Potential non-technical challenges and how to overcome them
01 Organization and time management skills
Changing careers is a project. It requires a strategic plan , a timetable , and specific (realistic) small goals .
Ask yourself the following questions:
-Why do I want to be a data scientist? Which subjects am I interested in?
-Should I quit my job to devote time to learning the skills I need, or do I need to make a transition at the same time as my current job?
-What am I good at? What is my weakness?
-How much time and money am I willing to spend on changing careers?
-What new skills and qualifications do I need to master on the new career path?
-What is my learning style?
After you answer these questions truthfully, you can establish a career change plan to find a method that suits you. Here are some preparations you can do :
-Print a weekly plan and post it on the wall.
-Arrange daily schedule on Google Calendar.
-Determine what the plan can be implemented and what needs to be readjusted in the plan. Maybe you underestimate the time required for a course, or the learning resources are not appropriate.
-Don't be afraid to change your plan. Go to online forums to find better learning resources, or read some articles on time management and how to set SMART goals.
-Eliminate tasks from your to-do list. This motivation can avoid the initial frustration of learning new things.
02 Become a better communicator
Another essential skill for a data scientist is communication.
Data scientists will a lot of data into decision-makers and stakeholders in the decision-making basis . However, not everyone who needs to communicate is a data scientist or has a STEM background.
An introvert may not be excited about public speaking or continuous conversation. Communication in data science requires not only to be a good speaker, but also to cultivate these habits and skills :
-Explain complex concepts well to non-data scientists.
-Record the code and analysis so that other team members can learn from it in the future (or even the future self).
-Get in the habit of writing everything you do in your career and cultivate yourself to be a good writer.
Documents are power, so keep writing them down. In the near future, the skills that need to be shown on your resume can be supported by a well-documented code base (for example, on GitHub), blog posts, webinars, or talking about work. The sooner you start, the faster you progress.
03 Get ready to fight psychological pressure
A common challenge faced by many career reformers is psychological pressure .
After having an established career, suddenly becoming a novice again is not an easy task.
In this case, it is entirely a question of mentality: learn new things that can keep you motivated and excited !
Data scientist Omdena hosted a webinar on how to overcome psychological stress, which included understanding the skills gaps that needed to be filled, and recognizing the skills she had learned from previous careers.
04 Build your relationships
It is not difficult to use interpersonal relationships to find a job. In fact, from statistical data, most job opportunities come from personal relationships .
When you build a network, you just connect with people with similar interests, let them understand the topic of data science, and have a preliminary understanding of their career, because these people may become your future colleagues, the worst There may be no response.
One of the easiest ways to build relationships is through a collaborative project . In this project, you have the opportunity to share knowledge, collaborate with experienced practitioners, gain insights from people in different roles, and have the opportunity to find work.
Another good way to connect is to establish connections through the following data science authorities, such as:
§ Omdena
§ Women Who Code
§ Women in AI
§ Super data science
§ DataCamp Slack Community
Contact any interesting people you meet-speakers on your favorite podcasts, teachers of online courses, or bloggers you like.
The most important one is: stand up and ask questions. This is the best way to get feedback.
to sum up
Switching to a new career in data science is not an easy task. It requires rigorous planning and a passion for new things. At the same time, you need to work hard to communicate, establish your own personal network, find the learning resources you need and suitable for you, have the courage to ask questions, and persist in improving your abilities.
● How to open the door to the data science industry (part 1)