here is to study data engineering and gain familiarity with its essential skills (some of which I listed in this paragraph) that would be useful in a financial position.

In my experience, financial engineers almost always hold a master's degree or above. From my experience working with them (eyeball to eyeball, so to speak), the vast majority of them may work in banking or the financial industry after completing their undergraduate degrees in order to gain very brief, valuable professional experience before pursuing a graduate degree. It's unusual to find a team of financial engineers, with most members holding merely a bachelor's degree. Nearly all of them will have at least a master's degree.

I would venture as far as to say that a graduate degree will be expected to become a financial engineer (almost for all roles). Various viewpoints on the minimum required level of education are emerging (as they should; not all positions call for advanced degrees, etc.) for data science as it expands into new fields and occupations. Such expansiveness is almost nonexistent in financial engineering: you either have the background, or you do not. It is likely that education will be an absolute minimum classifier. Thus, be on the lookout for those financial engineering roles by starting out with the education requirements.

An important lesson to be drawn regarding the educational requirements is that if you want to make a career change into financial engineering but don't yet have a graduate degree, you should seriously consider going back to school to get one.

Nevertheless, if you are already a data scientist, why should you pursue financial engineering? I'm not trying to convince you to do anything or not do anything with this post. There's a good chance you're intrigued by the idea because you discovered this post (or, more often than not, actually considering already making the switch).

Still, there are a few things I'd like to highlight. First of all, as a data scientist, you probably already know a significant number of useful facts, amount of expertise, and relevant (thus, directly transferable skills) about machine learning and AI. With such knowledge and familiarity with relevant technology, you'll be well-equipped to design efficient algorithms for a wide range of financial applications. As a data scientist, you already have a significant advantage thanks to your knowledge of concrete applications of machine learning. A head start like that just gives you more chances to succeed.

If you have any recommendations for this post or suggestions for broadening the subject, I would appreciate hearing from you. Simply, send me a private message anywhere on this post.

Also, here is my newsletter; I hope you will kindly consider subscribing.

If you enjoy reading stories like these and want to support me as a writer, consider signing up to become a Medium member and gain unlimited access to all stories on Medium.

Also, I have authored the following posts that you might find interesting:

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Career Switch: How to Become a Financial Engineer as a Data Scientist

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Learn about how to make the switch and the key requirements Perhaps a career as a data scientist was a perfect fit from the start. It's like looking for a needle in a haystack: this is an incredibly sought-after and competitive position, especially at the higher pay levels.

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Liz Simmons talking about  How to Become a Data Scientist 

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Liz Simmons talking about  How to Become a Data Scientist 

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A subfield of computer science, data science is the study of large quantities of data.

A relatively new and quickly growing field, data science offers excellent career opportunities. Glassdoor ranks data scientist as the third best job in the U.S. for 2022, citing high job satisfaction, top salaries, and abundant job openings.

- Felipe de Andrade Nascimento

https://www.computerscience.org/careers/data-science/how-to-become/

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A subfield of computer science, data science is the study of large quantities of data.

A relatively new and quickly growing field, data science offers excellent career opportunities. Glassdoor ranks data scientist as the third best job in the U.S. for 2022, citing high job satisfaction, top salaries, and abundant job openings.

This page explains how to become a data scientist. We cover experience and education requirements, look at certifications and job search strategies, and outline the steps to become a data scientist.

What Is a Data Scientist?

New technologies help organizations easily collect large amounts of data. But, they often do not know what to do with this information. Data scientists use advanced methods to help bring value to data. They collect, organize, visualize, and analyze data to find patterns, make decisions, and solve problems.

Data scientists need strong skills in programming, data visualization, communication, and mathematics. Typical job responsibilities include gathering data, creating algorithms, cleaning and validating data, and drafting reports. Nearly any organization can benefit from the contributions of a trained data scientist. Potential work sectors include healthcare, logistics, and banking and finance.

What Education Do Data Scientists Need?

Education requirements for data science professionals vary by position, employer, and industry. Data scientists typically need at least a bachelor's degree in computer science, data science, or a related field. However, many employers in this field prefer a master's degree in data science or a related discipline.

Data analysts and data engineers usually need a bachelor's degree. Becoming a data scientist or computer and information research scientist usually requires a master's.

Some data science professionals hold a mix of education levels. For example, someone might earn a bachelor's in computer science and complete a data science bootcamp. Or, they might complete a bachelor's in an unrelated field and then earn a master's in data science.

In general, career opportunities and salaries increase as people earn higher degree levels. A graduate degree can also help job applicants stand out from candidates with just a bachelor's.

How Much Experience Do Data Scientists Need?

Data scientist requirements for professional experience depend on the role and workplace. In general, data scientist positions are not entry-level jobs. Successful candidates need some relevant experience.

Before becoming a data scientist, some people start out in related information technology positions. These stepping-stone roles may include data analyst, market research analyst, or data engineer.

Data science degree programs and bootcamps often include internships, fellowships, and capstone projects. These experiences provide hands-on practice that can help graduates land a job offer. Some employers let job applicants substitute education for experience, or vice versa.

Professional Certifications

Professional certifications are not a requirement for becoming a data scientist. However, a certification can help you stand out and show your expertise to potential employers. Getting certified can help you earn a higher entry-level salary and open the door to more career advancement opportunities.

In some cases, a data science certification may persuade an employer to hire someone who does not meet all of a job's stated education or experience requirements.

This certification demonstrates the ability to use SAS and open source tools to manipulate big data. Certified professionals know how to use machine learning models to make business recommendations. Applicants complete the SAS data curation professional, advanced analytics professional, and AI & machine learning credentials to earn the data science certification.

Microsoft's data scientist associate certification shows knowledge and experience with Azure machine learning and Azure databricks. Applicants must pass the Designing and Implementing a Data Science Solution on Azure exam. The credential demonstrates skill in implementing machine learning, managing Azure resources, and deploying machine learning solutions.

The senior data scientist certification signifies data science knowledge and data leadership potential. Candidates pay a $775 fee that covers exam prep resources, a digital badge, and a credential kit. The certification requires 3-5 years of analytics and research experience and a bachelor's degree or higher.

