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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Data Science vs Software Engineer: Which is a Better Career?

A
Analytics Vidhya Last Updated : 14 Sep, 2023
8 min read

Introduction

In today’s tech-driven world, two professions have been making significant strides: Data Science and Software Engineering. While both play pivotal technological roles, they have distinct focuses, goals, and skill sets. This article will delve into the nuances that differentiate Data Science vs Software Engineering, shedding light on their fundamental differences, similarities, challenges, and future trends.

Table of contents

What is Data Science?

Data Science is an interdisciplinary field that extracts valuable insights and knowledge from data. It combines elements of statistics, mathematics, computer science, and domain expertise to analyze and interpret large datasets. Data Scientists are responsible for building predictive models, creating data-driven solutions, and providing actionable recommendations to drive decision-making.

What is Data Science
Source: Slide Team

Also Read: Is Data Science a Good Career?

What is Software Engineering?

Software Engineering, on the other hand, revolves around the design, development, and maintenance of software systems. Software Engineers primarily aim to create reliable, efficient, and scalable software applications. They write code, debug software, and ensure the end product meets user requirements and industry standards.

What is software engineering
Source: Krusche & Comapny

Key Differences Between Data Science vs Software Engineering

Focus and Goals

Data Science

  • Solving Complex Problems: Data Scientists use data analysis techniques, machine learning, and statistical modeling to solve intricate and often large-scale problems.
  • Improving Decision-Making: They work to enhance decision-making processes within organizations by providing data-driven insights and recommendations.
  • Creating Data-Driven Strategies: Data Scientists assist in formulating strategies and plans based on data, enabling organizations to make informed choices and seize opportunities.

Software Engineering

  • Designing Software: Software Engineers are responsible for creating the blueprint of software applications determining how they will function and look.
  • Developing Functional Products: They focus on writing code and building software products that meet specific requirements, are efficient, and function as intended.
  • Maintaining Robustness: Software Engineers ensure that software remains robust, reliable, and adapts to changing needs over time.

Skills Required

SkillsData ScienceSoftware Engineer
Technical SkillsProficiency in programming languages like Python or R, data manipulation, machine learning, and statistical analysis.Mastery of programming languages (e.g., Java, C++, or JavaScript), software development methodologies, and debugging techniques.
Professional SkillsStrong problem-solving, data visualization, and communication skills.Emphasis on software architecture, code optimization, and quality assurance.
Soft SkillsCuriosity, creativity, and adaptability to navigate complex data landscapes.Attention to detail, teamwork, and the ability to meet project deadlines.
Domain KnowledgeRequires domain-specific knowledge to contextualize data and insights within a given industry.It may require domain knowledge but is often more generalized across different projects.

Applications and Industries

Data Science

  • Finance: Data Science is used for risk assessment, fraud detection, algorithmic trading, and customer analytics in the financial sector.
  • Healthcare: It is crucial in patient diagnosis, drug discovery, predicting disease outbreaks, and optimizing healthcare operations.
  • Marketing: Data Science is applied in customer segmentation, targeted advertising, market basket analysis, and sentiment analysis.
  • E-commerce: It enhances user experience through recommendation systems, inventory management, and customer personalization.

Also Read: 14 Must Have Data Scientist Skills in 2023

Software Engineering

  • Mobile Apps: Developing applications for smartphones and tablets, ranging from social media apps to mobile games.
  • Web Development: Creating websites, web applications, and e-commerce platforms for businesses and individuals.
  • Embedded Systems: Designing software for embedded systems used in automotive control systems, IoT devices, and medical devices.

Workflow and Processes

Data Science

The workflow in Data Science typically involves several stages:

  1. Data Collection: Gathering relevant data from various sources, including databases, APIs, or web scraping.
  2. Data Cleaning: Preprocessing and cleaning the data to handle missing values, outliers, and inconsistencies.
  3. Data Analysis: Exploratory data analysis (EDA) to understand the data’s characteristics and patterns.
  4. Model Development: Building machine learning or statistical models to derive insights or make predictions.
  5. Deployment: Implementing the models into production environments for real-time use.
  6. Monitoring and Maintenance: Continuously monitoring model performance, retraining models, and maintaining the deployed solutions.

