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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

Welcome to Analytics Vidhya!

Kunal Jain
Kunal Jain Last Updated : 24 Apr, 2015
3 min read

analytics technology

Welcome to Analytics Vidhya!

For those of you, who are wondering what is “Analytics Vidhya”, “Analytics” can be defined as the science of extracting insights from raw data. The spectrum of analytics starts from capturing data and evolves into using insights / trends from this data to make informed decisions. “Vidhya” on the other hand is a Sanskrit noun meaning “Knowledge” or “Clarity on a subject”. Knowledge, which has been gained through reading literature or through self practice / experimentation.

Through this blog, I want to create a passionate community, which dedicates itself in study of Analytics. I share my learning and tips on Analytics through this blog.

[stextbox id=”section”]Why study Analytics? [/stextbox]

With growing data and complexity, data analytics will become a competitive advantage for companies doing it well.  Have you ever wondered how social networking sites like Facebook and Linkedin can suggest you friends in near time? How can Google use its search query data to identify areas under pandemic influence? How can a grocery store predict pregnancy of its customer even before it was known to her family?

All of these are examples of analytics at work in real time. And this is just the start. With increasing usage of mobile devices and digitization across the globe, advantages of investing in cutting edge analytics are unlimited.

According to Harvard Business Review (October 2012 edition), job of a data scientist is the sexiest job of 21st century. If you are still not convinced, look at some of the trends on Business Analytics related keywords (as per Google Trends):



[stextbox id=”section”]How can this blog help? [/stextbox]

While the need for analytics experts is clear, the shortage is shocking!

According to the McKinsey Global Institute (In a May 2011 report): “By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.”

Imagine what would be the number across the globe…

While this blog would not be able to cover shortfall of this magnitude, it can certainly provide resources for people willing to learn the subject.

[stextbox id=”section”]What can you find on this blog?[/stextbox]

I aim to provide as many resources as possible for learning analytics. These resources include:

  1. Training and tutorials: Stuff to get you going
  2. Tips and tricks related to Business Analytics and Business Intelligence tools
  3. Case studies: Case studies of problems and their analytical solutions
  4. Interviews of Business Analytics & Business Intelligence leaders

[stextbox id=”section”]Who am I?[/stextbox]

I am Kunal Jain, a post graduate from IIT Bomaby in Aerospace Engineering. I have spent more than 6 years in field of Business Analytics. My work experience ranges from mature markets like UK to a developing market like India. Over last 3 years, I have been instrumental in setting up a Business Analytics & Intelligence team in a multi national Insurance company. During these 6 years, I have worked with tools like SAS, SPSS, Qlikview, R and Matlab. For any queries ir questions, please feel free to reach out to me on [email protected]

Image credit: 123rf.com

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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.

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Responses From Readers

Clear

Solomon
Solomon

Kunal, this is an awesome place to start for someone who is new to this field Thanks

123

Kunal Jain
Kunal Jain

Sure. Hope to contribute to make analytics more mainstream through this :) Keep coming for awesome posts on Analytics and Intelligence. Kunal

123
Nikhat
Nikhat

Hi Kunal, Throw some more ideas on association rules in data mining. I am doing research in that field.So your ideas will help in that. Bye, Nikhat

123

Kunal Jain
Kunal Jain

Thanks Nikhat for the suggestion. Is there a specific aspect you are looking for? May be it provides me an idea for upcoming posts :) Kunal

123
Richa
Richa

Hi Kunal, I need your guidance relating to my careere in analytics.I am postgraduate in stat.I have nearly 6 years of total experiance.But out of 6.5,5 years I have given to analytical testing and 7 months back I have joined an analytics company.Now having nearly 6 years of experiance my new company is expecting more from me from analytics perspective.but in this total years I have lost my confidence and touch in analytics. I need your guidance how do I cope up with my new companies expectation.How can I improve my self and gain confidence that I can do anything which will come on my way. Also wanted to know what is expectation of a company from a employee who has 6 years of experiance in anaytics,by this I mean to say which techniques and what level of details of the same is expected. Thanks, Richa

123

Kunal Jain
Kunal Jain

Richa, As long as you can back up yourself - you are in good stead. You are one of the few lucky people to be part of this industry. There are a lot of people who want to get into analytics, but an't get a break. As far as learning is required - I would say start from data science specialization courses or Statistics1 course available on sas.com Make sure you spend 1 -2 hours daily with some more time over weekends and you will be up to speed before you realize. It is difficult to answer the expectation as it might vary from company to company and role to role. You can be a specialist in a few techniques (e.g. risk modeling) or you can be some one who has worked across several peoblems and industries. Hope this helps. Regards, Kunal

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