How to Land a Data Science Job With Little or No Experience
12 valuable tips to consider
A lack of experience doesn't mean you can’t land a job in data science even though the data science job market is highly competitive.
Even for entry-level positions, employers are looking for highly experienced candidates.
Undoubtedly, the demand for data scientists has been extremely high for the next five or ten years due to the following factors.
- Large quantities of data generated through smart devices
- Computational resources especially cloud computing provided at low prices
- Development of complex algorithms such as GANs
- Open-source data science frameworks
Another very special feature of the data science job market is that it has a diversity of industries meaning that data scientists are working in a wide range of industries such as healthcare, transportation, manufacturing, academic, business, environmental, security, and so on.
On the other hand, data science is a broad field that can be subcategorized into machine learning (ML), data analysis, data visualization, computer vision, deep learning (DL), neural networks, predictive analysis, and so on.
So, there are plenty of positions or opportunities that exist in the data science job market. The most popular job roles are:
- Data scientist
- Machine learning engineer
- Decision scientist
- Data analyst
- AI/ML writer
- Data engineer
- Business analyst
- MLOps Engineer
- Data Visualizer
- Predictive analyst
Getting a job in one of the above fields means that you’re getting a job in the field of data science.
So, you have plenty of opportunities. But, you must consider the following tips to land your first data science job!
1. Master essential skills
The following skills are needed for those who want to land a job in data science.
Skills: Problem-solving, Decision-making, Programming (R or Python), Statistics, Mathematics (especially Linear Algebra and Calculus), Machine Learning, Deep Learning, Data Visualization, Storytelling and Report-writing.
To master these skills, you can do online courses, read books or even follow a degree in a university.
Your goal should be to get a solid educational foundation in data science!
When you mention these skills on your CV, it will stand out and you have more chance to get selected!
2. Be familiar with data science libraries
Every data scientist or machine learning engineer should be familiar with the following data science libraries.
Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Tensorflow (Keras), Yellowbrick, XGBoost, CatBoost.
There are many people who know Scikit-learn, but only a few will know how to use Tensorflow (Keras) which is related to deep learning and neural networks.
So, go ahead and learn how to use Tensorflow (Keras). Before that, you will need to learn other basic libraries such as NumPy and Scikit-learn as prerequisites.
3. Learn continuously and be up to date
Even after you get a job, don’t pause learning.
You need to learn continuously to get up-to-date knowledge on new tools and technologies which is a common scenario in data science.
If you pause learning for months, you will need to restart from the beginning.
4. Be specialized in a certain subject area
Knowing general data science may not be enough to land your first job! You need to be specialized in a certain subject area.
As I told you earlier, data science is a broad field that consists of many sub-fields. Out of them, machine learning and deep learning stand out!
So, if you can specialize in those areas, it will be an added benefit for you.
5. Get hands-on experience by doing it yourself
“Learn by doing” is the best way to practice any unknown skill.
Mastery = Experience + Knowledge (Education)
When you learn machine learning algorithms, for example, you can write and execute code that produces the outputs of the algorithms with real data.
Then you modify the code, apply changes and get new outputs. You can compare them and find the reasons why you get those outputs.
In this way, you will experiment with what you know and then you learn or find new things. That’s a very effective way of learning new things.
Try it yourself!
6. Write what you learn
“Write what you learn” is another effective way to practice data science skills. You can also start a data science blog to build a personal brand and show your data science skills like I am doing here. This will:
- Help others to learn data science, AI and ML.
- Improve your knowledge of data since, AI and ML.
- Help you to make some money as a passive income.
Just start your blog today!
The secret of getting ahead is getting started — Mark Twain
7. Take online courses
You may take some online courses to develop your data science skills. Online courses are available in general data science, machine learning, deep learning, cloud computing, computer vision, and so on.
Currently, edX and Coursera are the most popular platforms that provide online courses in data science. They may even offer degree programs by partnering with the world’s top universities. Most of their services are paid, but the free version may be just enough for most learners who want to develop their skills.
8. Add a few professional certificates to your resume
When you follow a paid course, you will also receive a professional certificate for that course. Adding a few such certificates is a great way to prove that you have obtained the necessary skills through verified institutes.
9. Build a data science network
Building a data science network gives you more opportunities to land your first data science job.
The following platforms are great places to build a data science network.
- LinkedIn: You can share data science-related posts and build a network.
- Medium: You can create a data science-related blog and build a network.
- Kaggle: You can participate in data science competitions and build a network.
10. Complete projects
The projects you are doing should be relevant to the industry you expect to work in. For example, if you prefer to land a job in the computer vision field, you should do projects related to computer vision.
11. Target entry-level positions or internships
When you have little or no experience, you should target entity-level positions or internships as they do not require you to have much experience.
After getting some valuable experience, you can apply for a higher-level position that usually requires more experience.
12. Create an impressive resume
Employers just take about 5 seconds to scan your resume to pass it to the next round. So, your resume should be impressive to get passed!
The first impression of the resume will decide whether you win the job. Here is an example of such an impressive resume.
This is the end of today’s article.
Please let me know if you’ve any questions or feedback.
Read next (Recommended)
- Your dream data science job may not be a bed of roses
- AutoML will not replace your data science profession
- 10 Real Truths about Machine Learning
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Rukshan Pramoditha 2023–04–28