The principal data scientist credential offers four tracks for different applicants. It indicates high-level knowledge of data science and analytics technologies. Depending on the track they qualify for, candidates must pursue various exams and assessments to earn the credential. Once awarded, the designation never expires.

How Do I Become a Data Scientist?

There is more than one way to enter the data science field. Data scientist requirements differ by employer, industry, and location. Aspiring professionals can meet data scientist job requirements in various ways.

The typical process to become a data scientist includes at least a four-year college degree in computer science, data science, or a related field. Many data scientists also pursue graduate education, professional certifications, and bootcamps.

Most data scientist jobs require some relevant professional experience. Students can gain experience while still in school through internships, capstone projects, and fellowships.

Below, we outline in detail some of the steps to become a data scientist.

Steps to Becoming a Data Scientist

Several paths can lead to a career as a data scientist. Below, we list the steps to become a data scientist based on different paths.

Bachelor's Degree Path

1. Earn a bachelor's degree. Most data science jobs require at least a four-year bachelor's degree. Consider majoring in data science, computer science, or mathematics. Take classes in computer science, business, and statistics.

2. Complete an internship. Getting internship experience develops career-relevant skills and can lead to job offers.

3. Pursue professional certifications. Earning a professional certification is not required for becoming a data scientist, but it can help you prove your skills to potential employers.

4. Get entry-level professional experience. Apply for jobs like data analyst, data engineer, and market research analyst. Spending several years developing your skills can lead to better job opportunities in the future.

Data Science Bootcamp Path

1. Earn a bachelor's degree in any field.

2. Complete a data science bootcamp. Data science bootcamps provide intensive career training in less time than most traditional college programs. You may need to complete prerequisites before starting a bootcamp.

3. Complete an internship.

4. Pursue professional certifications.

Master's Degree Path

1. Earn a bachelor's degree in any field.

2. Earn a master's degree in data science. Data science master's programs usually take 1-2 full-time years to complete.

Professional Spotlight: Interview with a Data Scientist
Why did you choose to become a data scientist?
Prior to data science, I was a professor. But I (and many of my fellow young Ph.D.s) gradually realized that the academic job market has serious problems that prevent it from absorbing and properly utilizing all of the talented candidates who are getting their doctorates. Data science offers a way for people with strong mathematical and statistical backgrounds to apply their industry knowledge and research acumen to problems in the private sector in a much livelier job market (also for substantially better pay than is offered in academia). I also felt that data science, as a fast-growing, dynamic field, would allow me to expand my skills and insights faster than in academia.
Can you describe your path to a career in data science?
My first problem once I decided to switch careers was how, exactly, to transition. While I was highly educated, I had no specific certifications or qualifications that many jobs were looking for. That is why I chose to enroll in The Data Incubator. The Data Incubator specializes in taking candidates with strong academic backgrounds and helping them to learn how to conduct and communicate data science effectively in the private sector. They also help to match their students with prospective employers, which enabled me to get my first job in data science at Cova Strategies (which later transitioned to a role at NNData as senior data scientist).
What are some high and low points for this career? What challenges might a data scientist face?
While I am likely not in a good position to comment on career highs and lows (I have not been in data science for that long), I can say that the biggest challenge I faced in data science was believing myself to actually be qualified. Even after getting my first data science role, I felt much of the same imposter syndrome that plagues many people, especially those coming from academia.
What type of person does well in this role?
People who have a strong grasp of mathematics and statistics and can learn and apply new techniques rapidly. Data science is a rapidly evolving field; methods change, new techniques develop, and there is always something relevant to discover, understand, and integrate into new or even existing projects. No one can stay informed on every topic, so there will inevitably be times when you have to learn on the fly to use the latest or best techniques to solve a problem.
What advice do you have for students considering a career in data science?
First, as I mentioned earlier, data science is much more an exercise in mathematical and statistical reasoning than anything else, so don't neglect your mathematics! Second, be prepared to be a pioneer. While many people have attempted to solve almost any problem (Stack Overflow is proof of that), few have likely tried to solve the problems you will be facing with the exact intention that you have. Be prepared to combine solutions together, modify code, or apply technologies in ways they may not have been initially intended. That's what makes data science into a "data art," and that's what makes it fun! Third, especially if the student is coming from a graduate program, know your value. If you have (or are about to have) a Ph.D., you probably know something! You are more qualified than you likely give yourself credit for, and should not let yourself forget that. Finally, getting your foot in the door in any industry can be hard. Try and find some certification program, course, or something that signals to companies that you are serious and able to apply your skills to meet their needs. Beyond that, just remember that, like any career, a career in data science is a journey. Be prepared for the unexpected and to find your ideal niche in a company (and the wider industry) where you may not initially expect.

The Job Hunt

Places to look for data scientist positions include professional organizations, job fairs, and networking opportunities at annual conferences. Ask for job leads from mentors, alumni associations, and former supervisors and colleagues. Current students and recent graduates can apply for paid and unpaid internships to gain professional experience.

You can also search for openings on job boards. Below, we highlight five of the top job boards for the data science industry.

DataJobs: This job site posts openings in data science, data analysis, and data engineering. It matches companies with big data talent.

Open Data Science Job Portal: Job-seekers can find thousands of data science jobs here at over 300 companies. Candidates can submit their resumes and get matched automatically with relevant positions.

Ai-jobs.net: Find artificial intelligence, machine learning, and big data jobs around the world.

Digital Analytics Association: Browse for analytics openings by industry, job type, location, and experience level.

icrunchdata: This website posts analytics, technology, and data-related jobs worldwide. It also provides industry insights and career advancement information.

Questions About Data Science Requirements

Is data science a good career?

Yes, data science offers promising career paths. It is a fast-growing and relatively new field with higher-than-average salaries. The Bureau of Labor Statistics projects above-average job growth for many data science careers, including operations research analysts and computer and information research scientists.

Do data science jobs pay well?

Yes. Data scientists can earn significantly more than the average worker. The Bureau of Labor Statistics reports that data scientists earned a mean annual salary of $108,660 as of May 2021.

What are the education requirements for a data scientist?

