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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

Maintaining fearless monk-like attitude while leading Analytics teams

Kunal Jain
Kunal Jain Last Updated : 03 Sep, 2015
4 min read

One of my mentors had the following thought written in one of the presentation he was making:

“Your attitude, not your aptitude, will determine your altitude”

-Zig Zagler

attitude_quote2

Compared to some other industries, Analytics still has higher dependence on aptitude. As per my experience and observation, following is more appropriate for people in Analytics industry:

Your aptitude decides whether you can survive in Analytics industry and your attitude defines the altitude you achieve in life

In mathematical terms, if can be defined as something like this:

Altitude = b(Aptitude) x fn(attitude)

where b is a boolean function which becomes 1 over a certain threshold of aptitude. Once you have the right aptitude, your attitude defines how far do you reach in the journey. In more layman terms, your aptitude promotes you from an Individual contributor to a bigger role, but it is your attitude, which decides whether you can become a leader in this industry.

 

[stextbox id=”section”]   What is the right attitude to carry while leading Analytics teams?[/stextbox]

Before I proceed further, let me call out that this is my take on what is the ideal attitude to succeed while leading Analytics teams. If you have a difference of opinion, please feel free to start a discussion through comments below.

tips to lead your analytics team fearlessly

According to me, you need to maintain a fearless monk like attitude in order to do a good job while leading Analytics teams. Following are the reasons why I say so:

  • A monk aims to and is always curious to find out truth about a subject. This is the drive you need to find out those deeply burried insights.
  • A monk does not give up his search for truth in face of love, fear or any adverse situation. Similarly, an analyst needs to stay objective about his analysis without thinking about the impact of outcomes.
  • A monk is deeply passionate about learning and enhancing knowledge. You need to be on top of latest techniques and technologies in order to succeed as a leader.

 

[stextbox id=”section”]   Characteristics of a fearless monk:[/stextbox]

So, what are the characteristics of a fearless monk? I am sharing them with a hope that they will help bridge some gap between the demand and the supply of good Analytics leaders.

Here are the characteristics as I see them:

1. Stay calm, composed and objective with an uncluttered mind. As analysts, we often work in high pressure environment with tight deadlines. However, this should not impact the quality of the analysis being produced. You can not make an error, especially so when timelines are short! Maintaining calm, composed and uncluttered mind is the only way you can come out of this situation.

 

2. Keep it simple – Focus on what is important and work hard. As an analyst, you are expected to have passion for complicated problems and high attention to details. This makes us an easy prey in a situation where you can make things complex. More often than not, complex situations get solved by simple solutions. Once you know the problem, identify the 20% of area which will give 80% of benefit and work hard to make sure that gets delivered flawlessly.

 

3. Know your strength and weakness and stay confident. Everyone has their strengths and weakness. Knowing them, making sure that you focus on your strengths to deliver the results while being aware of your weakness can produce astonishing results. While writing it down is easy, only a person with deep understanding about himself can do this.

 

4. Enjoy the process and never shy away from expressing yourself. Until and unless you enjoy the process, you can not lead people. Most of the good leaders I know will have glitter in their eyes on mention of getting an opportunity to slice and dice data themselves. Whether it is data cleaning, collection or iterating your model for nth time, you have to love each part of the process. Also, during the process, you have to add the value you can. So if you can think of a solution, but not sure whether it will work out – at least try it out. Because, until it is tried out, you will never know whether it could have worked.

 

5. Lead by example. Your team gets the best form of motivation when this happens. Whether it is a difficult stakeholder to meet, or you want the team to spend time on learning. All of these are best demonstrated by leading the way. Once you have set an example, it is always easy for the team to follow.

 

6. Knowing your team members and backing them up. If you have ever observed a group of monks working together, you will know what I mean. There is infinite trust in the capability of team members. And this trust does not falter in face of difficult times, it actually makes things seamless and easy. You have to back up your team members when they are down after a bad meeting or after an undesired result.

 

7. Humility and levelheadedness – Last but not the least, you have to maintain humility as a leader. This is an aspect which defines you as a human and it develops on its own when you start caring about people. One of the best illustration of humility I have seen in recent past is last speech of Sachin Tendulkar before hanging up his boots from international cricket.

 

[stextbox id=”section”]   A role model:[/stextbox]

I am a big fan of Mahendra Singh Dhoni. For those of you who follow cricket (even by a distance), you would know whom I am talking about. For those of you who don’t follow cricket, here is his brief profile. If you have seen Dhoni play, he does not have the best playing technique (aptitude), but his success as a leader is down to his attitude. He is a perfect example of these characteristics personified.

 

What do you think about maintaining a fearless monk-like attitude? Any thoughts on the characteristics and how to foster them? Any practices to share? Please feel free to share them through comments below.

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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

Apurv
Apurv

I huge Dhoni fan, the calmness on his face when he hit that world cup winning six was EPIC. I not a cricket fan, partly because i am bad player and partly because i a huge basketball player & fan. interesting post though...

123

Kunal Jain
Kunal Jain

Thanks Apurv!

123

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