starting a career in data science : the ultimate guide

Are you starting a career in data science?

Just thinking about the first step can leave you dazed and confused, especially if you lack previous experience in the field.

With so many different data science careers to explore, you might find yourself wondering which is the right one for you and if you’ve got what it takes to fit the profile.

Is Data Science for Me?

Well, we’ve all asked ourselves that question when we were at square one of our data science learning path. And we haven’t forgotten that every expert was once a beginner.

So, this data science career guide has a three-fold purpose:

Show you why data science opportunities are worth exploring;
Inform you about the different careers in data science and boost your efficiency in discovering suitable data science roles;
Give you the know-how you need to pursue your professional data science path.

How to navigate the data science career guide

We did our best to give you the most comprehensive data science career guide out there.

We researched dozens of books, hundreds of articles, and thousands of report pages on careers in data to make sure you have everything you need to get on the track to data science career success. However, we realize that this is a truly massive amount of information. Of course, we’ll be ecstatic if you want to read the whole article from the headline to the last full stop. But just in case you wish to go straight to the parts that interest you most, you can use the navigation below.

Data Science Career Guide

What is the data science job outlook?

What data science qualifications will get you on the path to success?

How to get a job in data science?

Which data science industries you should consider?

Where to find data science positions?

What kind of data science organizations you should put on your radar?

Why is data science a good career to explore?

Advancements in technology helped data science evolve from cleaning datasets and applying statistical methods to a field that encompasses data analysis, predictive analytics, data mining, business intelligence, machine learning, deep learning, and so much more. Now, there still might be some who think that data science is just a trend and the hype around it will eventually go away. Of course, nothing could be farther from reality. The truth is, data science is just gaining speed as all businesses (and government organizations) use enormous volumes of data to improve what they do and how they do it. But what makes data science the magic ingredient for success? Here are two tangible examples.

Formula 1

The Brazilian Grand Prix 2012. Red Bull’s Sebastian Vettel suffers the worst possible start, his car spinning out of control and losing its front wing on his opening lap. Vettel suddenly drops down to last. All seems doomed for the champion. However, the German somehow manages to recover and finish sixth, which grants him just enough points to snatch the world title for the third year in a row in the most dramatic fashion. Fierce driving or luck?

Nope. That’s data science.

Turns out, by the time Vettel made it to his 10-th lap pit stop, a team of brilliant data engineers had already used modeled data to run simulations. Thus, they were able to analyze what adjustments were necessary to keep Sebastian’s Renault going for the remaining 70 laps. What a huge victory fueled by data!

Caesars Casino

Caesars Entertainment Corporation was the fourth-largest gaming company in the world in 2013, with annual revenues of $8.6 billion. Based in Nevada, US, it runs more than 50 hotels and casinos around the world, some of them at the most lucrative locations in Las Vegas.

But do you know what Caesar’s most valuable asset is?

It’s their database, giving indispensable insight into 45 million hotel and casino customers’ behavior and spending patterns.

The corporation used 17 years of gathering information to build “an automated, targeted marketing strategy for every individual customer, using data to understand who they are, and then predictive modeling to assess the best way to incentivize them to spend money.” (Marr, 2016)

What does this mean in practice?

Well, if you’re a client with a high lifetime value that happens to have lost all their luck, dignity, and money at the Blackjack table, you’ll most certainly be approached by a staff member. They’ll immediately lift your spirits by serving you your favorite drink. Or they’ll hand you free tickets to an evening entertainment program, and even offer you other free perks they know your heart desires. That way, Ceasar makes sure you’re happy and satisfied and keeps you coming back for more.

Why is data science important?

Data science isn’t just restricted to the F1 racetrack or the big casino business players. In fact, there is virtually no industry that can’t benefit from it. Retail and e-commerce, logistics and transportation, healthcare, finance, insurance, real estate – all these need a strong data science team that can leverage the data within their organization to gain a competitive advantage. That’s why, if you’re looking for a rewarding career with a strong impact on any business decision-making process, you should definitely explore the data science career path.

Who can use data science?

You can. And your employer certainly can, too.

In fact, the latter will welcome you with open arms, once they realize you’re brave and qualified enough to tame the tide of unstructured, semi-structured, and structured data, and use data insights to drive change. Of course, it goes without saying that those changes should lead to measurable results. After all, every company wants to ensure the well-being of their business, right?

Now, we want to help you be the person that every big data science company or fast-growing startup would gladly hire on their team. So, how do you get into the data science field? Fortunately, there are numerous ways to do that. Some involve waltzing through the impressive “Data Science Leviathan” door, whereas others help you secure a career in data science through less demanding paths.

