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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

Who are Citizen Data Scientists and What Do they Do?

A
Analytics Vidhya Last Updated : 22 Apr, 2024
11 min read

Introduction

In today’s data-driven world, the role of data scientists has become indispensable. But what if I told you that you don’t need a Ph.D. in data science to unravel the mysteries hidden within vast data sets? Enter the era of Citizen Data Scientists – a new breed of empowered individuals with the skills and tools to uncover valuable insights without formal training. Citizen Data Scientists are ordinary people with extraordinary abilities to transform data into actionable knowledge, revolutionizing how organizations make decisions. In this article, we will explore the rise of Citizen Data Scientists, their impact on businesses, and the exciting possibilities they bring.

Table of contents

What are Citizen Data Scientists?

Citizen Data Scientists are individuals without formal data science training who possess the skills and tools to analyze data and derive insights. They leverage self-service analytics platforms and intuitive tools to explore data, build models, and make data-driven decisions, democratizing the power of data within organizations.

Citizen data scientists
Source: ElegantJ BI

Why is There a Growing Demand of Citizen Data Scientists?


The growing demand for citizen data scientists can be attributed to several factors, all of which are influenced by the rapid advancements in technology, data availability, and the increasing importance of data-driven decision-making in various industries. Here are some key reasons why the demand for citizen data scientists is on the rise:

Data Sbundance

In recent years, the amount of data generated by individuals and organizations has exploded. This data comes from various sources such as social media, internet activity, sensors, and transactional records. To extract meaningful insights from this vast amount of data, there is a need for more individuals who can work with data effectively.

Shortage of Data Scientists

There is a significant shortage of trained data scientists in the job market. These professionals typically require specialized skills in programming, statistics, and machine learning, making them relatively scarce. Citizen data scientists can help fill this gap by taking on simpler analytical tasks and freeing up data scientists to tackle more complex problems.

Democratization of technology

The democratization of technology has made data analysis more accessible to non-experts. User-friendly data analytics tools and platforms have emerged, enabling individuals without extensive technical backgrounds to perform data analysis and create visualizations.

Cost-effectivenessw

Hiring and retaining professional data scientists can be expensive for some organizations. Leveraging existing employees as citizen data scientists can be a cost-effective way to make better use of available talent while still gaining valuable insights from data.

Agility and speed

In fast-paced business environments, waiting for data scientists to address every analytical need can slow down decision-making processes. Citizen data scientists can expedite the analysis process, leading to quicker responses to business challenges and opportunities.

Why Should Organizations Hire Citizen data Scientists ?

Data science is a vast field that brings significant benefits to organizations, and citizen data scientists play a crucial role in harnessing the power of data. Here are some reasons why companies need:

  1. Simplifying Data Analysis: Citizen data scientists are integrated into various departments or teams, allowing them to tackle specific business challenges and explore data relevant to their field. This leads to deeper understanding and better decision-making.
  2. Bridging the Gap: They possess domain expertise along with a solid understanding of data science. This combination allows them to bridge the gap between technical skills and industry knowledge, bringing context and insights to data analysis.
  3. Real-Time Insights: With their domain expertise and access to automated analysis tools, citizen data scientists can analyze data in real time and provide quick insights to decision-makers. This enables organizations to respond rapidly, seize opportunities, and mitigate risks effectively.
  4. Force Multipliers: By handling routine data analysis tasks, they free up data scientists to focus on more complex challenges and strategic initiatives. They act as force multipliers, supporting multiple teams and increasing overall productivity.
  5. Unique Perspectives: They bring their diverse experiences and expertise to data analysis, resulting in fresh perspectives and innovative problem-solving approaches. Their unique insights often lead to novel discoveries and improved decision-making.
  6. Agile Experimentation: Citizen data scientists have the flexibility to explore different methodologies, modify models, and test hypotheses efficiently. Their adaptability promotes innovation as they experiment with various analytical methods, driving progress within their respective fields.

