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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. Computer Science: A Comprehensive Guide

Ayushi Trivedi
Ayushi Trivedi Last Updated : 01 Aug, 2024
5 min read

Introduction

Lets place yourself in a tech conference; full of like-minded people, influential technocrats and IT enthusiasts. As you move through the crowd, you catch bits and pieces of two professionals discussing their work—one is a data scientist, who seems to be very passionate about the use of machine learning in predicting illnesses, the other is a computer scientist who also seems to be full of joy when explaining how he designed a new architecture for software. As you listen, you find that, even though their aims are related to technology, they use strategies and equipment unlike one another. This realization sparks your curiosity: what really defines data science from computer science? Welcome to embark on the journey with us to learn about those two intriguing fields, their specifics, and the possibilities for future technological experts.

Overview

  • Understand the fundamental differences between data science and computer science.
  • Learn about the educational paths and core skills required for each field.
  • Discover the various career opportunities available in data science and computer science.
  • Learn about the equipment and software that experts in these fields utilize.
  • Possess the knowledge necessary to decide which field would be the most suitable for your hobbies and professional objectives.

Table of contents

Understanding Data Science

Data science can be defined as a multidisciplinary practice, which uses methods for extracting information and value from data that is structured and unstructured. It integrates aspects of probability, computation, data analysis, and the specific field of study in deciphering data. Their ultimate aim is to make the data more analytical and accessible as a tool for decision-making and future planning for different businesses.

Understanding Computer Science

Computer science is defined as the scientific field that deals with computers and computational systems. It covers a vast area of disciplines like algorithms, languages of programming, software, artificial intelligence, and even designs of hardware. Computer scientists are involved in designing of algorithms, creation of software, and problem solving through computational ways.

Data Science vs. Computer Science

AspectData ScienceComputer Science
DefinitionConsiders the process of arriving at knowledge and discovering patterns or processing data using statistical and computational tools.Broadly includes the areas of computer science, computation, programming, and computer software engineering.
Core AreasStatistics, machine learning, data analysis, and data visualization.Mathematics and computing, computer software and application, computer system construction, cognition, and artificial intelligence.
Educational PathwaysAn undergraduate, master’s, and a PhD degree in data science, statistics, applied mathematics; certification in data analysis and machine learning.Computer science, software engineering, IT-related degrees; certification in programming, system architecture.
Programming LanguagesPython, R, SQL.Python, Java, C++, JavaScript.
Key SkillsStatistical analysis, programming, data visualization, machine learning.Coding, writing algorithms, developing software, system design.
Career OpportunitiesData analyst, data scientist, machine learning engineer, business intelligence analyst.Software developer, systems analyst, cybersecurity specialist, AI researcher.
Tools and TechnologiesPandas, NumPy, Tableau, Matplotlib, TensorFlow, Scikit-learn.Integrated Development Environments such as PyCharm, Eclipse, Git and GitHub, React, Angular, Spring.
FocusData analysis and interpretation, and identification of conclusions to guide decisions.Structuring, creating, and designing applications, algorithms, problem solutions.
Mathematics RequirementHigh emphasis on statistics and linear algebra.Concentration on combinatorics and the theory of algorithms.
Industry ApplicationsBanking and finance, healthcare, marketing, technology, e-commerce.Software development, network design, cybersecurity, artificial intelligence research.

Real-World Applications

Adding real-world examples can help illustrate the impact and significance of each field.

Real-World Applications of Data Science

Data science plays a crucial role in various industries:

  • Healthcare: Enhancing healthcare outcomes through the prediction of disease outbreaks, customization of treatment programs, and analysis of patient data.
  • Finance: Personalized financial advice, algorithmic trading, risk management, and fraud detection. Retail: Customer segmentation, inventory management, and recommendation systems.
  • Marketing: Analyzing consumer behavior, optimizing marketing campaigns, and sentiment analysis.

Real-World Applications of Computer Science

Computer science drives innovation across multiple domains:

  • Software Development: Developing applications that are used on different endpoints ranging from handheld gadgets such as mobile phones to the computers that make up server farms.
  • Cybersecurity: An information assurance plan that will address the issues of how to secure data and systems against various cyber threats through implementing measures such as encryption, use of secure code, and threat identification.
  • Networking: Having operational responsibilities for the construction of Internetworking, which comprises of routers, switches, and protocols.
  • Big Data: Designing systems to deal with large volumes of data for analysis and decision making, for example, constructing data marts, using real time analysis and developing architectures for large scale data processing.
  • Mobile Apps: Performance optimization of applications to runs on smartphones and tablets, user experience and graphical user interfaces, cloud service implementation while designing and developing applications for use on smart devices such as smartphones and tablets.
  • Web Development: Developing and sustaining websites and web-based apps that include manipulation of two layers of a website – client-side script and server-side script, focusing on the site’s navigability, as well as interacting with a database or another application program interface, known as API.

Trends and Future Outlook

Discussing current trends and future prospects can provide readers with a forward-looking perspective.

Trends in Data Science

  • Big Data Analytics: The growth in volume of data resulting in large amount of information to be analyzed and to determine the depth of the specific information required.
  • Automated Machine Learning (AutoML): Those that assist users in picking the most appropriate machine learning model, or predict and value its variables.
  • Explainable AI: Creating models that give clear and comprehensible outputs, which in the present digital word is absolutely essential for such fields as healthcare and finance.
  • Ethics and Privacy: Data protection and regulation in the use of data and the prevention of the misuse of data in various applications.
  • AI and Machine Learning Integration: Increasing integration of AI into various applications, enhancing their capabilities and functionalities.

Trends in Computer Science

  • Quantum Computing: Emerging technology that promises to solve complex problems beyond the capabilities of classical computers.
  • Edge Computing: Processing data closer to the source to reduce latency and improve real-time decision-making.
  • Cybersecurity Advances: New techniques and tools to combat evolving cyber threats.

Conclusion

In this article we saw Data Science vs. Computer Science. They are both somewhat interrelated fields when it comes to the contemporary technological world, although distinct in presenting their own set of problem-solving scenarios and processes. Data science is useful for those who love to analyze data and find relevant information, as well as like applying different methods of statistic and operations research to solve practical problems. On the other hand, computer science is for people who love the science as well as the practical aspect associated with it such as, software programming and system architecture. From the differences between these fields, one will be in a position to tell how the fields differ making choices in educational and career routes fitting the individual skills, interest and career goals.

Frequently Asked Questions

Q1. Can I switch from computer science to data science?

A. Yes, many professionals transition from computer science to data science, leveraging their programming skills and gaining additional expertise in statistics and machine learning.

Q2. Which field has better job prospects?

A. Both fields have strong job prospects, but data science is currently experiencing rapid growth due to the increasing importance of data-driven decision-making.

Q3. Do I need a Ph.D. to succeed in data science?

A. While a Ph.D. can be beneficial, many successful data scientists hold a master’s degree or even just a bachelor’s degree combined with relevant experience and skills.

Q4. What programming languages should I learn for data science?

A. Python, R, and SQL are the most commonly used programming languages in data science.

Q5. Is computer science only about programming?

A. No, computer science encompasses a broad range of topics, including algorithms, hardware design, artificial intelligence, and more.

Ayushi Trivedi
Ayushi Trivedi

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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