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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

Starting a big-data analytics practice? Answer these 5 questions first

Kunal Jain
Kunal Jain Last Updated : 24 Jun, 2022
6 min read

Are you a business owner who is wondering how can Analytics / Big data help me out? Or you are convinced that data mining can add value, but clueless about next steps? or have you attended multiple conferences under the headline of big data, but still have little idea about how to start it in your own Organization?

First of all, don’t be dis-heartened, you are not alone! Multiple surveys have shown that while there is a lot of talk on big data, only a few people are actually working on it.

How to start big data Analytics?

How to start big data Analytics?

This article lays out 5 questions every business head should answer before starting this journey. These questions are not a solution to your technical questions related to big data and would not provide you a remedy to all your questions but they will provide you enough stimulation to challenge and really understand the core of big data analytics proposition for you.

[stextbox id = “section”] Question 1: What business are you in? [/stextbox]

This might sound a really silly question to start with, but this is most powerful one as well. You need to look for an answer beyond your BAU business model. Let me give you a real life example to try and explain what I mean.

What business is McDonald’s in? If you said selling fast food, you’d be wrong. In 1974, Ray Kroc, the man who started the golden arch empire asked an MBA class at the University of Texas, Austin this same question. Of course, everyone said hamburgers. Kroc’s answer surprised everyone: he said he was in the real estate business.

Here’s the way he explained it: his primary business focus was to sell franchises of the restaurant. He knew, however that real estate and location were the most important factors of success for each franchise. A poorly placed restaurant would go broke. McDonald’s is now the single largest owner of real estate in the world.

Here is food for some more thought. List out what you think each of the business is in:

  • Amazon
  • Tesco
  • Netflix
  • Coursera
  • You?

Look for an answer beyond the obvious. Netflix is not in business of renting out / streaming movies, it is in business of understanding viewing habits of people. Similarly, Coursera is in business of understanding learning behaviour of people.

So, now again, Which business are you in?

In answering so, you might figure out that the real differentiator to your business is different from the traditional business model you had in your mind.

In today’s landscape, a credit card provider is not in a business of a Financial risk management. It is important, but its true business is likely to be understanding consumer spending. If they provide a spending offer a month after the customer needs it, they are doomed!

[stextbox id = “section”]Question 2: Can data / behaviour insights change the way you do or look at your business?[/stextbox]

Now that you have probably listed down which business are you in, you need to check how much data do you own in that business. Following are the questions you need to answer:

  1. Do you own the data and journey?
    1. If yes, can you increase the ownership? Can you store more information which can provide you information about what the customer wants?
    2. If no, how do you start owning the data and the journey?
  2. If you get insights on consumer behaviour, what kind of changes can you make today? What do you think is the potential revenue opportunity there?

[stextbox id = “section”]Question 3: Do you want to build in-house data capability or Outsource?[/stextbox]

The answer depends on how much importance / differentiating capability you attach to Analytics in your core strategy and how much revenue opportunity you put on it. While it is easy to take decisions on the extremes, it might be more difficult to make decisions if you lie somewhere in between. There are several hybrid models, which might work if you lie somewhere in between.

outsoucing

[stextbox id = “section”]Question 4: Do you need to invest in people or you have the required talent in-house?[/stextbox]

This might as well be a Question 3 b. Once you are clear whether you need to Outsource or not and in what capacity, you need to do a reality check versus your current talent pool. Needless to emphasize the importance of this step.

  • Scenario 1: If you think you have the right talent in-house, you should ask:
    • Why have we not started the journey already?
    • What have been the results of past initiatives these people / team have led?
  • Scenario 2: You do not have the right talent inhouse. In this case, create a job description, find out relevant candidates, take time to understand their perspective, involvement in transition in previous companies and ability to lead change.

[stextbox id = “section”]Question 5: What should be the plan for roll-out of this initiative?[/stextbox]

Once you have all the above questions answered, you need to think about how would you go about penetrating Analytics to the level in your Organization you really want? Following are some of the guidelines in creating this action plan:

  • Identify specific problems (hopefully core of what business are you in) and work on those problems
  • Communicate your vision to your team and create a buy-in with them
  • Identify a few low hanging fruits to increase engagement with business

Once you have answers to these questions, hopefully you will have all the weapons you need to create an action plan. Here are a couple of case studies with a few CEOs and their answers to the five questions:

[stextbox id = “section”]Case study: Credit card provider:[/stextbox]

Question 1: What business are you in?

There are 3 parts which define the core of our business proposition:

  • Identifying the right risk profile of customers to be underwritten on right products
  • Managing the customer life cycle effectively and efficiently. Some of the questions we try and answer here are:
    • Can we anticipate when a good customer needs an offer to shift his big spends on to our cards?
    • Can we identify transactions which illustrate worsening risk for customer?
  • Managing Fraud:
    • This is another space where application of big data comes in place. What are the patterns which might indicate dubious behaviour?

Question 2: Can data / behaviour insights change the way you do or look at your business?

  • For us, mining data to bring out behavioural insights is the core of solving business problems. We place a very high value on any insight generated in each of these areas highlighted above. We typically own the entire data in this journey, which is a big advantage compared to some other industries. We are increasing moving to a space where data is looked at as an asset on Balance sheet of our Organization.

Question 3: Do you want to build in-house data capability or Outsource?

For us the answer was very evident. Once we were clear on the value, the question was about finding the right talent to build in-house capabilities.

Question 4: Do you need to invest in people or you have the required talent in-house?

We invested heavily in specialists to build our analytics expertise. The Leadership team was actively involved in screening the candidates themselves. Once the team was in place, managing their attrition has been a key priority for our Organization.

Question 5: What should be the plan for rollout of this initiative?

Since, we were convinced about the value Analytics can add and there are Organizations across globe who have already created differentiation based on data (e.g. Capital One, American Express), we started work on key strategic areas immediately. While we did not necessarily look out for low hanging fruits, speed to execution was really key for us. If we know about a trend which might signify potential fraud, it had to be implemented quickly. You cannot sit on information like this.

So, what do you think about these questions? Would they help you provide direction in setting up an Analytics practice? Are there additional factors you would want to answer in initial stages of starting a data practice? Do let me know your thoughts.

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

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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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