Minimum education requirements for data science professionals usually include a bachelor's degree. Many data scientist positions require or prefer applicants with a master's degree in data science, computer science, or a relevant field.

How can I become a data scientist?

Various paths can prepare students for a data science career. The steps to become a data scientist may include earning a bachelor's degree, gaining work experience, and completing a professional certification. Many data scientists earn a master's degree or attend a bootcamp.

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How To Become A Data Scientist | Education And Experience Requirements

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A subfield of computer science, data science is the study of large quantities of data. A relatively new and quickly growing field, data science offers excellent career opportunities. Glassdoor ranks data scientist as the third best job in the U.S. for 2022, citing high job satisfaction, top salaries, and abundant job openings.

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"How Would I learn Data Science If I  had to Start Over", by Ken Jee.

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"How Would I learn Data Science If I  had to Start Over", by Ken Jee.

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In this video, the YouTuber Ken Jee talk about how he would learn data science if he had to start again from scratch.

Learning data science and machine learning is a little bit different for everyone, but hopefully this video will help you to build the necessary foundation to tackle these fields.

- Felipe de Andrade Nascimento

https://www.youtube.com/watch?v=4OZip0cgOho

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How I Would Learn Data Science (If I Had to Start Over)

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How to Become a Data Scientist in 2022: A Guide to Getting an Entry-Level Position

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How to Become a Data Scientist in 2022: A Guide to Getting an Entry-Level Position

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There’s no doubt that a career in data science can be both personally rewarding and financially lucrative, but you shouldn’t opt into it lightly. Make no mistake — becoming a data scientist takes time, effort, and dedication.

That said, if you’re determined to make your mark on the field, we can help you start on the journey! Below, we’ve covered the steps you’ll need to take when aspiring to enter the data science field.

1 - Choose a path
2 - Brush up on essentials skills
3 - Get an Education
4 - Know What to Expect From an Entry-Level Role
5 - Boost Your Hiring Potential

- Felipe de Andrade Nascimento

https://bootcamp.pe.gatech.edu/blog/how-to-become-data-scientist/

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Maybe you always knew you were a math person. You might have discovered your knack for logical puzzles as a child or realized in college that the data sets that bamboozled your friends weren't all that confusing to you. Or maybe the epiphany came later - after you spent a few years in a career that didn't speak to you in the way that math and analysis did.

In any case, if any of the above scenarios feels familiar, you've probably determined that you want to explore building a career in data science. The question is, how can you get there?

Everyone who wants to become a data scientist will need to take several key steps. These include choosing a career path and expanding your industry knowledge. Further education is a must; your options include a traditional college program or data science bootcamp, which has become a more popular choice among working adults.

In the below guide, we'll walk you through what you'll need to do to obtain an education, what to expect from an entry-level position, and how you can increase your employment potential.

5 Steps to Becoming a Data Scientist

There's no doubt that a career in data science can be both personally rewarding and financially lucrative, but you shouldn't opt into it lightly. Make no mistake - becoming a data scientist takes time, effort, and dedication.

That said, if you're determined to make your mark on the field, we can help you start on the journey! Below, we've covered the steps you'll need to take when aspiring to enter the data science field.

1. Choose a Path

Data science is a broad field that offers countless opportunities for growth and success. If you are looking to become a data scientist, it will be beneficial to identify which industry you ultimately want to work within.

Generally speaking, data scientists tend to perform similar duties across industries. However, the kind of information you will manage, what you analyze, and how you present your findings will vary according to your employer and sub-field. If you're planning to become a data scientist, it may be helpful to have a general idea of where you want to work - though you should be careful not to limit yourself too early in your career.

While data science is used across most industries to some degree, there are five primary sectors in which data scientists tend to thrive: retail, healthcare, finance, manufacturing, and transportation. These industries tend to have a high demand for data scientists because they need to collect massive amounts of data, conduct accurate, efficient analysis to identify trends, provide insights that assist in business decision-making, and optimize processes for their operations, clients, and customers.

Data scientist skill sets are relatively standard, but their applications may vary across industries. Here's a quick rundown:

Retail: Will need strong communication skills and an eye for detail, especially if they are expected to present their findings to business leaders or consumers.

Healthcare: Will need a thorough understanding of medicine and healthcare terminology as well as medical record management and spreadsheet analysis.

Finance: Will need to be proficient in Excel and data collection from diverse sources (including face-to-face interactions).

Manufacturing: Will need an advanced understanding of predictive analytics, as one of their primary roles will be determining when machines will need maintenance or repairs.

Transportation: Will need to be top-notch collaborators with exceptional data visualization and econometric modeling skills.

It may help prospective data scientists to look into these industries and determine which align most closely with their natural skills and interests. However, it is worth noting that data science is relevant and useful to the vast majority of industries - so don't feel as though you need to limit yourself!

2. Brush up on Essential Skills

In terms of essential skills for a data scientist role, mathematics and computer science rank at the top of the list.

If it has been several years since you took a class, or you don't regularly apply mathematical knowledge in your current career, refreshing your memory and studying relevant topics will be immensely beneficial - especially since most data science courses expect you to have a grasp of applicable principles and practices before coming to class.

Math skills aspiring industry professionals should brush up on include (but are not limited to):

As far as technical skills go, an understanding of coding languages such as Python, R, Java, SQL, NoSQL, and HTML will be necessary. Additionally, proficiency with industry-relevant software like Hadoop (which is useful for data storage and management) or Tableau (which allows data scientists to isolate data based on variables) can make aspiring data scientists more marketable to potential employers.

Soft skills such as problem solving, critical thinking, and communication are also crucial. Data scientists should be skilled at identifying obstacles, proposing and trialing solutions, and conveying their findings in a clear, comprehensive fashion.

If you don't have a handle on the mathematic, technical, or soft skills mentioned above, you may want to set aside some time to study up and ensure that you're prepared for your data science coursework. Even if you already have some relevant knowledge or experience, a dedicated effort to increase your understanding and improve your skills will be valuable.
Additional Resources:
3. Get an Education

Specialized education is a necessity for data scientists. It doesn't matter how much of a knack you might have for statistics or linear algebra - if you don't have certain industry skills and theoretical knowledge, no one is likely to hire you.

