09 Jun How to be a data scientist
If you’re interested in using data to make a difference in the world, you’ve come to the right place.
Whether you need to analyse complex data or just want to improve your reporting skills, data science is a growing field. It’s a great way to add value to your organization, and there are many ways to become a data scientist.
In this post, we’ll show you how to get started in the world of data science and how data science can be used to solve real-world problems.
What Is A Data Scientist?
Even if you have some background information, there is a lot more than meets the eye when it comes to what exactly a data scientist is.
To put it simply, imagine the outcome when mathematics, statistics, data analysis and computer science combine. A range of techniques from all these disciplines (and more!) are merged which forms data science in a nutshell. Other subject areas that are very relevant in data science include algorithms, data mining, artificial intelligence, and deep learning.
That’s why this job field is so interesting! Instead of having to narrow down your skills into one specific speciality, by becoming a data scientist you can apply your skills in a wide range of academic and creative ways to improve everyday operations.
What Are The Different Types of Data Scientists?
Data science is a vast field, and there are many specialties you can follow with this field that can help you achieve your goals.
You see, data science has been around for a long period of time, but it is only now that this job is rising to popularity. Mathematicians, digital analytic consultants, quality analysts and data scientists have all been working in the data science domain for decades, possibly without even realising.
It is only in the past few years that the data scientist career has soared into demand, generating huge potentials, and creating an ever-increasing number of jobs.
Depending on the organisation you wish to work in, a data scientist can be given other role titles. For example, an advertising agency would require a great data analyst to dig deep into target rating point and gross rating point data; whereas a market research company would need someone to analyse, manage and merge their data for enhanced strategy performance.
There are many other areas of data science you can work in, which we go into more detail below.
Financial data scientist
A great option if you are interested in financial and stock markets, which are ever-growing and are expected to hugely surge in growth over the next 5 years.
What to expect:
- Work with series data and perform data analysis to build financial modelling techniques.
- Dig into data cleaning and munging to better understand financial planning and determine effective insights.
- Analyse behavioural data and identify opportunities for business growth.
Health data scientist
This is a great branch to go into if you are interested in both the human mind and data. You can help to provide proper treatments to all patients and reduce the risk of treatment failure, all whilst easing the workflow of healthcare systems.
What to expect:
- Collect data from patients using descriptive image recognition algorithms.
- Analyse the needs of hospitals to optimize healthcare systems.
- Sequence and analyse human genes to find the best responding drug using tools such as Bioconductor.
Government data scientist
This is a great data science path if you are also interested in politics and making the world a greater place. With some data only available to government employees, you get the chance to put it to good use to serve the general public.
What to expect:
- Analyse data to identify and prevent waste, fraud, and abuse.
- Collect intelligence data to improve national defence systems.
- Create maps and visualizations to explain data findings in areas around the country.
Retail data scientist
What better way to generate more sales than to analyse the hottest trends to find out what people are buying? That’s exactly what a retail data scientist does to help continuously push up transactions to generate better margins.
What to expect:
- Conduct research to personalise offers to different customer segments.
- Use customer sentiment analysis techniques to provide better customer service.
- Utilize machine learning algorithms to identify and detect patterns and correlation among supply chains.
What Are The Responsibilities of A Data Scientist?
Even though there are many different paths you can take to become a data scientist, they all hold similar key responsibilities.
As a data scientist you can expect to generate and lead a wide range of key roles that help to solve problems faced by businesses worldwide. These include:
- Extracting, merging, and managing data
- Designing, building, and testing algorithms
- Using machine learning tools and statistical techniques
- Maintaining clear and coherent communication to write and interpret reports
- Evaluate the effectiveness and trustworthiness of sources
- Conduct research to develop prototypes, datasets, and coding materials
The responsibilities above are just a handful of what you can achieve as a data scientist. This job type is so broad that by applying the skills you develop, the possibilities and solutions that you can come up with are endless.
How To Become A Data Scientist
There is more to becoming a data scientist than just going to university and getting a degree. You need to acquire a range of skills and experience in order to qualify as a data scientist.
This evidently means that the more skills and experience that you build up, the better your CV will look which will make you stand out from the crowd when applying for jobs.
To break it down, there are five main steps that need to be taken to become a qualified data scientist.
1: Reinforce your foundations
It is pretty clear that whichever branch of data science you want to get into, you’ll need some sort of background in the subject area. A great starting point is having a degree or qualification in the subject area that interests you. For example, if becoming a financial data scientist takes your fancy, then a solid background in mathematics (i.e., an undergraduate degree) would be extremely beneficial.
Generally speaking, you will need an undergrad degree to become a data scientist. Employers are looking for someone who is qualified, has great planning and organisation skills, and is able to deliver under pressure, and having a degree shows just that.
