The Data Scientist Career Path: Everything You Need To Know
Nearly every type of organization from government, to retail, to healthcare needs data scientists. Data scientists organize and analyze raw data from various sources, enabling these enterprises to make informed decisions to ensure efficiency, boost profitability, and fuel growth.
Demand for data science professionals is expected to increase significantly in the next decade. The U.S. Bureau of Labor Statistics estimates 22 percent growth through 2030, which far exceeds the 7.7 percent projected increase for all occupations. That translates to a need for an average of 3,200 data scientists each year through 2030.
These positions tend to be clustered in Maryland and Virginia, as data scientists are in high demand with the federal government. Data scientists are also in high demand in New York, California, Texas, and Washington state.
The demand for data scientists coincides with a marked increase in the sheer amount of available data. According to Statista, just two zettabytes of data were created, copied, captured, or consumed in 2010, a number that is expected to increase to 79 zettabytes by the end of 2021, and then mushroom to 181 zettabytes by 2025.
Clearly, data scientists have a vital role one that will only continue to increase in importance and value over time.
Data Scientists Are Needed In Every Industry
With data playing an increasingly important part in the economy, data scientists are needed in every industry you can think of. From healthcare to sports, finance, and e-commerce , the applications are almost limitless. For keen lifelong learners, this makes data science a cornucopia of opportunities to practice and grow.
To learn more about specialized data science roles, start here: What does a data scientist in finance do?
Human Resources & Staffing
Maybe a little unexpected, but Human Resources & Staffing is in the top five industries for data scientists. What can you do there? First of all, you could analyze the job market and potential employees, which includes evaluating the candidatesâ skills and predicting their future performance and development. Also, you could predict employee turnover, plan their educational programs, measure their performance, anticipate the companyâs need for new employees, etc.
After covering the most common industries for a data scientist, itâs time to have a look at the data scientist career path options.
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Data Scientist Career Path
The path weâre going to show is concerned with the hierarchical level within the organization and less so with different types of jobs a data scientist can do. If youâre interested in the latter, itâs best that you take a look at 14 data science job titles. The data scientistâs career path usually starts as an individual contributor .
Theyâre not responsible for managing anyone, they are employees carrying out the operational work. On this path, you can be:
After the Associate level, youâve gained a respectable amount of experience. Now itâs time that you become leading teams and start the Management Track. There are three positions available here:
After becoming a Group Manager, the career path branches off into two leadership tracks. While both relate to leadership positions, they are different.
The first one is the Management Leadership Track, which focuses on managing a team. The three levels are:
- Senior Director
The other leadership track is the IC Leadership Track. This one relates to managing large projects as an IC. It also has four levels:
Weâll now go through all the positions and talk about what they are, how much experience and which skills they require, and what salary they offer. Before that, itâs essential that you understand what a data scientist does.
Hereâs an overview of the data science career path positions.
Do You Want A Career In Data Science
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Data Scientist Career Path And Progression In 2022
Q) How did you first get into data analytics?
Q) What do you suggest they do next after a data science course?
A) After a MOOC, put whatever data science skills that you’ve learned into practice by working on diverse real-world data science projects. If it is already a part of your work then that’s brilliant. It wasnt for me, so while doing the MOOC I learnt python for data science in parallel, and whatever SQL processes were happening in my line of work I would replicate that in Python to get hands-on experience. I was looking for an opportunity in this area and when that chance came I approached my manager and told him that this is an opportunity that I want to pursue and we can do this together. He was very supportive and thats how I transitioned to a data science career.
Q) Apart from the Andrew NG Coursera MOOC, are there other specific data science blogs, resources, and projects that you would refer back to?
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Q) When you talk about your background you mention being involved in end-to-end projects and it’s probably produced a pretty straightforward definition for you but then for the benefit of the readers, can you help us understand what is an end-to-end project. What are the different parts of that chain?
