Javarevisited: The 2024 Data Scientist and Data Engineering RoadMap

Hello guys, if you want to become a Data Scientist or Data Engineer in 2024 and looking for guidance like how to become a Data Scientist and Data Engineer in 2024 then you have come to the right place. In the past, I have shared best Data Science courses, tools, and best websites to learn Data Science and in this article, I am going to share the complete Data Scientist and Data Engineering RoadMap. This roadmap include all the skills, tools, and technologies you need to become a professional Data Scientist in 2024. While this roadmap is very comprehensive and contains tens of skills, tools, libraries, framework and programming language, they are divided into different categories like mandatory, good to know and other choices.

If you are a beginner you should focus on all the skills marked with yellow color as they are the mandatory skills you need to learn to become a Data Engineer in 2024.

Every organization has data stored in their service about their customers. They need to take advantage of this data to improve their service, better manage their marketing campaigns, but this is possible only by data scientists since they have the skills in math, programming, statistics to organize this extensive data and apply their knowledge to find hidden solutions in this data.

This article will show you the resources you need to learn to become a data scientist.

The 2024 Data Science and Data Engineering RoadMap

This is the full roadmap you can follow to become a Data Science and Data engineer in 2024. This roadmap is created by ErdemOzgen and its available for free on Github. I found this roadmap when I was thinking to create another one just like the 2024 Python Developer RoadMap and 2024 Data Analyst RoadMap I created earlier. When I see this I found it very relevant and comprehensive so I am sharing with you guys here.

Whenever I share any skill and ask people to learn a tool the big question comes where should I learn that skill, tool or library. To solve that problem, I am also sharing online resources where you can learn all these mandatory skills from the comfort of your office or home.

image_credit - https://github.com/ErdemOzgen/Data-Engineering-Roadmap

1. Learn Python Language

This journey starts with learning this fabulous programming language called python which almost every person who works as a data scientist should understand very well. This language is used a lot when working with data, such as collecting data from resources such as web scraping or the database. You will also need to visualize them and create a machine learning model for prediction.

There are a lot of python resources you could find on different websites, but I would like to suggest this course on Coursera that will help you a lot in learning this language:

1.1. Python For Everybody on Coursera

This is a good course for the basics of python language with no pre-requisites starting with the data types, python built-in data structures such as lists and dictionaries. Then you will learn to access the web by building a web scraper, which will be very useful when collecting the data and learning how to interact with SQL databases.

If you’ve mastered this programming language, then you’ve completed a long stage in this journey to become a data scientist. Still, there are many other things to learn, and let’s move on in this journey by understanding data visualization.

The course has more than a million students with a 4.8 rating score which is an excellent resource.

Though, if you like to learn Python from Udemy courses, you can join Angela Yu's 100 Days Of Python Bootcamp course and if you need more choices, you can also checkout this list of best Udemy courses to learn Python online.

2. Data Processing & Visualization

You can define data visualization as the process of converting your dataset after cleaning it into charts that have meaning and can drive decisions for offering better services, better user experience, understanding more about your customers, and the list is endless. There are a lot of data processing and visualization libraries that work with python, and let’s first explore two of the best data processing libraries:

2.1. Numpy

This is a python library developed to work with arrays. Numpy can use it for mathematical calculation, which is very important for knowing if you are a data scientist. It's also one of the essential Python library every Machine Learning Engineer and Data Scientis should learn. If you need resources, you can checkout this free NumPy courses and best NumPy courses to start with.

2.2. Pandas

This is used for working with tabular data such as CSV files, importing your data from different resources, and it is used a lot for data analysis and cleaning your data before using it. If you want to learn Pandas in 2024, you can check out these best Pandas online courses for Data Scientist and Machine Learning engineer.

And, if you need free resources then you can also see this list of free Pandas online courses to start with.

2.3. Matplotlib:

This is the most common and used python library for data visualization. It can create some fantastic graphs and charts with simple programming commands. It supports 3D visualizing, which makes it perfect for this purpose. Data Scientist and ML Engineer you should learn Matplotlib in 2024 along with NumPy and Pandas. If you need resources, you can see this list of best Matplotlib courses and tutorials to start with.

2.4. Tableau:

Tableau is a data visualization tool that doesn’t need any programming skills to use, and it is used a lot in the business intelligence industry. Non-technical people can use it for making customized dashboards. If you want to learn Tableau in 2024, you can join one these best Tableau online courses to start with.

2.5. Power BI:

Microsoft Power BI is a cloud-based data analytic and visualization service with a more incredible speed and efficiency offered by Microsoft. Many versions also work on the phone and desktop.

These are the best libraries and tools used among data scientists in their daily routine, but you explore more others if you want, such as Plotly and leaflet.

And, if you want to learn Power BI in 2024, you can also start with these best Power BI online courses where I have shared Power BI courses form both Udemy and Coursera for beginner and experience developers.

Top 5 Courses to Learn Microsoft Power BI in 2020- Best of Lot

Now, let’s move on to another important section in your data science journey: learning math.

3. Learn Math

You don’t need to have excellent skills in math to be a data scientist. Still, it would be best to have a basic understanding of math, such as linear algebra, calculus, probabilities, and statistics.

These skills will be beneficial when working with data, such as transforming it into another shape or performing operations using a numpy library. There are a lot of courses to learn math and statistics, but I will suggest to checkout these best Maths and Statistics courses on Coursera for learning these skills.

4. Machine Learning

Machine learning can be very useful if you want to become a data scientist since it will help you make predictions and it can make the machine take the right decisions without any human intervention. I will suggest some of the most used machine learning libraries learn:

4.1. Tensorflow

This is an open-source artificial intelligence library developed by Google and used a lot in deep learning models where you need to analyze a large amount of data. If you want to learn TensorFlow then you can checkout these best TensorFlow online courses where I have share best courses to learn TensorFlow from Udemy, Coursera, Pluralsight and Kaggle.

4.2. Scikit-Learn

This is the most used library among machine learning engineers and data scientists, which can be very useful in a small amount of data and easy to use compared to Tensorflow. And, if you want to learn Scikit learn library and need resources then you can also see this list of best Sciki-learn online courses where I have shared best Udemy and Coursera Courses to learn Scikit library.

Conclusion

This is an overview of the data science roadmap. You can learn more about programming languages used among data scientists such as R language, and deep dive into more about machine learning & deep learning.

Other Developer RoadMaps you may like to see

Thanks for reading this article, if you like this Data Science and Data Engineering RoadMap then please share with your friends and colleagues. IF you have any questions or feedback then please drop a note.

Javarevisited

Monday, August 21, 2023

The 2024 Data Scientist and Data Engineering RoadMap