Brian Kim

Brian Kim is a data scientist with a strong interest in marketing. As a data scientist specializing in marketing science, he's used a variety of machine learning and statistical models to extract "signal" from "noise" in various sorts of media data. Programmatic advertising and social media data have been among the data kinds used.

Starting Out in the Insurance Industry
Kim began his data science career in 2017 with Healthy Bytes, a New York-based healthcare business, where he performed exploratory data analysis on insurance claim data using the Python programming language. He also created an Extract-Transform-Load (ETL) pipeline to get data from the MongoDB document database. For corporate and management reports, he used Tableau software and Python to parse the data and build visuals. Kim also used machine-learning algorithms to analyze health insurance data in order to assure compliance with HIPAA regulations. He reported directly to the company's CEO and CTO while fulfilling all of these duties.

Science of Data
Following his time at Healthy Bytes, Kim worked at Metis, a data science and analytics business headquartered in New York. He produced proprietary datasets and used machine learning for a variety of initiatives at Metis.
One of these projects was detecting mobile click fraud. On a company's mobile ad platform, Kim employed machine-learning algorithms to precisely anticipate fake clicks. Using the analytics engine Apache Spark on Amazon Web Services, Kim was able to wrangle and resample a large-scale data frame (AWS). To get an 88 percent recall rate, he created at least 40 distinct features and modeled discrete data using XGBoost and GBM.
Kim earned a data science and machine learning certification from Metis in March 2018. (He also has a Google Analytics certification.)

Data Science in Marketing
Kim used SQL, Python, and R to extract insights from television, search, social media, and programmatic channels for numerous client vertical categories at New York-based integrated media firm Media Assembly. He presented the findings of a multi-touch attribution model to the company's top management in order to enhance media optimizations. Extant media mix models (MMM) were also constructed to improve the efficiency and measurement of digital marketing efforts.

Creating client conversion, revenue, and long-term value (LTV) models by combining important key performance indicators (KPIs) such as cost-per-click, click-through rates, and impressions to optimize the allocation of client media budgets was another achievement Kim handled at Media Assembly. Kim used Tableau to create graphics that assisted a big pharmaceutical client improve the efficacy of their television advertisements.

Kim managed a rewrite of ETL methods for proprietary agency DMP, working with a data engineering team to improve the company's data gathering and accuracy. He also co-wrote a "Point of View" paper for the agency that covered a variety of topics, including the possible effect of GDPR/CCPA legislation and changes to third-party cookie policies.
Kim conveyed the technical intricacies of multiple data-science initiatives to a variety of non-technical stakeholders, including strategy, planning, and investment teams, in a number of occasions. Kim took a risk by project-managing several analytics deliverables by requiring data scientists and data analysts to adhere to strict timelines in order to guarantee that projects were completed on time and accurately.

Donating to charity
Kim is now based in Queens, New York.
Kim enjoys art, design, music, and online education in addition to data science and digital marketing. In recent years, he's donated to two organizations that he really believes in: Planned Parenthood and Khan Academy.
Planned Parenthood is a nonprofit organization that provides reproductive health care services across the globe, particularly in the United States. It was founded in 1921 as the American Birth Control League (it changed its name in 1942). The organization, which has 159 affiliates (both medical and non-medical), maintains more than 600 health clinics in the United States and 12 other countries across the world. In the United States, Planned Parenthood is the biggest provider of reproductive health services, including abortion. Planned Parenthood participates in reproductive technology research and fights for the growth and preservation of reproductive rights in addition to providing direct health care. According to research, maternal death rates increased in a number of local areas when Planned Parenthood facilities closed.

Sal Khan, an American educator, created Khan Academy, a nonprofit online education corporation located in California. Khan Academy offers a set of free online tools to go along with over 6,500 instructional video courses and tutorials that users may view and download to learn about a range of academic disciplines. The company's initial concentration was on math and scientific disciplines, but it now covers a wide variety of subjects, including exam preparation and technological issues such as computer coding. Nonprofit organizations have delivered analog copies of the Academy's movies with different subtitled translations to rural regions in Latin America, Asia, and Africa. For educators who want to utilize the Academy's films in the classroom, the Academy has produced unique practice tasks and tools.

There are a lot of ways to become a data scientist, but not all of them require a lot of technical skills. Even people who don't have a lot of experience in the field can get jobs in the field, like entry-level analysts. There are a lot of jobs for data scientists in your city, which is good. In order to learn more, go to the Careers website and look for more. Then, look for a meetup in your area to learn more about the job.

According to Brian Kim, if you want to work in this field, getting a college degree can help you get a job. A post-graduate degree in data science can help you build a good network, and many businesses hire from within their own staff, so you can get a job there. People who know people in the field can also help you get a job. If you want to work in data science, you can also join or start a meetup group in your city.

After you get a degree, you should think about getting more education and experience. There are a lot of things you need to know about how the field works, even if you already have the technical skills. Analytical and technical skills are needed for this job. In addition, you should have some experience as a leader. People who work in the field of data science will be able to work with you and you will get paid for it.

Brian Kim described that, a basic knowledge of math is important, especially if you want to work with data. A good foundation in math is important, and linear algebra is the next step. To work with matrices and vectors next, you need to learn about how to do that, too. With time and practice, you'll be able to build a model and figure out what to do with it.