Salary And Job Outlook For Data Scientists
A data scientist earns an average of $102,599 per year. However, this amount may vary depending on your location, company and level of education. It may also depend on the amount of experience you have and what type of data scientist you are. For example, those in higher-level management roles can often earn a higher salary. Here are the salaries of some related job titles:
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What Is The Role Of Data Scientist
Data Science career is an interdisciplinary subject matter that utilises several statistical methods, scientific algorithms, information science, data assessment, machine learning theories, as well as other similar subjects. It involves the extraction of relevant information from a wide data group to generate a strategy for businesses to understand their information and analyse them. Individuals who opt for a career as a data scientist acquire an industry-specific understanding of analytical and programming skills. Students can pursue B.Sc, B.Stat, B. Tech to become data scientists. They write extremely complex algorithms to assemble big amounts of information. An organization plans its business strategy and plans, based on the performance provided by a Data Scientist.
Data Science Career Prospects
With more and more companies spanning across all the sectors adopting Data Science and Analysis, they are hiring Data Scientists by hordes.
In the Analytics ecosystem, 70% of the job postings are for data scientists with less than 5 years of work experience.
Though the demand for Data Science is intense, despite that companies are inefficient at meeting the shortfall in talent. It is either because the aspirants arent aware of the benefits of approaching a future-proof data Science career path or they dont know how to.
In the latter half of the post, you will know the reasons why you should make Data Science as a career path and how.
To bring clarity, here are the big four Data Science profiles that you can get into.
Data Science Training – Using R and Python
- Detailed Coverage
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Data Scientist Skill Set
- Experience and Fluency in many of these computer/coding programs: SAS, SPSS, MATLAB R, Python, Java, C/C++, Hadoop Platform, SQL/NoSQL Databases.
- Business Savviness: Data scientists need to understand the business sector they are working in and create solutions to complex problems that align with business logic/objectives.
- Communication skills: A data scientist can clearly and fluently translate their technical and analytical findings to a non-technical department. They must also be able to understand the needs of their non-technical departments in order to analyze the data correctly. A data scientist must empower the business to make decisions by presenting robust and verifiable information.
- Expert Technical skills in the following:
- Machine learning tools and techniques
- Data visualization and reporting techniques
- Unstructured data techniques
Are You A Data Analyst
Data analysts are generalists, which means they get to work in different teams and roles. They enjoy working on clearly defined, structured problems. They use data to extract and produce reports that are valuable to a business. Successful data analysts generally enjoy some level of complexity, but not as much as data scientists. Heres how you can tell if you are fit to become a data analyst:
- You are a generalist.
- You enjoy working cross functionally.
- You enjoy solving concrete problems.
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What Do Each Of The Data Science Roles Do
Ive written extensively about the differences between each of the data science and analytics job roles here, but Ill briefly recap in this table:
Data Science Job Roles Uncovered
We can see that its more typical for the hard-core engineering disciplines to use Python versus the more business analytical disciplines to use R/Python and Excel/PowerBI/Tableau.
If you are looking to move from Business Analysis to Data Science, we can see from the chart that you should add: R or Python to your skillset.
Get A Bachelors Degree
If you are leaving school and considering a move to higher education to train as a data scientist, there are plenty of bachelors degrees options you can consider. Computer science, for one, is widely available in most academic institutions and is a good foundation for your career. Another good degree would be mathematics specifically, statistics would be perfectly aligned to the role.
Many data scientists choose physics, because of how it draws upon several different relevant areas such as mathematics, problem solving, analysis and programming. Economics or business degrees also have relevance to the results side of data science.
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The Field Of Data Science Is Constantly Evolving
Data scientists dont have a single defined role. Since the position varies from business to business there are always exciting new problems to solve. Whether this means building brand new algorithms from scratch, creating data architectures, or just working in an area thats completely novel to you, youll certainly never get bored.
Look For Data Science Internships
Now is an excellent time for them try getting data science internships. Many people view internships as paths to their future jobs, and indeed, they can be if people impress employers enough.
However, anyone who’s working toward a data science career must realize that most internships are valuable even when participants don’t directly get job offers from them.Depending on the length of the internship and the needs a company has, people might get to build data visualizations and create reports, among other tasks. Doing these things might feel overwhelming at times, but the experience gets individuals ready for their futures.
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Job Roles In Data Science
So, what did you learn?
Roles in a data science team arent exclusively technical. While programming and statistics are needed for the core stages of the process, contextual skills are essential for the planning and reporting stages.
Indeed, the data scientist role is a crossover between many different disciplines. Data scientists are multi-talented professionals, who can see the big picture, while also being programmers, statisticians, and good data storytellers.
