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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

Top 10 Data Science Startups in USA

A
Analytics Vidhya Last Updated : 20 Sep, 2023
7 min read

Data science has emerged to be a promising field. Moving beyond the theoretical predictions of its ability to revolutionize human society, numerous startups have come forward to exhibit its immense potential. The article enlists top data science startups in USA.

Table of contents

Top Data Science Startups in USA

Startup NameFunding AmountNo. of yearsSearch Growth
Logz.Io$121.9M (Series E)9Peaked
Featurespace$107.9M (Grant)15Peaked
Zencity$51.2M (Series Unknown)8Exploding
ComplyAdvantage$108.2M (Series C)9Exploding
Databricks$3.5B (Series H)10Exploding
BioCatch$253.7M (Secondary Market)12Regular
Solidus Labs$83.5M (Series B)6Regular
DoiT International$100M (Series A)12Regular
Unsupervised$53.5M (Series B)6Regular
AgriTask$35.1M (Series B)13Peaked
Dune Analytics$79.4M(Series B)5Peaked
Contentsquare$1.4B (Series F)11Exploding
Fivetran$853.1M (Debt Financing)11Exploding
Coin Metrics$59.4M (Series C)6Peaked
Signal AI$101.6M (Series D)10Exploding
Uptake Technologies$218M (Series D)9Peaked
Cyclica$25.6M (Grant)10Regular
Tellius$33M (Series B)7Regular
Metricool$5.5M (Series Unknown)8Exploding
Kinetica$774M 7Regular

Top 10 Data Science Startups in USA

  1. Logz.Io
  2. Featurespace
  3. Zencity
  4. ComplyAdvantage
  5. Databricks
  6. BioCatch
  7. Contentsquare
  8. Fivetran
  9. Signal AI
  10. Metricool

Also Read: Top 10 Data Science Companies in USA

Logz.Io

The SaaS or Software As A Service based company provides cutting-edge specialized services in log management and observability solutions for business. They are of benefit for monitoring cloud-native businesses and securing the environment with comprehensive open-source tools. They promise the conversion of high-cost, low-burden value to high-value and low-cost business outcomes through a focus on logs, metrics and traces. 

Homepage of Logz.io | Data Science Startups

Role of Data Scientist in Logz.io

The data scientist team works with DevOps teams for the automation of processes like the extraction of information from huge volumes of data. Their focus is to quickly identify the anomalies using the algorithms, pace up the debugging and troubleshooting for lower interruptions in service and recognize the anomalous data spikes for cost reduction. 

Key Points of Logz.io

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Featurespace

The company provides services to safeguard the company’s accounts and money. Providing expert-based service in preventing financial crime, they work using behavioral analytics and deep behavioral network technologies. It monitors to detect anomalies and block fraud attacks without harming genuine users. 

Homepage of Featurespace

Role of Data Scientist in Featurespace

They are also focused on data analysis. Data scientists are supposed to generate advanced statistical models and algorithms for the analysis of customer behavior. They are also associated with end-to-end processing and huge dataset modeling. 

Key Points of Featurespace

Zencity

Zencity is a community engagement platform and GovTech startup powered by AI. The prime focus is to aid the local governments in decision-making based on the resident’s problems. They work through data collection, analysis, and subsequently acting on the information received by more than 150 cities and counties. 

Homepage of Zencity

Role of Data Scientist in Zencity

Focussing on raw data obtained from the general public, the most important tool used here is Natural Language Processing. The requirement and processing of generated data also varies based on specific demands. The data scientists deal with Human-in-the-loop training for categorization models and sentiment analysis.

Key Points of Zencity

ComplyAdvantage

It is a RegTech company leveraging the power of AI and ML. They work to recognize and prevent fraud and money laundering activities. ComplyAdvantage identifies criminals and financial risk-associated individuals such as exposed politicians, terrorists and others to avoid potential financial issues. The customers are generally banks, cryptocurrency industries and insurance companies. 

Homepage of ComplyAdvantage | Data Science Startups in USA

Role of Data Scientist in ComplyAdvantage

Working in the finance sector, the ML will be used here for the development of data metrics for different ML modules depending on the specific requirements. The data scientists here also work to generate SassS-based anti-money laundering solutions, entity resolution, adverse media, risk information graphs and other tasks.

