- \n
- Identifying business problems<\/li>\n\n\n\n
- Writing hypothesis<\/li>\n\n\n\n
- Performing data mining and data wrangling<\/li>\n\n\n\n
- Cleaning and classifying data<\/li>\n\n\n\n
- Conducting statistical analysis<\/li>\n\n\n\n
- Building algorithms and testing them<\/li>\n\n\n\n
- Writing code and deploying in production<\/li>\n\n\n\n
- Making presentations and visualisations<\/li>\n\n\n\n
- Collaborating with business teams etc. <\/li>\n<\/ul>\n\n\n\n
This might appear hard because it often is. As a data scientist<\/a>, you\u2019re responsible for creating the data-driven intelligence and predictions that are needed to make business decisions. This means that your output has to be both error-free and rooted in a real-world business context. For beginners, this can be challenging. <\/p>\n\n\n\n
A good data scientist will take the challenge in their stride and develop the skills and aptitude needed for it.<\/em><\/p>\n\n\n\n
#2 Is it hard to gain data science skills?<\/h4>\n\n\n\n
People ask this question because data science is not a singular skill<\/a>. As far as technical skills go, data scientist jobs combine mathematics, statistics, programming and domain expertise. Being able to meaningfully bring together the relevant skills across these fields can be a hard task for young professionals. However, you must understand that these are all related skills, building on top of one another, making them a coherent whole. <\/p>\n\n\n\n
![\"is](\"https:\/\/www.springboard.com\/blog\/wp-content\/uploads\/2021\/10\/is-data-science-hard-data-science-skills-set.png\")
<\/figure>\n\n\n\nFor instance, let\u2019s say you\u2019re a data scientist at a bank. You need not know everything there is to know about banking. But you need to understand financial concepts, within the context of your project. If you\u2019re using DS for fraud detection in credit card purchases, you need to have in-depth knowledge of the credit landscape and fraud patterns.<\/p>\n\n\n\n
Data science<\/a> skills are a lot more than that. Some other skills you need are:<\/p>\n\n\n\n
- \n
- Critical thinking and problem-solving:<\/strong> Being able to critically analyse the situation at hand and device innovative ways to solve business problems.<\/li>\n\n\n\n
- Business acumen:<\/strong> Understanding the real-world context in which the business works to make data-driven decisions to business problems.<\/li>\n\n\n\n
- Communication:<\/strong> Making presentations, building consensus among stakeholders, persuading team members, etc.<\/li>\n\n\n\n
- Self-learning:<\/strong> Being able to adapt to changing circumstances and keeping oneself updated on technological developments.<\/li>\n<\/ul>\n\n\n\n
Once you have a foundational grasp of each of these skills, you will develop an intuition for data science, which you can hone with practice and experience.<\/em><\/p>\n\n\n
Get To Know Other\tData Science Students<\/h4>
![\"Lou](\"https:\/\/res.cloudinary.com\/springboard-images\/image\/upload\/v1629225421\/Student%20Success\/Lou_Zhang_icon.png\")
<\/a>Lou Zhang<\/p>
Data Scientist at MachineMetrics<\/p><\/div>
<\/div>Read Story<\/a><\/p><\/div><\/div>
![\"Hastings](\"https:\/\/res.cloudinary.com\/springboard-images\/image\/upload\/v1648517255\/Student%20Success\/Hastings_Reeves_3.png\")
<\/a>Hastings Reeves<\/p>
Business Intelligence Analyst at Velocity Global<\/p><\/div>
Read Story<\/a><\/p><\/div><\/div>
![\"Corey](\"https:\/\/res.cloudinary.com\/springboard-images\/image\/upload\/v1680712086\/Corey_Wade_LinkedIn.jpg\")
<\/a>Corey Wade<\/p>
Founder And Director at Berkeley Coding Academy<\/p><\/div>
Read Story<\/a><\/p><\/div><\/div><\/div><\/div>\n\n\n\n
#3 Is it necessary to have experience to start a data science career?<\/h4>\n\n\n\n
While experience is non-negotiable, it doesn\u2019t have to be \u2018job experience\u2019. Hands-on practice with concepts of DS is good enough. You can gain this kind of experience in myriad ways:<\/p>\n\n\n\n
- \n
- Data science projects<\/a>, which you can use to showcase your skills\/interests.<\/li>\n\n\n\n
- Competitions and hackathons, which offer opportunities to solve real-world problems with data<\/a>.<\/li>\n\n\n\n
- Data science internships<\/a>, which give you a professional environment to learn the practice of this field.<\/li>\n\n\n\n
- Freelance projects<\/a>, which allow you to take on small assignments and work independently.<\/li>\n\n\n\n
