Data Science Roadmap.
Tags: analytics, datascience, datanerd, bigdata
Published: 2023-09-29 13:41:23




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.


**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.

**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. 

**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.  

**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 2023 along with NumPy and Pandas. 

**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. 

**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. 

**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. 

**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. 

**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. 

**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. 

**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.