Software Engineering

Software Engineering follows a structured process:

  1. Requirements Gathering: Understanding the software requirements and defining what the software should accomplish.
  2. Design: Creating a detailed system design, including architecture, data structures, and user interfaces.
  3. Coding: Writing the actual code for the software application based on the design.
  4. Testing: Rigorous testing to identify and fix bugs, ensure functionality, and validate against requirements.
  5. Deployment: Deploying the software to production servers or distributing it to end-users.
  6. Maintenance: Ongoing support, updates, bug fixes, and enhancements to ensure the software remains functional.

Roles and Responsibilities

Data Science

  • Data Analysis: Data Scientists collect and clean data, perform exploratory data analysis (EDA) and prepare data for modeling.
  • Model Development: They create machine learning and statistical models to extract valuable information from data.
  • Feature Engineering: Data Scientists engineer features to improve model performance and accuracy.
  • Data Visualization: They use data visualization tools to communicate findings effectively.
  • Deployment: Data Scientists deploy models into production environments for practical use.
  • Domain Knowledge: They often require domain-specific knowledge to understand and interpret results in context.

Software Engineering

  • Requirements Analysis: Software Engineers gather and analyze requirements from stakeholders to understand project needs.
  • Coding: They write code to develop software applications, adhering to coding standards and best practices.
  • Testing: Software Engineers ensure software’s functionality, performance, and security through testing.
  • Deployment: They deploy applications to production environments and may be involved in DevOps practices.
  • Maintenance: Engineers provide ongoing support, bug fixes, and updates to keep software running smoothly.
  • Collaboration: They collaborate with cross-functional teams, including designers and product managers.

Job and Salary

Data Science

PositionExperience (years)Average Salary per annum (INR)
Data Engineer/Associate Data Engineer/ Data Engineer II2-45 – 13 lakhs 
Senior data engineer/Mid-level data engineer/ data engineer III4-510 – 24 lakhs 
Lead data engineer/Team lead data engineer5-717 – 30 lakhs 
Principal data engineer/Senior staff data engineer/Section lead data engineer8+23 – 40 lakhs 

Software Engineering

PositionExperience (years)Average Salary per annum (INR)
Software Engineer (Entry Level)0-23 – 7 lakhs
Mid-level Software Engineer2-58 – 18 lakhs
Senior Software Engineer5-815 – 30 lakhs
Lead Software Engineer8+25 – 45 lakhs

Educational Background and Training

Data Science

Data Science typically requires a solid educational background in mathematics, statistics, or computer science. Many Data Scientists hold advanced degrees such as Master’s or Ph.D. These degrees provide a deep understanding of the mathematical and statistical foundations for data analysis and modeling. Additionally, Data Scientists often undergo specialized training in data manipulation, machine learning, and data visualization tools.

Software Engineering

Software Engineering also necessitates a solid educational foundation, primarily in computer science. While a bachelor’s degree or higher in computer science is common, entering the field through other related disciplines is possible. The education focuses on programming, software development methodologies, algorithms, and system design. Software Engineers often engage in continuous learning to keep up with evolving programming languages, frameworks, and technologies.

Similarities Between Data Science vs Software Engineer

Despite their differences, Data Science and Software Engineer share some commonalities:

  1. Problem Solving: Both fields require strong problem-solving skills. Data Scientists and Software Engineers must identify and address complex challenges, whether optimizing a machine learning model or debugging software code.
  2. Programming: Proficiency in programming languages is crucial for both roles. While the languages may differ (e.g., Python for Data Science, Java for Software Engineering), the ability to write efficient and maintainable code is a common requirement.
  3. Mathematics: Data Science vs Software Engineering often involve mathematical concepts. Software Engineers use math for algorithm design and optimization, while Data Scientists rely on statistics and linear algebra for data analysis and modeling.
  4. Team Collaboration: Collaboration within cross-functional teams is a shared aspect. Both roles involve working with product managers, designers, and other professionals to develop and implement solutions.
  5. Continuous Learning: Both fields require continuous learning and updating with emerging technologies. To remain competitive, Software Engineers and Data Scientists must adapt to new programming languages, frameworks, and tools.
  6. Agile Practices: Agile methodologies like Scrum and Kanban are employed in both disciplines to manage project development efficiently. Agile fosters iterative development, constant feedback, and adaptation to changing requirements.