Key Skill Required 

The skill set required for being a successful citizen data scientist includes the following analytical, technical and subject-specific skills: 

  1. Citizen data scientists should be equipped to interpret and present data visually through programs like Tableau, Power BI, or Python libraries like Matplotlib or Seaborn.
  2. They must have basic programming skills to work with data, apply statistical methods, and develop simple machine-learning models. Being familiar with programming languages such as Python or R is advantageous. 
  3. They must also be proficient in a few other areas, including statistics, data modeling, data visualization, etc., to evaluate and interpret data and produce more useful and effective insights.
  4. They must also be able to lay out goals, create hypotheses, and evaluate data to get knowledge and address corporate issues.
  5. Given the dynamic nature of data science, they must keep up with the most recent tools, techniques, and market developments and actively look for opportunities to diversify their skill set.
  6. They must be able to communicate effectively with non-technical users and convey complex analyses in a brief, straightforward, and visually pleasing method.
key skill required for citizen data scientist
Source: Teradata

Job Responsibilities 

  1. Citizen data scientists use statistical approaches, work with datasets, clean data, and find correlations, trends, and patterns across the data. 
  2. They are essential in developing precise problem statements and hypotheses centered on their subject-matter knowledge.
  3. They work directly with data teams, industry stakeholders, and subject matter experts. They collaborate to establish project goals, collect pertinent information, and corroborate conclusions. 
  4. They maintain the integrity and confidentiality of data, follow safeguarding data laws, and manage highly classified data with utmost care.
  5. They highlight the significance of data-driven insights, engage coworkers on data utilization, and support the implementation of analytical tools and data for supporting decision-making.
  6. These professionals compile their findings and discuss them with corporate leaders and decision-makers. They use visualization approaches to present their findings in a way that the technical or product development teams may easily comprehend.
  7. When addressing any challenge, they don’t always follow the book. They are responsible for coming up with unique approaches on a number of occasions, which requires them to think creatively. 

Also Read: How to Successfully Make a Data Science Career Transition – Everything You Need to Know

Tools and Technologies for Citizen Data Scientists

Popular Data Science Tools Accessible to Non-experts

The availability of user-friendly data science tools has significantly increased in recent years, enabling citizen data scientists to learn about, analyze, and present data without having a deep understanding of coding or statistics. Some of the popular tools best for citizen data scientists are as follows: 

  1. Tableau: Tableau’s array of user-friendly tools and intuitive interface makes it possible for citizen data scientists to efficiently explore and display data findings.
  2. Power BI: Power BI, a Microsoft business intelligence application, allows users to have access to a variety of data sources, generate dynamic representations, and share reports. The wide collection of predefined connectors and easy-to-use interface make it a viable option for citizen data scientists.
  3. Knime: It is a user-friendly open-source analytics tool utilized by citizen data scientists. They are reusable by nature and might be readily incorporated into the data science process. It could simply be integrated with R, Python, and machine learning. 
  4. Google Data Studio: It is a free program that allows users to build interactive and eye-catching reports and dashboards. It effortlessly connects with additional Google services, making data from Google Analytics, Google Sheets, and other Google services simple to analyze and visualize.

Automation and Augmented Analytics for Simplified Workflows

Automation and augmented analytics technologies can help citizen data scientists by streamlining challenging data analysis procedures and providing intelligent insights. These technologies automate repetitive tasks and use machine learning algorithms to speed up data analysis.

  1. Alteryx: It is a complete data analytics solution that integrates refined analytics, data blending, and data preprocessing. It provides an interactive workflow interface and a wide selection of tools for automating data transformation, cleaning, and predictive modeling processes.
  2. H2O.ai: The platform enables the creation of ML models, tracking, and sharing via the app marketplace. Its AI cloud helps generate novel concepts using the results of solving complicated business problems.
  3. Rapidmine: It provides a graphical interface to generate prediction models. It provides a wide range of automated procedures and machine-learning algorithms to construct and implement predictive models lacking a lot of coding.

