Want to become a data scientist? Five ways to get that data science job

If you’re looking to give your tech career a boost, you’ve probably considered getting into data science. There has been a 56% increase in job postings in the United States over the past year, according to LinkedIn. So how can you get started in data science? Five industry experts, who spoke at the recent Big Data World event in London, provide advice on best practices.

1. Take a look at the free online courses …

Richard Freeman, chief data and machine learning engineer at fundraising specialist JustGiving, says there is a big buzz in the field of data science. He advises all interested IT or business professionals to get started with some of the free online education platforms.

“The difference between now and when I did my PhD is that there is more information. It used to be that you had to do some training – like with someone like IBM – and you get perfected that way. , the lessons are free, ”he says.

Freeman says all kinds of people want to develop their data science skills. These people are not just graduates. Professionals in existing jobs from a traditional business background also want to upgrade their skills.

“People know this is a very exciting field – they think, as the Harvard Business Review suggests, that this is the sexiest job of the 21st century. On the internet, there are more courses on platforms like Coursera and Udemy than ever before, ”he said. said.

2.… but develop depth to gain competitive advantage

Alejandro Saucedo, chief scientist at the UK think tank Institute for Ethical AI & Machine Learning, said the first thing future data scientists should understand is that data science is probably not as sexy as they think it is. . It is also likely to be more difficult to learn than they perceive.

“Data science isn’t as simple as just jumping into a Coursera session – you won’t become an expert. The most important thing to understand is that you need depth, not just breadth – you need to. specialize yourself, ”says Saucedo.

He says the job title “data scientist” is poorly defined and can mean a lot of things, from an analyst in a field who understands business metrics to someone who can actually create code and extract information from them. data.

SEE: Sensor’d company: IoT, ML and big data (ZDNet special report) | Download the report in PDF format (TechRepublic)

Saucedo says some data scientists are digging deeper into operations, including those learning how to fine-tune the Apache Spark analytics engine in detail. If you want to get into data science, eventually you will need to specialize.

“Get a holistic understanding and experiment with different areas to find out what works best for you. Don’t step into something you hate. Take the initiative to become more than a pure data scientist. Just be able to work in one industry. specific vertical is no longer enough, ”says Saucedo.

3. Getting your hands dirty while working with code

Mohammad Shokoohi-Yekta, who was until recently a Senior Data Scientist at Apple, and who now teaches a course at Stanford University called “An Introduction to Data Science,” advises applicants to start working with code as quickly as possible.

“For people who don’t have a lot of experience in this area, the first thing I always recommend is to get your hands dirty on the code. Get used to problem solving and coding if you want to learn how to do it. apply data science, ”he said. said.

Shokoohi-Yekta says data science is a hot topic. His course is currently the most popular at Stanford, with 500 people on the waitlist each term. Those interested in data science also recognize that there are plenty of job opportunities. But learning data science theory is just the start.

SEE: Job description: Data scientist (Tech Pro Research)

“It’s more than just learning the concepts, looking at slides, and thinking you know what machine learning is. Our course at Stanford covers 50% of the concepts and the other half is getting your hands dirty on the code, especially in R and Python, ”says Shokoohi-Yekta.

“If you are thinking of getting into data science, you absolutely need to be very comfortable with data science code and application, rather than being theoretical. “

4. Make sure you have a solid business understanding.

Claus Bentsen, executive director of pharmaceutical giant Astra Zeneca, hires data scientists for his organization and agrees that the practical component is essential when reviewing candidates. He advises people wishing to enter the field to work on projects, whether solo, studying or as part of a business environment.

“You have a lot of people who can talk, but we take people through our selection processes and we test them to make sure they can really fix the issues. That’s how we sort the winners from the losers,” explains Bentsen.

SEE: How to implement AI and machine learning (ZDNet special report) | Download the report in PDF format (TechRepublic)

Astra Zeneca also trains internal staff to become data scientists. Bentsen says it can be difficult to recruit people with generic math, machine learning and data science skills and push them into different areas of the pharmaceutical business. So the company often hijacks the approach and instead seeks to shift people with general business knowledge to data science.

“Understanding the business is very important when it comes to problem solving,” Bentsen says. “We have people who have worked in the company and whom we are transforming into data science skills through various training programs. And again, we’re making these people work on real issues. “

5. Create a great portfolio of experiences

Krish Panesar, CTO of health specialist Diabetes Digital Media (DDM), explains that the key element in getting you started in data science is experience. “Whether at home or in a business, it doesn’t matter where. Get experience and get a portfolio, ”he says.

Other experts suggest it makes sense to join Tableau Public, which is a free service that allows anyone to publish interactive data visualizations to the web. This service allows data scientists to share their work with interested parties, and a portfolio in Tableau Public is often seen as a critical asset for job seekers who manipulate data.

SEE: Feature Comparison: Data Analytics Software and Services (Tech Pro Research)

Panesar, who was responsible for machine learning at DDM before becoming CTO last year, said there have been many occasions when his organization has encountered machine learning candidates who did not have a portfolio.

“It’s quite shocking. If you are a newbie in this field, familiarize yourself with the basics and the production pipeline and the general overview of the stages of this pipeline,” says Panesar.

“And if you are looking to improve your skills, or have moderate experience levels, I would definitely consider picking a specific part of the production line and specializing in that area.”


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