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Starting a Career as a Data Scientist
Tips on what your Data Science career could look like
Data science has become one of the most in-demand fields in recent years. As companies increasingly collect and generate data, they need individuals who can sift through this information to identify trends and make predictions.
While a data scientist’s job may vary depending on the industry, their responsibilities usually fall into one of three categories: Type A, Type B and Type C. In this article, we’ll explore the three main areas that data scientists should focus on. In addition, we’ll describe a possible strategy to start a career in Data Science and some tips to learn new skills.
The article is organized as follows:
- Type A — the Analyst
- Type B — the Builder
- Type C — the Consultant
- Starting a career in Data Science
- Learning new skills
Type A — the Analyst
Type A stands for Analyst. As the name suggests, these individuals excel at analyzing data. They’re great at taking large amounts of data and make sense of it all.
Analysts are often thought of as “traditional” data scientists. They are highly skilled in statistics and modeling, and they use these skills to extract insights from data. Analysts are often very good at communicating their findings to non-technical audiences since they understand both the technical details and the business implications of their work.
Initially, data analysts come from a very heavy research background. Over time the companies realized that these people were really rare. For this reason, currently, their actual study background doesn’t matter so much, it is more about their work experience. However, you can still have people with technical degrees, such as computer science degrees and statistics that would work as data analysts, although in most cases, these people fall in the Type B category.
A data analyst performs data analysis, being able to look at insights, do data visualization, make some dashboards, and try and figure out what’s going on with the data. That’s a very large component of data science.
Another important skill for analysts is programming. Programming languages like R and Python are designed specifically for working with data. If you want to be able to manipulate data effectively, you need to know how to program.
Third, you need to have an open mind. Data science can be challenging, and it is important to be willing to learn new things. If you are closed-minded, you will likely find yourself struggling with data science.
Last but not least, a good data analyst should be curious. Once you have collected data, you should be curious about the data and start asking questions. Questions can lead to more questions, and this is how hypotheses are formed. For anyone who wants to improve their curiosity and keep learning more different things, you just have to make the decision that you want to learn more things. It’s as simple as that. Everyone can do their own version of curiosity.
Type B — the Builder
Type B stands for Builder. Builders are essentially software engineers, focused in the realm of data science applications. You do stuff in the cloud, set up the infrastructure, manage workflows, work with the data engineering team to make sure all the pipelines are solid, force other members of your team to write clean code, unit tests, functional tests and different things to make sure that the code will run in production.
You can also think of them as the “new age machine learning engineers”. They build those systems and the Machine Learning ops, automated machine learning programs and different things like that.
One of the key differences between the Building type and the Analyst type is that the Building type always wants to work in the production environment. There’s almost a different mindset.
As a Type B scientist, you should know frameworks like Git, Docker, and cloud computing platforms. These are important tools for managing and analyzing data.
Git is a version control system that lets you track changes to your code. Docker is a tool that helps you package up your code so it can run in different environments. Cloud computing platforms like Amazon Web Services and Google Cloud Platform provide scalable resources for storing and processing data.
Differences between Type A and Type B
Type A and Type B have quite different mindsets. Type A is more exploratory, and Type B focuses more on production.
The type A data scientist will be very happy to play around in Jupyter Notebooks: take data from a CSV file, explore and see what’s there, and try to apply some machine learning to it straight away — after figuring out what the problem is, of course. But the Type B data scientist doesn’t want to do that. They think of all of that stuff as a waste of time. They’re going to refactor it, to change it somehow.
Type C — the Consultant
Type C stands for “Consultant”. In the past, a data scientist was the intersection of the programmer, mathematician, and business domain expert. This type C is essentially that person in the middle, but with very strong consulting skills.
As the name suggests, consultants are all about giving advice. But unlike other types of data scientists, who tend to focus on either the technical aspects of data analysis or the business applications, consultants have really strong stakeholder engagement and communication to make sure that the business problems will be solved adequately with the right solutions.
