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Career Paths: Full Stack Development vs. Data Science: Role, Roadmap & Salary

Table of Contents

Who is a Full Stack Developer?

Who is a Data Scientist?

Full Stack Developer v/s Data Science

Full stack Data Science roadmap

Full stack data scientist Salary

Salary for data scientist


Career Paths: Full Stack Development vs. Data Science: Role, Roadmap & Salary

Are you curious about the career possibilities in Full Stack Development and Data Science? Want to know what kind of salaries you can expect in these fields? You've come to the right place! In this engaging discussion, we'll be your guide, sharing expert insights and salary information that will help you steer your career towards success. Let's dive into the world of Full Stack Development and Data Science , and uncover the pathways to your professional aspirations. Your exciting journey starts now!

Who is a Full Stack Developer?

A Full Stack Developer is a versatile software professional with expertise in both front-end and back-end web development. They can handle the entire web application development process, from designing user interfaces and writing client-side code (HTML, CSS, JavaScript) to configuring server infrastructure, managing databases, and implementing server-side logic (using technologies like Node.js, Python, or Ruby). Full-stack developers possess a comprehensive understanding of web technologies, enabling them to build, deploy, and maintain complete web applications independently or as part of a development team.

Who is a Data Scientist?

A Data Scientists is a professional skilled in collecting, analyzing, and interpreting complex data to extract meaningful insights and solve problems. They use statistical and computational techniques, machine learning, and domain knowledge to inform data-driven decision-making in various fields, such as business, healthcare, and research.

Full Stack Developer v/s Data Science

Aspect Full Stack Developer Data Science
Purpose and Focus Building and maintaining web and mobile applications. Extracting insights and knowledge from data, solving complex problems
Skills Front-end (HTML, CSS,Javascripts) Back end(e.g. Node.js,Python)

Database, UI/UX design
Programming (Python Or R), Data Manipulation, Statistics, Machine Learning, Data Visualization
Tools and Technologies Web development frameworks(e.g., React, Angular),databases (e.g. MySQL, MongoDB),Git Jupyter Notebooks,Pandas,TensorFlow,scikit-learn, Matplotib
Projects Web applications, Mobile apps, Software system Data analysis, predictive modeling , recommendation system, data- driven decision- making
Career Path Specializations in Front-end, Back-end, or DevOPS Machine learning engineer, data engineer, data architect
Demand and Job Market High Demand for web and app development High demand for data-driven decision-making and AI applications
Primary Focus Developing Complete web applications Analyzing and extracting insights from data
Role Works on both Front-end and Back-end Focuses on data analysis, modeling, and Visualization
Programming Languages HTML,CSS,JavaScripts,Python,Java,etc Python, R, SQL, and other data-related languages
Core Skills Web development, Database, Server-side programming Statics, machine learning, data manipulation

Full stack data science roadmap

To become proficient in all aspects of data science , including data collection, pre-processing machine learning and deployment you need to follow a roadmap. Here's a step, by step guide with explanations for each stage:

Building a Foundation:

Start by mastering the fundamentals of statistics. This includes understanding statistics, probability concepts and hypothesis testing. Get acquainted with terms like mean, median, variance, and standard deviation and p values. Develop a grasp of linear algebra and calculus as they form the basis for comprehending machine learning algorithms.

Programming Skills:

Learn Python programming since it is widely used in the field of data science. Familiarize yourself with libraries like NumPy, Pandas and Matplotlib for data manipulation and visualization.

Data Analysis:

Become proficient in using Pandas for data manipulation tasks such as cleaning up messy data sets or transforming them.Acquire knowledge of techniques to handle values, outliers and inconsistent data during the process of cleaning.

Data Visualization:

Gain expertise in using Matplotlib and Seaborn libraries to create appealing plots and charts that effectively communicate insights, from your data. Optionally you can consider familiarizing yourself with data visualization tools such, as Tableau or Power BI. These tools allow for business oriented visualizations.

Dive into Exploratory Data Analysis (EDA):

Conducting EDA will provide insights into your data, help you identify patterns and assist in selecting features for modeling.

