Want to Become a Data Scientist? Part 1: 10 Hard Skills You Need
A quick 10-step hard skill guide on what you need to become a Data Scientist.
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You may come across a lot of comprehensive articles on how to become a data scientist. They provide a lot of good information, however, they can be very overwhelming. Especially as a beginner, you just want to know what you need to know and get cracking.
This is exactly what this blog will be about. I will go through the 10 hard skills you need to become a data scientist.
If you do not know how to code in any programming language, your first step will be to learn how to code. My recommendation will be Python, as it is arguably the most popular programming language for data science.
A topic that some people say you don’t need in the world of coding. But I believe that is truly wrong. I did a BootCamp that did not touch on the mathematical side - and I definitely realized it played a big weakness in my proficiency in the field.
Areas of math that you will need for data science are linear algebra, linear regression, probability and statistics. Learning the math behind data science will be highly beneficial for your data science career and noticed by your employer.
Learning math can be nerve-wracking, so I completely understand your hesitance. Have a read of How To Overcome The Fear of Math and Learn Math For Data Science to ease your mind.
Integrated Development Environments (IDE)
An Integrated Development Environment (IDE) is a software application that has a comprehensive environment that has a combination of tools and features specifically for software development. IDEs will help you execute data analysis, visualization, and machine learning tasks. Choosing the right IDE for you is more down to your preference, for example, there are:
Your IDE is where you will learn how to become proficient in your programming language, learn math, and all the below. Jupyter Notebook and Visual Studio Code are my favorites! These will also be highly beneficial when you get a job as employers expect you to know popular IDEs.
Coding has been made so much easier over the years, and this is down to the variety of libraries available. These libraries are tools that you can use to streamline the data analysis and machine learning processes.
If you have decided to learn Python, these are the libraries I would suggest you learn:
- NLTK (Natural Language Toolkit)
- Beautiful Soup
The reason I am providing you with a list of libraries at the start is that as you go through your data science learning journey, you will start to see these libraries a lot. Learn what each of them provides and you will see where you can apply it. For example, Matplotlib can be used for data visualization.
Exactly what it says - transforming your data. Data transformation is an important phase for a data scientist as you will spend a lot of time taking raw data and modifying, adjusting and converting it into a format that can be used for analysis and other tasks.
You will need to learn about normalization, standardization, scaling, feature engineering, and more.
An article you can read: Data Transformation: Standardization vs Normalization
Data visualization is an important aspect of data science, as you will need to be able to convey your findings in more than one way other than coding. Not everybody on your team will be technically inclined, therefore presenting your findings in visuals will help with this and also the decision-making process.
The next thing you want to learn is machine learning. There are a variety of aspects within machine learning, and you won't be able to be an expert in everything - but it's still good to be a jack of all trades within this area. Brace yourself, because there’s a lot to learn.
You will want to start with the fundamental concepts such as supervised learning, unsupervised learning, classification and regression tasks. Once you have a good understanding of these and can differentiate them, you will then want to learn more about the different machine learning algorithms, such as support vector machines and neural networks.
Once you understand machine learning models, you will need to learn:
- Building a Machine Learning Model
- Model Evaluation
- Model Interpretability
- Overfitting and Underfitting
- Hyperparameter Tuning
- Validation and Cross-Validation
- Ensemble Methods
- Dimensionality Reduction
- Regularization Techniques
- Gradient Descent
- Neural Networks and Deep Learning
- Reinforcement Learning
As I said, there’s a lot to learn in this area, so I would advise you to take your time and practice!
Here’s an article that can help you: Top 15 YouTube Channels to Level Up Your Machine Learning Skills
Big Data Tools
Having all this knowledge is great, but some tools can take your data science career to the next level. Understanding different technologies, where they can be used and the pros and cons will make your data science journey more efficient.
There are a variety of tools and technologies out there that can be of great benefit to anybody working with data. However, I will list a few popular ones, such as Apache Spark, TensorFlow, PyTorch, Hadoop, Tableau, Git, and more.
Cloud computing is a very important element of data science because all the projects and tasks that you will be working on will turn into products. Cloud computing services enable scalable storage, and computing power and provide easy access to tools and services.
Other cloud computing aspects you will need to be knowledgeable about are data storage, databases, data warehousing, big data processing, containerisation, and data pipelines.
Have a read of:
- Beginner’s Guide to Cloud Computing
- How to Efficiently Scale Data Science Projects with Cloud Computing
I am going to add projects as the last hard skill you need as it showcases all of the above. Don’t go and do a bunch of projects just because you want to put it on your resume and land yourself a job. Yes, that is the end goal, but ensure that you fully understand your projects.
In an interview, you will be asked about your projects, the ins and outs and you need to be prepared to answer with as much knowledge as possible. Use your projects to showcase your skills, and how you identified your weaknesses and worked on them.
Have a read of:
Wrapping it up
I tried to keep this article as condensed as possible so you don’t feel overwhelmed. I hope I have succeeded and provided you with enough detail and resources to go and kickstart your data science journey!
Have a look out for Part 2 for the soft skills you need as a data scientist.
Nisha Arya is a Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.