ML Platforms for Citizen Data Scientists

These platforms often come with pre-built machine-learning processes and algorithms, making it possible for beginners to use machine-learning methods to analyze their data. Several well-known machine learning tools for citizen data scientists include:

  1. Microsoft Azure Machine Learning Solutions: It has a user-friendly drag-and-drop user interface for creating and implementing machine learning models. It features a large selection of algorithmic features and combines easily with additional Azure services.
  2. Google Cloud AutoML: Personalized machine learning models can be trained and deployed without requiring a lot of coding. It provides simple tools for activities, including tabular data analysis, natural language processing, and image categorization.
  3. Amazon SageMaker: It streamlines the development, learning, and application of machine learning models. Citizen data scientists can use it since it has an intuitive user interface and supports a variety of machine learning algorithms.

Also Read: 22+ Data Science and ML Tools for Beginners

Citizen Data Scientist vs Expert Data Scientist

Citizen Data ScientistExpert Data Scientist
TrainingLimited or no formal data science trainingExtensive formal education and training
BackgroundNon-technical backgroundTechnical background
Skill LevelBasic understanding of data scienceAdvanced knowledge and expertise
Domain ExpertiseDeep understanding of a specific domainBroad understanding of various domains
Depth of AnalysisFocuses on specific analysis tasksConducts complex and in-depth analysis
Problem SolvingSolves simple to moderate complexity problemsSolves complex and challenging problems
ResearchConducts basic researchConducts advanced research

Role of Citizen Data Scientist Across Industires

As the data is expanding, the importance of having a citizen data scientist is increasing across various industries:

Finance

Citizen data scientists in banking and finance may study consumer transactions, market developments, and financial data to identify fraud, evaluate credit risk, and improve investment portfolios. They have the skills to create predictive models that can be used to forecast market trends, evaluate the effects of economic issues, and help with risk management strategies.

Supply Chain and Manufacturing

Citizen data scientists in manufacturing industries can analyze manufacturing data, quality indicators, and supply chain data to improve operational efficiency, streamline production processes, and eliminate inaccuracies. Predictive analytics can be used to estimate demand, enhance inventory control, and reduce interruptions in the supply chain.

Citizen data scientist in manufacturing
Source: Braincube

E-commerce

Citizen data scientists can study customer data, transaction histories, and browsing patterns across the retail and e-commerce industries to determine consumer needs, projected consumer demand and improve pricing strategies. Additionally, they can do market basket research to identify relationships between products, facilitating more precise up-selling and cross-selling strategies.

E-commerce
Source: Marketpro

Healthcare

In order to find patterns and trends, citizen data scientists in the healthcare sector could analyze patient data, health records, and clinical study findings. They can promote evidence-based medicine by helping determine the effects on patients, optimize treatment strategies, and improve patient care. By studying population health data and detecting illness risk factors, they can also advance medical research.

Citizen data scientist in healthcare
Source: Microsoft

Marketing

Citizen data scientists across marketing and advertising can categorize consumers, target specific populations, and customize promotional initiatives by analyzing customer demographic data, campaign effectiveness metrics, and social networking analytics. They can measure the efficacy of marketing campaigns, identify significant marketing channels, and optimize marketing budgets.

Environment and Energy 

Citizen data scientists working in energy and environmental study may study weather patterns, operational statistics, and data regarding energy consumption to enhance energy consumption, discover inconsistencies, and enhance energy conservation initiatives. By analyzing sensor data and finding patterns that help prevent machinery failures and save delays, they may lead to proactive repair.

Transportation

In the transportation sector, citizen data scientists are able to explore traffic patterns, logistical procedures, and transportation data to strengthen mapping routes, minimize fuel costs, and boost general supply chain effectiveness. They can help with automobile routine maintenance, facilitating preventative repairs to minimize malfunctions.