However, in a data science team, there are people with diverse roles, and they all contribute in different ways. If the data scientist career path is the ultimate goal, there are various ways you can get there.
Taking the data scientist career path: Find out what role fits you best
Data analysts, for example, are more involved in day-to-day tasks, focused on gathering data, structuring databases, creating and running models, trend analysis, making recommendations, and storytelling.
Business intelligence analysts, on the other hand, should be able to see the big picture, situate the business unit in the market, having considered its trends. Normally, BI analysts have expertise in business, management, economics or a similar field. However, they must also speak data. BI analysts work with tons of information, and spend most of their time analyzing and visualizing the data they have gathered from multiple sources.
The Data Science Process
The Main Steps of the Data Science Process Any data science project is a process understanding this fact is important if you want to find your way through the data science maze.
First, come the project objectives.Have you identified a business issue or an attractive market opportunity? You need to be clear about what you are trying to accomplish in order to help your company gain a competitive edge.
Then, you need to figure out where to collect data from, to plan resources, and to coordinate people to get the job done.
Part three is data preparation. Data needs to be carefully cleaned and explored. Associations will start to emerge the sample and the variables will be refined. Then, comes the creation of models, their validation, evaluation, and potential refinement.
Finally, you need to communicate the teams experiences during the data science process. Data needs to take a compelling form and structure. At the final reporting stage, visualizations are necessary to tell the full story.
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Data Scientist Job Description
While data science projects and tasks may vary depending on the enterprise, there are primary job functions that tend to be common among all data science positions such as:
- Collecting massive amounts of data and converting it to an analysis-friendly
- Problem-solving business-related challenges while using data-driven techniques and tools.
- Using a variety of programming languages, as well as programs, for data collection and analysis.
- Having a wealth of knowledge with analytical techniques and tools.
- Communicating findings and offering advice through effective data visualizations and comprehensive reports.
- Identifying patterns and trends in data providing a plan to implement improvements.
- Predictive analytics anticipate future demands, events, etc.
- Contribute to data mining architectures, modeling standards, reporting and data analysis methodologies.
- Invent new algorithms to solve problems and build analytical tools.
- Recommend cost-effective changes to existing procedures and strategies.
Build A Strong Foundation In Statistics And Mathematics
Everything that you do as a data scientist will involve some amount of statistics and other math. So make sure that you have a good grasp of probability, linear algebra, calculus, and statistics. These skills will also help if you want to work in more advanced areas like artificial intelligence or neural networks.
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The Final Pep Talk Permalink
It might look like a lot of work but this is a great field to get into. Career progression through the many different areas of data science is good and well compensated. Getting a foot in the door is the hard part but its well worth it and there is no better time to start than today.
Overall, Id say just work hard on picking up just enough new skills to be able to demonstrate you can use them. Dont wait until you consider yourself an expert to get applying for jobs.
The real learning, as in any field, is done on the job as you are adapting to real world situations. And, in the immortal words of The Carpenters, on that front weve only just begun
What Job Titles Can You Earn In Data Analytics
If youre pursuing a data analyst career path, its important to know that job titles vary across industries. You could find yourself analyzing animal grazing trends, healthcare insurance data sets, or measuring global fashion trends. Theres a wide range of data analytics jobs .
Here are the most common job titles that youll be qualified for once you finish our Data Analytics Thinkful course:
- Business Intelligence Analyst
Some of these titles, such as Data Analyst, Business Analyst, and Operations Analyst, are pretty standard. Others, like Financial Analyst, Data Journalist, and Marketing Analyst, are specific to a particular industry.
Even though job titles vary, a core component of the role remains: translating data into valuable business insights.
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A Complete Guide To Data Scientist Career Path
In this guide, weâll talk about where the data scientist career path could lead and what are the industries ideal for building this path.
Donât think of this guide as a rigid rule book proscribing how your data scientist career path should look. How your career path will look like depends on you: your interests, your educational background, your skills as a data scientist, the industry youâre in, the company you work in, and, of course, a little bit of luck in achieving whatever you want in your professional life.
We want to give you a general overview of what the data scientist career path often looks like. It can unveil some possibilities you didnât know existed and provide you with direction and new insights into the possibilities as a data scientist.
You can build your data career only where they want you. Thatâs why itâs a good idea to talk about the industries that give you the most possibilities as a data scientist.