Career in Data Science Ultimate Guide Part 2

Who is a data engineer? Interested in big data career opportunities? Data engineers are the ones to take things further up the data science pipeline. They use the data architects’ work as a steppingstone and then preprocesses the available data. They are the people who ensure the data is clean and organized and ready for the analysts to take over. Of course, once you hit the 4-6 years’ experience mark, you can expect your compensation to rise to $117,000 (plus, you’ll be eligible for additional bonuses in the region of $10,000). Looking for a data engineer job in the UK? According to PayScale research, even if you have less than 1-year experience, you can get average pay of £30,000 (this includes bonuses and overtime pay). Naturally, with experience comes a higher salary. A data engineer with 1-4 years of experience earns an average total compensation of £41,000. And it only gets better! Once you have 5-9 years of experience, your annual pay can hit £54,000. Big data, big rewards! What does a data engineer do? Data engineers are the Jedi Knights of data science. They rely on a blend of analysis, wisdom, experience, and judgment to make key decisions for a company’s success. A data engineer is a self-starter who is inspired to complete more than your usual number of tasks. But what does that mean? Let’s briefly go over some of the things a data engineer does. Data engineers implement complex, large scale big data projects with a focus on collecting, managing, analyzing and visualizing large datasets. All that massive amount of overwhelming raw data? Well, they are the ones turning it into insights using various toolsets, techniques, and cloud-based platforms. Data engineers are responsible for building and maintaining ETL pipelines which make crucial data accessible for the entire company. They also lend a helping hand to BI analysts by designing and supporting BI platforms. Who makes sure all big data applications are available (and performing properly)? Again, data engineers. And, to top it all, they are great team-players. A data engineer knows how to actively collaborate with data scientists and executives to build solutions and platforms that meet, or even exceed a company’s business needs. How much does a data engineer earn? How much do data engineers make? We asked Glassdoor and PayScale to give you the answer. In the U.S., the average pay for a data engineer who’s just getting started in his career is $103,000. “Data engineers test large-scale processing systems, develop processes for data modeling, and recommend ways to increase data efficiency.” What’s the data engineer career path? If you’re excited about big data career opportunities, this career path could be the one for you. A developer’s or software engineering experience can be the gateway to a data engineer’s job. But what if you’re new to the field and you’re not sure you’ve got what it takes to get there? Don’t worry. Here are the education and qualifications that will open the door to a data engineer career. What education do you need? A degree in software engineering, computer science, or information technology will give you a flying start. However, if that’s not the case, you can still make the cut. But you need skills in computer programming and software design, statistical modeling and regression analysis, Python, SQL, and Machine learning. Now, before you rush into writing off your dream job, you should know that acquiring these skills is absolutely possible, even for complete beginners. All you have to do is enroll in a qualification program or commit to a comprehensive data science training. Once you follow the curriculum and gain experience with real-world exercises and projects, you will have the skills, confidence, and the portfolio to apply for a data engineer position. Data Engineer: top priority Building and maintaining ETL pipelines which make crucial data accessible for the entire company Median salary: USA Entry level: $103,000 4-6 years of experience: $117,000 Median salary: UK Entry level: £30,000 1-4 years of experience: £41,000 5-9 years of experience: £54,000 How to Become a data engineer? The data engineer path is one of the best choices you can make if you’re driven to succeed in the data science field. But how to become a data engineer? This overview will help you learn about the responsibilities of a data engineer, the education requirements, the skills you need to acquire (or just sharpen, if you have previous experience) and the expected salary of a data engineer in the U.S. and the UK. What data engineer qualifications you should acquire? “A data engineer knows how to actively collaborate with data scientists and executives to build solutions and platforms that meet, or even exceed a company’s business needs.” A data engineer job comes with certain responsibilities. Here’s a list of competences and skills you need to become a data engineer who knows their stuff. Technical skills: What about machine learning engineers? • Well, if you want to tread the machine learning career path, you’ll certainly need solid knowledge of machine learning theory and understanding of deep learning. However, both aren’t exactly entry-level positions. So, if you want to pursue a data mining career, or land a job machine learning, you can start off with learning the fundamentals of software engineering and gradually build your way up through careers in big data. • • • • Knowledge of data modeling for both data warehousing and Big Data; Experience in the Big Data space (Hadoop Stack like M/R, HDFS, Pig, Hive, etc.); Ability to