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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

Tools for improving structured thinking (for analysts)

Kunal Jain
Kunal Jain Last Updated : 26 Mar, 2019
4 min read

Tools for improving structured thinking (for analysts)

There are 4 ingredients required to make a good an awesome business analyst:

  1. Passion for Business Analytics
  2. Structured thinking
  3. Love for statistics and numbers
  4. Business domain  knowledge

awesome_business_analyst

We recently shared a few tips to train your mind on analytical thinking, the tactics and practices mentioned there improve your number crunching abilities and help you apply analytical thinking in day to day activities. Today, I am going to share a few tools and exercises I use for improving structured thinking.

 

[stextbox id = “section”]   What is structured thinking?[/stextbox]

Structured thinking is a process of putting a framework to an unstructured problem. Having a structure not only helps an analyst understand the problem at a macro level, it also helps by identifying areas which require deeper understanding.

I had written an article on how structured thinking can help an analyst by reducing iterations and turn around time on projects. Today I’ll share some tools and practices which have helped me immensely in improving my structured thinking.

 

[stextbox id = “section”]   Tools / Exercises for improving structured thinking:[/stextbox]

Following are some of the best practices I have used and applied for better structured thinking. I am sharing simple tools rather than the most sophisticated mind map software out there. Idea is to get you started on this habit of structured thinking. Once you are used to the process, you can explore advanced tools and software:

 

[stextbox id = “section”]   1. Layout a framework on paper before looking / creating your datasets:[/stextbox]

If there is only one takeaway you want to carry from this article, it should be this. As a practice, you should spend 1 – 2 hours of distraction free time (no emails, messages and phone calls!) just laying out the structure, possible hypothesis, story flow and a tentative business presentation. Here are a few pics of how I typically layout structure and story flow on paper (Business problem statement: Your Organization has observed increased credit risk in last few months. You need to analyze why this has happened and recommend next steps):

Putting framework on paper

Putting framework on paper

Here is how tentative story looks at this stage:

Tentative presentation flow

Tentative presentation flow

 

[stextbox id = “section”]   2. Use whiteboards for brainstorming (again before touching the data):[/stextbox]

Typically, I do a session / meeting with stakeholders laying out all possible hypothesis and action items after I have spent time laying out things on paper. Call all stakeholders, layout discussion for them, make sure they are engaged (and not busy on smartphones), note thoughts on whiteboard and capture all the thoughts in the framework we created on paper (before this brainstorming).

By end of this brainstorming, you should have a clear framework agreed and discussed with stakeholders.

 

[stextbox id = “section”]   3. Practice back of the envelope calculations (anywhere and everywhere):[/stextbox]

This is the tool I use the most. Any business you come across (e-Commerce websites to the laundry service you might be using), try and chalk out their business model in your mind. Some of the questions you can use to get yourself started are:

  • What would be the revenues this business might be making?
  • How many customers are they touching? Repeat customers? Loyalty scheme?
  • What would be the cost structure?
  • How has the business scaled up / down in last few months / years?

Perform all these calculations at back of your mind (and not on paper). Layout a structure in your mind first, then answer these questions and come up with answers. A side benefit of doing this is that you improve number crunching abilities as well.

 

[stextbox id = “section”]   4. Usage of a note / document to conduct meetings / discussions:[/stextbox]

This is one of the tricks one of my mentors used to enforce on his team. As a rule, he did not look at any powerpoint presentation. For doing any discussion with him, you were enforced to create a note explaining the problem and the solution, send it to him before hand and then he would get involved in discussion. I think (am not completely sure), a similar practice is followed in Amazon for all meetings with Jeff Bezos.

If you have not tried this before, give it a shot. You will realize the amount of clarity which comes by preparing a simple note!

 

These tools and tricks have served me well over years. Do let me know in case you have any trick / tools, which have helped you become more structured in your thinking.

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.

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

Deepesh Jain
Deepesh Jain

Nice article. I sincerely follow the first point illustrated above. I prefer to call this process flow- the process that I tend to deal with the problem and determining its causes and possible solutions and recommendations.

123

sawantika
sawantika

I really liked the article would like to read more on these articles

123

AJ Ferrara
AJ Ferrara

Great, practical information.

123

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