Starting a new career in any domain is always challenging and exciting. In the field of data science, it is even more stressful and confusing because of different technologies that update every week, the intersection of various studies, and hard work during not only office hours. All these factors scare beginners, causing them often to make the wrong decisions about building a career in this field. In this paper, we will try to understand which skill set you should have at the very beginning of the path in data science.
Hi there! If you read this paper, I think you plan to build a perfect career in data science and looking for some help to start from. So we will not waste your time and start discussing what you need. Below, you will find categories of knowledge that will help you to have a good starting point. Of course, each company can provide its own list of requirements, but according to my technical background and experience in interview procedures, the following list will be enough to pass the average interview.
Desire above all
As we already know, Data Science is a dynamic field that requires always to be in the context of changes in the world. This means that you always need to update your knowledge and obtain new skills to be in demand on the market. Without this, in 1-2 years, your professional skills will be outdated, and you will have problems searching for a new job. Moreover, I will give an advantage to the candidate with less technical skills who desires to grow professionally compared to the guy who has better technical skills but thinks he already has all the required knowledge.
Programming
Data Science is a field directly linked to coding. So, this skill is mandatory even before you start to learn data science basics. Without the ability to write code, you will not build a model, write an inference script, or integrate your work with other system blocks.
Which programming language to select? I would say definitely Python. Potentially, during your career, you will face different languages (personally, I implemented DS projects in Python, Java, Swift, R, C++, and C#), but the majority of cases can be covered by Python, especially if we speak about the beginner level.
The final advice here: you should know the core functionality of Python language.
Version Control
Sharing your work with colleagues is as important as well as writing code. So before starting to work in any company, you should be able to operate with code repositories. The simplest way is to be familiar with git syntax and GitHub repositories. You should be able to commit, push, merge, pull the code, create a new branch, and some other stuff that you can find in various git tutorials.
Object-Oriented Programming
Every day, data science solutions become more structural and well-organized. If several years ago this direction was more exploratory with a relatively free code structure, now we need to follow rigid procedures to make the project really efficient. So, writing the code using OOP principles and design patterns is critical to make your code clean, scalable, and easy to support.
Algorithms
To my mind, algorithms are the first stage when you start coding, not depending on whether it is data science or software engineering. Background in algorithms will help you to write your code efficiently and think about the solution from different angles. A lot of data science projects require the implementation of different classical algorithms under the hood.
Development process
Each project is not only writing the code and training models - it is a complex system that should work according to some rules. So, you should be familiar with DS project methodologies (at least CRISP-DM), task management, and estimation procedures. These skills will help you to manage your work efficiently on personal and team levels.
Math
Of course, math is mandatory for any specialization related to computer science. It is important to have knowledge of linear algebra, probability theory, and statistics.
Classical Machine Learning
Not depending on the specialization in data science you select, you should have good knowledge of classical machine learning. It is classification and regression on tabular data, clustering, time series, feature engineering, hypothesis testing, etc. These skills are often required in projects related to computer vision and NLP.
Deep Learning
Without deep learning, you will not be able to work in any Data Science related fields. All state-of-the-art models for computer vision and natural language processing tasks are deep neural networks. So, you should understand the building blocks for models, how to select correct hyperparameters, and which architecture should be applied to your specific problem.
Computer Vision
Computer vision is a large field with a lot of branches. In the beginning, it will be great if you know the basic operations on images (it will be enough to go over documentation and examples in openCV) and the deep learning part of computer vision. It is how convolutional networks work, what is the meaning of convolution operation, and which architectures exist today. Also, it would be nice to solve object detection and segmentation problems.
Natural Language Processing
Today NLP thanks to LLM is on the top of popularity and hype. A lot of customers come with requests to build a chatbot of different levels of maturity. So you should have at least a basic understanding of LLM and its applications including external API and your own models.
Besides this, you should be also familiar with older NLP tools and tasks. You should be able to work with BERT and LSTM architectures, solve text classification and summarization, NER, relation extraction, and question answering problems.
Deployment
Model and app deployment is also a part of the data scientist's job. For the beginner level, it will be enough to have basic Docker knowledge as well as an understanding of the deployment in the cloud. Also, it will be good to have some skills in frameworks for inference optimization (like TensorRT or others).
Databases
Of course, any data scientist can't perform his work without databases. What you should know here - I think from the start it will be enough to write and optimize queries basic queries. And yes, important to have knowledge of both relational and document-oriented DBs.
As you can see, there are a lot of skills and knowledge that you should have to get your first job in data science. But it doesn't mean you can't even try to go to the interview in case you have not all items from the list. Performing the interview is also an experience that will help you in the future. Continue working, and very soon, you will get your first job!
Thanks for reading this paper, and I hope this information was helpful to you! See you soon on new papers in Data Science Factory.
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