Thankfully, accessing a data science education is easier than ever. Below, we've outlined the three common academic paths that data scientists often take on their way into data science. Each comes with its own set of pros and cons, so think carefully before deciding on a path!

College Education

Time Commitment: 4-7 years
Budget: $$$
Flexibility: Full-time, online options

While a formal degree isn't always necessary for aspiring data science professionals, obtaining one is a common educational choice and can help off set a lack of professional experience. According to a 2020 Burtch Works study (PDF, 1.1MB), the majority (83 percent) of predictive analytics professionals (PAPs) hold advanced degrees - this breaks down to 68 percent holding a Master's degree, and 15 percent holding a doctorate.

To lead a successful data science career, you need to ensure your knowledge of relevant principles and practices is sufficient. If you already have a background in mathematics or coding, pursuing a Master's in data science may be a suitable course. If you want to earn a comprehensive and accredited education in the field, such programs are a good choice; today, many even offer flexible online options for busy learners.

However, if you're just starting and plan to enroll in an undergraduate degree, don't worry. The Burtch Works study also noted that the importance of additional schooling has declined in recent years, as the number of people who have a bachelor's as their highest degree has increased by three percent year-over-year. You may be able to get an entry-level data science degree without advanced schooling.

Naturally, this education path is the most expensive and time-consuming, so if you have limitations in these regards, you may want to consider alternatives.

Data Science Bootcamp

Time Commitment: 3-6 months
Budget: $$
Flexibility: Part-time, full-time, online options

For working adults, individuals with an unrelated undergraduate degree, or motivated hobbyists, a data science bootcamp presents an appealing option to start on your path into the field. These courses take anywhere from 3 to 6 months - and while they are short, they are thorough and intensive.

A background in related fields will help those attending bootcamps, but it is not strictly necessary. This accessibility makes these intensive programs suitable for dedicated novices and individuals looking to further their existing knowledge or give themselves a refresher course on data science topics.

Bootcamps look good to employers, too. According to HackerRank's 2020 Developer Skills Report (PDF, 2.4MB), employers typically view individuals who have completed a data science bootcamp as qualified, hard-working high performers. Roughly 40 percent of surveyed developers believe that bootcamp-trained candidates are just as qualified as their college-trained peers, and 33 percent believe that bootcamp alumni are more qualified.

While bootcamps are considerably shorter and less expensive than a college education, they do require participants to commit a substantial amount of time and effort to their lessons during the course and to their experience building and job search afterward. As with any other formal education, interested individuals should understand that they will need to be disciplined and diligent to reap the most benefits from the bootcamp.

Self-Guided Learning

Those who have reservations about committing to a formal education may instead opt for a self-guided education. This is comparatively the most flexible and inexpensive option. However, unlike the other two highlighted above, independent study does not provide a concrete "graduation" that marks learners as being adequately prepared to handle the job duties of a data scientist.

Additionally, some employers may prefer job applicants who have a degree or a bootcamp certification that "proves" capability. Lacking these credentials may put you at a disadvantage during the job search, so pursuing additional certifications as you learn on your own is a good idea.

You should only opt for a self-guided education if you are intrinsically motivated and self-accountable; those who do not fall into these categories may find themselves procrastinating their lessons to the point of arresting their career journey.

4. Know What to Expect From an Entry-Level Role

Before applying to open data science positions, you should know what to expect from an entry-level position.

While the ways companies use data will vary, data scientists often provide analysis to improve cost-efficiency and financial performance, influence strategies, manage risk levels, and assess product or service quality and success. Knowing how to use the tools of the trade is a must, so you should be confident in your abilities and be able to demonstrate your expertise if prompted.

The primary responsibilities of an entry-level data scientist include:

Setting up and managing studies to facilitate data collection

Building tools to automate data collection

Reformatting data to make analysis easier

Parsing large data sets for usable information

Using data to develop strategic business conclusions

Creating reports and presentations

Another point to consider is industry specifications - when considering job listings, you should identify the elements of data science used in the role. Data scientists provide a vital service to companies, government entities, and other organizations by processing and distilling insights from immense amounts of data. That said, each business or organization may have different priorities, goals, and restrictions, and any existing knowledge you have will serve you well.

At the end of the day, an entry-level role requires data scientists to understand data: where it comes from, as well as how to process, gather, and analyze it. You will undoubtedly encounter nuances distinct to each open position, but entry-level data science positions tend to present similar expectations. With these in mind, you can better tailor your resume, professional portfolio, and other materials before submitting a job application.

5. Boost Your Hiring Potential

The demand for data scientists is growing, but the industry is also highly competitive. It's not enough to have the skills and the will to use them; you also need to stand out among your peers.

Let's walk through a few methods of doing so.

Networking

In any field, networking is a key part of launching your career.

Connecting with data science professionals on social media sites like LinkedIn or Handshake, where you can connect with alumni from any programs you have completed, is one of the simplest and most effective ways to forge professional relationships. Doing so will expose you to more opportunities than conducting an Internet search or posting on a job board.

While the pandemic has rendered some traditional networking practices temporarily obsolete, you can still network through digital conferences and lectures. At the very least, attending or participating in such events can expand your knowledge and give you a better understanding of the industry at large. You don't need to wait until you're ready to look for jobs, either! Keep an eye out for any relevant events while pursuing your data science education.
Additional Resources:
Create a Portfolio

Another way to boost your potential of securing an entry-level data scientist role is to compose a portfolio of your work. This tactic will be especially beneficial for those who opt for a self-guided education, but it can be productive for anyone looking to enter the industry. A well-stocked portfolio can overcome a newcomer's lack of job experience and give them a platform for demonstrating their skills.

Your portfolio should spotlight your completed data science projects and demonstrate your technical capabilities. The purpose of this portfolio is, after all, to show that you are qualified for any entry-level data science role.
Additional Resources:
What's the Difference Between Data Science and Data Analytics?

While similar, it is crucial to recognize that data science and data analytics are not interchangeable.

In a sense, data science is an extension of data analytics, building upon the responsibilities of managing and analyzing data to include the construction, development, and maintenance of machine learning models designed to facilitate data analysis and prediction.

