You’ve decided to get trained in data science and analytics, smart move.
As you are working hard to achieve your data science and analytics qualifications – there are additional efforts to ensure your future big data career is prepped and primed for success:
1. Complete additional online training to enhance your learning experience
Completing additional online training while gaining formal data science qualifications will allow you to enhance your learning experience while giving you the opportunity to practice what you’re learning in class, independently. Learning independently and on the job is a part of the data science professional’s day to day.
So whether you’re enrolled in a full time or part time education program, short course, or you’re teaching yourself online, do that little bit extra. Do the extra reading, try the extra task, make time to practice your skills and apply the knowledge you’re learning week to week. Data Scientists are constantly learning new techniques, so you should get into the habit of teaching yourself with new materials.
For example, you could complete some extra modules on SQL / Python / R via DataCamp to accelerate your learning process or participate in Kaggle competitions to practice working under pressure and collaborating with fellow data scientists in a digital setting.
Taking the initiative to push and practice your proficiencies while you’re still will prepare you for when it’s time to be tested in class, in an interview or as a future professional.
2. Become familiar with data science & analytics news and released technology
Staying informed about new data science news, technologies and industry advancements will give you a competitive edge in interviews and enable you to make connections by relating to what’s happening in the industry. Keeping up with new technologies and practicing them in your own time will give you a head start on understanding new techniques and processes . It will demonstrate that you are a self-motivated learner and are willing to adapt your skills to suit industry, employer, and project needs.
As the big data industry is still evolving and regularly creating new jobs across industry sectors – it is important to become familiar with data science and analytics new terminology. This is important for a few reasons:
- You will be able to identify the kind of job you want to pursue after graduating. This is because you will be able to decipher the terminology used in different job descriptions on job boards
- By researching and understanding complex data science and analytics language – you will be capable of simplifying this jargon to efficiently communicate with stakeholders / clients / managers that may not be versed in big data language. It will help you understand your findings in order to make evidence based business decisions.
To become more knowledgeable in big data news, terminology and technologies, we recommend subscribing to industry blogs and podcasts, reading big data project briefs / existing job descriptions and examining case studies, new AI start ups and industry reports.
3. NETWORK! Find a mentor, build a study group, ask questions & make friends
Networking is the smartest thing you can do with your career outside of learning.
Finding a mentor to guide your data science and analytics journey is an invaluable resource because they would have already gone through a similar process in their career. A mentor can offer you first hand insight and advice. You can find a mentor through your education program, at a networking event, by reaching out online or through a mutual contact.
Building a study group while you’re learning will actually double up as your data science and social network once you graduate and enter the industry.
Here are 5 benefits of building a study group while getting qualified:
- You can work together on structured and original projects to include in your portfolio and pitch ideas to gain constructive feedback from individuals in your field
- You can build your communication skills working with diverse individuals to meet deadlines and design data driven solutions – similar to an industry team dynamic
- You can reaffirm what you’ve learned by explaining complex ideas simply to each other, the same way you would be expected to present complex data insights to key decisions makers in a data science job
- You will have a support group during class, this will give you the confidence to ask questions about assignments and course content, and share your ideas with the course trainer, as many students usually have similar questions that need answering
- It is much easier to ask a friend for help when studying for an exam, if you need an extra pair of hands for a project or a referral for a job – your study group will become your close network as you enter the industry
Up to 85% of jobs are found through networking, so do not underestimate the value of this on your career!
4. Research, plan, create, and test an independent project
The data science industry is on high alert for data science professionals who are equipped with job-ready skills that can demonstrate their initiative and ability to think and perform like a data scientist even before they are hired and paid to be one.
The best way to stand out from the crowd when applying for jobs and during the interview process is to have a professional portfolio with projects you’ve created using the tools (Python, SQL, R, Hadoop, Spark) for the job. Collaborations are also valid, as long as you can demonstrate which aspects of the project you worked on.
Researching, planning, building and testing an original data based project will give you the opportunity to go through the process of completing tasks you would be required to on a daily basis in a job. This process will also allow you to determine which areas of data science you enjoy the most (data handling, analytics, AI/machine learning, text analytics, automation, data modelling / visualisation, statistical research, data engineering etc.).
Here are some project ideas:
- Find an existing data set online – clean, organise, filter, predict and present your findings
- Find an existing data driven project and improve on it (bonus points if it’s a project for the business you’ve applied to)
- Find an industry sector / topic that interests you and build a project from the ground up using all the practical skills you’ve learned
5. Create a career plan, apply for internships, participate in industry activities & plan ahead
While you’re becoming trained in the practical skills needed for a job in the data science industry, now is also the perfect time to plan ahead and write down all the steps you need to complete in order to launch your data science career.
Here is what your career plan could look like:
- Enrol into and complete an industry recognised data science program
- Complete extra learning modules to practice using data science tools / skills
- Keep up with industry news and tech advancements
- Build a study group and start creating portfolio projects
- Identify which types of jobs you want to apply for after graduating
- Find a mentor / references
- Start putting together job applications (compile portfolio /resume / write cover letters)
- 8Apply for internships / entry level positions to gain experience
- Attend networking events/participate in industry initiatives (hackathons/online forums)
- Keep applying to jobs you match the job descriptions for / keep up with your study group
6. Take a break & appreciate your achievements
This may be one of the hardest steps to take while gaining your qualifications but it’s necessary. Don’t forget to take a moment to appreciate how far you’ve come in your data science journey and all the little victories you’ve had along the way. It takes time, determination, focus, and an aptitude for data, to get where you are right now. So, take a break, appreciate your achievements, contemplate the next steps in your plan, and then get right back learning and progressing your career.
Taking the initiative to implement these extra steps while studying and earning your qualifications in data science and analytics, will enable you to prepare your environment to help accelerate your job search and career progression once you have graduated. You will be equipped with the knowledge, training, network, and data scientist mindset necessary for a successful big data career transition.