Data science is a very popular industry and it continues to grab the attention for a lot of people. You get to harness and quantify data, while also getting an amazing paycheck. That being said, getting into this field can be difficult. Which is why you want to target some entry level data science jobs at first. However, you need to make sure that you avoid any possible mistakes that might appear.
Which are the most common mistakes?
One of the main issues is that people tend to spend a lot of money on theory and they code too many algorithms from scratch. You need to practice as much as you can, because that’s how you will get to improve your knowledge and obtain better results. When it comes to coding, try to use libraries, avoid coding from scratch since you can waste a lot of time.
You also want to avoid jumping into the deep end or just using too much technical jargon in the resume. When a HR professional will interview you, they will focus less on your technical jargon from the resume or your academic degree. What matters is your knowledge and how you apply it. Domain knowledge in particular can be very helpful. Then there’s also the fact that a lot of people neglect communication skills, and that alone can be a major problem.
3 technologies that are must-have
When you try to find a great Entry-level data science job, it can be very important to know what skills you want to pursue. While there are many skills that you could focus on, I believe that AI and machine learning basics, R Programming and SQL database/coding are pivotal for this field. Not only do they give you a very good grasp of how you can acquire and process data, but they also make it easy to reflect over the results you provide and how they can be used further.
R Programming/ Python
R Programming is very important for any type of data science work. The reason is simple, R is very good at solving problems in the data science field, around 43% of all data science professionals are using it to solve statistical problems. The challenge with R programming is that it can have a bit of a learning curve. That’s particularly true if you already mastered a programming knowledge to begin with. Thankfully, there are training solutions online so you can get up to speed with everything and also practice as much as you can. I recommend the Data Science Training with R Programming Language from Simplilearn, which is a very simple and convenient approach.
You will also need Python coding in data science. Around 40% of data scientists are relying on Python as their primary language. Why is Python so relevant? It’s versatile, and on top of that you can take a variety of data formats, then import all the SQL tables into your own code. With its help you will be able to design and create data sets as you see fit. Once you harness the power of Python and R Programming, results can be incredible.
SQL Database/Coding
Although you will notice a lot of data science professionals using Hadoop and NoSQL, as an Entry-level data science professional you need to know how to work with SQL. This programming language is all about database management. You are expected to know how to delete, include or acquire any type of data from a database. With SQL you can also carry a variety of analytical actions, you can even work on data structures in a creative manner.
Learning as much SQL as you can will make quite the difference, since it helps you learn how to query a database and when to do that. You will also have access to commands that will help save time. Lastly, SQL will be able to help you understand relational databases. If you want to work as a data scientist, this is a very important thing to keep in mind.
Basics of Machine Learning and AI.
Machine learning topics like logistic regression, decision trees or supervised machine learning help you identify and resolve a variety of data science problems. Learning the basics of AI and machine learning can be the entry point in the data science field, since you will get to solve issues based on predictions. You can also try and expand to more advanced skills in machine learning and AI. These include adversarial or reinforcement learning, time series, outlier detection, natural language processing, survival analysis and many others.
How should you search for entry-level data science jobs?
The first thing you want to know is where you can find the entry-level data science jobs. You want to go on the large job boards like Glassdoor, Indeed or even LinkedIn. Granted, there’s a lot of competition here, but you will be able to reach more employers this way.
Maybe the best way to access entry-level data science jobs is to visit dedicated data science job boards. Kaggle Jobs, Data Science Central, AnalyticsVidhya, Analytics India Jobs, Stats Jobs, DataJobs, StatisticJobs UK or KDNuggets Jobs are a great starting point for many entry-level data science jobs. You can also check AngelList and GitHub Jobs, just to be safe. Then you can send messages and your resume to data science companies via their website. Sometimes this works, other times less so, but it’s worth a try.
Networking can also help you find more exclusive entry-level data science jobs. Then there’s also social media, where you can post your interest about a certain job or even connect with businesses directly. The potential is limitless!
Conclusion
I recommend you to take your time as you try to find the best entry-level data science jobs. It’s important to improve your skills as much as possible and ensure that you deliver the best results. This field is ever-changing, so the willingness to improve and boost your knowledge can really take things to the next level. Then you also need to know where to find the right data science jobs too. Use that to your own advantage, follow these tips and you will find it a lot easier to find the best entry-level data science jobs to suit your needs!
Let me know what you think! Thanks for reading!