It's not unusual for data scientists to begin their careers as data analysts. After all, many of the skills required for the former role - programming, analysis visualization, etc. - also apply to the latter. Here's the difference: while data analysts focus on identifying trends and drawing strategic conclusions from large data sets, data scientists use algorithms, predictive models, and prototyping to design and construct new data modeling processes.

Demand for both roles is relatively high, though competition tends to be somewhat higher for data science than data analytics. Companies that want to accomplish niche tasks such as incorporating advanced technology, artificial intelligence, and machine learning into their data models will seek a data scientist over an analyst. However, analysts may find more opportunities for work among companies that have less of a technological focus.

Who Can Become a Data Scientist?

The good news for anyone looking to make a career shift to the data science field is that there are no limitations on who can gain the skills, education, and experience needed to become a data scientist. Your age or current field of study or occupation do not restrict your potential. Whether you have just finished school or are closer to retirement age, you can become an entry-level data scientist with dedication, hard work, and the right attitude, resources, and education.

While it is beneficial to have a background in mathematics or computer science and you will need to put in extra dedication, it is possible to acquire the right knowledge and experience to become a data scientist in an entry-level role without a formal degree. The timeline for securing an entry-level data scientist role may be shorter if you already have some relevant knowledge and training. However, there is no impending deadline for your data science career to begin.

How Much Do Data Scientists Make in Atlanta, GA, Compared to Nationally?

When committing to a career change, especially one that will require additional schooling, you should consider how much you can expect to make in a new role. This process can help you determine the job prospects in your new field; by conducting this research, you can better plan for your future from a financial perspective.

The average salary for data scientists in the United States currently stands at $100,560. Salary prospects can vary greatly and can depend on a number of factors, including background, experience and location. States like California, Illinois, and New York are among those that employ the most data scientists. On average, data scientists make above the national average in these states.

In Georgia, data scientists can expect to earn less, on average, than the national average, with a mean salary of $81,520. That being said, data scientists in the urban hub of Atlanta can expect to make more than the state average - the mean salary for data scientists in the city tops $90,040. Even though the salary prospects are generally lower in Atlanta, GA, the industry is expected to continue growing rapidly, thereby increasing the potential for a more competitive job market and higher wages.

As we navigate the ongoing pandemic, the future of the data science industry is still coalescing. The trend of remote work is likely to continue and will almost certainly impact wage disparities across state boundaries.

With data scientists able to work from home, their geographic location will have a less significant impact on their employment opportunities. Faced as they now are with a larger pool of qualified candidates and an influx of competitors across the nation, companies may be compelled to offer competitive salaries to prospective data scientists to secure top talent and attract the right people.

Over time, as the remote work trend continues, we can expect the data scientist salary deviations to steadily become more balanced across the nation. Because of this knowledge, aspiring data scientists can work toward their goals without continually worrying about needing to move for better employment opportunities or higher wages.

Throughout the pandemic, the data science industry has remained strong, demonstrating its durability, demand, and flexibility.

Summary

If you are looking to make a significant career change this year, becoming a data scientist may be a worthwhile option. It is possible to make this transition over a few months if you have preliminary knowledge or experience and are dedicated and disciplined enough to hold yourself accountable.

The job prospects, salary range, and potential for initiating meaningful changes within this role are undeniably appealing, and by taking the aforementioned advice into account, you can increase your chances of succeeding in your pursuit of an entry-level data scientist role in 2022. If you want to kickstart your education, check out Georgia Tech Data Science and Analytics Boot Camp!

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How to Become a Data Scientist in 2022: A Guide to Getting an Entry-Level Position | Georgia Tech Boot Camps

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Maybe you always knew you were a math person. You might have discovered your knack for logical puzzles as a child or realized in college that the data sets that bamboozled your friends weren't all that confusing to you.

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C++ is a high-level programming language developed by Bjarne Stroustrup at Bell Labs. C++ adds object-oriented features to its predecessor, C. C++ is one of the most popular programming language for graphical applications, such as those that run in Windows and Macintosh environments. Your knowledge is strictly necessary if you want to become an automation developer, like Arduino, for example. Start now! Use the attached link to get a better understanding of the language:
https://www.codecademy.com/catalog/language/c-plus-plus?g_network=g&g_device=c&g_adid=518718870684&g_keyword=cpp%20tutorials&g_acctid=243-039-7011&g_adtype=search&g_adgroupid=102650142713&g_keywordid=kwd-303740143579&g_campaign=ROW+Language%3A+Basic+-+Exact&g_campaignid=10074200771&utm_term=cpp%20tutorials&utm_campaign=&utm_content=518718870684&utm_id=t_kwd-303740143579:ag_102650142713:cp_10074200771:n_g:d_c&utm_source=google&utm_medium=paid-search&utm_term=cpp%20tutorials&utm_campaign=&utm_content=518718870684&gclid=Cj0KCQjwmouZBhDSARIsALYcouq75o-u0-Sfy1_sT3WzDIv83ySrbqekmU1jm-IDD4HBleWTiOs1WhYaAknTEALw_wcB

Pedro e Vinícius

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

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

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Statistical analysis and computing

Machine Learning

Deep Learning

Processing large data sets

Data Visualization

Data Wrangling

Mathematics

Programming

Statistics

Big Data

Some data scientists have a Ph.D. or Master’s degree in statistics, computer science, or engineering. This educational background provides a strong foundation for any aspiring data scientist and also teaches the essential data scientist skills and Big Data skills needed to succeed in the field

Felipe C. e Kaique

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data science is not a simple job, always keep studying on your own, technology is always evolving so it is very important that you are prepared

Felipe C. e Kaique

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Focus on what you do, only then will you evolve at work, becoming better at what you do every day

Felipe C. e Kaique

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You should listen to some professionals of the area to learn a little more

Felipe C. e Kaique

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To become a data scientist is important to secure that all of these areas have a good foundation in your education

Felipe C. e Kaique

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You should choose a area to focus on, keeping an eye open and getting yourself updated with the always changing technological market will help you decide which area to focus. Even though having a focal point is important, a broad vision is necessary to not get limited

