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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?
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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

Top 18 Companies Hiring Data Professionals through Hackathons

Yana Khare
Yana Khare Last Updated : 18 Jul, 2024
5 min read

Introduction

Forget endless resumes and interviews! Hackathons are now the new way for companies to find the best data professionals. These intense coding competitions bring together talented folks to tackle tricky problems. It’s a chance to show off your skills and think outside the box under friendly pressure.

But it’s not just about bragging rights.

Companies are on the lookout for the brightest minds to join their teams. So, if you’re an aspiring data pro, hackathons are a great way to level up your skills and get noticed by top companies. In this article, we’ll explore some of the top companies that actively seek and hire data professionals through hackathons, providing a gateway to exciting career opportunities.

Companies that hire through hackathons

Table of contents

What is a Hackathon?

A hackathon mainly lasts for 24 to 48 hours or sometimes may go up to a week and is an event where people from computer programming, design, project management, and other fields come together to develop software or hardware. The term ‘’hackathon’’ derives from ‘’hack’’, which means experimental programming, and ‘’marathon’’, as such an event requires endurance to engage in such a meeting. A hackathon is generally an event that concentrates on one or more technologies, fields, problems, or issues within a shorter period and typically concludes with briefings or exhibitions of the completed solutions. A lot of companies conduct hackathons to encourage individuals to apply and find the best candidate suited for the job.

Benefits of Participating in a Hackathon

Participating in a hackathon offers a range of benefits that can significantly enhance one’s professional skills and opportunities. Here are some of them:

  • Hands-on learning and practice of new programming languages, tools, and technologies.
  • Development of problem-solving, teamwork, and communication skills.
  • Networking opportunities with professionals, mentors, and recruiters.
  • Demonstrating initiative, creativity, and technical proficiency to employers.
  • Rapid transformation of ideas into tangible prototypes.
  • Potential for developing fully-fledged products or services from hackathon projects.
  • Monetary prizes or other incentives for top-performing projects.
  • Access to informative sessions, workshops, and presentations by industry experts.

18 Companies that Hire through Hackathons

Here are the top companies that hire through hackathons and are known for providing excellent opportunities for participants to get noticed:

IBM Data Science and AI Elite Team Hackathon

  • Conducted By: IBM
  • Annually Positions: Roles in Data Science, AI, Data Engineering
  • Cost: Free
  • Eligibility: Open to students, professionals, and data scientists
  • Stages/Process: Training sessions, project development and submission
  • CTC Offered: Varies; competitive with excellent benefits
  • Audience: Students, data scientists, professionals interested in data science and AI
  • Click here for the link.

Major League Hacking (MLH) Hackathons

  • Conducted By: Major League Hacking (MLH)
  • Frequency: Regularly throughout the year, globally
  • Positions: Internships, full-time jobs
  • Cost: Generally free to participate
  • Eligibility: Typically open to university students, but some hackathons may also be open to professionals.
  • Stages/Process: This usually involves ideation, hacking/building, and presentations/judging.
  • Audience: Open to global participants.
  • Click here for the link.

TechCrunch Disrupt Hackathon

  • Conducted By: TechCrunch
  • Frequency: Annually
  • Positions: Full-time jobs, startup opportunities
  • Cost: Participation fee varies, typically around $200
  • Eligibility: Generally open to individuals with technical backgrounds, often requires registration.
  • Stages/Process: Includes initial pitching, hacking, and final presentations to judges.
  • Audience: Global, but physical attendance may be location-specific.
  • Click here for the link.

Microsoft Imagine Cup

  • Conducted By: Microsoft
  • Frequency: Annually
  • Positions: Internships, full-time jobs
  • Cost: Free to participate
  • Eligibility: Open to students globally, requires registration and team formation.
  • Stages/Process: Involves project submission regional and global finals with presentations.
  • Audience: Global, students from universities worldwide participate.
  • Click here for the link.

HackMIT

  • Conducted By: Massachusetts Institute of Technology (MIT)
  • Frequency: Annually
  • Positions: Internships, full-time jobs
  • Cost: Free to participate
  • Eligibility: Open to college students worldwide, with a competitive entry process.
  • Stages/Process: Hackathon format with hacking sessions, mentorship, and judging.
  • Audience: Global, but physical attendance is required at MIT.
  • Click here for the link.

ETHDenver

  • Conducted By: ETHGlobal
  • Frequency: Annually
  • Positions: Internships, full-time jobs
  • Cost: Generally free to participate
  • Eligibility: Open to blockchain enthusiasts globally; registration required.
  • Stages/Process: This includes hacking/building blockchain projects, workshops, and final presentations.
  • Audience: The audience is global, and participants come from the blockchain community worldwide.
  • Click here for the link.

