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So you want to learn data science? You’ve made a great decision!
Data science is one of the most in-demand skills in the world today – and it’s only growing in 2023 and beyond!
Fortunately, it’s not too difficult to learn if you follow the right steps. I find that many are looking to learn data science but not many people cover it in blogs.
That’s why in this article, I will cover how to learn data science in seven easy steps. I’ll explain why each step is important and provide examples of how to do it.
By following these steps, you’ll gain the experience and confidence you need to become a successful data scientist!
Read on for more information:
How to learn Data Science?
1. Choose the Right Learning Platform
The first step to learning data science is choosing the right learning platform. There are many different ways to learn data science, but not all of them are created equal.
Here are some learning platforms for picking up data science:
(a) Data science online courses
When it comes to learning data science as quickly as possible, you want to learn by taking a course. If you want to self-teach yourself data science through free resources only, it’s going to take a long time for you to grasp the basics.
Just take an online course from the following sites to get started:
Also, don’t forget to include any analytics or AI certifications that you finish in your LinkedIn profile.
(b) Data science bootcamps:
These are short, intensive courses that teach you everything you need to know about data science in a matter of weeks. Bootcamps can be expensive, but they’re worth it if you want to learn data science quickly.
Some of the best data science bootcamps include:
- General Assembly
- Flatiron School
- App Academy
- Fullstack Academy
- Codecademy Pro Intensive courses
(c) Data Science Books
If you want a more traditional learning method, then data science books are for you. Data science is a vast subject, and there are many different books that cover different aspects of it.
Here are some popular data science books:
Data Science from Scratch by Joel Grus
Python Data Science Handbook by Jake VanderPlas
R for Data Science by Hadley Wickham and Garrett Grolemund
Doing Data Science by Cathy O’Neil and Rachel Schutt
Data Wrangling with Python by Jacqueline Kazil and Katharine Jarmul
(d) Data science YouTube channels
Another great way to learn data science is by watching YouTube videos. There are many great channels that cover different aspects of data science.
Some of my favorites include:
These data science YouTube personalities are what help you demystify data science and learn how data scientists think, talk, and just the language they use on a daily basis.
Do make sure to check out some of their day-in-the-life videos for you to learn whether data science is the right career for you!
(e) Data science Discord groups
If you want to learn data science with others, then joining a Discord group is a great idea. In these groups, you can ask questions, share resources, and collaborate on projects with other members.
Some of the best data science Discord groups include:
These are just some of the ways you can learn data science. Choose the platform that works best for you and get started today!
Credit to Any Instructor for this list.
2. Get Familiar with Basic Data Science Concepts
The next step is to familiarize yourself with basic data science concepts. These include things like statistics, data modeling, and machine learning.
If you’re not familiar with these concepts, don’t worry! There are plenty of resources that can help you learn them.
Some resources for learning these concepts include:
Data Science from Scratch by Joel Grus
Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Gé.
These are just a few of the resources you can use to learn data science concepts. Use whichever resource works best for you and dive in!
Once you’re familiar with basic data science concepts, you can move on to the next step: learning how to code.
3. Start Coding!
The next step is to start coding!
The 2 most popular data science programming languages you’ll use are R and Python. You’ll also use Structured Query Language (SQL) really often if you are working with querying directly from databases.
When many people think of data science, they think that learning how to code is everything that a data scientist will need to do. However, you need to start thinking of coding as a tool to get to the answer and solution that you need.
Almost every problem given to you as a data scientist will be very applicable-based and solves a business need. Coding is merely the means to solve that problem.
Having said that, if you’re not familiar with how to code, don’t worry! There are plenty of resources that can help you learn.
In the online courses that I mentioned at the start of the article, there are some parts of the courses that teach you how to code.
If you want to break into data science for the biology and healthcare industry like I did, you’ll want to take up R as your first programming language. If you want to enter any other industry that requires more flexibility and deployability, then you’ll want to learn Python instead.
Also, I think learning how to code in SQL is very important and also at the same time, very simple. It’s not as hard as you think!
Although you won’t be querying from SQL databases all the time as a data scientist, there will be times when you’ll need to make automated queries from relational databases to obtain data for training data models.
To make sure you find the best data science IDE for your use case, read this article!
If you find yourself not having enough time to learn how to code, there are data analysts out there that solely use data analysis programs like Tableau, KNIME, or Power BI for daily data processing.
If you want to find out where to learn Power BI course, read this article:
4. Learn Good Data Analysis and Visualization Practices
After you’ve learned how to code, the next step is to learn good data analysis and visualization practices.
This step is important because it will help you understand how to effectively communicate your findings to others.