- Online programs that include portfolio projects solving real-world problems.<\/li>\n<\/ul>\n\n\n\n
The only way to show that you\u2019re a data scientist is through practical application. Gaining this can be a little hard, but it is crucial.<\/em><\/p>\n\n\n\n
#4 Is getting a data science job really hard?<\/h4>\n\n\n\n
Data science is a competitive field \u2014 recruiters are looking for the best candidates, making the interview process rigorous. The qualifications needed for these positions are also stringent. It is very unlikely that you go to a walk-in interview and walk away with an offer. This doesn\u2019t mean that getting a job is hard.<\/p>\n\n\n\n
With the right skills and data science portfolio<\/a>, you will be able to land your dream job. Here are a few pointers to keep in mind while applying for data science jobs<\/a>:<\/p>\n\n\n\n
- \n
- Make a CV<\/a> that presents your strengths confidently<\/li>\n\n\n\n
- Customise your CV to suit the job you\u2019re applying for<\/li>\n\n\n\n
- Present a portfolio of projects you\u2019ve worked on, based on the job you\u2019re applying to, present the relevant ones prominently<\/li>\n\n\n\n
- Prepare for commonly asked data science interview questions<\/a> and practice your answers<\/li>\n\n\n\n
- Follow up diligently and seek feedback<\/li>\n<\/ul>\n\n\n\n
\nhttps:\/\/www.youtube.com\/watch?v=7YuTmLvs1Dc&t=1s\n<\/div><\/figure>\n\n\n\nRemember that sending the same templated CV or portfolio to all job vacancies is unlikely to get you interview calls. Customising your resume and presenting relevant portfolio is crucial.<\/em><\/p>\n\n\n\n
#5 Is it hard to grow as a data scientist?<\/h4>\n\n\n\n
As an emerging field, career growth as a data scientist is still not set in stone. Without a clear growth path to follow, it might appear like an adventurous career path. However, this field opens the door to myriad opportunities. <\/p>\n\n\n\n
- \n
- You can lead data science teams, training and mentoring young professionals in your team. If you have leadership aspirations and a knack\/patience for teaching the next generation of data scientists, this path is perfect for you. <\/li>\n\n\n\n
- Become a super-specialist in specific domains or use cases. For instance, being a data scientist in medicine might need you to gain in-depth knowledge of legalities and compliance. But once you do, you can consult with multiple organisations at high levels. If you already have domain experience, this is a great path for you. Here are some tips for making a seamless career transition.<\/li>\n\n\n\n
- Build products leveraging data, like analytics engines, predictive tools, etc. If you have an entrepreneurial spirit in you, the world of data science is your oyster. You can build, scale and grow your own startup in the field.<\/li>\n\n\n\n
- Move into machine learning\/artificial intelligence and so on. After a few years in data science, you can take on more complex projects in predictive analytics, internet of things or edge computing too.<\/li>\n<\/ul>\n\n\n\n
Figuring out how to grow within the data science field can seem difficult. Once you learn the possibilities and decide what you want, you can follow a clear path to success.<\/em><\/p>\n\n\n\n
\u2018Hard\u2019 is a subjective concept. What might appear hard for someone without the necessary skills\/qualifications might be easy for someone with many years of experience. However, in data science, \u2018hard\u2019 takes a different meaning. As an emerging field, data science can be challenging and ambiguous. To navigate this, you need a mentor, who can answer your questions, identify gaps, offer career advice, and guide you in the right direction. It is for this reason that Springboard\u2019s Data Science Career Track<\/a> includes 1:1 mentorship, project-driven approach and career coaching. At Springboard, you will not only learn data science as a subject but also become a data scientist by the end. Check out the program now!<\/p>\n\n\n\n
Companies are no longer just collecting data. They\u2019re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists \u2013 the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.<\/p>\n\n\n\n
In this article, we\u2019ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you\u2019re more drawn out to the creative side or interested in the strategy planning part of data architecture, there\u2019s a niche for you. <\/p>\n\n\n\n