The future of industrial analytics is poised to embrace the rise of Citizen Data Scientists. With the increasing utilization of open-source data and the emergence of these data enthusiasts, the field of data science is undergoing significant transformation. Data scientists must embrace a mindset of continuous learning and professional growth to keep up with the ever-evolving nature of the discipline. Staying relevant and updated with emerging technologies, evolving approaches, and market trends will be crucial for remaining efficient and competitive.

Citizen Data Scientists will play a pivotal role in creating compelling visualizations, dynamic dashboards, and data-driven narratives that resonate with diverse stakeholders. Leveraging pre-trained algorithms, automated machine learning platforms, and AI-driven solutions, these data enthusiasts will continually enhance their data analysis skills as artificial intelligence and machine learning technologies evolve. Embracing these advancements will enable citizen data scientists to unlock new levels of insight and drive innovation in industrial analytics.

Challenges and Limitations

Potential Pitfalls

Citizen data scientists might not be as knowledgeable as qualified data scientists. Inadequate training and direction could result in inaccurate data analysis, poor interpretations, and inaccurate results. 

They could derive inaccurate or biased conclusions from the data if they don’t thoroughly understand statistical methods and data constraints. Additionally, they can have issues obtaining dependable, high-quality data, which could affect the accuracy of their conclusions.

Source: Freshworks

Addressing Ethical and Privacy Concerns

As citizen data scientists engage with data, it is paramount to address privacy and ethics concerns. Safeguarding individual privacy requires careful handling of confidential and personally identifiable information (PII) through appropriate anonymization and encryption techniques. Ethical considerations should guide their research practices, refraining from unethical, discriminatory, or distressing use of data. Obtaining explicit consent from individuals or organizations involved in handling sensitive data is essential to uphold ethical standards. By adhering to privacy regulations and ethical principles, they can maintain trust and ensure responsible data utilization.

Balancing Technical Skills

To enhance the value and credibility of their findings, citizen data scientists should constantly optimize their technical abilities, including competency in essential tools, coding languages, and statistical methods. 

They should work collaboratively with experienced data scientists or professionals with complementary skills to create more robust analyses and interpretations. Organizations must offer them the necessary training and assistance. Regular supervision and advice from data specialists might help them successfully navigate technological challenges.

Source: Procurious

Should You Consider a Job as a Citizen Data Scientist?

Considering a job as a citizen data scientist? If you like playing with data and finding cool patterns, it might be for you. You don’t need to be an expert, just have some basic skills in math and maybe coding. Lots of tools online can help you learn. Many industries, like healthcare and finance, need people who can understand data. You’ll get to work on different projects and share your findings with others. Plus, you’ll always be learning new stuff. So if you’re curious and like solving puzzles, being a citizen data scientist could be a fun job for you!

Conclusion

The democratization of data science tools and the proliferation of user-friendly analytics platforms have significantly increased the prevalence of citizen data scientists. Within an organization, individuals from diverse backgrounds can step into the role of citizen data scientists. However, possessing prior expertise in the company’s operations or the subject matter relevant to the analytics work can provide a distinct advantage. For those looking to enhance their skills as citizen data scientists, Analytics Vidya offers an intriguing program – Blackbelt Plus, which equips individuals with the necessary knowledge and capabilities to excel in this field.

Frequently Asked Questions 

Q1. What is a citizen data engineer?

A. It is a type of data engineer who is not a full-time part of the engineering team but contributes their skills and knowledge to the data engineering tasks when needed.

Q2. Why do citizen data scientists matter?

A. Citizen data scientists help to bridge the gap between IT technical professionals and business stakeholders in any organization. 

Q3. What are the 3 types of citizen science?

A. The 3 main types of citizen science include contributory, co-created and collaborative.

Citizen Data Scientist vs Expert Data ScientistCitizen Data Scientistsdata sciencedata scientist
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