Venturing into Machine Learning:

  • Take a look at Scikit Learn which offers a range of machine learning algorithms including regression, classification, clustering and dimensionality reduction.
  • Additionally you might want to explore the world of learning by using libraries, like TensorFlow or PyTorch to build network based models.
  • Model Deployment:

  • Web Frameworks: Deploy machine learning models via Flask or Django framework for web applications you want to learn about.
  • Cloud Services: For scaling model deployments, explore cloud solutions like AWS, Azure, or Google Cloud.
  • Advanced Topics:

  • Time Series Analysis: Through proper analysis training, you will have the skill set needed.
  • Reinforcement Learning: Learn about decision-making via reinforcement learning algorithms.
  • Ethical Considerations and Bias:

    Data science requires an understanding of ethics, particularly when it comes to biases, privacy, and justice.

    Career Paths: Full Stack Development vs. Data Science: Role, Roadmap & Salary

    Continuous Learning:

    Keeping current with the latest studies and instruments, data science fields advance quickly.

    Domain Knowledge:

    To effectively apply data science in any field, gain specialization knowledge (finance, healthcare).

    Soft Skills:

  • Communication: Effective communication requires strong skills; use them to share findings and insights.
  • Problem Solving: To solve complex problems related to data, improve your ability to tackle them head-on.
  • Full stack data scientist Salary:

    Organization Expected Salary
    Aganitha Cognitive Solutions 8 Lakhs per annum
    Wipro Above 5 Lakhs
    Jio 7-8 Lakhs
    WidasConcepts 17-19 Lakhs
    Markytics 6-7 Lakhs
    Velocity Clinical Research 30-32 Lakhs 17-18 Lakhs
    Robozz Lab 55-59 T

    Career Paths: Full Stack Development vs. Data Science: Role, Roadmap & Salary

    Salary for data scientist:

    The salary for a Data Scientist can vary widely depending on factors such as the location of the job, the level of experience, the industry, and the specific organization. Data scientist salaries also tend to change over time due to economic conditions, demand for data science skills, and other factors. However, I can provide you with a general idea of the salary range for data scientists in different types of organizations as of my last knowledge update in September 2021. Keep in mind that these figures are approximate and can vary significantly:

    Companies Salaries offered
    (0-7 yr exp)
    7.4 Lakhs
    (0-7 yr exp)
    11.5 Lakhs
    (1-9 yr exp)
    12.0 Lakhs
    (1-7 yr exp)
    8.5 Lakhs
    (1-7 yr exp)
    7.9 Lakhs
    (1-7 yr exp)
    7.7 Lakhs
    (1-7 yr exp)
    8.4 Lakhs
    Tech Mahindra
    (2-14 yr exp)
    7.3 Lakhs
    (1-6 yr exp)
    11.8 Lakhs
    (1-10 yr exp)
    39.4 Lakhs
    Tiger Analytics
    (0-6 yr exp)
    15.7 Lakhs
    Ab Inbev
    (3-7 yr exp)
    20.8 Lakhs
    Emst & Young
    (1-7 yr exp)
    11.6 Lakhs
    HCL Tech
    (2-7 yr exp)
    8.9 Lakhs

    In the end, it becomes a matter of taste, desires and personal objectives, where one opts for full stack development, while another goes for data science. Full Stack Developers are some of the most sought after because they can develop applications that run offline, mobile and online. Data Scientist primarily engage in analytical data processing, including model building; and it is data-oriented management decisions which form the essence of their work. It is important to note that there are numerous factors for example years of experiences, location, or company that lead to a significantly different pay by industry. For any success, continuous learning technology is required. Lastly, one should go for what suits their interests and anticipated goals in an ever-changing IT environment.


    Full Stack Developers focus on building and maintaining web and mobile applications, while Data Scientists extract insights and knowledge from data to solve complex problems.

    Full Stack Developers need expertise in front-end technologies (HTML, CSS, and JavaScript), back-end development (e.g., Node.js, Python), database management, and UI/UX design.

    Data Scientists need skills in programming (Python or R), data manipulation, statistics, machine learning, data visualization, and domain knowledge.

    Full Stack Developers use web development frameworks (e.g., React, Angular), databases (e.g., MySQL, MongoDB), and version control systems like Git.

    Data Scientists use tools like Jupyter Notebooks, Pandas, TensorFlow, scikit-learn, and Matplotlib for data analysis and machine learning.

    Yes, Full Stack Developers should stay updated with the latest trends and technologies in web development to remain competitive and adapt to changing user expectations.

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