Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

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

Startups bringing analytics and data science closer to you!

Kunal Jain
Kunal Jain Last Updated : 11 Dec, 2015
5 min read

Introduction:

Data Science and analytics are changing every industry as you read this article! If you have been following Analytics Vidhya lately, I am sure you would know how strongly I believe in it. You can look at some exciting start-ups coming out, which are using analytics and data science to solve problems more efficiently than ever before.

However, finding the right talent and tools is still difficult for a lot of companies wanting to use analytics. For example, how many SMEs can use dashboards to make real time decisions? or how many bloggers across the world know reading habits of their readers?

This week I bring you a collection of products / companies, which are making anaytics more accessible to an end user. They are taking the data from customers, doing the heavy lifting and delivering results in simple and impactful manner – what else would a business owner ask for! Check them out below:

 

Hotjar

hotjar

Most organizations rely on Google Analytics, Omniture or MixPanel to understand their customers, while they are on the company’s website. But, all of them feel restrictive in some way or the other. Hotjar aims to solve this problem. Hotjar is a new and easy way to truly understand your web and mobile site visitors. Identify your hottest opportunities for improvement using Heatmaps, visitor recordings, funnels, forms, polls, surveys & user testers. If you just wat one take away from this article – check them out.

 

PredictionIO:

PredictionIO

PredictionIO bring predictive models for developers, ready to deploy out of box. It is an open-source machine learning server for developers and data scientists to create predictive engines for production environments, with zero downtime training and deployment. It is built on top of Apache Spark, HBase and Spray.

 

Grow

grow

Simple BI dashboards for small and medium businesses. The interfaces of the product looks really intuitive. I think Grow has actually understood their customer well and have designed the product to take care of a lot of inherent needs of the customer. I definitely feel compelled to recommend the product to a lot of SMEs who don’t own a lot of data expertise in house.

 

Proximus

proximus

Analytics for the retailers and real world. Proximus allows retailers to accurately understand the behaviour of their shoppers in-store. Their technology is based on two things: a unique indoor location and navigation engine and a great BigData architecture that analyse the behaviour of thousands of shoppers every day. Check out their site for a nice visual story about how it is delivered.

 

Smarking

smarking

Big Data meets parking problem. Smarking allows parking managers to optimize pricing and staffing based on historical and predicted demand, improving revenue and reducing costs. They aim to combine parking data with traffic data, weather data and other data sources to create a prediction for parking managers. This should then help these managers price optimally for parking space.

 

Prayas Analytics

prayas

A Y combinator company, which is enabling A/B testing based on computer vision. The best part is that they use existing CCTV camera, so the store owners can start seeing the benefits before investing any thing in hardware. They are enabling brick and mortar business test in similar way as an e-Commerce company does.

 

SumoMe

sumome

SumoMe provides content Analytics for blogs. With an integration in less than 37 seconds and reasonably priced plan, you get insights into reading behaviour and engagement of your customers. The primary application of SumoMe being Heatmaps of your content, which would help you decide your content strategy. They integrate with almost every blogging platform and are being used by more than 150,000 websites! How did I miss them before?

 

Insights Web Analytics

insightwebanalytics

Insights web analytics acts as a layer above Google Analytics and convert the data from Google Analytics to actionable insights. For example, they convert the effect of higher bounce rate on a page to the impact on traffic, customer and ultimately sales! Insights Web analytics changes the way people use Google Analytics, which happens to be the most commonly used product for web analytics.

 

Neuron

neuron

Neuron aims to apply machine learning to create segmentation of customers. they aim to drive personalization for customers to solve problems like customer attrition and life cycle management.

 

Data Stories

datastories

Create data stories on click of the button. DataStories wants to crunch the time taken to convert an excel sheet with data to a story. The end user can select the kind of story he is looking from the data and get data stories on click of a button.

 

End Notes:

Here was a collection of startups which are making data science and analytics more accessible to common people. Some of them are trying to change the data science industry itself – exciting times, as I said!

Have you come across products / services which are making Data Science more accessible? If yes, I would love to hear more about them from you.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page

Analyticsdata productsstartupsweb analytics
Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Ritesh
Ritesh

Very nice article, Thanks for sharing Kunal. This helps in giving an insight about the latest industry trends, what new is going on in the analytics and how people are solving different challenges using analytics.

123

leonardolegorreta
leonardolegorreta

Thank you Kunal. By the way, on a related note, have you seen the vast number of data science "specializations" on Coursera? One from every major university,or so it seems. Anyway we can get some reviews on these? Thanks again for you terrific work Kunal!

123

Ramdas
Ramdas

Excellent article, it is interesting to see how data science is used in different ways.

123

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details