write, analyze, and debug SQL queries; Experience with at least one scripting language (e.g. Python); Mathematics; Practical skills: • • • • • • • • • Data visualization skills (make Tableau your best friend); Analytical skills; Ability to make sound decisions, even in the absence of complete information; Ability to follow through on commitments and make sure others do the same; Personal responsibility for decisions, actions, and failures; Establishment of clear processes for monitoring work and measuring results; Design of feedback loops into work; Strong attention to detail; Ability to think critically and conceptually. Soft skills: • Very strong communication skills in a variety of settings. Well, now you’re in-the-know about what it’s like to be a data engineer and how to get there. Of course, better preparation means higher chances of success. So, if you want to gain more useful insight into your future career options, you can find our resume and portfolio tips in the later sections of the data science career guide. We believe they will give you the information you need to make the first steps into a rewarding data science career. Author’s note: Some of you may wonder what the differences between a data engineer, a data mining engineer, and a machine learning engineer are. Although they’re all in the realm of data science engineering, these roles all have specific responsibilities that distinguish them from the rest. Data engineers are typically more focused on back-end solutions and data sources integration. Data mining engineers, on the other hand, are also involved in developing models to extract actionable insights from a data set A data engineer must have a highly technical mindset, be in the know about current and emerging technologies, and thrive as a collaborative team player. Learn Data Science from experts. Preview the training Starting a Career in Data Science: Ultimate Guide Who is a data architect? If you’ve seen the 1999 cult movie The Matrix, you probably recognize the Architect as the creator of the utopian world for human minds to inhabit. Much like their blockbuster counterpart, data architects create the database from scratch. They design the way data will be retrieved, processed, and consumed. What does a data architect do? Do you have an analytical and creative mind? Are you a problem solver, driven by curiosity and excitement to build complex database systems? Then you might be perfect for a career as a data architect. Data architects are technical experts who adapt dataflow management and data storage strategy to a wide range of businesses and solutions. They’re in charge of continually improving the way data is collected and stored. Furthermore, data architects control the access to data (all you corporate spies out there – now you know who to look for). Data architects are also responsible for design patterns, data modeling, service-oriented integration, and business intelligence domains. They often partner with fellow data scientists and IT guys to reach the company’s data strategy goals. A data architect constantly seeks out innovations to provide improved data quality and reporting, eliminate redundancies, and provide better data collection sources, methods, and tools. How much does a data architect earn? According to Glassdoor’s salaries report, the average annual pay for entry-level data architects in the U.S. is approximately $104,000, while professionals with 4-6 years of experience make more than $125,000 (and can easily get bonuses in the region of $10,000 a year). What about the UK? As a data architect early in your career, you can earn an average total compensation of £45,000. However, a mid-career Data Architect with 5-9 years of experience can get as much as £55,000 (including bonus and overtime pay). So, you’re in for solid growth! What’s the data architect career path? a succinct overview of everything you need to make the first bold steps on the data architect career track. You will learn about the responsibilities of the data architect job, the education requirements, the necessary skills, and the salary you can expect at various stages of your data architect career. If you want to go into the data architect realm, first you should know what education and qualifications will help you get there. What education do you need to become a data architect? How to Become a Data Architect? If you want to become a data architect, a Bachelor’s degree in If you’ve set your data science career goals in that direction, we have two things to say. First off– great choice! The data architect role is on the rise with its increasing importance for enterprises and their business success. And, second – this article is the right place to start, as it gives you Data architects design powerful database models and implement solutions to store and retrieve company data in an effective way. information systems, computer science, engineering, or related field will definitely give you a competitive edge. But don’t be quick to despair if you’ve studied something entirely different. It turns out, these are by no means prerequisites to get the job. According to LinkedIn job offers, employers are much more interested in relevant work experience than in shiny degrees. In their own words, “Bachelor`s degree in Computer Science is desirable, but will waive this requirement entirely if you have significant and applicable real-world experience.” So, how can you get there? Well, first of all, master Python or R, SQL, Tableau, and gain experience in data modeling and machine learning. The good news is, you can build up your knowledge from scratch