What are the top 5 data science career opportunities to explore?

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.

Who is a data scientist?

Ever wondered how to become a data scientist? Also known as the Data Science Unicorn, a data scientist offers an unmatched blend of skills, much to the satisfaction of their employer. Data scientists not only understand the language of data, but they can also analyze it and draw actionable insights from it. Moreover, they’ve mastered the art of data storytelling to a level that makes both management and stakeholders nod in agreement and plan their strategy accordingly.

What does a data scientist do?

Practically everything.

Data scientists have a strong curiosity and a passion for achieving practical business impact. In addition, they boast exceptional judgment combined with an analytical mindset.

But what sets apart the best from the rest is a knack for creative problem solving and willingness to learn new technologies and skills.

Why is that so important?

Because data scientists must use machine learning models to solve challenging problems in all business areas. Moreover, they are real champions in utilizing statistical natural language processing to mine unstructured data and extract insights. Which doesn’t mean they don’t also deal with structured data. In fact, it’s quite the contrary. Data scientists model structured data with advanced statistical methods and algorithms to perform analyses. Then, they interpret the results and visualize the data to tell the most compelling stories to management and stakeholders to achieve the company’s business goals.

Data scientist salary – How much does a data scientist earn?

How much do data scientists make?

Here are the numbers, according to Glassdoor and Payscale.

As a data scientist in the U.S., even with zero experience, you can get a $10,000 annual bonus on top of your $100,000 average annual pay. And, just in a few years’ time, your earnings can increase to $118,000, plus $12,000 in bonuses! (Pay for data scientists varies by state and region. If you want to take a closer look, you can find a detailed list in UC Berkeley's article.)

You've set your gaze to London’s Big Ben and the UK? With no previous experience as a data scientist, you can expect to earn an average total compensation of £33,813. And once you have 1-4 years behind your back, your average total compensation will rise to £39,573.

Overall, the data scientist career outlook is undeniably positive. No wonder “data scientist” was coined the sexiest job out there!

What’s the data scientist career path?

You can start as data architects or data analysts, and gradually work their way up to this coveted job. In any case, if you’re aiming at the data scientist position, here’s everything you need to know to get started on the right career track.

How to become a data scientist?

Everybody wants to know how to be a data scientist. Well, maybe not literally everybody, but this is undeniably the most sought-after career in the data science field at the moment, as the demand for data scientists is constantly growing. Data scientists are independent and impactful, and If you want to get hired as one, you’re probably wondering what the data scientist duties are, how to acquire the necessary skills to apply for data scientist positions and if the salary will meet your expectations.

Is data scientist a good career? To save you browsing through job boards and career websites to find the various information you need, we created this ‘data scientist snapshot’. We believe it will give you the insights you need to decide if the data scientist role fits your career needs and aspirations.

What education do you need to become a data scientist?

For starters, you don’t need a Master’s or a Ph.D. degree to become a data scientist. If you already have it – great! It’s certainly a plus! However, a Bachelor’s degree is good enough to get you on the data scientist path.

According to our extensive research on how to be a data scientist, a background in the following disciplines increases your chances of landing a data scientist job:

Economics and social sciences, which includes economics, finance, business studies, politics, psychology, philosophy, history, and marketing and management;
Natural science, including physics, chemistry, and biology;
Statistics and mathematics;
Computer science;
Engineering;
Data science and analysis, which includes machine learning.

The good news is that even students from entirely different areas of studies, hold a very good chance of becoming data scientists. According to data from successful data scientists’ LinkedIn profiles, 43% have completed at least one data science online course with 3 certificates being the average.

So, if you’ve never written a line of code in your life, you can still make up for it with determination and commitment to learning…And ultimately start a career in data science.

What data scientist qualifications you should acquire?

Data scientists are famous for their robust skillset and competences. So, here are the must-have qualifications you need to become a data scientist.

Technical skills

Excellent programming skills in R or Python and its data science libraries (Pandas, scikit-learn);
Experience with relational databases and SQL;
Experience in MATLAB;
Advanced practical knowledge of data science and machine learning /AI development frameworks;
Excellent analytical and learning skills;
Experience in Deep Learning frameworks (e.g. TensorFlow);
Experience with NLP algorithms and techniques is an advantage.