Daniel e João Vitor

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Knowledge and expertise of tools and programs like RapidMiner, DataRobot, Google Cloud Prediction API, Paxata, Trifacta and BigML may or may not be required, depending on the industry and position. So you shouldn't be too scared and you can learn some things as you go.
- Eduardo e Ricardo

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The government can also hire you. You should check out your national resources for jobs from the government and you can be a data scientist in the public sector.
- Eduardo e Ricardo

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Basically all industries can get you hired as a data scientist, so the possibilities are endless. A few examples include health care companies, ecommerce businesses, financial firms, social networks and science-based organizations like drug companies, among many others. You should check out all of your options as there are plenty of places you can work at.
- Eduardo e Ricardo

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You ought to be a criative person whilst continuesly solving programming problems, these are the key elements to be a better data scientist and problem solver

Daniel e João Vitor

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Specifically when it comes to data science, Python is the most used language in that field. Knowing it inside and you will give you the necessary skills for the job and might even give you an edge over other people competing for your job. You should study this language emphatically and make sure you are comfortable using it.
- Eduardo, Ricardo

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The language that should be taught to beginners.

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The language that should be taught to beginners.

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This video talks about the language that a beginner should learn and improve programming.

Guilherme Silva e Guilherme Souza

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MINHA OPINIÃO: Qual a melhor linguagem para começar a programar?

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Importance of Communication in Data Science

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Importance of Communication in Data Science

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Communication is key to any data science role. As a data scientist, you will be relied upon to communicate your findings and recommendations to non-technical colleagues. This may include senior management, other departments within your company, or even customers. It is therefore essential that you develop strong communication skills.

Guilherme Souza e Guilherme Silva

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To learn how to program better, it is good to use the W3school website.

Guilherme Silva e Guilherme Souza

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3 Técnicas Que Eu Uso Para Aprender a Programar Qualquer Coisa (mesmo sem ter o dom da programação)

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Math is a core educational pillar for data scientists, regardless of your future industry career path. It ensures you can help an organization solve problems and innovate more quickly, optimize model performance, and effectively apply complex data towards business challenges.

Guilherme Souza e Guilherme Silva

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One of the most important skills you should have is the programming logic, to develop such skill you should use math, solving math problems helps you understand some issues you might have while programming.

Daniel e João Vitor

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You should have comprehensive knowledge of machine Learning

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You should have comprehensive knowledge of machine Learning

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Machine learning is a must-have ability for any data scientist. Predictive models are created using machine learning. If you want to forecast how many clients you’ll have in the upcoming month based on the previous month’s data, for example, you’ll need to employ machine learning techniques. You can begin with simple linear and logistic regression models before progressing to sophisticated ensemble models such as Random Forest, XGBoost, CatBoost, and others.

Filipe Muniz, Wesley Terrão.

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You must have knowledge of data wrangling and data exploration

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You must have knowledge of data wrangling and data exploration

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The initial phase in your data analysis process is exploratory data analysis (EDA). Here, you’ll figure out how to make sense of the data you have, as well as what questions you want to ask and how to phrase them, as well as how to best modify your data sources to get the answers to the problem currently being considered. This is done by looking at patterns, trends, outliers, unexpected outcomes, and so on. Data manipulation and wrangling, on the other hand, can take a long time but can ultimately help you make better data-driven judgments. Missing value imputation, outlier treatment, correcting data types, scaling, and transformation are some of the common data manipulation and wrangling techniques used.
So, a data scientist must be familiar and confident in concepts of data wrangling and data exploration.

Filipe Muniz, Wesley Terrão

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Don't focus on data science, but on solving business problems.

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Don't focus on data science, but on solving business problems.

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To be a data scientist, you need to become a computer technician to get a feel for programming and study.
Like any self-proclaimed data scientist, he/she must be an expert in hacking, math, and statistics, and still have substantive expertise in the field she works in.

Guilherme Souza e Guilherme Silva

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O termo "cientista de dados" se tornou uma expressão coloquial na linguagem empresarial moderna.

Usamos essas palavras quando tentamos definir o cargo de uma pessoa que, teoricamente, deve possuir todas (ou quase todas) as habilidades relacionadas a dados.

Na realidade, as organizações que desejam contratar cientistas de dados procuram por "unicórnios" - profissionais tão profundamente habilidosos que chegam a não ser reais.

O Diagrama de Venn de Data Science (popularizado pelo cientista de dados Drew Conway) ajuda a visualizar esse amplo conjunto de habilidades:

("A) 'habilidades de hacking' em vermelho, B) 'expertise substantiva' em azul, e C) 'conhecimentos em matemática e estatística' em verde. Na interseção de A e B temos a área roxa, denominada de 'área perigosa'; entre A e C está o 'Machine Learning' e entre B e C está o 'skill de pesquisa tradicional'. Na interseção dos três círculos está o 'Data Science'").

Quando a maioria das pessoas olha para este diagrama, elas pensam instantaneamente que o esquema define um "cientista de dados".

Como qualquer pessoa que se autodenomina cientista de dados, ela deve ser especialista em hacking, matemática e estatística, e ainda ter especialização substantiva no campo em que trabalha.

Na prática, são poucos os indivíduos que são verdadeiramente especialistas em todas as três áreas (e eles provavelmente estão distantes de nós. Se você for um verdadeiro ou verdadeira especialista em todos os três, provavelmente está trabalhando no Google, Microsoft ou Facebook).

O diagrama nunca foi concebido para definir um cientista de dados (a pessoa), mas em vez disso, define a ciência de dados (o campo, a área em si).

Para ser um(a) cientista de dados, é necessário ser excepcional em pelo menos uma ou duas das categorias e ter conhecimento suficiente nas outras para realizar qualquer que seja o objetivo determinado.

E algumas empresas estão superando o desafio de encontrar bons cientistas de dados ajustando suas ofertas de empregos, tornando-as mais adequadas.

O Airbnb, que emprega uma das equipes de Data Science mais maduras do mundo, recentemente dividiu suas equipes de Data Science em três áreas (Analytics, Algorithms e Inference) para comunicar e estruturar o valor das áreas com mais eficácia.

