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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

Analytical Concepts: Must Known For Every Professional & Fresher

G
Guest Blog Last Updated : 27 Feb, 2024
5 min read

Introduction

The use of analytical concept have gained immediate importance in the last few years. The practice of gaining useful insights from data have helped several companies improve their business performance.

Analytics allow companies to obtain a clear picture of events in the past and the future of their performance. A peek into the future helps companies to get prepared for the misfortune (if any) about to arrive.

Using analytics, companies can find answers to three main questions: ‘What has happened’, ‘What is happening’ and ‘What will happen’. It wouldn’t be incorrect to say that the rise in data has fueled this outrageous penetration of analytics use.

Analytics is not just limited to deriving insights from the past, but also enables predicting future outcomes and optimizing business resources. As a result, the more advanced forms of analytics namely, predictive and prescriptive have assumed greater importance in supporting organizations’ decision making needs.

In this article, I’ve explained the 3 major forms of analytics which categorizes all forms of analytical models applied across countries.

According to a study, organizations that focus on basic automation to expand their reporting capabilities can improve their ROI by 188 percent. But, adding advanced analytic deployments that enhance organization strategy can extend their ROI by as much as 1209 percent.

analytical concepts

What are the different types of analytics?

1. Descriptive Analytics

Let’s start with the most basic type of analytics i.e. descriptive analytics. The objective of descriptive models is to analyze historical trends and figure out relevant patterns to gain insights on the population behavior. Descriptive analytics involve finding answers to ‘what has happened?’. It is the most commonly used form of analytics by organizations for their day to day functioning and is generally the least complex.

Descriptive models use basic statistical and mathematical techniques to derive key performance indicators that highlight historical trends. The primary purpose of the model is not to estimate a value, but gain insight on the underlying behavior. Common tools used for running descriptive analysis include MS Excel, SPSS, and STATA.

A typical example in the Banking industry would be customer segmentation. Historical data is mined to analyze customers’ spending patterns and wallet share to enable a targeted approach to marketing and sales. Such models are powerful tools to profile the population, but are limited in their predictive ability with respect to the individual members’ behavior from the same population.

Helpful resources:

  • Online resources for learning basic descriptive statistics can be found on Khan academy: Link
  • Here’s a video on running descriptive statistics in SPSS: Link
  • A must-do MOOC on Coursera- Data Scientist’s Toolkit: Link

2. Predictive Analytics

Predictive analytics use statistical modeling to determine the probability of a future outcome or a situation occurring. It involves finding answers to ‘What could happen?’.

Predictive models build up on descriptive models as they move beyond using historical data as principal basis for decision making, often using structured and unstructured data from various sources. They enable decision makers to make informed decisions by providing a compressive account of an event’s likelihood to occur in the future. They encompass various advanced statistical models and sophisticated math concepts like random forests, GBM, SVM, GLM, game theory, etc.

A predictive model builds on a descriptive model to predict the future behavior. However, unlike a descriptive model that only profiles the population, a predictive model focuses on predicting a single customer behavior.

Tools used to run predictive models vary by the nature of the model’s complexity, however some of the commonly used tools are RapidMiner, R, Python, SAS, Matlab, Dataiku DSS, amongst many others. Online resources on using these tools can be found on Coursera.

A typical example in the banking industry would be advanced campaign analytics. It can help predict the likelihood that a customer would respond to a given marketing offer to improve the cross-selling and up-selling of products. Another example would be predicting the probability of fraud on credit cards.

Helpful resources

  • MOOC on Coursera on R for beginners: Link
  • A comprehensive guide to Python for beginners: Link
  • Predictive model building on Coursera: Link

3. Prescriptive Analytics

Prescriptive analytics is the most sophisticated type of analytics that uses stochastic optimization and simulation to explore a set of possible options and recommend the best possible action for a given situation. It involves finding answers to ‘What should be done?’.

Prescriptive models go beyond descriptive models that only address what is going on, and beyond predictive models that can only tell what will happen, as they go on to advise what actually should be done in the predicted future. They quantify the effect of future actions on key business metrics and suggest the most optimal action.

Prescriptive models

Prescriptive models synthesize big data and business rules using complex algorithms to compare the likely outcomes of a number of actions, and choose the most optimum action to drive business objectives. Most advanced prescriptive models follow a simulation process where the model continuously and automatically learns from the current data to improve its intelligence.

These models are typically the most complex in nature and hence are being used by some progressive and large companies, as they are difficult to manage. However, when implemented correctly, they can have a strong impact on a company’s decision making effectiveness and hence, its bottom line.

That said, technical advancements such as super computers, cloud computing, Hadoop HDFS, Spark, in-database processing, MPP architecture, etc. have made deployment of complex prescriptive models using structured and unstructured data much easier. Tools used to run prescriptive models are mostly the same as predictive models however, require advanced data infrastructure capabilities.

Common example of prescriptive models

The retail banking industry is the optimum allocation of sales staff across various branches of the bank to maximize new customer acquisitions. Marrying geo-location information with every branch’s performance and potential, the model can prescribe the most optimum allocation of sales staff across all the branches.

A more sophisticated prescriptive modeling approach is used in the airline ticket pricing systems to optimize the price of air tickets based off travel factors, demand levels, purchasing timing, etc. to maximize profit margins, but also at the same time not deter sales.

According to a research, about 10% organizations use some form of prescriptive analytics at present, this figure has increased from 3% in 2014 and is expected to rise to 35% by 2020. Factors like massive investments in predictive analytics, expansion of IoT capabilities that compliment prescriptive analytics are driving this growth and expanding the scope of prescriptive models.

Helpful resources (in addition to the ones on Predictive analytics):

  • Guide to building a recommendation engine in Python: Link
  • MOOC on Coursera for practical hands-on Machine Learning: Link
  • Guide to learning random forests: Link

Conclusion

In this article, I’ve discussed 3 different versions of analytics used across industries today. These are the building blocks of the analytics industry across the world. It is fair to say that all models, developments, and discoveries made using data can be classified under any of these three categories.

This article is meant to help people who are new to analytics or plan to switch over to analytics to get a clear view of the domain. I hope the mentioned resources help you get started with learning.

Business Analyticsdescriptive analyticspredictive analyticsprescriptive analytics
G
Guest Blog

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Responses From Readers

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David
David

Hello Sajan, Very interesting article! Could you provide the source for your data on increasing ROI via basic automation and advanced analytics? Thank you very much.

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