Data Science for Beginners: 2023 - 2024 Complete Roadmap
Published: 2023-09-30 12:56:43




![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/a7aaabkzge8s5nh5vfxb.jpg)
###<u>Introduction</u>

Data science is a changing field that continues to evolve at a fast pace. It offers exciting opportunities for everyone looking to embark on a career in the data-driven world of tomorrow. Whether you are a recent graduate, a career switcher, or just someone interested in harnessing the power of data, this comprehensive roadmap will guide you through the essential steps and resources to become a proficient data scientist in 2023-2024. Follow along if you want to become a data scientist.


####1. Understanding Data Science.

This will give you insights into data science and its application in real life.
 > - [<u>What is Data Science</u>](https://www.ibm.com/topics/data-science)
> - [Key skills and concepts](https://www.simplilearn.com/what-skills-do-i-need-to-become-a-data-scientist-article)
> - [Why choose Data Science](https://www.linkedin.com/pulse/why-should-you-choose-data-science-your-career-aashi-parashar#:~:text=A%20career%20in%20data%20science%20is%20challenging%20and%20stimulating.,and%20society%20as%20a%20whole.)

####2. Getting Started

This will help you in knowing the prerequisites required for data science. It will also help in setting up your environment and the essential tools required.
> - [Prerequisites](https://www.nobledesktop.com/learn/data-science/prerequisites)
> - [Setting up your environment](https://www.knowledgehut.com/blog/data-science/data-science-environment#:~:text=The%20first%20step%20of%20any,keep%20track%20of%20your%20code.)
> - [Tools required](https://medium.com/javarevisited/10-essential-tools-data-scientists-should-learn-in-2022-acbae6558643)

####3. Learn a Programming Language.

For data science, you will choose between Python and R programming. If you are coming from a social or arts-related field R programming will be easier for you. If you are from a scientific field, Python programming will be easier for you. Here are resources for you.
_**Python Resources**_ 
> - [Python as a data science language](https://www.geeksforgeeks.org/python-for-data-science/#:~:text=Benefit%20Of%20Using%20Python%20For,has%20excellent%20machine%2Dlearning%20libraries.)
> - [code academy](https://www.codecademy.com/learn/learn-python-3)
> - [Coursera's Python for everybody](https://www.coursera.org/specializations/python)
> - [Python.org](https://www.python.org/)

After learning the basics of Python, you dive into the following:

<u>_Data Manipulation and Analysis_</u>

You will get an introduction to Python libraries like Pandas and NumPy. You will also learn Data Cleaning and Preprocessing and finalize with Data Visualization with Matplotlib and Seaborn.
Here are some resources:
> - [Pandas Documentation](https://pandas.pydata.org/pandas-docs/stable/index.html)
> - [NumPy Documentation](https://numpy.org/doc/stable/)
> - [Data visualization in Python](https://www.simplilearn.com/tutorials/python-tutorial/data-visualization-in-python)

_<u>Machine Learning Fundamentals</u>_

You then learn machine learning. You will focus on Supervised, Unsupervised, and Reinforcement Learning. You also know Scikit-Learn for Machine Learning.
Resources are provided here:
> - [Scikit-Learning Resources](https://scikit-learn.org/stable/documentation.html)
> - [Coursera's Machine Learning](https://www.coursera.org/learn/machine-learning)

_<u>Deep Learning and Neural Networks</u>_

Learn Introduction to TensorFlow and PyTorch. You also Build and Train Neural Networks.
Resources include:
> - [TensorFlow Documentation](https://www.tensorflow.org/guide)
> - [PyTorch Documentation](https://pytorch.org/docs/stable/index.html)

_**R Programming Resources**_
> - [R for Statistical Computing](https://www.r-project.org/about.html)

####4. Learn a database querying Language.
As a data scientist, you will be interacting with database a lot. There are several types of database which include [Relational Database](https://www.geeksforgeeks.org/relational-model-in-dbms/) or [noSQL](https://www.geeksforgeeks.org/introduction-to-nosql/).
Learn SQL for interacting with databases.
Here are the resources;
> - [SQL Tutorial](https://www.w3schools.com/sql/)
> - [SQL commands](https://www.codecademy.com/article/sql-commands)
> - [Coursera's SQL for data science](https://www.coursera.org/learn/sql-for-data-science)

####5. Data Science Projects.
For you to stay relevant in the field of data science, you will have to build projects, participate in hackathons, contribute to open sources, and also take part in competitions like the one for Kaggle.
You can store your code in [Github](https://github.com/)

####6. Statistical Analysis

You will need to have some statistical information for data analysis. This will include Hypothesis Testing and Bayesian Inference.
Resources include;
> - [Coursera's Statistical inference for data science](https://www.coursera.org/learn/inferential-statistics-intro)
> - [Bayesian Methods](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)

####7. Big Data Technologies

Big Data Technologies are defined as software utilities that are primarily designed to analyze, process, and extract information from large datasets with extremely complex structures that traditional data processing technologies cannot handle.
You can learn more about Big data technologies here, [Big Data Technologies](https://www.interviewbit.com/blog/big-data-technologies/)
####8. Continuous Learning and Networking

Stay updated and join data science communities for networking.
Start your data science journey today!