Muitas empresas agora listam o título de trabalho "Machine Learning Engineer" ou "Research Scientist" para enfatizar a disparidade de funções em todo o continuum da definição de cargos em ciência de dados.

Mas, embora estejamos cada vez melhores nessa definição e na segmentação de seus muitos subdomínios em cargos, funções e tarefas, ainda estamos atrasados no processo.

Afinal, ciência de dados não é sobre algoritmos, habilidades técnicas avançadas ou diplomas especializados - ciência de dados é, basicamente, resolver problemas.

As organizações estão mais preocupadas com a complexidade acadêmica e técnica de suas equipes de ciência de dados do que com o valor que elas agregam aos negócios.

Na verdade, eu apostaria que 2 em cada 3 de vocês que lerem essa postagem podem pensar em um projeto de Data Science em sua empresa que tenha um investimento significativo, mas que ainda não mostrou valor para o negócio.

Por que isso acontece?

As empresas querem o pão fresco, mas estão contratando engenheiras elétricas em vez de padeiras.

Cassie Kozyerkov, engenheira chefe de inteligência de decisão do Google, explica a analogia:

"Imagine que você quer um pão, e, para isso, você contrata uma chef para construir um forno para você ou um engenheiro elétrico para fazer o pão para você. Quando se trata de Machine Learning, esse é o tipo de erro que vejo as empresas cometendo continuamente.

Se você estiver abrindo uma padaria, é uma ótima ideia contratar um padeiro experiente, versado nas nuances de fazer pães e doces deliciosos. Você também quer um forno, com certeza. Mas embora essa seja uma ferramenta crítica, aposto que você não colocaria como tarefa para a sua chefe de confeitaria construir aquele forno.

Então, por que sua empresa está fazendo o equivalente em Machine Learning?

Você está fazendo pão? Ou está fazendo fornos?".

A Sra. Kozerkov continua explicando que o motivo pelo qual as empresas falham com tanta frequência no Machine Learning é devido a uma compreensão insuficiente de pesquisa versus aplicação, ou seja, construir o forno versus o assar pão.

Especialistas em pesquisa com diplomas altamente especializados podem ser incrivelmente valiosos na situação certa (por exemplo, se seu negócio ou produto for o algoritmo), no entanto, a maioria das empresas não precisa deles.

O que as empresas precisam é de um "padeiro" - alguém que possa "assar pão", colocar a mão na massa, vendê-lo e distribuí-lo de forma eficaz usando a cozinha que já foi construída em algum outro lugar.

Se você gerencia uma equipe e deseja contratar o especialista em pesquisa, vá em frente, mas certifique-se de colocá-lo em contato com alguém que possa realmente resolver o seu problema.

Caso contrário, não reclame quando não estiver obtendo o valor que esperava.

Se você está dizendo a si mesmo: "preciso de alguém que seja um ótimo padeiro e, ao mesmo tempo, um excelente engenheiro", boa sorte. Embora essas pessoas existam (imagino que existam algumas!), elas provavelmente estão ganhando mais do que você, trabalhando no Google ou no Facebook.

Em vez de caçar o "unicórnio", monte uma equipe especialmente desenhada para os problemas específicos do seu negócio.

E se você está atualmente em uma função de ciência de dados, se pergunte: quais desafios sua empresa enfrenta? Você entende esses desafios extremamente bem? Você pode medir claramente o valor comercial dos projetos em que está trabalhando?

Para aspirantes a cientistas de dados, ficam outras questões - em quais tipos de problemas você está interessado ou interessada? De qual área? Antes de se aprofundar em um curso de tecnologia ou em um doutorado, você sabe quais são os problemas que gostaria de resolver?

Você quer construir fornos ou finalmente assar o pão?

Ao contrário da crença popular, a IA não é uma bala mágica.

Na reportagem de capa da Harvard Business Review de julho de 2017, o chefe de IA do Facebook de The Business of Artificial Intelligence, Joaquin Candela (falando sobre aqueles "unicórnios") expressa suas frustrações:

"O que me frustra", diz ele, "é que todo mundo sabe o que é um estatístico e o que um analista de dados pode fazer. Se eu quiser saber: 'ei, qual o segmento de idade que se comporta dessa ou daquela maneira?', eu chamo o analista de dados".

Ele continua: "(...) então, quando as pessoas pulam essa etapa e vêm até mim e dizem: 'me dê um algoritmo de Machine Learning que fará o que nós fazemos hoje', eu fico tipo: 'como assim? Que problema você está tentando resolver? Qual é o seu objetivo? Quais são as compensações desse processo?'. Às vezes, as pessoas se surpreendem com a existência de compensações! E se o indivíduo não tem respostas para essas perguntas, eu fico pensando: 'que diabos você pensa que é IA?'".

"Eles pensam que tudo funciona como um passe de mágica", Candela completa. "Mas não é assim que nada acontece. Esse é o momento em que digo às pessoas: 'você não precisa de Machine Learning. Você precisa montar uma equipe de ciência de dados que o ajude a pensar em um problema e aplicar testes para solucionar esse problema. Sente-se com sua equipe. Observe seus dados. Se você não consegue nem saber o que está acontecendo, se não tem intuição, se não sabe construir um sistema muito simples e baseado em regras (por exemplo, se uma pessoa tem menos de 20 anos e vive nesta geografia, vamos fazer isso), então fico receoso até de falar sobre IA na cara do seu problema'".

Os insights de Candela são incrivelmente pungentes em nossa obsessão atual com todas as coisas de IA e ML.

A dura verdade é esta: você provavelmente não precisa de IA - pelo menos não ainda. Antes de pensar em Machine Learning para impactar seus negócios, você precisa ter um problema muito específico e bem definido.

Na minha experiência pessoal, a maioria das empresas não definiu o problema bem o suficiente para sequer aplicar um conjunto de regras simples a ele.

Como o Sr. Candela afirma enfaticamente: se você ainda não chegou nesse ponto, como podemos começar a discutir sobre alguma aplicação de IA?

Simplesmente não podemos.

Esqueça a ciência de dados por um minuto e faça um esforço concentrado para desvendar problemas e fazer planos sobre como resolvê-los.

Se você fizer isso, algo engraçado acontecerá: a tecnologia/algoritmo/ou a técnica que você precisa aplicar se tornará aparente.