with some additional qualification courses or by enrolling in an online certificate data science training. • • • What data architect qualifications you should acquire? • Here are the qualifications and skills you need to become a data architect that truly stands out. Technical skills: • • • • • • • Strong data modeling skills; Database architecture; Data warehousing experience; Data governance know-how; Experience with Python or R; Experience with SQL; Good command of data visualization tools (e.g. Tableau); Knowledge of data retention concepts and practices; Automation of data flows and integrations. Understanding of XML and Java. Practical skills: • • • • • • • Ability to lead multiple, highly complex efforts simultaneously; Establishment and implementation of standards and guidelines; Ability to determine key architecture solutions that meet performance, scalability, reliability, and security needs; Research and evaluation of emerging data management technologies for applicability and adoption; Ability to present technical concepts to upper-level management and officers; Ability to work independently; Strong problem - solving skills; Strong attention to detail. Soft skills: • • Strong communication skills; Provision of technical leadership and guidance to the project team as appropriate. Now you know what it takes to become a data architect. However, some useful career tips are never too much. So, if you want to take your job preparation to the next level, just scroll down the data science career guide. We believe the information on employment opportunities we’ve provided will help you with the first steps into a successful data science career. Data architects are responsible for creating secure frameworks for databases that can serve the needs of hundreds or thousands of people. Starting a Career in Data Science: Ultimate Guide What’s the Data Science job outlook? Data science is an ever-evolving field, as technologies change quickly. Therefore, you should be prepared that any data science job will require continuous learning and development of competences and skills What’s the Demand for Data Science Professionals? We understand that before you invest all the time and effort into a new data science career, you would want to know if the data scientist job outlook (or any other data science and analytics job outlook) is positive enough to guarantee you secure occupation and future growth. So, to put your Shakespearean doubts to rest, we decided to dig into the numbers. We used data and insights from the extensive joint research performed by IBM, Burning Glass Technologies, and Business-Higher Education Forum (BHEF) - The Quant Crunch: How The Demand For Data Science Skills Is Disrupting The Job Market. Here’s everything you need to know about the data science career outlook in a nutshell. What’s the future of data science jobs? According to the report, by 2020 the number of data science and analytics job listings is estimated to grow by nearly 364,000 listings to about 2,720,000 openings. In fact, there is a serious concern that the data science skill gap will continue widening and the supply of data scientist and data analytics career talent won’t be able to catch up to the industries` demand. Therefore, now is literally the best time to become a data science specialist. What’s the future of data science jobs? Indeed, the future of data scientists and data engineers is brighter than ever. According to the report, they are the jobs most likely to suffer from supply shortages, along with BI analysts and other data science, analytics, and system developer positions. Moreover, the data indicates this tendency will continue to be strong for the years to come. What’s the job growth in data science? Once again, we can say that the only way is up when it comes to data science and analytics jobs. As the study shows, the skills projected to grow the fastest include advanced analytical compe-tencies, such as data science, machine learning, tableau, big data, data visualization. Of course, skills in SQL, data analysis, financial analysis, data management, mathematics, data warehousing, SQL Server, database administration, business intelligence, and ETL firmly remain among the most demanded analytics skills. All things considered, the law of supply and demand indicates the data science job outlook is more than optimistic (and so should be you in pursuing your data science career goals). Starting a Career in Data Science: Ultimate Guide What data science qualifications will get you on the path to success? To help you sort things out, we’ve featured the top 5 in-demand types of data science jobs – data analyst, BI analyst, data engineer, data architect, and, of course, data scientist. What are data scientists made of? According to research, academics may gradually give way to real-world, industry-relevant experience. “Fortunately, there is more than one way to learn data science.” Education Let’s call a spade a spade - the path to data scientist success involves a good amount of studying. And, although data science pros come from different backgrounds, there are certain academic areas that will make your entry into data science easier. Those include computer studies, economics, finance, business studies, statistics, and mathematics. Nevertheless, our research shows these are not prerequisites to land a data science job. With that said, you can make up for lost college time by instead gaining the skills required by data science employers. • Good command of programming languages, such as SQL, • • Python, and R; Ability to work with visualization tools (e.g. Tableau); Machine learning and deep learning fundamentals. But how do you master all of these if you’re starting from scratch? Fortunately, there is more than one way to learn data science. For instance, you can enroll in a qualification program or study with private tutors. However, if going back to school is not an option, an online certificate program can be the best start of your professional journey. Certificates According to The Data Scientist Profile 2019 (research based on 1,001 LinkedIn profiles), 43% of data scientists boast at least 1 online course in their resume, with 3 certificates being the average. Furthermore, those include even graduates of prestigious universities! And that is quite an accurate indicator of the importance of self-preparation and a proactive approach towards a data science career. Skills Now, whether you choose the role of data scientist, data analyst, or data mining engineer, there’s a basic skill set you must acquire in order to be competitive in the data science job market: • Excel proficiency; • Statistics knowledge; • Maths understanding (specifically calculus and algebra – the subfields that are heavily employed in data science); In addition to education and technical skills, a data scientist needs a solid business acumen, in order to identify and solve the business problem at hand. Starting a Career in Data Science: Ultimate Guide How to get a job in data science? If you’ve come this far down the article, it’s safe to say you’re serious about data science as a career. So, without further ado, let’s briefly go through the job application steps that will get you where you want to be. How to write a data science resume? They say you only have 3 seconds to make a great first impression. And you can be sure that applies to your job resume, especially in the super-competitive field of data science. A well-thought-out and carefully designed resume isn’t just a pass to a job interview. As a matter of fact, it can pre-determine what questions you will be asked during the interview, and, ultimately, whether you’ll get the welcome-to-the-company handshake. So, if you want to create an outstanding resume that will present you in the best light, you can check out our pro tips and data science resume recommendations. We believe those will prove useful in your job application process. How to build a data science project portfolio? While a resume matters, your data science career prospects heavily depend on your project portfolio. Why? Because it’s the real-life proof of what you can do. And that’s what potential employers look for when scouting for data science talent. So, how can you build a project portfolio if you lack previous experience in the field? Here’s what you can do. School projects. Those are a good place to start. However, school projects are the fruit of teamwork and solutions are provided in the end. So, usually, you don’t get to choose the project and you can’t truly showcase your individual talent. It is good practice, to begin with, mostly because there’s always someone you can approach with a question. But overall, it is far better to include personal projects in your portfolio; Personal projects. This is where you show what your data science dreams are made of. Personal projects truly set you apart from the rest and demonstrate that you’re serious about getting into data science. Just find a problem you’d like to solve, collect the necessary data, and apply your data science skills to find the solution. Course projects. If you’re enrolled in an online data science certificate course, you’ll have the chance to build a portfolio by completing the larger hands-on projects at the end of the course sections. Volunteer work. Although work pro-bono isn’t for everyone, some free-of-charge projects for a company or an NGO can do wonders for your data science portfolio. Such experience will add extra points to your resume and will boost your data science skills. How to prepare for a data science interview? Data science interviews comprise tons of topics! There are coding and machine learning questions, statistics and mathematics tasks, case study scenarios… Not to mention the background check and behavioral questions that you can’t possibly escape. As always, preparation is key. So, if you want to know what to expect from a data science job interview, you can read more on the subject in our resource Data Science Interview Questions. We believe it will help you maximize your chances to land the job you desire. How to get a data science internship? Internships are a great steppingstone that will boost your chances for a successful career in the long run. As an intern, you will not only hone your practical data science skills but will also be able to work on a variety of projects. And, as we mentioned above, the latter is a deal-maker when you apply for a data science job. Is data scientist a good job? An internship at a high-profile company will give you the answer. So, if you want to explore the data science opportunities you’ll get as an intern, learn more in our article How to Get a Data Science Internship. Starting a Career in Data Science: Ultimate Guide Which data science industries you should consider? Where do data scientists work? As it turns out, everywhere! The data boom creates demand for data scientist roles across an ever-growing variety of industries. From Finance and insurance, through Agriculture and Forestry, to Construction – every organization needs solid data science skills to exploit their data and gain a competitive edge. So, what type of data scientist would you like to be? Here are 3 top