There is no specific degree that you need to have, as a wide range of subject areas give you the freedom to venture into the data science path. The list of degrees below are all considered acceptable foundations when building up your data scientist career:
- Brain and cognitive science
- Computer science
2: Learn the basics
There are a few essential technical skills that you need to have experience in to become a data scientist. You will need to become proficient in using programming tools which are considered the ‘basics’ in the data science world.
Hadoop, SQL, and Python are a few of the database interrogation and analysis tools that you need to be familiar with.
SQL is a domain specific language used in data handling for the extraction, addition, and deletion of data. Due to the fact that it isn’t as complex as other programming tools, knowing how to use it proficiently is considered a must-have skill by most employers. Learning SQL will help you to better understand relational databases and boost your profile as a data scientist.
Hadoop is an open-source software framework that provides massive database storage and holds an enormous processing power which gives you endless opportunities when working on tasks. Having experience with this programming tool is not essential, but heavily preferred in most cases. This is because when working as a data scientist, you will have to deal with so much data that it may exceed the memory capacity of your system or server. That’s why Hadoop is so useful for data storage, as well as data exploration, data filtration, and data sampling.
Python is one of the most common coding tools available. It is a great programming option for data scientists, especially beginners, as the syntax is very similar to the English language and therefore easy to read and understand. Python is an extremely versatile tool that can be used for almost all processes and responsibilities you will hold as a data scientist. Compatible for use on different platforms including Windows, Mac, and Linux, it runs on an interpreter system which means that code can be quickly executed as soon as it is written.
3: Study machine learning
By following this step and gaining experience in machine learning and algorithms, you are sure to stand out from other data scientist applicants.
Machine learning is essentially using patterns hidden inside of data to build predictive models for the future. A data scientist works with large numbers of data sets, and so having the skills and experience to solve different data science problems that are based on predictions of major organizational outcomes is very beneficial.
To build up your machine learning skills, it is best to start small with simple linear and regression models. Once you feel that you have grasped those concepts well, you can gradually increase the complexity and move onto K-means clustering and Classification and regression trees (CART) models.
4: Gain Experience
As with any job or career type, having experience is a great way to show potential employers that you have the skills and knowledge they are looking for.
With data science continuously growing in popularity, gaining work experience is not as hard as you may think.
Big companies, especially those in the retail, finance, and travel industries, offer internships which provide you with invaluable experience. Bear in mind that these programmes can be extremely competitive, and places are limited, so anchoring down your CV with basic skills and having an ‘above and beyond’ working attitude can help your application go further.
Medium and small companies may not have as many internship programmes but may offer shadowing opportunities. These can be a great way to watch an experienced data scientist at work, so you can soak up all the techniques and skills they use in their daily job.
Also, large firms such as Kaggle and Topcoder frequently host competitions which aim to spot new and emerging talent. By entering and competing in these events you can show potential employers your eagerness and flexibility towards becoming a data scientist.
If you are only just starting out in your career as a data scientist, you can always rely on conferences and seminars as a starting point. These events can give you a greater insight on what becoming a data scientist means, as well offering you with great networking opportunities with potential employers.
5: Keep up to date
As stated before, data science is an exciting and every-changing career field with so many new opportunities becoming available every day. It’s really important to be confident in your skills and abilities so you know you are making a difference in your data science career.
Having said so, even the most skilled data scientists sometimes need to top-up their knowledge. There are various online courses and bootcamps available that you can attend which keep you up to date with the latest algorithms and techniques.
Also, a postgraduate qualification such as a Masters or a PHD is very common among data scientists. By gaining extra certifications, you are giving yourself an edge over other candidates which makes you more likely to be chosen for promotions in the future.
What Is The Salary of A Data Scientist?
Salaries depend on a range of factors including location, the sector you work in, as well as your experience and qualifications.
Generally speaking, a junior data scientist can expect to make between £25,000 and £30,000 a year when they first start out.
As you gain more skills and experience, you can expect your salary to rise to around £40,000 per year.
Many leading data scientists and chiefs in their area of expertise earn approximately £65,000 per year, with some positions reaching more than £100,000.
The figures stated above are a guide only, but you can expect other benefits of working with certain companies such as private medical insurance, joining the company pension scheme, as well as performance bonuses.
Is Data Science A Good Career?
Becoming a data scientist is a good career that can set you up for a comfortable and happy life.
According to burning glass technologies, the demand for data scientists and data engineers tripled over the past five years, rising 231%. This just goes to show how quickly the data science field is expanding, with jobs readily available for those with the right set of skills and qualifications.
If you feel that becoming a data scientist is right for you, then gain the relevant experience and knowledge and take the plunge! You never know where this exciting and new career path may take you.