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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 Engineer vs Data Scientist: Which Career to Choose?

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Analytics Vidhya Last Updated : 09 Dec, 2024
7 min read

In the world of data, two crucial roles play a significant part in unlocking the power of information: Data Scientists and Data Engineers. But what sets these wizards of data apart? Welcome to the ultimate showdown of Data Scientist vs Data Engineer! In this captivating journey, we’ll explore the distinctive paths these tech titans take to transform raw data into valuable insights.

Data Scientists use statistical expertise and machine learning magic to unearth hidden patterns and predict future trends. On the other hand, Data Engineers are the architects, building robust data pipelines and infrastructure to ensure smooth data flow and storage. Together, they form an unstoppable force that fuels the engines of innovation.

Table of contents

What is Data Engineering?

Data engineering refers to the procedure comprising data organization, storage and processing. Data engineering aims to leverage the potential of data in decision-making through varying analysis methods. Skilled and trained data engineers use advanced tools and technologies to carry out the process. 

Types of Data processing
Source: Integrate.io

What is Data Science?

Data science is a multidisciplinary field that dives deep into the field. With a more research-oriented perspective, it functions on the algorithms, processes, scientific methods and systems for knowledge and data extraction. It also utilizes advanced tools and techniques. However, the aim here is data analysis through statistics, artificial intelligence and machine learning. 

What is Data Science
Source: Slide Team

Data Engineering vs Data Science – Overview

AspectData EngineeringData Science
Primary FocusBuilding and maintaining data pipelines and infrastructureAnalyzing and interpreting data to extract insights
Role ObjectiveEnsuring data is collected, stored, and processed efficientlyLeveraging data to make data-driven business decisions
Skills RequiredDatabase management, ETL (Extract, Transform, Load)Statistics, Machine Learning, Data Visualization
Tools and TechHadoop, Spark, SQL, NoSQL databasesPython, R, SQL, TensorFlow, Pandas
Data ManipulationEmphasizes efficient data processing and storageFocuses on data analysis, modeling, and visualization
OutputStructured, clean, and accessible dataValuable insights, predictions, and actionable outcomes
Key ResponsibilitiesDesigning data architectures, data integration, data warehousingExploratory data analysis, predictive modeling, data visualization
Industry ApplicationData infrastructure, data pipelines, big data solutionsBusiness intelligence, predictive analytics, data-driven decision-making
CollaborationCollaborates closely with Data Scientists for data accessibility and qualityCollaborates with Data Engineers for data access and pipeline optimization
GoalSets the foundation for effective data analysisApplies analysis to drive data-based decision-making

Role and Responsibilities for Data Engineer and Data Scientist

Data Engineer Job Role

  • Work on the complex and new problems arising regularly 
  • Develop big data infrastructure for analysis 
  • Design, build, integrate and test data 
  • Manage, maintain and optimize it as per the individual data requirements
  • Build data pipelines 
  • Write complex queries and data mining 
  • Use ETL or Extra Transform Load for the development of large data warehouses

Data Scientist Job Role

  • Perform online experiments and develop hypotheses
  • Apply statistical analysis and machine learning algorithms on data for trend identification and creating forecasts
  • Visualize and communicate your findings to a technical and non-technical audience 
  • Develop compatible models for

Skills Required

Data Engineer Skills

Technical Skills

  • Deeper understanding and usability of programming languages such as Python, SQL,
  • Ability to handle frameworks  like NoSQL, Data streaming, MapReduce, Hadoop, Hive and Pig
  • Cloud computing 
  • Familiarity with data warehouse platforms such as IBM’s Db2 warehouse and Amazon’s Redshift 
  • Working knowledge of Linux along with Microsoft Windows

Soft Skills

  • Logical mind
  • Ability to identify the data requiring processing and analysis 
  • Able to smoothly function with cross-functional teams 

Data Science Skills

Technical Skills

  • Expertise in programming languages like SAS, R, Python and Java 
  • Proficiency in Big Data frameworks like Spark,
  • Knowledge of the basics of advanced technologies, including Machine Learning and deep learning
  • Ethical knowledge comprising security, biases and privacy 

Soft Skills

  • Out-of-the-box thinking
  • Ability to clearly and concisely explain the technical information in layman’s terms 
  • Ability to work independently 
  • Problem-solving 
  • Broad knowledge of advanced and important concepts 

Data Engineer vs Data Scientist Salary

Data Engineer

The salaries for different levels of experience of data engineers are as follows: 

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 

Data Scientist

The salaries at different experience levels for the post of a data scientist are tabulated as follows: 

PositionExperience (years)Average Salary per annum (INR)
Data scientist/data scientist II/Associate data scientist2-47 – 18 lakhs 
Senior data scientist/data scientist III4-516 – 30 lakhs 
Lead data scientist5-718 – 32 lakhs
Principal data scientist8+30 – 60 lakhs

Data Engineer and Data Scientist – companies that will hire you

Engineer:

  • Tech Giants: Google, Microsoft, Amazon, Apple, Facebook
  • Consulting Firms: Accenture, Deloitte, McKinsey & Company, PwC, EY
  • Financial Services: Bank of America, JPMorgan Chase, Goldman Sachs, Citigroup, Barclays
  • Healthcare: Johnson & Johnson, Pfizer, Merck, Abbott Laboratories, UnitedHealth Group
  • Retail: Walmart, Amazon, Target, Home Depot, Kroger
  • Telecommunications: AT&T, Verizon, T-Mobile, Sprint, Comcast

Scientist

  • Tech Giants: Google, Microsoft, Amazon, Apple, Facebook
  • Consulting Firms: Accenture, Deloitte, McKinsey & Company, PwC, EY
  • Financial Services: Bank of America, JPMorgan Chase, Goldman Sachs, Citigroup, Barclays
  • Healthcare: Johnson & Johnson, Pfizer, Merck, Abbott Laboratories, UnitedHealth Group
  • Retail: Walmart, Amazon, Target, Home Depot, Kroger
  • Telecommunications: AT&T, Verizon, T-Mobile, Sprint, Comcast

Startups

  • Data Engineering Startups: Databricks, Confluent, DataRobot, Snowflake, Cloudera
  • Data Science Startups: Palantir, Kensho, Dataiku, Datadog, Stitch

These are just a few examples of the many companies that hire Data Engineers and Data Scientists. The specific companies that hire you will depend on your skills, experience, and location.

Similarities Between Data Engineering and Data Science 

Regardless of the difference between data engineer and data scientist, there are some common points when considering data engineer vs machine learning engineer. They are enlisted as follows:

  • Programming: Knowledge of programming languages for building data pipelines and maintaining databases 
  • Data handling: The common skills here involve
  • Collaboration: They have to collaborate concerning data structure, deciding its compatibility with data analysis and pattern identification 
  • Data quality: Ensuring accuracy and consistency in data is an important task that both professionals need to perform 
  • Business understanding: Domain knowledge is essential for efficient functionality and understanding of the exact requirements 

Educational Background Required for Data Scientist and Data Engineer

Data science and data engineering are both great fields, but they require somewhat different educational backgrounds. Here’s a breakdown:

Data Scientist:

  • Master’s or Ph.D. More Common: For data scientist roles, employers often prefer a Master’s degree or even a Ph.D. [+] This is because data scientists need strong analytical skills in statistics, mathematics, and sometimes even business acumen.
  • Relevant Bachelors Degrees: That said, it is possible to land an entry-level data science job with a Bachelor’s degree in a field like computer science, mathematics, statistics, or economics.

Data Engineer:

  • Bachelor’s Degree is Common: For data engineer roles, a Bachelor’s degree in computer science, information technology, or a related field is typically sufficient. Some employers might prefer a Master’s, but it’s less common than in data science.
  • Engineering or Math Background Preferred: Data engineers benefit from a strong foundation in software engineering principles and may also have studied engineering disciplines, applied mathematics, or physics.

Certifications:

  • Both Can Have CS Backgrounds: While there are some differences, there’s also overlap. Both data scientists and data engineers can have computer science backgrounds.
  • Certifications Can Be Valuable: Regardless of your educational background, certifications in data science or data engineering can be valuable additions to your resume.

Conclusion

Effective data handling is crucial for any organization, and skilled professionals are essential for both Data Engineering and Data Science roles. These positions are in high demand, offering many opportunities for career growth and success. Interestingly, a common skill set in these fields allows for a smooth transition between the two, depending on one’s interests and aspirations. Whether you become a data engineer or a data scientist, honing your expertise in either domain promises a bright future filled with promising career prospects. Embrace the world of data, and open the doors to endless possibilities in shaping the fate and reputation of companies through data-driven decisions. Your journey into the world of data begins with boundless potential and opportunities! At the End of these Article you will understand difference between Data Scientist and Data Engineer.

Analytics vidhya offers a wide range of courses for data professionals to excel in their careers. You can access these data engineering and data science courses here

Frequently Asked Questions

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

A. Both fields are important and rely on each other for data handling. The ‘better’ field among the two depends on one’s interests, skills and career goals. 

Q2. Is data engineering harder than data science?

A. The challenges in both fields vary. While data engineers encounter problems in data processing, pipeline and infrastructure development, data scientists have to deal with ML algorithms, statistics and others. 

Q3. What pays more for data science or data engineering?

A. Data scientists are at senior level and hence are paid comparatively more than data engineers. 

Q4. Can a data engineer be a data scientist?

A. Yes, switching fields is easier by acquiring analytical skills, learning machine language and programming languages and working on data science projects. 

Q5.Who earns more data scientist or MBA?

Data scientists generally earn more than those with an MBA. The specialized skills in data science are in high demand, contributing to higher salaries compared to the broader roles associated with an MBA. If you’re looking for solid earning potential, a career in data science is worth considering

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Analytics Vidhya

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