Consultants are also experienced in working with a variety of different businesses, so they can quickly adapt to your company’s needs.
Usually, type C scientists are either people leaders or project managers. Or they’re very good at persuading and talking to business. If you’re a strong talker, you’re more likely to be a people leader. Usually, the type C data scientist will be your manager of data science, your head of data science, or your chief data scientist.
There are different types of consultants as well. You can think of them as true consultants. They aren’t working very deep into the data. They’re talking about the process to solve problems, work closely with the stakeholders to figure out what the problems are and make sure that their concerns and issues are all addressed. But often we see that some leaders are not very technical.
The most important thing for the Type C data scientist will be a commercial mindset: commercial acumen of being able to make decisions, people leadership, being able to talk to businesses, convince leaders to do something, as well as a storytelling piece.
Starting a career in Data Science
Here are a few things to keep in mind if you’re wondering if a career in data science is right for you:
- Data science is a team sport — No matter how talented you are, you won’t be able to do everything on your own. Data science is a collaborative field, so you’ll need to be able to work well with others. This means being able to communicate effectively and being open to different perspectives.
- The field is constantly changing — If you’re the type of person who likes stability and hates change, data science may not be the right fit for you. The field is constantly evolving, so you’ll need to be comfortable with change. This means being open to new ideas and technologies and
In data science, it is often said that domain expertise is key. This is true to some extent — if you want to be a data scientist working on medical data, it helps to have a background in medicine. However, domain expertise is not always necessary to get started in data science.
Where to start
As a fresher, breaking into the data science field can seem daunting. However, with the right attitude and some hard work, it is definitely possible to make a name for yourself in this exciting and ever-growing industry. Here are a few tips on how to get started:
1. Learn Python or R. These are the two most popular programming languages for data science and are relatively easy to learn compared to other languages.
2. Get familiar with basic statistical concepts. Understanding means, median, mode, variance, etc. will be helpful in your journey to becoming a data scientist.
3. Start playing around with data! Use public datasets to practice your new skills or try out different data analysis techniques. Kaggle is a great resource for finding datasets and participating in competitions.
4. Learn machine learning. This is a huge field of study within data science and can seem daunting at first, but there are plenty of resources available to help you get started (including our own course!).
5. Networking is another key component of breaking into data science. Attend meetups and conferences, and connect with people already working in the field — they may be able to offer advice or even help you land your first job.
6. Stay up to date with the latest news and advancements in data science. Read blogs, listen to podcasts, watch talks online… there are lots of ways to stay informed about what’s going on in the world of data science.
Learning new skills
There are many skills that are needed in order to work as a data scientist. However, there are also many skills that can be learned in order to become a data scientist. Many people interested in data science may not have the opportunity to learn these skills at their job. However, there are many ways to learn the skills outside of work.
One way to learn the skills needed to become a data scientist is through online courses. There are many online courses that teach the basics of data science. These courses can be found on websites such as Coursera and Udacity. In addition, there are many blog posts and articles that can be found online that can help someone learn the basics of data science.
Another way to learn the skills needed to become a data scientist is through conferences and meetups. There are many conferences and meetups that discuss data science topics. These events can be found in cities all over the world. Attendees of these events can learn from presentations and network with other people who are interested in data science.
Finally, another way to learn the skills needed to become a data scientist is through books. There are many books that have been written about data science.
Congratulations! You have just learned how to start a career in Data Science!
While a data scientist’s job may vary depending on the industry, their responsibilities usually fall into one of three categories: Type A, Type B or Type C.
In this article, we’ve explored the three main areas that data scientists should focus on. We’ve also described a possible strategy to start a career in Data Science and some tips to learn new skills.
If you’re interested in pursuing a career in this exciting field, then be sure to keep these things in mind!
The content of this article has been inspired by the podcast episode The ABC’s of Data Science with Danny Ma at DataTalks.Club.
Originally posted on DataTalks.Club.
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