- 1 Focus too much on theory
- 2 Using too many technical jargon in your resume
- 3 Not focusing on presentation and communication skills
- 4 Coding from scratch
- 5 Relying on your degree for a job
- 6 Not being up to date with the latest trends in data science
- 7 Narrowing your job search
- 8 Not networking with data science professionals
- 9 Not discussing your projects during an interview
- 10 Being thorough with domain knowledge
- 11 Jumping straight towards the deep end
- 12 Trying to learn multiple tools at once
Every day, all over the world, people are generating 2.5 quintillion bytes of data. That is huge!
In today’s world, data science has become a lucrative field and a lot of professionals are looking to either start their career in data science or switch to this field. The job title of Data scientist has been named as the sexiest job of the 21st century. Every industry needs data science but there are not many professionals in the field, which is why the demand for talent in this field is constantly increasing.
Data science professionals derive valuable insights from data sets and perform analysis and visualization on these insights. Becoming a data scientist is not easy. One needs to have skills in mathematics, programming, and other technical skills. If you are someone who is planning to begin their career in data science, pursuing a Data Analyst certification course would be a good place to start.
Whether you are from a technical background or a non-technical background, it can be difficult for anyone to jump into this sector. At the beginning of your data science career, you must try to avoid these common mistakes –
Focus too much on theory
When you are a beginner, you tend to spend too much time learning all the concepts and forget to practice those concepts through live projects. It is okay to have partial knowledge and move ahead from there. You will end up learning a lot of things as you go, with hands-on experience by working on projects and data sets that utilize the theoretical concepts.
Using too many technical jargon in your resume
Your resume is an on-paper version of what you can bring to the table and the impact that you can make. Do not make the mistake of filling it up with technical jargon. Instead of listing the programming languages or the tools you know, mention how you used these in projects and what results you gained from that. Also, do not clutter your resume with unnecessary information. Only list the most important skills and projects related to data science that you have worked on, to highlight them.
Not focusing on presentation and communication skills
Apart from technical skills, presentation, as well as communication skills are also an important part of a data scientist’s job role. Other than gaining valuable information from raw data, you should be able to present this information concisely and simply as not everyone is well versed with technical jargon. You should practice these skills by presenting sample projects and presentations.
Coding from scratch
A common mistake that entry-level data scientists tend to make is coding from scratch. Instead of doing that, one can pick up coding libraries as it is not necessary to code from scratch but that the correct algorithms are implemented in the right manner.
Relying on your degree for a job
A degree in data science will surely be beneficial, but a degree alone will not help you land a job in this field as it is an applied field. Apart from theoretical knowledge, one should also know the practical applications of the skills they have learnt. Showcasing practical knowledge through projects and working on real datasets will be more beneficial.
Not being up to date with the latest trends in data science
Since data science is such a fast-paced field, it is always evolving. With it, data science professionals also need to stay current with the latest happenings in the industry. Having the most current skills and knowledge in this field will make you a more preferable candidate for various job roles in data science.
Narrowing your job search
Sometimes, job titles do not include the words data, analyst or scientist and we simply ignore it without looking at the job description. It so happens that job titles may vary but they happen to require the very same skills such as data visualization or machine learning. Hence, do not limit your job search by looking for data analyst openings or data scientist openings. Instead search for jobs by required skills, technologies used, job responsibilities or by different job titles such as “quantitative analyst”.
Not networking with data science professionals
One of the biggest mistakes that an individual wanting to start their career in data science can make is to not network with professionals in the field. You can use resources such as LinkedIn to connect with other professionals. You can also try to connect with other students if you are in a learning program.
Not discussing your projects during an interview
When you are in an interview, you need to be prepared to discuss your projects. Thus, review and practice talking about past projects that you undertook during internships or as part of your classwork.
Being thorough with domain knowledge
Every individual aspiring to begin their career in data science should possess knowledge about machine learning and other areas as well as technical skills, but that is not enough. They should also be thorough with knowledge about the position in the domain that you are applying for.
Jumping straight towards the deep end
When beginning your career in data science, everyone aspires to build big things – robots, self-driven cars and so on. But for that, one needs to master the fundamentals first and then slowly move towards the areas of natural language processing and deep learning.
Trying to learn multiple tools at once
When you try to learn too many tools at once, you may get a basic knowledge of each of them, but you will not be able to master one tool. Instead, you can focus on learning one tool at a time and use it to solve complex data science problems. Nowadays, recruiters are slowly moving towards hiring based on skills rather than tools-and-technology based hiring, since data science requires a combination of skills rather than just knowledge of different tools.