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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

11 Amazing Applications / Uses of Data Science Today

A
Analytics Vidhya Last Updated : 17 Dec, 2024
6 min read

Introduction

In today’s data-driven world, the power of data science is unbelievable. From uncovering hidden patterns to predicting future trends, application of data science in real life is revolutionizing industries. In this article, we will explore the fascinating and diverse uses of data science, unlocking its potential to transform businesses, healthcare, finance, and more. Get ready to discover the limitless possibilities of harnessing data science for innovation and success.

Learning Objective

  • Common data science uses
  • Advanced applications
  • Data science tools and techniques
  • Future impact of data science

Table of contents

Top 11 Uses of Data Science

Using data science, companies have become intelligent enough to push & sell products as per customers purchasing power & interest. Here’s how they are ruling our hearts and minds:

When we speak of search, we think ‘Google’. Right? But there are many other search engines like Yahoo, Bing, Ask, AOL, Duckduckgo etc. All these search engines (including Google) make use of data science algorithms to deliver the best result for our searched query in fraction of seconds. Considering the fact that, Google processes more than 20 petabytes of data everyday. Had there been no data science, Google wouldn’t have been the ‘Google’ we know today.

Google Search

2. Digital Advertisements (Targeted Advertising and re-targeting)

If you thought Search would have been the biggest application of data science` and machine learning, here is a challenger – the entire digital marketing spectrum. Starting from the display banners on various websites to the digital bill boards at the airports – almost all of them are decided by using data science algorithms.

This is the reason why digital ads have been able to get a lot higher CTR than traditional advertisements. They can be targeted based on user’s past behaviour. This is the reason why I see ads of analytics trainings while my friend sees ad of apparels in the same place at the same time.

3. Recommender Systems

Who can forget the suggestions about similar products on Amazon? They not only help you find relevant products from billions of products available with them, but also adds a lot to the user experience.

A lot of companies have fervidly used this engine / system to promote their products / suggestions in accordance with user’s interest and relevance of information. Internet giants like Amazon, Twitter, Google Play, Netflix, Linkedin, imdb and many more uses this system to improve user experience. The recommendations are made based on previous search results for a user.

recommender systems data science

4. Image Recognition

You upload your image with friends on Facebook and you start getting suggestions to tag your friends. This automatic tag suggestion feature uses face recognition algorithm. Similarly, while using whatsapp web, you scan a barcode in your web browser using your mobile phone. In addition, Google provides you the option to search for images by uploading them. It uses image recognition and provides related search results. To know more about image recognition, check out this amazing (1:31) mins video:

5. Speech Recognition

Some of the best example of speech recognition products are Google Voice, Siri, Cortana etc. Using speech recognition feature, even if you aren’t in a position to type a message, your life wouldn’t stop. Simply speak out the message and it will be converted to text. However, at times, you would realize, speech recognition doesn’t perform accurately. Just for laugh, check out this hilarious video(1:30 mins) and the conversation between Cortana & Satya Nadela (CEO, Microsoft)

6. Gaming

EA Sports, Zynga, Sony, Nintendo, Activision-Blizzard have led gaming experience to the next level using data science. Games are now designed using machine learning algorithms which improve / upgrade themselves as the player moves up to a higher level. In motion gaming also, your opponent (computer) analyzes your previous moves and accordingly shapes up its game.

motion gaming data science

7. Price Comparison Websites

At a basic level, these websites are being driven by lots and lots of data which is fetched using APIs and RSS Feeds. If you have ever used these websites, you would know, the convenience of comparing the price of a product from multiple vendors at one place. PriceGrabber, PriceRunner, Junglee, Shopzilla, DealTime are some examples of price comparison websites. Now a days, price comparison website can be found in almost every domain such as technology, hospitality, automobiles, durables, apparels etc.

8. Airline Route Planning

Airline Industry across the world is known to bear heavy losses. Except a few airline service providers, companies are struggling to maintain their occupancy ratio and operating profits. With high rise in air fuel prices and need to offer heavy discounts to customers has further made the situation worse. It wasn’t for long when airlines companies started using applications of data science in real life to identify the strategic areas of improvements. Now using data science, the airline companies can:

  1. Predict flight delay
  2. Decide which class of airplanes to buy
  3. Whether to directly land at the destination, or take a halt in between (For example: A flight can have a direct route from New Delhi to New York. Alternatively, it can also choose to halt in any country.)
  4. Effectively drive customer loyalty programs

Southwest Airlines, Alaska Airlines are among the top companies who’ve embraced data science to bring changes in their way of working.

