Does Data Science Require Coding? Skills, Languages & Beginner Guide

Yes, to work as a Data Scientist, you will need to learn one or two programming languages to dig deeper into the world of data science. While data science is one of the most in-demand fields in the world, from predicting customer behaviour to improving healthcare outcomes, data science powers modern decision-making across industries.

When newbies come to this field of data science, they always have this question in mind: “Does Data Science require me to learn coding?”. The answer is yes, but not as much as you might think. While you can still work on a basic level, if you want to be a perfectionist in this field, you will most likely need to learn coding. As we are talking about data science, let's see what programming languages are required to start your career in this field of Data Science.

The Honest Truth About Coding in Data Science

The Reality: Most Data Science Roles Do Require Some Coding 

The term "Data Science" indicates that your work will revolve around data, and to handle this data effectively, you will need to learn coding. Let’s explore the types of data you will be working with as a Data Scientist:

1. Data Collection and Cleaning: As a data scientist, you will be working on raw data that is provided to you, which is often incomplete and messy. You will be required to work on formation, cleaning the data, and removing duplicate values from the data, which is very time-consuming work, but can be done easily by coding.

2. Exploratory Data Analysis (EDA): As a data scientist, you are required to analyse the data, which requires you to first understand the data. Which can be easily done if you know coding, you can create visualisations like statistics, histograms, a detailed summary statistics to uncover patterns, spot anomalies, and form hypotheses.

3. Training and building a Model: In this, you will be required to write code to build machine learning models, in a simple linear regression or a complex neural network. You’ll also be using libraries and frameworks to train the model on your data, to evaluate its performance, and fine-tune its parameters.

4. Execution: Once you are done creating your model, you can deploy it to make it available for all, which requires you to build an application programming interface (API) that will allow other software to send data to your model and get outcomes.

5. Featured Engineering: In this, you will be creating new variables for the existing ones to improve their functions and performance. With the help of coding, you can create and test many features, while with no coding knowledge, your work and knowledge will be limited.

Which  Programming Language Will Give a Boost to Your Data Science Career?

While there are many programming languages you can learn, like C, C+, C++, Java, Python and R, languages are often used in Data Science, and compared to all the programming languages, Python is easy to learn.

• Pandas: Pandas in Python is a library for data manipulation and for analysing data, such as DataFrames similar to SQL tables.
• NumPy: NumPy is a fundamental package for numerical computation in Python. It's most essential for working with arrays and matrices.
• Matplotlib and Seaborn: Often used for creating statistics and interactive data visualisation.
• Scikit learn: Often used to go to the library for classification, regression, clustering, etc.
• TensorFlow and PyTorch: These libraries are often used for deep learning and to build and train complex neural networks.

R language is mostly designed for creating statistics and graphics, mostly popular in academic and statistical analysis.

• Tidyverse: This is a collection of packages, including dplyr for data manipulation and ggplot2 for visualisation.
• Shiny: A Popular framework for creating interactive web applications directly from the R language, and easy to share your analysis.

Data Science With Little or No Coding Knowledge. Is this Possible?

To become a perfect Data Scientist, you will be required to learn coding. Still, some tools can do minimal work for you without coding, like KNIME, RapidMiner, Tableau, Power BI and GoogleAutoML, which will allow you to drag and drop data components, run models and create visualisations with no coding.

While these tools can do minimal work for small businesses for getting insight and analytics, they come with limitations and can only be used if you are just exploring the field, not for complex work/coding.

• Limited flexibility compared to complex coding.
• It can't be used for running large database sheets.
• Difficult to run advanced machine learning models.

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Step-by-Step Guide To Become A Perfect Data Scientist for Beginners 

To become a good Data Scientist, you will be required to learn these things:

1. Python Basics: The beginners are required to learn the basics of the Python language.
2. Learn SQL: Use the Online Sandbox to learn databases for free.
3. Practice with database sheet: Use Kanggle or other government open Data portals for practical knowledge.
4. Start with small projects: Analyse weather patterns, social media trends or movie ratings.
5. Use visualisation tools like Tableau and Power BI.

To get a strong grip on this field of Data Science, you need to practice with these tools regularly.

Career Opportunities In Data Science 

Students of this field of Data Science can become:

• Data Scientist 
• Data Analyst 
• Business Intelligence Analyst 
• Machine learning Engineers

Conclusion

The answer to your question, "Does data science require coding?" is a resounding yes. To excel in the field of data science, it is essential to have a solid understanding of programming languages. Without coding knowledge, it is challenging to become an effective data scientist.

Beginners can start with foundational knowledge by becoming familiar with tools such as Tableau, Power BI, Excel, and Google Data Studio. This will help build their confidence as they gradually progress to learning coding and advancing their careers in the field of Data Science.