Practical skills

Ability to start initiatives;
Integrity and confidentiality;
Desire to drive innovation and generate unique solutions;
Growth mindset;
Ability to track and share external trends, best practices or ideas;
Drive for performance;
Accountability;
Ability to thrive under pressure and fast pace;
Ability to take an unpopular stance.

Soft skills

Strong written and verbal communication skills
Leadership skills;
Ability to develop and leverage relationships with stakeholders to achieve a company’s business goals;
Ability to inspire others and support others' development to achieve full potential;
Readiness to collaborate closely with engineering and BI teams.

Well, now that you’ve got a good idea of what it’s like to be a data scientist and how to become one, you should feel more confident and determined on your path. However, if you feel you need additional data scientist career tips and advice, just scroll down the data science career guide. We believe you’ll find information that can help you at any stage of your data scientist job application process.

Who is a data analyst?

Data analysts are the real troopers of data science. They’re the ones who are involved in gathering data, structuring databases, creating and running models, and preparing advanced types of analyses to explain the patterns in the data that have already emerged. A data analyst also overlooks the basic part of predictive analytics.

What does a data analyst do?

Well, quite a lot. A data analyst is both a thinker and a doer who doesn’t hesitate to roll up their sleeves and dig into the numbers. Data analysts extract and analyze data with a “can do” approach and then present data-driven insights to underpin decision making. They also develop and build analytics models and approaches as the basis for a company’s strategy and vision. On top of that, they are often responsible for identifying and extracting key business performance, risk and compliance data, and converting it into easy-to-digest formats. So, as you can see, agility to shift between strategic projects and operational activities a must.

Sounds a bit lonely? Think again. Data analysts are great team players and work closely with various departments and leaders within the organization. That’s super important if they want to be effective in this role. So, the ability to communicate well and influence is critical.

Data analyst salary – How much does a data analyst earn?

How much do data analysts make?

Glassdoor and PayScale were kind enough to share their insights.

If you’re taking the first steps in your data analyst career, you can expect an average pay of $57,000.

As you reach 4-6 years of experience, your compensation will also go higher ($68,000 median annual salary and an average bonus of $4,705).

You’re based in the UK? The average compensation for data analysts with less than 1 year of experience (including bonuses and overtime pay) is £23,870. In terms of data analyst job growth, if you already have 1-4 years of experience as a data analyst, you can expect annual earnings of £25,853. So, it looks like the data analyst path is worth exploring!

What’s the data analyst career path?

A budget analyst or a compensation and benefits analyst can be a starting point to your data analyst career. But, in any case, if you wish to advance on the data analytics career path, here’s all you need to learn in terms of education and data analyst skillset requirements.

How to become a data analyst?

Considering a career as a data analyst?

That’s certainly a great option to explore, both on its own and as a gateway into data science. However, there are a few questions you need the answers to if you want to make sure that a career in data analytics is the best career path for you. So, this overview is designed to confirm the fundamentals of data analytics careers for you: what a data analyst does, what education is preferable or required, how to get into data analytics, and what skills will get you hired. We also discuss what salary you can expect over your data analyst career progression. Effectively, this overview gives you the insight you need to make up your mind and initiate the first steps towards a successful data science career.

What education do you need to become a data analyst?

If you consider becoming a data analyst, a Bachelor's degree in IT, computer science or statistics will give you a strong advantage. However, equivalent experience in data and business analytics also fit the bill. The good news is, even if you lack the background and the experience, you still have a good chance of getting a job as a data analyst. There are various ways to learn, such as taking qualification courses or enrolling in a comprehensive online training covering everything from statistics and Excel to SQL, Python, and Tableau.

In fact, the latter will increase your chances to land an internship at a high-profile company and build your career from the ground up. Maybe an entry-level position isn’t how you imagined launching a successful career as a data analyst. But this just may be the best way to achieve your goal. In most companies, you’ll be able to gain valuable experience and take advantage of many in-house training opportunities. So, the important thing is to stay curious and keep on learning. Real-world experience, exposure to the latest software programs, and communicating with experts in the field will expand your knowledge and put you on the data analyst track.

What data analyst qualifications you should acquire?

As a data analyst, you’ll have plenty of tasks to juggle on a daily basis. So, here are the qualification and skills you need to become a data analyst and handle all that data.

Technical skills:

Programming and coding language skills using Python, R, etc.;
Expertise in SQL and a good understanding of how relational database management systems work;
Experience with large datasets and distributed computing;
Excellent Excel skills and ability to use advanced analytics and formulas;
Experience with extracting and analyzing data from diverse sources (multiple data marts and file formats);
Knowledge of statistics and statistical software packages, quantitative methods, confidence intervals, sampling and test/control cells;
Predictive modeling.