Career Challenges

Data Science

Data Science faces several challenges in its practice:

  • Data Quality Issues: Data Scientists encounter challenges related to data quality, such as missing values, outliers, and inconsistent data formats. Ensuring data accuracy and reliability is crucial for making meaningful insights and predictions.
  • Ethical Concerns: The ethical use of data is a significant challenge. Data Scientists must address concerns related to privacy, bias in algorithms, and the responsible handling of sensitive information to build trust with stakeholders.
  • Continuous Learning: Data Science is a rapidly evolving field. Professionals must continuously learn to keep up with new tools, techniques, and algorithms. Staying updated is essential to remain competitive.

Software Engineering

Software Engineering also deals with its unique set of challenges:

  • Security: Ensuring the security of software applications is a top priority. Software Engineers must protect systems from vulnerabilities, data breaches, and cyberattacks, which can severely affect users and organizations.
  • Code Complexity: As software systems grow, managing code complexity becomes challenging. Engineers need to maintain clean, maintainable, and scalable code to facilitate development and reduce the risk of bugs.
  • Meeting Project Deadlines: Software development projects often face tight deadlines. Managing resources, scope, and timelines is a constant challenge to deliver quality software on time.

AI and Machine Learning: AI and machine learning will continue to dominate both fields. They enable the development of smarter applications, automation of tasks, and predictive analytics, enhancing decision-making processes.

  1. Big Data: Handling and deriving insights from vast data will remain central. Advanced data storage, processing, and analysis technologies will evolve to address the challenges of managing and extracting value from big data.
  2. Automation: Automation tools and practices will become increasingly prevalent. In Data Science, automation streamlines data preprocessing, model training, and deployment. In Software Engineering, it accelerates testing, deployment, and maintenance tasks, improving efficiency.
  3. Ethical AI: Ethical considerations related to data privacy, transparency, and fairness will gain prominence. Both fields will focus on responsible AI development, ensuring that algorithms and applications are ethically sound and do not perpetuate bias.

These trends reflect the ongoing convergence of Data Science vs Software Engineering, as both disciplines leverage similar technologies and methodologies to harness data’s power for practical applications. Professionals in both fields will need to adapt to these trends to remain competitive and contribute to the evolving landscape of technology.

Conclusion

In conclusion, Data Science vs Software Engineer, while distinct in focus and goals, share commonalities in problem-solving and programming skills. Both fields face unique challenges, such as data quality in Data Science and security concerns in Software Engineering. The future promises continued growth with AI, Big Data, and automation shaping both domains. Ethical considerations will play a pivotal role. As these fields converge, professionals must adapt to remain competitive and contribute to the evolving tech landscape, ensuring that the power of data is harnessed responsibly, leading us toward a smarter, more data-driven future.

If you want to make a career in Data Science, all you have to do is enroll in our BlackBelt Plus program!

Frequently Asked Questions

Q1. Which is better, data science or software engineering? 

A. The choice between data science and software engineering depends on your interests and career goals. Data science focuses on data analysis and modeling, while software engineering involves designing and building software applications. Both fields offer rewarding opportunities, so it’s a matter of personal preference.

Q2. Is data science harder than software engineering? 

A. Both data science and software engineering can be challenging but in different ways. Data science requires strong mathematical and statistical skills for data analysis and modeling. Software engineering demands proficiency in coding, problem-solving, and software development practices. The difficulty depends on individual strengths and interests.

Q3. Is it easy to become a data scientist or software engineer? 

A. Becoming proficient in either field requires dedication and continuous learning. While it may not be easy, it is achievable with the right resources, training, and practice. Choose the path that aligns with your interests and commit to ongoing skill development for success in either data science or software engineering.

A
Analytics Vidhya

Analytics Vidhya Content team

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