Learning how to perform good data analysis also means understanding how data types work and how you can analyze them. This can include learning how to wrangle text data using Natural Language Processing (NLP) techniques.
Here’s a good video to learn what data analysis is:
The data analytics process is perhaps more important than learning how to train the model right. As a budding data scientist, this is what you need to master more than any other skill. In fact, data scientists spend a good amount of time checking and cleaning data before they begin any data processing. This is all part and parcel of the data analysis process.
Here’s a good video to describe what data visualization is:
Basically, it’s the production of graphs, charts, and figures that make sense to others without the need for much explanation. Good data visualizations will need to be easily interpretable and not have much visual clutter.
This is an entire art and many data scientists can get really into it, even up to the shade of colors to use and how to highlight specific data points you want to stress upon.
5. Learn How to Write Machine Learning Algorithms
Next, you’ll need to learn how to write machine learning algorithms.
This is important because it will allow you to create your own models and improve upon existing ones.
Okay, I know that this may sound quite foreign to you, but what are machine learning algorithms?
Machine learning algorithms are a set of instructions that a computer can use to learn how to do something without being explicitly programmed to do so.
In simpler terms, it’s a method of teaching computers how to learn from data. This is done by feeding the computer training data which contains a set of input and output values.
The computer will then try to find patterns in the data and use them to make predictions on new data.
I found this video on YouTube where a data scientist succinctly explains what machine learning is to different age types and expertise:
There are many different types of machine learning algorithms, but the most common ones are linear regression, logistic regression, decision trees, and support vector machines.
These are just some of the more commonly used machine learning algorithms. As you become more experienced, you’ll learn about more sophisticated ones.
But for now, just focus on understanding how these basic ones work.
In order to write machine learning algorithms, you’ll need to have a good understanding of linear algebra and calculus. These concepts are important in deriving the equations that are used in machine learning algorithms.
You don’t need to be a master of these concepts, but you should at least know the basics. If you need to brush up on your math skills, there are plenty of resources online that can help you out.
In my opinion, you should also get the best laptops for data science if you value portability, as some laptops can be too slow to train or even load large datasets.
6. Master Big Data Processing Techniques
The next step is to master big data processing techniques.
This is important because it will allow you to effectively handle large amounts of data. This is a common task in data science as many datasets can be quite large.
There are many different ways to process big data, but the most common ones are MapReduce and Hadoop.
MapReduce is a programming model that allows for the parallel processing of large datasets. It consists of two steps: map and reduce.
The map step takes in a dataset and breaks it down into smaller chunks which are then processed by different nodes in parallel. The results from the different nodes are then combined in the reduce step.
Hadoop is an open-source framework that allows for the storage and processing of big data. It consists of two parts: a distributed file system (HDFS) and a MapReduce programming model.
Both MapReduce and Hadoop are very powerful tools that can help you effectively process large amounts of data.
If you want to learn more about them, I would recommend checking out this tutorial:
And this one:
In newer and more modern companies, data scientists work in a modern data stack that consists of:
- cloud-based data warehouse (Amazon RedShift, BigQuery, Snowflake)
- data integration (ELT) tool (FiveTran, Integrate)
- data science tool (dataiku)
- reverse ELT tool (hightouch)
- data transformation tool (dbt)
- business intelligence tool
I know this might sound like a lot to take in, but essentially you’ll only be working with a data science tool as a data scientist. You’ll be building machine learning models in it. Other data professionals such as data analysts, data engineers, cloud engineers, and DataOps engineers will be working at the other various parts of the modern data stack.
7. Prepare for a Data Science Career
The final step is to prepare for a data science career.
This includes things like creating a strong resume, preparing for interviews, and networking with other data scientists.
Creating a strong resume is important because it will give you a chance to showcase your skills and experience. Make sure to highlight your experience with data science tools and techniques.
Preparing for interviews is also important as it will give you a chance to practice your interviewing skills. There are many resources available online that can help you prepare for data science interviews.
And finally, networking with other data scientists is a great way to learn about new opportunities and to stay up-to-date with the latest trends in data science.
And that’s it! I hope this article has been a great starting point for you to discover what it would take to learn data science.
Although the journey may not be easy, it’s definitely worth it!
As demand for data science and data professionals increases over the next few years, you’ll be in demand for jobs in companies all over.
I hope this article has helped you understand how to learn data science!
Thanks for reading and happy learning!
Justin is the founder and author of Justjooz. He is a Nanyang Technological University (NTU) alumni and a former data analyst.
Now, Justin runs the Justjooz blog full-time, hoping to share his deep knowledge of business, tech, web3, and analytics with others.
To unwind, Justin enjoys gaming and reading.