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Is Data Science Hard? Taming the Data Science Dragon
Data Science

Is Data Science Hard? Taming the Data Science Dragon

6 minute read | October 28, 2021
Sakshi Gupta

Written by:
Sakshi Gupta

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It was only twelve years ago that the title ‘data 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 the business, what skills do they need are all commonly asked questions among aspirants — be it fresh graduates, entry-level professionals or mid-level managers seeking a career transition.

Among all the questions we hear, there is one that’s heavily loaded: Is data science hard? While there is great enthusiasm and interest in the opportunities opened up by data science, there is also scepticism. In this blog post, we endeavour to answer all the questions around building a data science career.

Is Data Science Hard?

Our mentors and counsellors believe that this question comes more from a place of misunderstanding. Given that this field is still maturing, there are only a few mentors/ seniors in the field, and most traditional colleges still don’t offer data science as a structured course, it leaves aspirants with many unanswered questions. Let us answer them one by one.

#1 Will a data science job be hard?

A generalist data scientist is a jack of many trades. Their job includes: 

  • Identifying business problems
  • Writing hypothesis
  • Performing data mining and data wrangling
  • Cleaning and classifying data
  • Conducting statistical analysis
  • Building algorithms and testing them
  • Writing code and deploying in production
  • Making presentations and visualisations
  • Collaborating with business teams etc.

This might appear hard because it often is. As a data scientist, you’re 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.

A good data scientist will take the challenge in their stride and develop the skills and aptitude needed for it.

#2 Is it hard to gain data science skills?

People ask this question because data science is not a singular skill. 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. 

is data science hard, data science skills set

For instance, let’s say you’re 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’re using DS for fraud detection in credit card purchases, you need to have in-depth knowledge of the credit landscape and fraud patterns.

Data science skills are a lot more than that. Some other skills you need are:

  • Critical thinking and problem-solving: Being able to critically analyse the situation at hand and device innovative ways to solve business problems.
  • Business acumen: Understanding the real-world context in which the business works to make data-driven decisions to business problems.
  • Communication: Making presentations, building consensus among stakeholders, persuading team members, etc.
  • Self-learning: Being able to adapt to changing circumstances and keeping oneself updated on technological developments.

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.

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#3 Is it necessary to have experience to start a data science career?

While experience is non-negotiable, it doesn’t have to be ‘job experience’. Hands-on practice with concepts of DS is good enough. You can gain this kind of experience in myriad ways:

The only way to show that you’re a data scientist is through practical application. Gaining this can be a little hard, but it is crucial.

#4 Is getting a data science job really hard?

Data science is a competitive field — 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’t mean that getting a job is hard.

With the right skills and data science portfolio, you will be able to land your dream job. Here are a few pointers to keep in mind while applying for data science jobs:

  • Make a CV that presents your strengths confidently
  • Customise your CV to suit the job you’re applying for
  • Present a portfolio of projects you’ve worked on, based on the job you’re applying to, present the relevant ones prominently
  • Prepare for commonly asked data science interview questions and practice your answers
  • Follow up diligently and seek feedback
https://www.youtube.com/watch?v=7YuTmLvs1Dc&t=1s

Remember 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.

#5 Is it hard to grow as a data scientist?

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.

  • 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. 
  • 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.
  • 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.
  • 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.

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.

‘Hard’ 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, ‘hard’ 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’s Data Science Career Track 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!

Companies are no longer just collecting data. They’re 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 – 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.

In this article, we’ll 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’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

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’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

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.

Data Scientist

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 – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s 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. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

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.

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.

General Requirements

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 to acquire these skills. 

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.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

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.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

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.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s 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. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

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).

General Requirements

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. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s 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. 

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.

General Requirements

If you’re aiming to excel as a database architect but don’t 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’t forget programming languages – SQL, Python, or Java. 

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.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

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. 

General Requirements

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.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

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. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

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. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

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.

General Requirements

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. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

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’s context. 

General Requirements

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.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

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.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

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.

Online Communities

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.

Network And LinkedIn

Don’t 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 “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s 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.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

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.

What Skills Do You Need To Land an Entry-Level Data Science Position?

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’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Sakshi Gupta

Sakshi is a Managing Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer with experience in the Indian and US markets.