Is Data Science A Good Career?<\/h2>\n\n\n\n
Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It\u2019s an interdisciplinary field that keeps the work varied and interesting.<\/p>\n\n\n\n
10 Data Science Careers To Consider<\/h2>\n\n\n\n
Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.<\/p>\n\n\n\n
<\/div><\/div><\/div><\/div>\n\n\n\n<\/p>\n\n\n\n
Data Scientist<\/h3>\n\n\n\n
Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research \u2013 all depending on the field they operate in.<\/p>\n\n\n\n
They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. <\/p>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
A data scientist\u2019s career starts with a solid mathematical foundation, whether it\u2019s interpreting the results of an A\/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. <\/p>\n\n\n\n
Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses<\/a> and certifications that demonstrate their expertise and willingness to learn.<\/p>\n\n\n\n
Average Salary<\/h4>\n\n\n\n
The average salary of a data scientist in the US is $156,363<\/a> per year.<\/p>\n\n\n\n
Data Analyst<\/h3>\n\n\n\n
A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.<\/p>\n\n\n\n
A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.<\/p>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps<\/a> to acquire these skills. <\/p>\n\n\n\n
For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.<\/p>\n\n\n\n
Average Salary<\/h4>\n\n\n\n
The average base salary of a data analyst is $76,892<\/a> per year.<\/p>\n\n\n\n
Business Analyst<\/h3>\n\n\n\n
Business analysts often have an essential role in an organization, driving change and improvement. That\u2019s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. <\/p>\n\n\n\n
A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.<\/p>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master\u2019s degree, especially in more complex industries or corporate environments.<\/p>\n\n\n\n
Employers also value a business analyst\u2019s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.<\/p>\n\n\n\n
Average Salary<\/h4>\n\n\n\n
A business analyst can earn an average of $84,435<\/a> per year.<\/p>\n\n\n\n
Database Administrator<\/h3>\n\n\n\n
The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. <\/p>\n\n\n\n
A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.<\/p>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it\u2019s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. <\/p>\n\n\n\n
Average Salary<\/h4>\n\n\n\n
Database administrators earn an average salary of $77,391<\/a> annually.<\/p>\n\n\n\n
Data Engineer<\/h3>\n\n\n\n
Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).<\/p>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. <\/p>\n\n\n\n
Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.<\/p>\n\n\n\n
Average Salary<\/h4>\n\n\n\n
Data engineers earn a whooping average salary of $125,180<\/a> per year.<\/p>\n\n\n\n
Database Architect<\/h3>\n\n\n\n
A database architect\u2019s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. <\/p>\n\n\n\n
Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.<\/p>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
If you\u2019re aiming to excel as a database architect but don\u2019t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don\u2019t forget programming languages – SQL, Python, or Java. <\/p>\n\n\n\n
If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard<\/a>.<\/p>\n\n\n\n
Average Salary<\/h4>\n\n\n\n
Data architecture is a very lucrative career. A database architect can earn an average of $165,383<\/a> per year.<\/p>\n\n\n\n
Machine Learning Engineer<\/h3>\n\n\n\n
A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. <\/p>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.<\/p>\n\n\n\n
Average Salary<\/h4>\n\n\n\n
Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K<\/a> per year, one of the highest-paying AI careers<\/a>.<\/p>\n\n\n\n
Quantitative Analyst<\/h3>\n\n\n\n
Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. <\/p>\n\n\n\n
They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.<\/p>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. <\/p>\n\n\n\n
Average Salary<\/h4>\n\n\n\n
A quantitative analyst earns an average of $173,307<\/a> per year.<\/p>\n\n\n\n
Data Mining Specialist<\/h3>\n\n\n\n
A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.<\/p>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. <\/p>\n\n\n\n
