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How to Land a Job As a Data Scientist in 2019

How to Land a Job As a Data Scientist in 2019

It’s the buzz!

Everyone is talking about data science as the dream job that they want to have!

Yes, the “100K $USD annual package” is a big draw.

Furthermore, the key focus of self-help and self-improvement literature coming out in the last decade speak about doing what you enjoy and care about – in short, a job you love to do – since there is the greatest possibility that you will shine the brightest in those areas.

Hence many students and many adventurous challenge-hunting individuals from other professions and other (sometimes related) roles are seeking jobs that involve problem-solving. Data science is one solution since it offers both the chance to disrupt a company’s net worth and profits for the better by focusing on analytics from the data they already have as well as solving problems that are challenging and interesting. Especially for the math nerds and computer geeks with experience in problem-solving and a passionate thirst to solve their next big challenge.

So what can you do to land yourself in this dream role?

 

Fundamentals of Data Science

Data science comprises of several roles. Some involve data wrangling. Some involve heavy coding expertise. And all of them involve expert communication and presentation skills. If you focus on just one of these three aspects, you’re already putting yourself at a disadvantage. What you need is to follow your own passion. And then integrate it into your profession. That way you earn a high amount while still doing work you love to do, even at the level of going above and beyond all the expectations that your employer has of you.  So if you’re reading this article, I assume that you are either a student who is intrigued by data science or a working professional who is looking for a more lucrative profession. In such a case, you need to understand what the industry is looking for.

From http://news.mit.edu/2018/mitx-micromasters-program-statistics-and-data-science

a) Coding Expertise

If you want to land a job in the IT or data science fields, understand that you will have to deal with code. Usually, that code will already have been written by some other people or company in the first place. So being intimate with programming and readiness to spend hours and hours of your life sitting before a computer and writing code is something you have to get used to. The younger you start, the better. Children pick up coding fastest compared to all other age groups so there is a very real use-case for getting your kids to code and to see if they seem to like it as young as possible. And there is not just coding – the best choices in these cases will involve people who know software engineering basics and even source control tools and platforms (like Git and GitHub) and have already started their career in coding by contributing to open source projects.

If you are a student, and you want to know what all the hype is about, I suggest that you visit a site that teaches programming – preferably in Python – and start developing your own projects and apps. Yes – apps. The IT world is now mobile, and anyone without knowledge of how to build a mobile app for his product will be left in the dust as far as the highest level of earning is concerned. Even deep learning frameworks, that were once academic, have migrated to the mobile and app ecosystem. That was unthinkable a mere five years ago. If you already know the basics of programming, then learn source control (Git), and how to build programs for open source projects. And then contribute to those projects while you’re still a student. In this case, you will actually become an individual that companies go hunting for before you even complete your schooling or college education. Instead of the other way around!

Mentoring

If you are a student or a professional who is interested in this domain, but don’t know where to start – well – the best thing to do is to find a mentor. You can define a mentor or a coach as someone who has achieved what you aim to achieve in your life. You learn from their experience, their networking capabilities, and their tough sides – the way to keep up your ambition and motivation when you feel the least motivated. If you want to learn data science, what better way than to learn from someone who has done that already? And you will gain a lot of traction when you show promise, especially on your networking side for job placement. For more on that topic (mentoring) – I highly recommend that you study the following article:

https://dimensionless.in/how-to-find-mentors-for-data-science/ 

b) Cogent Communication (Writing and Speaking skills)

Even if you have the world’s best programming expertise, ACM awards, Mathematics Olympiad winning background, you name it – even if you are the best data scientist available in the industry today for your domain – you will go nowhere without communication skills. Communication is more than speaking, reading and typing English – it is the way you present yourself to others in the digital world. That is why blogging, content creation, and focused interaction with your target industry – say, on StackOverflow.com – are so important. A blog really resonates with those to whom you seek a job. It shows that you have genuine, original knowledge about your industry. And if your blog receives critical acclaim through several incoming links from the industry, expect a job interview offer in your email before too long. In many countries but especially in India, the market is flooded with graduates, postgraduates, and PhDs who might have top marks on paper but have no marketable skills as far as their job requirements demand.

Overcome your fears!

Right now it is difficult to see the difference between a 100th percentile skilled data scientist and a 30th percentile skill level by just looking at documents that you submit to a company. A blog testifies that you know your field authoritatively. It also means that you have gained attention from industry leaders (when you receive comments). A StackOverflow answer that is highly rated or even a mention in technology sites like GitHub indicate that you are an expert in your field. Communication is so critical that I recommend that you try to make the best use of every chance you get to speak in public. This is the window the world has on you. Make yourself heard. Be original. Be creative. And the best data scientist in the world will go nowhere unless he or she knows how to communicate effectively. In the industry, this capacity is known as soft skills. And it can be your single biggest advantage over the competition. If you are planning to join a training course for your dream job, make sure the syllabus covers it!

c) Social Networking and Building Industry Connections through LinkedIn

Many sources of information don’t focus on this issue, but it is an absolute must. Your next job could be waiting for you on LinkedIn through a connection. Studies show that less than 1% of resume submissions are selected for the final job offer and lucrative placement. But the same studies show that at least 30% of internal referrals from within a company get placed into the job of their dreamsNetworking is important – so important that if you know the job you’re after, please reach out and research. Understand the company’s problems. Try to address some of their key issues. The more focused, you are the more likely it is that you will get placed in the company you aim for. But always have a plan B – a fallback system, so that in case you do not get placed, you will know what to do. This is especially important today with the competition being so intense.