7. Fraud and Risk Detection

One of the first applications of data science originated from Finance discipline. Companies were fed up of bad debts and losses every year. However, they had a lot of data which use to get collected during the initial paper work while sanctioning loans. They decided to bring in data science practices in order to rescue them out of losses. Over the years, banking companies learned to divide and conquer data via customer profiling, past expenditures and other essential variables to analyze the probabilities of risk and default. Moreover, it also helped them to push their banking products based on customer’s purchasing power.

8. Delivery Logistics

Who says applications of data science in real life has limited applications? Logistic companies like DHL, FedEx, UPS, Kuhne+Nagel have used data science to improve their operational efficiency. Using data science, these companies have discovered the best routes to ship, the best suited time to deliver, the best mode of transport to choose thus leading to cost efficiency, and many more to mention. Further more, the data that these companies generate using the GPS installed, provides them a lots of possibilities to explore using data science.

9. Miscellaneous

Rows of shelves with boxes in modern warehouse

Apart from the applications mentioned above, applications of data science in real life is also used in Marketing, Finance, Human Resources, Health Care, Government Policies and every possible industry where data gets generated. Using data science, the marketing departments of companies decide which products are best for Up selling and cross selling, based on the behavioral data from customers. In addition, predicting the wallet share of a customer, which customer is likely to churn, which customer should be pitched for high value product and many other questions can be easily answered by data science. Finance (Credit Risk, Fraud), Human Resources (which employees are most likely to leave, employees performance, decide employees bonus) and many other tasks are easily accomplished using data science in these disciplines.

10. Self Driving Cars

Check out this ~3 min video to know more:

11. Robots

Check out this ~3 min video of robots which resembles Humans

Imagine a world, where we are surrounded by robots like these. Will they do any good for us? Or these would lead to repercussions that mankind will have to endure. Let’s have a discussion on this !

Conclusion

The pervasive influence of data science is evident across various domains, as showcased by the top 11 uses highlighted in this list. From enhancing internet search experiences to revolutionizing transportation with self-driving cars and robots, data science continues to reshape industries and drive innovation. Whether it’s optimizing digital advertisements, improving customer recommendations, or detecting fraud, the applications of data science are vast and impactful. As we move forward, the role of data science will only continue to expand, unlocking new possibilities and addressing complex challenges across diverse sectors of our society.

Frequently Asked Questions

Q1. What are the benefits and uses of data science?

A. The benefits of data science include extracting valuable insights from data, making data-driven decisions, improving efficiency, identifying patterns and trends, and enabling predictive modeling.

Q2. How is data science used in real life?

A. Most common data science uses in real life are – 

1. Personalized recommendations (e.g., Netflix) 
2. Fraud detection (e.g., credit card transactions)
3. Predicting disease outbreaks (e.g., epidemiology) 
4. Optimizing supply chains 
5. Sentiment analysis (e.g., social media monitoring)
6. Improving customer experience

Q3. Where is data science most used?

A. Applications of data science in real life is most commonly used in industries such as –

1. Healthcare (clinical decision support, patient monitoring)
2. Finance (risk assessment, algorithmic trading)
3. Marketing (customer segmentation, targeted advertising)
4. Technology (predictive maintenance, recommendation systems)

Q4. What are the 3 main uses of data science?

A. The three main uses of data science are:

1. Descriptive analytics (summarizing and understanding data)
2. Predictive analytics (forecasting future outcomes)
3. Prescriptive analytics (providing recommendations and optimizing decisions based on data)

A
Analytics Vidhya

Analytics Vidhya Content team

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Responses From Readers

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Andrea Maria
Andrea Maria

Yes, I agree with this article. Data science is a blend of data inference, algorithmm development, and technology in order to solve analytically complex problems.

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pabbathi srinivas
pabbathi srinivas

nice bro.its very easy to understand.this article is very useful for freshers who want to make their carrer in data science

123

Arti Kadu
Arti Kadu

Good information on the data science.

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