You can also learn how to do things with your hands. During a bootcamp, you can learn how to do basic math and statistics with data. After you finish the program, you should use what you learned to improve your job. Then, you can move on to more complicated parts of the job. Anybody, no matter what their background is, should be able to tell a data scientist's resume apart from theirs. As a data engineer, you might also want to think about working in the department. You can apply for a job there as well.

Also, you can start a job in data science at a company that already exists. It may not pay as well to be a data scientist, but there are a lot of benefits to becoming one. You can start by learning about the different ways to show data, like R. In addition to learning technical skills, you can also make a resume that shows off your work history. This will make you stand out in the field.

An impressive resume for a data scientist must be on it. This will show that the data scientist can do a lot of different things. The more projects you do, the better your CV will be. The more experience you have in real life, the more likely you are to get a job in this field. People who work in this field can make a lot of money, so be sure to think about it.

Brian Kim explains, you'll also need to learn machine learning and other important data science skills while you're at it. This means that it isn't enough to be a great coder. You also need to learn how to build projects with data. People who work in data science should be able to deal with the difficulties that come with their jobs. Once you know how to do these things, you can apply for a job as a data scientist. Employers will be impressed when they see what you've done in your job history.

In order to be good at what you do, you need a strong mind. Data science is a field where you can improve your personality and mental skills. It's possible for people who haven't gone to college to learn the skills they need by taking online classes. If you want to be a data scientist, there are a lot of things you can do to learn more about what it is. The most important thing is to do what you love.

3/15/2022
As per Brian Kim, learn about the numerous tools and databases that are available to you in order to learn how to become a data scientist. As well as being familiar with SQL queries and processes, you should be familiar with Microsoft Excel and IBM SPSS, among other software programs. You should also be familiar with several forms of data analysis and interpretation techniques, as well as different classification and clustering strategies. Aside from that, you should have outstanding communication skills and a strong desire to learn new things.

If you enjoy working with numbers, you might want to explore pursuing a career as a data scientist. When it comes to spreadsheets and solving mathematical problems, this is a field that you should consider pursuing. There are numerous advantages to pursuing a career as a data scientist, and it may be a very gratifying one. For those interested in the topic, you might begin by finding promising prospects and developing your own case studies. If you're not sure where to begin, you might also look for an internship opportunity. This will provide you hands-on work experience in a real-world setting. You can also assist government agencies, non-governmental organizations, and small enterprises.

You should have a fundamental understanding of mathematics, which serves as the foundation for data science careers. It is critical to understand statistical concepts such as variance and standard deviation. Along with this, you should have a solid understanding of linear algebra as well as calculus. Aside from that, you should have a fundamental understanding of decision trees and logistic regression. If you want to work as a data scientist, you should be prepared to put your knowledge and talents to use. However, if you're just starting out, it may be preferable to start with a more general bachelor's degree.

Brain Kim describes programming, statistics, and maths are all necessary skills for a data scientist to possess. You must also be able to articulate the outcomes of your research and the significance of your discoveries. You should concentrate your efforts on developing relationships with the company's executives, designers, marketers, and software engineers. You must be adaptable and flexible in order to succeed in any environment. In addition, you'll need to become familiar with the R programming language, which is used for statistical processing. At the end of the day, you'll have to learn how to generate digestible reports based on the data you've collected and analyzed.

When pursuing a career in data science, you do not need a doctorate in the discipline to be successful. Individuals with no postsecondary education in STEM subjects can still be interested in the field, and it is not necessary to have a degree in mathematics to be interested. As long as you have a genuine interest in data science, you will almost certainly be successful. If you have a strong interest in data science, you'll be able to handle the issues that arise in the sector and establish an excellent portfolio of your own creation.

In order to work in either a startup or an established firm, you'll need to become familiar with the most up-to-date software available. Your chances of becoming a data scientist increase as your knowledge of the sector grows. You'll need to learn how to work with a variety of various sorts of data, as each sector has its own unique set of data requirements. Understand the business processes of your chosen organization in order to be able to discover the types of data that are the most relevant to your situation.

A solid portfolio and the requisite skills are required to become a data scientist in today's competitive job market. You should be able to complete your own project successfully. Decide on a topic matter that interests you and create a project that answers a question using data. To demonstrate your abilities, you should post your results on GitHub. Attending an online training course will provide you with all of the most up-to-date technologies and data science abilities.

Brian Kim explains to work as a data scientist, you should have a bachelor's degree in mathematics or computer science as a prerequisite. Additional qualifications include a master's degree in a relevant field and work experience. If you want to be able to land your desired career, regardless of your background, you must also have the appropriate education. There are numerous advantages to working as a data scientist, and there is a great deal you can accomplish once you get started.

In order to make the shift from a programmer to a data scientist, you must be able to demonstrate your abilities in the appropriate manner. In order to be successful in your application, you must ensure that your CV reflects your greatest qualities. The most effective technique to demonstrate your worth is to demonstrate that you are passionate about the area and have a genuine interest in it. Make the most of your first impression by putting your best foot forward on your CV.