The Facebook of the Workplace

One place where you can be noticed is through industry connections in social networks. You might miss this, even if you are an M.S. from a college in the US. LinkedIn profiles – the Facebook of the technology world – are especially important today. More and more, in an environment saturated with high-quality talent, who you know can sometimes be even more important as what you know. Connecting to professionals in the industry you plan to work in is critical. This can occur through meetups, through conferences, through technological symposiums and even through paid courses. Courses who have instructors with industry connections are worth their weight in gold – even platinum. Students of such courses who show outstanding promises will be directed to their industry leaders early. If you have a decent GitHub profile but don’t know where to go after that, one way is to go for a course with industry experienced experts. These are the people who are the most likely to be able to land you a job in such a competitive environment. Because the market for data scientists – in fact for IT professionals in general – is highly saturated, including locations like the US.

Conclusion

We have not covered all topics required on this issue, there is much more to speak about. You need to know Statistics – even at PhD levels sometimes, especially Inferential Statistics, Bayes Theorem, Probability and Analysis of Experiments. You should know Linear Algebra in-depth. Indeed, there is a lot to cover. But the best place to learn can be courses tailored to produce Data Scientists. Some firms have really gone the extra mile to convert industry knowledge and key results in each subtopic to create noteworthy training courses specially designed for data science students. In the end, no college degree alone will land you a dream job. What will land you a dream job is hard work and experience through internships and industry projects. Some courses like the ones offered by www.Dimensionless.in have resulted in stellar placement and guidance even after the course duration is finished and when you are a working professional in the job of your dreams. These courses offer – 

  1. Individual GitHub Profile Creation & Mentoring (Coding Expertise)
  2. Training in Soft Skills
  3. Networking Connections on LinkedIn
  4. Instructors with Industry Experience (not academic professors!)

It’s a simple yet potent formula to land you the job of your dreams. Compare the normal route to a data science dream job – a PhD from the US (starting cost Rs. 1,40,28,000.00 INR for five years total, as a usual range) – to a simple course at Rs. 50K to Rs. 25K (yes, INR) from the comfort of taking the course from wherever you may be in the world (remote but live tuition – not recorded videos) with a mic on your end to ask the instructor every doubt you have – and you have a remarkable product guaranteed to land you a dream job within six months. Think the offer’s too good to be true? Well; visit the link below, and pay special attention to the feedback from past students of these same courses on the home page.

Last words – you never know what the future holds – economy and convenience are both prudent and praiseworthy. All the best!

 

 

How to Find Mentors for Data Science?

How to Find Mentors for Data Science?

Introduction

Although Data Science has been around us ever since the 1960s, it has only gained traction in the last few decades. This is one of the main reasons why budding data scientists find it quite challenging to find the right mentors. However, this scenario is drastically changing now. With the right approach and by looking at the right corners, you can find data scientist mentors who can help you bridge the gap between theoretical and practical applications of data science.
In this article, we will be looking at why there is even a need for individuals to have mentors in data science and how can we find them.

Why does one need a mentor in Data Science?

Earlier for data science jobs if you had a technical grad degree, you could brush up on your Python skills, fill a small portfolio with scikit-learn projects, and more or less watch the offers roll in. But this is not the case anymore. Data science industry has made a lot of advancements in a small span of time. These advancements have basically done two things here. First, they have resulted in organizations looking for more than basic skills from the data scientists. Secondly, it has created a huge demand for data scientists which have resulted in a lot of competition between different job seekers

  1. Take you under their wing and help you to stay motivated and discover the path that you may need to take.
  2. Understand what it takes to get to the top and be a valuable resource by answering your career or work related questions and providing good advice.
  3. Help you to be passionate about your success and brand.
  4. Provide you with a wealth of knowledge and resources and help you to connect with various Subject Matter Experts (SMEs).
  5. Be your own personal cheerleader and help you discover new opportunities.

Where to find mentors?


Available Online Courses

Dimensionless Technologies provides best online data science training that provides in-depth course coverage, case study based learning, entirely Hands-on driven sessions with personalized attention to every participant. We provide only instructor-led LIVE online training sessions and not classroom training.
These programs are led by Kushagra (IIT, Delhi – 10+ years experience in Data Science) who is a machine-learning practitioner, fascinated by the numerous application of Artificial Intelligence in the day to day life. He enjoys applying my quantitative skills to new large-scale, data-intensive problems. Also, he is an avid learner, keen to be the frontrunner in the field of AI.

