I have summarised in this text best tips for data scientists to progress along their career. Whether you’re just starting or you want to go from junior to mid or mid to senior, there’s something for you.

Portfolio of GitHub projects

First of all you should build a portfolio of open-source projects on GitHub. I recommend to create three projects:

1. A classification project where you use a public database (you can download one from Kaggle — more about that in the next paragraph) of images/texts to sort them and hone your skills with supervised/unsupersived learning (from PCA to neural networks, through DBScan, KNN, etc.).
2. An NLP project which would analyse sentiments from Tweets from a particular subject and classify them accordingly into positive/neutral/negative. That’s a classic one — choose a topic interesting to you, so you have a good story to tell about it.
3. A scraping project, where you scrape different sources to extract information — that can be scraping sports news if you’re a sports fans, financial fluctuaction if you’re into finance or data science novelties. The endgoal might be creating an automated website with scraped and extracted content. Easy to show during a job interview.

If you’re more advanced then you definitely should play around with latest machine learning algorithms. For example you can:

• try GANs (Generative Adversial Networks) and generated some faces or cats.
• try Reinforcement Learning with easier games.
• try GPT-2 and text generation.

You have endless opportunities here. You definitely should show that you’re well-versed in neural networks and their fundamentals like Keras, PyTorch, TensorFlow.

Know what you know and what you don’t know

Even if that sounds obvious you should be able to answer questions about your knowledge, particular technicalities or problems.

Actually the most important question you’ll be asked is what was the hardest in a given project. If you were struggling with a particular technical issue, why and what it was. How did you overcome it? Those are the basic kind of questions that you should be prepared to answer.

Be ready to talk about algorithms in more detail, different methods you have used in the past. Be open and share also what was problematic for you. This can only help during a job interview.

Polish your LinkedIn profile

The last thing which is often overlooked by data scientists is putting up a coherent LinkedIn profile with explanation of what you did in the past and where you’re at currently. If you have any gaps in your career be sure to be asked about them — there’s nothing wrong with 6-months stay on Bali surfing, you should just be honest about what motivated you back then and what you wanted to achieve. I imagine that a good argument would be you wanted to have a break from your screen, wanted a restart, wanting to work as a digital nomads — there are plenty of reasons to explain why this option was the best also for your career.

You should also ask for recommendations from your past employers. They can write it directly on LinkedIn — just a couple of sentences is enough. If your last departure wasn’t really planned and you feel bitter about, try to explain why and what you expect from your next employer.

You should be able to say precisely why you want to change your job. Is it because you’re looking for new challenges? If yes, why you can’t have them at your current job? Speaking clearly about your previous work experiences is a huge asset.

Shine!

Summing up there are 3 things you should do to really master a job interview for a data scientist position:

• build a porfolio of GitHub projects;
• know what was the hardest part of each of your project and how you overcame it;
• polish your LinkedIn profile.

Good luck!

And if you want to know more, read my other articles about becoming a Data Scientist:

• 5 ways to become a data scientist
• How to start with Data Science
• 3 common mistakes Data Scientists make
• Practical guide to become a Data Scientist

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Data Scientist is one of the hottest job on the market right now. Demand for data science is huge and will only grow, and it seems it’s growing much faster than the actual number of data scientist. So if you want to make a career change and become a data scientist, now is the time.

Who is a Data Scientist?

A data scientist is an expert with a deep knowledge of data, algorithms and data visualization. To be a data scientist, you need to possess the ability to work as part of a team, understand data structure, analyze data, design and create charts and graphs, and write concise code.

What is a Data Scientist Salary?

A Data Scientist is one of the most sought after jobs in the industry right now, and it comes with a lot of perks. They can earn anywhere between $120,000-$180,000 per year. For comparison, a Software Developer makes between $110,000-$135,000.

Of course a Data Scientist’s salary depends on their specific role, but they typically work in the field of analytics or machine learning, often working with large data sets.

They need to have excellent analytical skills, experience in programming or databases, and strong writing skills.

A Data Scientist Job Description

A Data Scientist’s job is to develop and analyze data, and then analyze that data to create insight. These insights can be used in a variety of ways, including in various business decisions, and are often used to make recommendations or to help make a business case for a new product or service. Data scientists work across a variety of different data science topics such as

business intelligence, web analytics, natural language processing, social media analysis, predictive analytics, machine learning, data mining, and so on.

You can find a list of some of the key positions and how to apply to these positions on LinkedIn or AngelList or various other sites which allow you to browse job offers in your area.

What skills you’ll need to become a Data Scientist

Data Scientists are in high demand, so you’ll need to have a well-rounded skill set if you’re interested in this type of job.

Some of the skills you’ll need include:

– Excellent analytical skills, including the ability to understand data

– Good project management skills, including the ability to plan and manage projects and communicate with others

– Good communication skills, including the ability to write clearly and concisely

– An understanding of the importance of data integrity and privacy issues, as well as the importance of knowing your users

– Excellent quantitative skills, including the ability to use data to gain insight and communicate the results clearly

– Ability to understand complex information and interpret results

– The ability to think critically and problem solve, both in terms of understanding the big picture and in terms of making specific decisions on how to solve a particular problem

Technical skills of Data Scientist

From more technical standpoint you will also need some of the following — especially if you’re applying for non-junior positions:

– Expertise in Excel, SAS, R or Python (note: Excel is a programming language)

– Experience in machine learning

– Experience with databases (e.g., relational databases or NoSQL databases)

– Experience in data visualization

– A strong technical background and/or background in computer science

– Ability to learn quickly

In addition, data scientists typically work in teams, which means you’ll be asked to learn a lot of things quickly.

It’s not all about data, but rather information, so you’ll need to be an active learner if you want to develop the skills necessary to be successful in this industry.

3 steps to become a Data Scientist

Now for the very practical things:

1. Build your repository on GitHub and start an open-source project. You can take a dataset from Kaggle and build something around that. Usually classification problems tend to be easier. This will allow you to hone your skills and show a potential employer your engagement.
2. Engage in Facebook/LinkedIn groups about Data Science and Machine Learning. Try finding meetups and conferences near and attend them to meet more people. It’s always good to have someone to guide you.
3. Write more code! Data Science is a practical skill in the end. Share it on your social media — update your LinkedIn profile to have better chances to find a job.

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