industries consider. Finance and Insurance Healthcare Finance and Insurance is, in fact, the industry with the highest demand for unique data science and analytics talent. But what distinguishes a financial data scientist? Their experience and financial domain knowledge. So, if you want to earn the title financial data scientist, you should definitely be in-the-know about financial markets, portfolio management, risk analysis, profitability, delinquency, and closure, among other concepts. Now, you have to keep in mind that this is not a job you land without a few years of experience in the field. However, if you start as a financial analyst, study hard, and keep your eyes on the prize, you can certainly work your way up to the coveted financial data scientist position. According to a study published in the Journal of the American Medical Informatics Association (JAMIA), the healthcare industry’s demand for skilled data scientists is increasing, along with the growing quantity and importance of big data in health care. Big data is the key to discovering innovative solutions and improving the quality of care. At the same time, it can lead to greater efficiency, thus reducing healthcare expenditures. That said, it comes as no surprise that the primary data science skills expected from a healthcare data scientist are: “statistics, R, machine learning, storytelling, and Python.” (Note that this summary is based on the job posting research sample.) Of course, gaining some prior experience as a big data scientist in any other industry will certainly open the door to a data science career in healthcare. Manufacturing Have you heard about Oden Technologies? This startup company has developed a revolutionary Industrial Internet of Things (IIoT) platform that helps manufacturers optimize their processes and see how the changes they make affect their production in real-time. And that’s just one example of how data science is becoming an indispensable factor for the success of present-day manufacturers. AI-powered industrial robots, product development, and computer vision monitoring systems are bound to become more common. Which makes perfect sense: they increase quality and thus bring more benefits to the companies. Therefore, if you’re looking for great career opportunities in the long run, manufacturing is definitely an industry you should pay attention to. And one last thing! If you want to become a healthcare data scientist, don’t forget honing your data mining skills. This is a specific prerequisite for data science healthcare jobs, as much of the unstructured clinical data is in note format. Starting a Career in Data Science: Ultimate Guide Types of organizations to consider? When browsing data scientist job openings, it’s important to be in-the-know about the types of data science organizations, what they offer, and if they ultimately suit your career needs. After all, you don’t want to spend hours researching data center careers, when there are plenty other great options out there. Data science consulting companies Data science consultancies offer outstanding remuneration packages and development opportunities. In fact, if you’re on the career path for data scientist, you shouldn’t sell yourself short. On the contrary – start researching the top companies in the field. This will tell you if a big consultancy is the right environment for you. Author’s note: If you want to learn more about the big players, you can read our article about the Top 15 Data Science Consulting Companies Hiring Now. Data science startups Startups give you the chance to face challenges independently and wear multiple hats. Working in a startup can help you grow your skillset tremendously in a relatively short period of time. This rings especially true if you compare it to working in large corporations where it may take years of repetitive tasks before you climb up the data science ladder. Government data science jobs Although this may not be the most popular career choice, an increasing number of countries realize the importance of data driven innovation and the socio-economic benefits that it can bring. For example, in their project the U.S. Climate Resilience Toolkit, the U.S. Federal Government has developed a catalog of more than 200 digital tools to boost climate resiliency. And that certainly indicates a positive outlook for data science careers, most prominently data analysis careers, careers in data analytics, and careers in big data analytics. So, how to get a data science job for the government? As a first step, you can visit the following government jobs directories: USA JOBS – if you’re based in the U.S. and you want to explore a wide scope of data science hiring paths; The UK – The U.K. government is also recruiting top-notch data science talent (make sure you check for job openings in the Office for National Statistics, the Office of Gas and Electricity Markets, and the Cabinet Office); GovtJobsPortal – Although finding a government data science job in India is challenging, there are plenty of IT and computer science positions that can be a great entrance to future data science job openings. Now that you’re aware of the data science employment options, types of data scientists, roles, career paths, and job outlook, you’re equipped with all the knowledge you need to take the first step on your data science career journey. However, there’s one more thing to keep in mind – anything worthwhile takes time and hard work. So, keep learning and improving your skills, don’t stay still, master different technologies, and opportunities for a career in data science will show up.