LinkedIn

Learning data science will never be easy without any help from the community or from someone who is willing to help beginners. These someones are the ones that are making up our amazing LinkedIn Data Science Community.
Kate Strachnyi ♕ – If you’re in pursuit of having a data science career and learning data visualization. You must follow her in a heartbeat. Her hashtag#makerovermonday posts are amazing and she shares a lot of information that can help you in your journey.

Randy Lao ☁️ – He has been serving the community from as long as I know there was one for data science in LinkedIn. He shares the best resources for everything in data science. Starting from libraries in Python to courses on machine learning. You will find a lot of useful resources in his posts. Also, check out his collaboration with Kyle.

Kyle McKiou – Top-notch data science influencer. Can’t afford to not follow him if you want to learn from books to pick for data science to interview tips and tricks. Constantly helping beginners with his experience and content.

Mentors at work/college

Experienced working with professional developers can make or break your ability to land a data science position.
The best strategy we’ve found is called income share: basically, aspiring data scientists work with an expert mentor on an industry-level project, and they pay their mentor a small share of their future income in exchange (but only if they actually get hired as a result).
Income share has two benefits: first, it means that you can get expert instruction at no upfront cost. You only pay when you can afford to, and if you don’t get a data science job within a certain time limit (usually 24 months) you don’t pay anything at all.
Second, income share aligns the incentives of the mentors and mentees. Even after the formal mentorship period ends, mentors still have a stake in your future success, which means they’ll look out for opportunities for you by default.
Income share mentorships make new opportunities accessible to people who can’t afford the expert time or find professional data scientists to learn from.

Paid mentorship available at different websites

If you have a disposable income to spend then I’d highly recommend hiring a mentor who can walk you through your problems. When I was just starting to learn data science, I found having a paid mentor (via Thinkful) was incredibly helpful as it allowed me to ask all the dumb questions that I otherwise would’ve been too embarrassed to ask on a community forum.
Learn code & data science 1-on-1 with a mentor
Instant hands-on programming help available 24/7
Clarity – On Demand Business Advice

What if you are not able to find one?

Take ownership of your career

People who think a mentor is a key to success may lack confidence in their own ability to take initiative. Carreau advises taking control of your own growth. “Create your own plan to take control of your career and your life, and rise above the average,” she recommends.”Howard Schultz is a great example of someone from humble beginnings who took control of his destiny early. He has famously said, ‘I had no mentor, no role model, no special teacher to help me sort out my options.

Get value from peers

Even though a mentor is not necessary to success does not mean that you should ignore the opportunity of turning to your peers for guidance. In fact, Carreau advises joining a peer-mentoring group. “The combination of great networking and feedback makes participation in these kinds of circles invaluable, rather than the standard networking coffee meetings between two people,” she stated. “Getting feedback from not just one, but many people is much more valuable, and leads to better solutions and ideas. The dialogue in these groups consistently impresses me. And the business outcomes I see from promotions to career changes to overcoming major career challenges are substantial.

Learn from the youth

As technology continues to accelerate the rate of change, many would-be mentors are finding their approach outdated and obsolete. They find they actually have a lot to learn from younger generations. Instead of using experienced senior leaders as mentors to younger colleagues, some companies are reversing roles. “The younger person becomes the mentor, and the senior professional becomes the mentee,” explains Carreau. “In India, they have found that this concept has re-energized senior professionals and showed them a lot about technology and the social media world we live in today.

Combine activities to maximize time

For real value to take place, mentorship requires focused time, which is a valuable commodity. Carreau recommends looking for ways to combine learning activities to save time and be productive all at once. “Instead of going for a jog and then meeting a friend for coffee, why not go for a jog with your friend?” notes Carreau. “Instead of letting your commute be wasted time, listen to a podcast, relevant news or language tapes. Leveraging the power of multipliers lets you accomplish more by overlaying tasks that make sense together.”

Become a better networker

Building a network is not an intuitive skill for most people. It is also an iterative process; you are never finished, and the way you develop your network will change as your career progresses. When you begin networking, you are still figuring out your interests and career goals,” elaborates Carreau. Because of this, you must cast as large a net as possible among the people you can. Hence you should contact-family friends, school alumni and more. As you understand your ambitions better, you will become a better networker as well. Thus you are able to quickly spot the diamond in the rough among your contacts. You should not focus on networking and wasting your time on superficial contacts. Rather, make ensure you are engaging in an authentic and helpful way with them both online and in person.

Conclusion

Data science is one of the areas where this idea is starting to take off. Data scientists remain rare, and students may find it hard to get access to information. Mentoring bridges that gap and enables students to improve their skills and understanding of using data science in business.