Average Salary<\/h4>\n\n\n\n
Data mining specialists earn $109,023<\/a> per year.<\/p>\n\n\n\n
Data Visualisation Engineer<\/h3>\n\n\n\n
Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data\u2019s context. <\/p>\n\n\n\n
General Requirements<\/h4>\n\n\n\n
Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.<\/p>\n\n\n\n
Average Salary<\/h4>\n\n\n\n
The average annual pay of a data visualization engineer is $103,031<\/a>.<\/p>\n\n\n\n
Resources To Find Data Science Jobs<\/h2>\n\n\n\n
The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.<\/p>\n\n\n\n
Job Boards<\/h3>\n\n\n\n
When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. <\/p>\n\n\n\n
Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.<\/p>\n\n\n\n
Online Communities<\/h3>\n\n\n\n
Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.<\/p>\n\n\n\n
Network And LinkedIn<\/h3>\n\n\n\n
Don\u2019t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the \u201cEasy Apply\u201d options, as you\u2019ll be competing with thousands of applicants who bring nothing unique to the table.<\/p>\n\n\n\n
FAQs about Data Science Careers<\/h2>\n\n\n\n
We answer your most frequently asked questions.<\/p>\n\n\n\n
Do I Need A Degree For Data Science?<\/h3>\n\n\n\n
A degree is not a set-in-stone requirement to become a data scientist<\/a>. It\u2019s true many data scientists hold a BA\u2019s or MA\u2019s degree, but these just provide foundational knowledge. It\u2019s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.<\/p>\n\n\n\n
Does Data Science Need Coding?<\/h3>\n\n\n\n
Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.<\/p>\n\n\n\n
Is Data Science A Lot Of Math?<\/h3>\n\n\n\n
It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.<\/p>\n\n\n\n
What Skills Do You Need To Land an Entry-Level Data Science Position?<\/h3>\n\n\n\n
To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you\u2019re acing problem-solving, critical thinking, and effective communication.<\/p>\n\n\n\n
Since you\u2019re here\u2026<\/strong>Are you interested in this career track? Investigate with our free guide to what a data professional actually<\/em> does<\/a>. When you\u2019re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp<\/a> which will help you land a job or your tuition back!<\/p>\n","protected":false},"excerpt":{"rendered":"
It was only twelve years ago that the title \u2018data scientist’ was coined. The practice of data science has not existed for much longer either. As a young field, though growing rapidly around the globe, the role of a data scientist is often misunderstood. What exactly do data scientists do, how do they contribute to […]<\/p>\n","protected":false},"author":100,"featured_media":18973,"comment_status":"closed","ping_status":"closed","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"_eb_attr":"","_eb_data_table":"","footnotes":""},"categories":[67],"tags":[],"marketing_tags":[],"class_list":{"0":"post-13198","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science"},"acf":[],"_links":{"self":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/13198"}],"collection":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/users\/100"}],"replies":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/comments?post=13198"}],"version-history":[{"count":4,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/13198\/revisions"}],"predecessor-version":[{"id":48069,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/13198\/revisions\/48069"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media\/18973"}],"wp:attachment":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media?parent=13198"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/categories?post=13198"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/tags?post=13198"},{"taxonomy":"marketing_tags","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/marketing_tags?post=13198"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
- Competitions and hackathons, which offer opportunities to solve real-world problems with data<\/a>.<\/li>\n\n\n\n
- Business acumen:<\/strong> Understanding the real-world context in which the business works to make data-driven decisions to business problems.<\/li>\n\n\n\n
- Critical thinking and problem-solving:<\/strong> Being able to critically analyse the situation at hand and device innovative ways to solve business problems.<\/li>\n\n\n\n