7 min read

In our first resolution, we talked about learning the building blocks of data science i.e developing your technical skills. In this second resolution, we walk you through steps to stay relevant in your field and how to dodge jobs that have a high possibility of getting automated in the near future.

2nd Resolution: Stay relevant in your field even as job automation is on the rise (Time investment: half an hour every day, 2 hours on weekends)

Once you have got your fundamentals right, it is important to stay relevant through continuous learning and reskilling. In addition to honing your technical skills, you must also deepen your domain expertise and keep adding to your portfolio of soft skills to stay ahead of not the just human competition but also to thrive in an automated job market. We list below some simple ways to do all these in a systematic manner. All it requires is a commitment of half an hour to one hour of your time daily for your professional development.

1. Commit to and execute a daily learning-practice-participation ritual

Here are some ways to stay relevant.

  • Follow data science blogs and podcasts relevant to your area of interest. Here are some of our favorites:
  • Data Science 101, the journey of a data scientist
  • The Data Skeptic for a healthy dose of scientific skepticism
  • Data Stories for data visualization
  • This Week in Machine Learning & AI for informative discussions with prominent people in the data science/ machine learning community
  • Linear Digressions, a podcast co-hosted by a data scientist and a software engineer attempting to make data science accessible
  • You could also follow individual bloggers/vloggers in this space like Siraj Raval, Sebastian Raschka, Denny Britz, Rodney Brookes, Corinna Cortes, Erin LeDell
  • Newsletters are a great way to stay up-to-date and to get a macro-level perspective. You don’t have to spend an awful lot of time doing the research yourself on many different subtopics. So, subscribe to useful newsletters on data science. You can subscribe to our newsletter here. It is a good idea to subscribe to multiple newsletters on your topic of interest to get a balanced and comprehensive view of the topic. Try to choose newsletters that have distinct perspectives, are regular and are published by people passionate about the topic.
  • Twitter gives a whole new meaning to ‘breaking news’. Also, it is a great place to follow contemporary discussions on topics of interest where participation is open to all. When done right, it can be a gold mine for insights and learning. But often it is too overwhelming as it is viewed as a broadcasting marketing tool. Follow your role models in data science on Twitter. Or you could follow us on Twitter @PacktDataHub for curated content from key data science influencers and our own updates about the world of data science. You could also click here to keep a track of 737 twitter accounts most followed by the members of the NIPS2017 community.
  • Quora, Reddit, Medium, and StackOverflow are great places to learn about topics in depth when you have a specific question in mind or a narrow focus area. They help you get multiple informed opinions on topics. In other words, when you choose a topic worth learning, these are great places to start. Follow them up by reading books on the topic and also by reading the seminal papers to gain a robust technical appreciation.
  • Create a Github account and participate in Kaggle competitions. Nothing sticks as well as learning by doing.
  • You can also browse into Data Helpers, a site voluntarily set up by Angela Bass where interested data science people can offer to help newcomers with their queries on entering the required field and anything else.

2. Identify your strengths and interests to realign your career trajectory

Generative Adversarial Network

OK, now that you have got your daily learning routine in place, it is time to think a little more strategically about your career trajectory, goals and eventually the kind of work you want to be doing.

This means:

  1. Getting out of jobs that can be automated
  2. Developing skills that augment or complement AI driven tasks
  3. Finding your niche and developing deep domain expertise that AI will find hard to automate in the near future

Here are some ideas to start thinking about some of the above ideas.

The first step is to assess your current job role and understand how likely it is to get automated. If you are in a job that has well-defined routines and rules to follow, it is quite likely to go the AI job apocalypse route. Eg: data entry, customer support that follows scripts, invoice processing, template-based software testing or development etc. Even “creative” job such as content summarization, news aggregation, template-based photo-editing/video editing etc fall in this category. In the world of data professionals, jobs like data cleaning, database optimization, feature generation, even model building (gasp!) among others could head the same way given the right incentives. Choose today to transition out of jobs that may not exist in the next 10 years.

Then instead of hitting the panic button, invest in redefining your skills in a way that would be helpful in the long run. If you are a data professional, skills such as data interpretation, data-driven storytelling,  data pipeline architecture and engineering, feature engineering, and others that require a high level of human judgment skills are least likely to be replicated by machines anytime soon.

By mastering skills that complement AI driven tasks and jobs, you should be able to present yourself as a lucrative option to potential employers in a highly competitive job market space.   

In addition to reskilling, try to find your niche and dive deep. By niche, we mean, if you are a data scientist, choose a specific technical aspect in your field, something that interests you. It could be anything from computer vision to NLP to even a class of algorithms like neural nets or a type of problem that machine learning solves such as recommender systems or classification systems. It could even be a specific phase of a data science project such as data visualization or data pipeline engineering. Master your niche while keeping up with what’s happening in other related areas.

Next, understand where your strengths lie. In other words, what your expertise is, what industry or domain do you understand well or have amassed experience in. For instance, NLP, a subset of machine learning abilities, can be applied to customer reviews to mine useful insights, perform sentiment analysis, build recommendation systems in conjunction with predictive modeling among other things.

In order to build an NLP model to mine some kind of insights from customer feedback, we must have some idea of what we are looking for. Your domain expertise can be of great value here. If you are in the publishing business, you would know what keywords matter most in reviews and more importantly why they matter and how to convert the findings into actionable insights – aspects that your model or even a machine learning engineer outside your industry may not understand or appreciate.

Take the case of Brendan Frey and the team of researchers at Deep Genomics as a real-world example. They applied AI and machine learning (their niche expertise) to build a neural network to identify pathological mutations in genes (their domain expertise). Their knowledge of how genes get created and how they work, what a mutation looks like etc helped them feed the features and hyperparameters into their model.

Similarly, you can pick up any of your niche skills and apply them in whichever field you find interesting and worthwhile. Based on your domain knowledge and area of expertise, it could range from sorting a person into a Hogwarts house because you are a Harry Potter fan to sorting them into potential patients with a high likelihood to develop diabetes because you have a background in biotechnology.  

This brings us to the next resolution where we cover aspects related to how your work will come to define you and why it matters that you choose your projects well. 


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