So, you have decided to pursue a data science career, but learning data science can be intimidating, especially when you have just started your career. As we all know, with time, business is becoming data-driven, the world is rising connected, and every business needs data science practice. Thus, the job sector for data science is booming and evolving.
But, in a hurry to become a data scientist, many students will face fatal mistakes that will eat the most valuable things, i.e., time, energy, and motivation. That’s why in this data science career guide, we ensure to save you from making these mistakes.
Beginner Mistakes to Avoid While Starting a Career in Data Science
Let’s look at the 9 data science mistakes usually made by beginners in the initial stage of a data science career. You can avoid these mistakes and grow in your career.
- Not Spending Time to Understand The Problem
Probably this mistake is followed by every junior data scientist in their career. From the beginning of the project, they tend to jump headfirst into data and code without taking a step back and thinking about adding value they can bring.
But it is advisable that scoping the complete delivery of the project right from the start is highly crucial, and later have a clear vision of what data science can bring at each step. However, spending time understanding the problem will help in the following ways:
- Mapping the available data resources
- Define the data project assumptions
- Provide a realistic roadmap to achieve goals
- Describe project assumptions.
- Underestimating the Formal Education
While searching for data science degrees, most of them require education. However, there are many institutes and boot camps available that will complement the resume. Even multinational companies’ recruiters are looking for candidates with a technical or master’s degree in data science. The brightest side is that institutes are offering data science certifications and provide you with that level of knowledge to apply for a job confidently.
- Spending Too Much Time on Theory
Many beginner data scientist learners fall into this trap of spending too much time on theory. Even if it is related to math (linear algebra, statistics, etc.) or machine learning, this approach is inefficient mainly due to three reasons:
- It is time-consuming to learn every line or also understand it.
- Being human, remembering all the concepts is next to impossible. The data science career is practical work, so it is better to practice more.
- At last, there is a considerable risk that will demotivate you if you feel how this learning will be applied to the problem.
- Trying to Fly to the Top of The Ladder
People enter the data science world with high expectations of working in self–driving cars and medicines. But, it would help if you had in-depth learning and knowledge, which does not come overnight. To work as a data science professional, you must obtain experience with simple datasets, building machine learning algorithms, and many more.
- Coding is An Added Advantage
Assuming that coding is a prerequisite for performing data analysis is a myth. To excel in a data science career, coding skill is not only required. Along with coding, there are other aspects to cover that allow you to perform extensive functions, and sometimes it does not require coding at all. Coding is an added benefit to performing a complex task for which automatic tools are unavailable.
- Coding Too Many Algorithms from Scratch
Many data science beginners are enthusiastic about creating everything from scratch because they want to experience everything. But, this mistake causes students to miss the forest for the trees. Initially, you don’t need to code every algorithm from scratch.
Creating code from scratch can be cumbersome and consume much energy. If you are doing it for learning purposes, then it is fantastic. But now, thanks to machine learning libraries and cloud-based solutions, many practitioners never code algorithms from scratch. The only important thing understands how to apply the suitable algorithms in the correct settings.
- Ignore the Value of Domain Knowledge
While pursuing a career as a data scientist, it is imperative to understand the fundamentals functioning of the domain and make valuable decisions. Thus, selecting the correct parameters for modeling data science is used to improve the business and how the data science career is changing the other players in the market.
- Neglecting Communication Skills
Even though you might discuss technical areas like data mining, algorithms, and data analysis, strong communication is a hidden job description in the data scientist role. However, the entire business planning depends on how data science employees demonstrate the insights and pain points in language.
You are probably presenting your work to a business sponsor, and this person will not be able to understand the technical terms. They only listen to what is related to them. That’s where communication skills come into play.
- Search for a Job Effectively
It is also the most common mistake data scientist make. So, it is recommended not to apply through a job title; use your skill to help your search. Searching for a job related to skills will narrow your search and save time and energy.
Conclusion
Being a data scientist is always a journey of learning and evolving with a new problem. Never be afraid of these mistakes while begins your data scientist journey. However, taking the proper steps will lead to exponential growth. This list does not include all mistakes but is the most common one a data scientist makes.