Key Points of ComplyAdvantage

Databricks

Another software platform with services to enhance business operations. They are a unified open analytics platform to provide AI solutions in different aspects to build, deploy, share and maintain enterprise-grade data and analytics. These are further useful to harness the power of big data and make data-driven decisions. The services expand to both cloud-based and on-premises solutions. 

Homepage of Databricks |

Role of Data Scientist in Databricks

The data scientists here deal with transforming data into ideas for product design, formulating customer acquisition and retention strategies and optimizing the efficiency, performance and stability of engineering systems while deploying algorithms to the Databricks platform.

Key Points of Databricks

BioCatch

It is a cyber security company that merges human information with AI to deliver the safety of users and data. They work through human-device interaction analysis to reduce online fraud and cyber threats. The idea is to keep the enhanced user experience untouched while analyzing the physical and cognitive digital behavior of users across the web and mobile channels. 

Homepage of Biocatch | Data Science Startups in USA

Role of Data Scientist in BioCatch

At BioCatch, the data scientists analyze data and develop models to contribute to an organization’s growth and innovation. They develop data-driven solutions and work on statistical models and ML algorithms. Exploratory data analysis and data visualization are also critical expertise utilized in daily tasks. 

Key Points of BioCatch

Contentsquare

Using the power of AI, big data and behavioral data, the company offers analytics into the digital experience of businesses. It informs about the intent, behaviors and feelings of users for effective customer experience management. Representing themselves as capable of providing faster growth, happier customers and greater agility, they simultaneously focus on privacy and accessibility as well. 

Homepage of Contentsquare | Data Science Startups in USA

Role of Data Scientist in Contentsquare

Working on the behavioral aspects, the Data scientists at the company contribute to product building to deliver practical insights and recommendations for businesses. They work on the dataset generated through user interaction and digital content. The different projects at the company involve automating UI/UX performance analysis, understanding user behaviors, modeling content and layouts for app/web and characterization of e-commerce products. 

Key Points of Contentsquare

Fivetran

The company helps translocation of data throughout the cloud data platforms. Helping in the time-consuming aspect of business operations, their offerings deal with the automation of ELT processes, including extracts, schema drift and transformations. They provide the effortless transition and replication of data into the warehouse. 

Homepage of Fivetran | Data Science Startups in USA

Role of Data Scientist in Fivetran

Data scientists at Fivertan are concerned with developing prototypes for AI and ML models. They emphasize on areas of predictive and descriptive analytics to identify trends and patterns. They also build and maintain data pipelines and infrastructure. 

Key Points of Fivetran

Signal AI

The startup alters the serving businesses’ course of action by making them aware of the risks and opportunities to help in strategy modification. They aid in decision-making through information on external noise. It is an External Intelligence company that helps to outcompete the competitors.

Homepage of Signal AI | Data Science Startups in USA

Role of Data Scientist in Signal AI

The company works by data extraction from huge datasets to gain meaningful insights. Thus, the data scientists with their team focus on information retrieval and hence NLP, text analytics, topic classification and entity recognition.

Key Points of Signal AI

Metricool

The startup provides the tool to analyze the digital content or performance of online content digital campaigns. Thus, providing expertise in the marketing and advertising sector, they offer services that guide to strategic development of digital content and campaigns at the company. 

Homepage of Metricool | Data Science Startups in USA

Role of Data Scientist in Metricool

The company acts in the field of user behavior analysis, and hence, the data scientist’s job role focuses on predictive analytics, customer segmentation, sentiment analysis, and campaign optimization through A/B testing. The expected qualification will mainly be in computer vision, recommendation systems, time series forecasting and reinforcement learning.

Key Points of Metricool

Conclusion

Data science is a broad subject encompassing multiple sub-fields and specializations. With a promising approach towards human benefit, the data science startups in USA explored in the article provide innovative and effective usage of functions of data science. The wide utilization is prominent in sectors like finance, growth and expansion of companies and driving the decision-making to align with companies’ goals and objectives. 

Want to work at these Data Science startups in USA? Enroll into our Blackbelt Plus program and master all the skills required to ace in this field.

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Top 10 Data Science Companies in USA
Top 10 Data Science Companies in USA
Top 10 Data Science Companies to Work for in 2024
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