WeAreDevelopers World Congress Hackathon

  • Conducted By: WeAreDevelopers
  • Frequency: Annually
  • Positions: Internships, full-time jobs
  • Cost: Participation fee varies, typically around $100
  • Eligibility: Open to developers globally, often requires registration.
  • Stages/Process: Hackathon format with coding, ideation, and presentations.
  • Audience: Global; participants can join from anywhere, but physical attendance may be required for some aspects.
  • Click here for the link.

Uber HackTag

  • Conducted By: Uber
  • Frequency: Annually
  • Positions: Internships, full-time jobs
  • Cost: Free to participate
  • Eligibility: Open to developers and tech enthusiasts
  • Stages/Process: Problem-solving, coding, and presentations
  • Audience: Global participants
  • Click here for the link.

Microsoft Generative AI Hackathon

  • Conducted By: Microsoft
  • Frequency: Annually
  • Positions: Internships, full-time jobs
  • Cost: Free to participate
  • Eligibility: Open to developers and tech enthusiasts
  • Stages/Process: Bug fixing, coding challenges, and presentations
  • Audience: Global participants
  • Click here for the link.

Myntra HackerRamp We-for-she (For Women)

  • Conducted By: Myntra
  • Frequency: Annually
  • Positions: Internships, full-time jobs
  • Cost: Generally free to participate
  • Eligibility: Open to women in tech, developers, and tech enthusiasts
  • Stages/Process: Ideation, coding challenges, and presentations
  • Audience: Global participants
  • Click here for the link.

American Express Makeathon

  • Conducted By: American Express
  • Frequency: Annually
  • Positions: Internships, full-time jobs
  • Cost: Generally free to participate
  • Eligibility: Open to developers and tech enthusiasts
  • Stages/Process: Problem-solving, coding, and final presentations
  • Audience: Global participants
  • Click here for the link.

Amazon AWS Hackathon

  • Conducted By: Amazon Web Services
  • Annually Positions: Internships, full-time roles in AI, Data Science, Data Engineering, Data Analysis
  • Cost: Free
  • Eligibility: Open to developers, data scientists, and students
  • Stages/Process: AWS resource training and Project Development
  • CTC Offered: Varies; highly competitive with substantial perks
  • Audience: Developers, data scientists, students interested in cloud computing and AI
  • Click here for the link.

JPMorgan Chase Code for Good

  • Conducted By: JPMorgan Chase
  • Frequency: Annually
  • Positions: Internships, full-time jobs
  • Cost: Generally free to participate
  • Eligibility: Open to developers and tech enthusiasts
  • Stages/Process: Social impact challenges, coding, and presentations
  • Audience: Global participants
  • Click here for the link.

Google Girls Hackathon (For Women)

  • Conducted By: Google
  • Frequency: Annually
  • Positions: Internships, full-time jobs
  • Cost: Generally free to participate
  • Eligibility: Open to women in tech, developers, and tech enthusiasts
  • Stages/Process: Problem-solving, coding challenges, and presentations
  • Audience: Global participants
  • Click here for the link.

EY Techathon

  • Conducted By: EY (Ernst & Young)
  • Frequency: Annually
  • Positions: Internships, full-time jobs
  • Cost: Generally free to participate
  • Eligibility: Open to developers, tech enthusiasts, and business professionals
  • Stages/Process: Business problem-solving, coding, and final presentations
  • Audience: Global participants
  • Click here for the link.

Microsoft AI for Climate Hackathon

  • Conducted By: Microsoft
  • Annually Positions: Internships, full-time positions in AI and Data Science
  • Cost: Free
  • Eligibility: Open to students, professionals, and researchers interested in AI and environmental sustainability
  • Process: Ideation, project submission and final presentation
  • CTC Offered: Varies; typically competitive salaries and benefits
  • Audience: Students, professionals, researchers in AI and environmental sustainability
  • Click here for the link.

Google Cloud x Formula E AI Hackathon

  • Conducted By: Google
  • Annually Positions: Internships, full-time roles in AI, Data Science, Data Engineering
  • Cost: Free
  • Eligibility: Open to students, developers, and professionals
  • Stages/Process: Training sessions, development, final presentation
  • CTC Offered: Varies; competitive salary and benefits
  • Audience: Students, developers, and professionals interested in AI, ML, and cloud computing
  • Click here for the link.

Airbnb Engineering and Data Science Hackathon

  • Conducted By: Airbnb
  • Annually Positions: Roles in Data Science, Data Engineering, Data Analysis
  • Cost: Free
  • Eligibility: Open to students, data scientists, and professionals
  • Stages/Process: Project development and submission
  • CTC Offered: Varies; competitive with great benefits
  • Audience: Students, data scientists, professionals interested in data science and data engineering
  • Click here for the link.

Conclusion

Hackathons provide extensive advantages, including sharpening technical abilities and establishing important industry relationships. These hackathons for jobs serve as springboards for future developers and innovators, offering opportunities for turning ideas into physical models. Hackathons are crucial in promoting technological innovation and shaping the future of different industries globally, whether looking to advance one’s career or seek a collaborative challenge.

Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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