Practical skills:

Strong attention to detail and ability to quality check your own work to ensure data mistakes are caught prior to work delivery;
Advanced analytical and data interpretation skills;
Ability to stay organized and deliver within agreed timeframe and scope;
Hands-on, problem-solving skills;
Ability to initiate and drive projects to completion with minimal guidance;
Ability to communicate the results of analyses in a clear and effective manner;
Confidentiality and personal integrity (a key competency, especially if your job requires managing sensitive data);
Confidence to challenge thinking and offer opinions, thoughts, and insight;
Prioritization skills;
A proactive approach to resolving problems;
Quick learning skills.

Soft skills:

Excellent communication skills – both verbal and written;
Ability to articulate complex concepts in a clear and concise manner;
Ability to collaborate effectively in any work environment;
Good listening skills.

Now you’re aware of the most important aspects of a data analyst job and how to become one. However, in order to be successful, you still may need additional career advice on top of the skills needed for data analyst. So, if you feel like you need further preparation, you can jump to the later sections of the data science career guide. We are sure those will come in handy as a quick reference once you start sending out application forms and going to interviews.

Who is a BI analyst?

A BI analyst is the best friend of business performance, focusing primarily on analyses and reporting of past historical data. Once the relevant data is in the hands of the BI Analyst (monthly revenue, customer, sales volume, etc.), they must quantify the observations, calculate KPIs and examine the measures to extract insights from their data.

What does a BI analyst do?

Data is business and business is data. That’s probably every BI analyst’s motto.

BI analysts possess a blend of business vision, consultant abilities and profound understanding of data. Not to mention they are fierce Tableau ninjas. Their job often requires work alongside senior management to shape and develop a data strategy. Analysis of Key Performance Indicators (KPIs), accurate overview of business performance and identifying areas that need improvement are all in the BI analyst’s domain.

But that’s not all. Another part of a BI analyst’s job is to continually improve their company’s competitive positioning. Therefore, they examine their competitors, data trends, seasonality, and other random effects to quickly identify issues and best practices.

On top of that, they create killer graphs and dashboards to review major decisions and measure effectiveness. Do you want to have an impact on the business world? Become a BI analyst!

BI Analyst Salary – How much does a BI analyst make?

How much do BI analysts make?

We found the answer in Glassdoor and PayScale.

If you’re new to the profession (with 1 year of experience or less), you can count on $66,000 average pay. Once you’ve gained a few years of experience and you’ve honed your persuasion skills, your median annual pay can reach $79,000 (plus $5,185 average bonus).

Do you want to work in the UK? An entry-level BI analyst can expect to earn an average total pay of £26,000. And, by all means, 1-4 years of experience will grant you an average total compensation of £29,000. Sounds like advanced excel skills truly pay off in the end!

What’s the BI analyst career path?

If you want to pursue the business intelligence analyst career path, you can first consider gaining some experience as a data analyst or a business analyst. A BI analyst career has a lot to offer but you definitely need some high-level skills. So let’s see what education and qualifications will pave your way to success.

How to become a Business Intelligence analyst?

Is the business intelligence career path right for you? Well, if you’re interested in business performance, calculating KPIs and extracting insights from past historical data, this might be an opportunity worth looking into. However, a BI analyst has many other responsibilities and tasks to complete daily. That’s why we created this article - to provide insight into what a BI analyst does, what education and skills you need to fit the bill, and how much you can earn in this role. Practically, everything that will get you past those first initial steps on your way to a new career.

What education do you need to become a BI analyst?

Generally speaking, landing a job as a BI analyst should come relatively easy, if you have a bachelor’s degree in one (or more) of the following:

computer science;
engineering;
mathematics;
statistics;
finance;
business administration;
economics or related field.

But don’t get discouraged if your background is in contemporary dance or Classical Indian literature.

Fortunately, there are no limits to learning. And it’s never late to acquire the crucial skills for a BI analyst position, such as Excel, SQL, Python, and Tableau. Today, there are plenty of specializations and all-around online certificate courses that will give you the knowledge and confidence required for the job. And here’s a small tip: if you’re new to the data science field, an internship in the financial industry would be a great start. It will help you understand all business processes. Plus, you’ll get an idea of the kinds of projects a BI analyst undertakes.

What BI analyst Qualifications you should acquire?

A data science job is all about the skillset, and the BI analyst path makes no exception. So here’s a list of skills you need to become a BI analyst.