10 Min Read
December 23, 2021
Every time you interact with a mobile app, use the internet, or visit a website data is generated. Every day large amounts of data are generated from millions of users, and nearly all of this data is valuable.
Understanding the data generated by customers leads to data-driven insights on products and services. As these swaths of data prove to be ever more useful to businesses, the demand for qualified professionals to handle and understand this data will only continue to grow.
Enter data scientists, who earn high salaries deciphering this highly valuable data. It’s no surprise that the demand for data scientists is at an all-time high. Because of this, more and more people are considering how to become a data scientist.
Here, you’ll find all the information you need to decide on whether you want to pursue a career in data science, and what steps are needed to get one. We’ll also give you key facts about careers in data science, such as the expected salaries, and training programs available to you.
A data scientist is someone who works with data to solve a particular problem. They use mathematics, algorithms, and machine learning to create insights from a data set. While some data scientists do work in a traditional academic setting, the data science job title considered here takes place in a business setting.
The data scientist will use all of the information an application, website, or any other source has generated to help an organisation understand its users, improve their services, or draw other conclusions.
Data scientists may use statistical analysis, mathematics, and Machine Learning techniques to effectively analyse a set of data and derive the insights they need.
Data scientists are responsible for collecting and analysing data produced by a program or system. They develop custom algorithms and build models to produce valuable insights from trends in this data. Data scientists will then use these insights to solve problems within an organisation.
or example, a data scientist could use information about user preferences to recommend products a user may want to buy on an e-commerce site. Data scientists could also take user interaction data from an app to find ways to make the app’s UX more user friendly.
Data scientists will do more than just analyse data. They have to derive insights from a dataset and present these findings to other departments. If a data scientist finds out users do not go ahead with buying products they have put in their basket, they’ll have to figure out why and tell the engineering and design teams so they can improve the user experiences.
While data science is an attractive career field, like many STEM fields it isn’t for everyone. While anyone can learn data science, here are a few of the traits shared by many data scientists that love their job:
While it’s not essential to have every item on this list, if you have none of them you may not enjoy a career in data science.
Demand for data scientist and data engineers in the UK has tripled over the past five years, rising 231%. That’s much faster than job postings overall in the UK, which rose 35%, according the Royal Society outlined in their report, “Dynamics of data science skills”.
The job outlook for data scientists is strong, as experts expect more companies to hire people who will help them analyse their data.
Data scientists earn high salaries. According to Reed.co.uk, the average salary for a data scientist is over £71,000, whilst entry-level data scientists usually earn around £35,000, and more experienced data scientists can earn salaries of up to £210,000.
The exact salary you can expect to earn will depend on the company for which you work and the place you live. Further, your salary will depend on the experience you have in data science. And as you get more experience, you’ll be able to unlock higher salaries. It’s important to note that these figures do not include stock options or other perks such as private health insurance offered by some technology companies — you should keep that in mind when you’re searching for a job.
Some data scientists, rather than working in an office, will work as an independent consultant. There are many consulting opportunities available for data scientists, especially at larger companies. These types of jobs can pay thousands for only a few hours of work, which can make this a lucrative career path.
There are a number of steps to becoming a data scientist. Here are the main steps you’ll need to go through in order to pursue a career in data science.
The first step is to consider what kind of work you would like to do as a data scientist. There are many applications for data scientists, from machine learning engineers to enterprise architects. Here are three of the most popular data science career paths:
Data Scientist: Data Scientists are people who develop solutions for difficult problems and create programs to analyse data. They’ll use technologies such as SQL, R, Python, Hadoop, MongoDB, Tableau, or Scala.
Data Engineer: Data Engineers are responsible for creating the methods used to analyse data at scale. This will involve maintaining databases, creating queries, and creating pipelines to store data.
Data Analyst: Data Analysts are often entry-level data scientists who are just starting in their career. They’ll be analysing data and writing recommendations, but they usually don’t have to create their own technical programs to solve problems.
There are many paths you can take to learn data science. Here are the most common paths people take when they are getting started in a career in data science:
Each path has its own benefits and drawbacks, and many data scientists have used a combination of the above methods.
In the past, pursuing a computer science degree was the main path toward becoming a data scientist. However, over the last few years, another popular option has emerged: coding bootcamps.
Coding bootcamps are short-term, intensive programs designed to help you develop the skills you need to thrive in a specific career in the computer science industry. Instead of attending university for four years and taking out loans, bootcamps are designed to teach you the practical skills you need to thrive in a career in the tech industry in a short period of time. During a bootcamp, you’ll also be given the career support you need to thrive in a career in technology.