Você se tornará mesmo um especialista ou uma especialista nisso porque não estará apenas hackeando ou calculando, estará resolvendo um problema real e prático.

Aqui estão algumas sugestões para cientistas de dados ou membros das equipes de análise para aplicar essa ideia de forma mais completa:

1. Torne-se um(a) cientista do negócio. Gaste um pouco menos de tempo aprendendo novos algoritmos e pacotes Python e mais tempo aprendendo as alavancas que fazem seu negócio específico subir ou descer (e as variáveis ​​que impactam essas alavancas). Identifique as fontes de dados que contribuem para essas variáveis ​​ - geralmente, na interseção, você encontrará oportunidades de alto valor.

2. Seja implacável ao priorizar e aceitar projetos. Antes de avançar em um projeto, avalie A) a ação que será realizada e B) o valor comercial que será criado com base nessa ação. Se a ação não for clara e o valor não for alto, não perca tempo. Nota: Data Science NÃO é Business Intelligence, BI é uma importante função de TI que mantém a integridade das fontes de dados e painéis. Seu trabalho como cientista de dados é resolver os problemas do negócio.

3. Não espere que as partes interessadas sempre (ou alguma vez) sejam capazes de definir o problema. Em minha opinião, esta é a habilidade número um mais importante para um(a) cientista de dados acima de qualquer conhecimento técnico: a capacidade de avaliar e definir claramente um problema. A maioria das partes interessadas tem problemas, mas não pensou sobre eles por tempo suficiente para ser capaz de definir o processo por trás deles. Este é o momento em que você vai dominar o Machine Learning e a IA, trazendo-os para a sua organização. Decifre as necessidades do negócio em etapas onde a ciência de dados pode ser aplicada de forma eficaz.

4. Torne-se parte do negócio. E, sob nenhuma circunstância, fique isolado ou isolada. Envolva-se de forma proativa com a unidade de negócios como um(a) parceiro(a), e não como um mero "suporte".

É isso. Boa sorte na resolução de problemas!

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Não foque em ciência de dados, mas na resolução de problemas do negócio

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O termo "cientista de dados" se tornou uma expressão coloquial na linguagem empresarial moderna. Usamos essas palavras quando tentamos definir o cargo de uma pessoa que, teoricamente, deve possuir todas (ou quase todas) as habilidades relacionadas a dados. Na realidade, as organizações que desejam contratar cientistas de dados procuram por "unicórnios" - profissionais tão profundamente habilidosos que chegam a não ser reais.

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You must have knowledge of programming language

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You must have knowledge of programming language

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Data scientists must be proficient in advanced statistical modeling tools and have a deep understanding and knowledge of programming, in addition to having a strong foundation in mathematics and statistics. There are a variety of programming languages that are preferred for the role of a data scientist.

Filipe Muniz, Wesley Terrão

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In this area a degree is not necessary, but it will give you an edge when trying to get hired. So getting a degree will get you more opportunities to get a job.

Gabryel Rickheim, Gustavo Mansur

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This is a nice platform that you should look into to learn algorithms and programming languages

Daniel e João Vitor

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"Parabéns professor, vc e sua equipe fazem um trabalho maravilhoso e de ótima qualidade. Patrocínio merecido. Espero que outras empresas siga o exemplo do youtube e da Hostnet e patrocinem o canal cada vez mais. Foi através do seu canal que me apaixonei pela programação. Obrigado por tudo!"

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Curso em Vídeo - Aprenda informática com cursos grátis.

# data[].attributes.attachment_link.description
"Parabéns professor, vc e sua equipe fazem um trabalho maravilhoso e de ótima qualidade. Patrocínio merecido. Espero que outras empresas siga o exemplo do youtube e da Hostnet e patrocinem o canal cada vez mais. Foi através do seu canal que me apaixonei pela programação. Obrigado por tudo!"

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You  should get a basic understanding of data analysis, tools and techniques.

# data[].attributes.subject
You  should get a basic understanding of data analysis, tools and techniques.

# data[].attributes.body
When learning to write sentences, you must be familiar with grammar in order to construct proper sentences. Similarly, before you can create high-quality models, you need to understand statistics. Machine Learning begins with statistics and evolves. Even the concept of linear regression is a statistical analysis concept that has been around for a long time.

Filipe Muniz, Wesley Terrao

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You should learn how to fluently speak english, being able to get along with different people from different countries will expand your possibilities of networking. Also, english fluency will help you learn programming languages faster.

Daniel e João Vitor

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In the vast world of data science, there are many different fields of expertise one can specialize in. You should find the one that is right for you.

Gabryel Rickheim, Gustavo Mansur

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You'd better get familiar with any programming language

# data[].attributes.subject
You'd better get familiar with any programming language

# data[].attributes.body
Before starting, you should get familiar with any programming language, even if it's a really basic one. This is great so that you have a basis once you start learning more advanced languages, because the logic you learned carries over to any language.

Gabryel Rickheim, Gustavo Mansur

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You'd better make sure that you actually like working in that area.

# data[].attributes.subject
You'd better make sure that you actually like working in that area.

# data[].attributes.body
You can be the best data scientist there is, but it's no use if you don't like working as one. You should make sure you know what you're getting into before commiting to it.

Gabryel Rickheim, Gustavo Mansur

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Reference https://www.knowledgehut.com/blog/data-science/data-science-roadmapAvatar de Mariana Santa Maria                           Filipe Muniz, Wesley Terrão

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Skills you should master to be a data scientist

# data[].attributes.subject
Skills you should master to be a data scientist

# data[].attributes.body
Filipe Muniz, Wesley Terrão

# data[].attributes.headline
You should get familiar with the way computers work. 

# data[].attributes.subject
You should get familiar with the way computers work. 

# data[].attributes.body
If I were you, I'd start by learning the basics of computer programming. You can do this by taking free online courses, like the one included in this post.

Gabryel Rickheim, Gustavo Mansur

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Think like a computer: the logic of programming

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Have you been thinking about a career in software development? Do you find computers and computer lingo a bit foreign? This course demystifies jargon and puts computer programming within reach!