In order to succeed in a career in data science, you’ll need to practice what you’ve learned while growing your skills. There are very few hiring managers looking for data scientists without any experience, so finding ways to build some is important while at the same time growing your portfolio.
In addition, let’s take a look at some of the data science skills you’ll need to grow during this time to succeed in a data science career, starting with technical skills.
There are a couple of technical skills you’ll need to become a successful data scientist. These skills include programming languages, data analysis techniques, and other technical concepts you need to know.
Ability to Prepare Data. As a data scientist, you’ll likely encounter cluttered datasets. You’ll need to know how to prepare data for analysis effectively so you can solve a specific problem. This will involve sourcing, arranging, processing, modelling, and synthesising data, and amending data into a readable format. They will also have to use data mining to find the right information to solve through a particular problem. Data scientists will often have to read and interpret big data as well — large data sets which could hold deep insights into a particular problem.
Basic Statistics. Data scientists need to have a good understanding of the fundamentals of statistics and statistical analysis. This will involve knowing about distributions, probabilities, A/B statistical testing, and other statistical concepts. This will make it easier for you to analyse datasets and identify relationships between data in a dataset.
Data Wrangling/Munging. Often, data scientists are given datasets in formats they do not expect. In this case, you’ll need to know how to process that data in a way your programs and systems can read. This will include responding to inconsistent formatting, missing values, and other problems in a dataset. You may also have to format big data sets, which are large in nature and require algorithms to sort through.
Data Visualisation Whether you are a junior data scientist or a senior data engineer, you’ll need to know how to visualise data. This skill is particularly important because you’ll need to be able to present your findings to other members of an organisation, who would rather see a graph than a dataset. You’ll need to be able to generate a functional visualisation based on the information in a particular dataset, and use tools such as d3.js and Tableau to do so.
While data scientists don’t code as much as software developers, using code to build your own algorithms is an important part of the job. While some companies will have specific languages they expect you to know, these are the most common ones you can expect:
Python - Python is the goto language for machine learning and the burning star of data science. While it’s far from the only language used in data science, it will likely be the one you see the most.
R - R is nearly as popular as Python for handling and displaying data, but while Python has other uses R is designed almost exclusively for this task.
SQL - Structured Query Language SQL is less of a programming language and more of a series of commands used to handle the storage and manipulation of vast amounts of data.
Java - While Python and R hold the king and queen position among data scientists, Java is also regularly used because of its age and quality libraries. Beyond this, much of the most well used data science software is programmed in Java.
To be a successful data scientist, you’ll need more than just technical skills. You’ll need to have a strong set of interpersonal (“soft”) skills. Here are a few of the soft skills you can expect to use in your job.
Communication. As a data scientist, you’ll need to communicate with other data scientists to share your findings. You’ll also need to work with other departments to help them solve their data problems. For example, the marketing department may ask you to analyse data from a campaign, or the development team may ask you to figure out why people are having trouble using a certain webpage.
Business Mindset. Data science is all about solving problems by using data. Thus, you’ll need to know how to process business problems, and use that information to help you craft programs to accomplish a certain goal. You should be able to approach problems as if you were an executive, and present your findings in a way non-technical people will understand if necessary.
Critical Thinking. Data scientists need to be able to use critical thinking skills to evaluate data and find insights in large datasets. You’ll need to be able to think about how to design a solution to a complex problem and think about those problems from different angles and perspectives.
Now that you’ve developed your skills, grown your portfolio, and gained some experience, it’s time to start applying for your first real data science job. Here are a few useful tasks and tips that will help you in landing a job:
Prepare a solid technical resumeYour resume is your first impression with most hiring managers, and having a clean resume that well represents your skills is essential.
Clean up your portfolioYour portfolio acts as proof of your data science skills, and a display of the quality of your work. You want to only include the projects you are most proud of, ditching quantity for quality.
Prepare for a technical interviewWhile your portfolio may look good, hiring managers will want to make sure that you’re well rounded in your understanding of data science beyond the projects you’ve completed. A technical interview gives them the chance to test out your knowledge with technical questions.
The good news is that if you’ve chosen to pursue an education in data science through a bootcamp, you won’t have to worry too much about these steps. Most bootcamps include robust career services that include mentorship, interview prep, portfolio help, and resume guidance.
Knoma would be delighted to help you! Explore data scientist courses here.
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