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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

Five habits of highly successful analysts

Kunal Jain
Kunal Jain Last Updated : 27 Aug, 2021
5 min read

I have interacted with various successful analysts over last 7 years. During these interactions, I found out some common habits in them. After observing these habits, I have tried ways to inculcate these habits myself. Some of these habits were also emphasized by my mentors, while the others were learnt the hard way!

analytics, business analytics, data science

This article lays down 5 habits I have observed in highly successful analysts and provides some of the ways in which I practice these habits. On one hand, I hope that some one should have made me aware of these habits at start of my career and the long lasting impact these could have. On the other hand, these habits have helped me immensely and I hope they will do the same for any one adapting them:

Habits of successful analysts

Habits of successful analysts

[stextbox id=”section”]Habit 1: Keep high bar on project delivery. Walk that extra mile and deliver your best[/stextbox]

Quality thinking differentiates a high performing analyst from a low performer. Successful analysts provide enough quality “brain time” to any project they deliver. By brain time, I mean distraction-free time devoted to analytical problem solving. This is the time when you strive for going beyond what is expected. This is the time when an analyst asks some of these questions to himself:

  • Is there a better way to structure this problem? Will that make the solution better or more intuitive for business?
  • Is there a better way to present / summarize the findings of the project? How can I visualize the outcomes in best possible manner?
  • Instead of simply highlighting the insights from a project, can I use these insights and chalk out business actionables? Can I size the impact from these actionables and tell them to business upfront?
  • Is there any aspect of analysis I might have ignored?
  • Is there any implicit assumption I have made which is impacting the result?

Remember, the value of analytics is recognized through the value it generates for the business and the amount of time spent asking these questions will have direct impact on the business value created.

There have been times when I have changed the presentation flow, performed additional analysis, verified and re-verified all the numbers / insights till the night before project presentation. All of that is done to make sure the project creates the impact it deserves. And, nothing beats the feeling you go through after creating that impact.

So, don’t leave any stone un-turned and make sure there are no gaps in your thinking on every project you work on.

[stextbox id=”section”]Habit 2: Segment, till you can![/stextbox]

Successful analysts never work on averages. Every time they see an average, they think if there is an underlying segmentation at work, which could explain things better? By not segmenting the average, there is value left on table. Successful analysts never do that.

This article contains various examples and the way to create a segmentation.

[stextbox id=”section”]Habit 3: Triangulate numbers, perform back of the envelope calculations and think what do they mean for business[/stextbox]

As an analyst, you deal with numbers day in and day out. You need to pick out that one cell in which the formula is wrong from a file containing thousands (if not millions) of formulas. The only way you can do it is by triangulating numbers and by making sense of what they mean for business.

While this might sound obvious, you will be surprised to see the number of times this is overlooked. While triangulating numbers deserves a post in itself, I’ll briefly mention some of the questions I ask to triangulate numbers:

  • Ask yourself, can I reach this number through a different framework / calculation? Do the numbers tie up or they are different by a magnitude?
  • Are there process dependencies which can give you a sense of numbers? Can you issue 2000 credit cards every month, if you only get ~1800 applications every month?
  • What do these number mean for business? Do they tie in with the infrastructure and resources business has?

Triangulation is like any other skill, it will look difficult to start with. But the more you practice, the better you become at it.

[stextbox id=”section”]Habit 4: Test out your hypothesis (even if you think they make complete business sense)[/stextbox]

There are times when you tend to overlook the need for testing. Just adding a live chat functionality to your website? Sounds like a good thing to do with no down side. Test it out and you will know. The customers might not like it! Here is another example:

One of the leading travel portal in India saw this in their data: 90%+ flights booked have departing location same as city from which tickets are being booked (determined by the I.P. address). They thought of making this location pre-populated (obviously with an option to change). This sounds like a nice idea which would help provide a better customer experience. Thankfully, they tested it out. Booking conversion dropped by double digit percentage with in weeks of making this change. Possible reason: The customers are used to filling To and From location. Removing one of them adds to the confusion.

So, next time if you are implementing results from any analysis or hypothesis, test it out!

[stextbox id=”section”]Habit 5: Learn something about analytics everyday[/stextbox]

While this habit will not bear immediate results like other habits mentioned above, you will benefit the most from this in long run. Analytics is a dynamic and evolving field. A new tool / technology / update arrives almost every 2 – 3 months. Being up to date with latest updates in industry helps not only stay on top of it, but creates a huge gap from analysts who don’t stay updated.

Some of the topics I enjoy reading whenever I get time are:

  • What are the latest developments in Big data?
  • How to analyze unstructured data from Social media? How can we make visualizing this better?
  • What are the statistical concepts behind the algorithms used by various tools?
  • How can you design and analyze a design of experiments?

The list is endless.

Start reading and you are bound to run out of time!

Additional Habit: One additional habit worth mentioning is “Being customer centric”. Hearing what the customers are saying and keeping that in mind is a trait of every successful professional. The same applies to an analyst. Since it is not an analytics specific habit, I have not mentioned it in the list.

While the list of habits could have been lot longer, I have deliberately restricted it to 5. If you think any other habit, please add them in comments.

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

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Zahiruddin Khazi
Zahiruddin Khazi

Hi Kunal, All 5 habits successful analysts put into place. Question lingering in my mind that a project awarded to a develop a application to a company, without knowing the strength later it was noticed that they do not have domain expertise totally different domain from (Financial to Telco). So my question is how segmentation comes into effect in these type project scenario. Pls. Comment. Thanks and Regards, Zahiruddin Khazi

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Kunal Jain
Kunal Jain

Zahiruddin, I am not sure I understand your question completely. If you are asking that if you are creating an application for a customer in a domain where you do not have experience, I would suggesy to do the following: * Spend time understanding the business by spending time with customer and the team responsible for MIS / Analysis. This should provide a good list of hyoothesis to start with. * Once you have possible set of hypothesis and you have the data, you can quickly check the data to understand how is the population segmenting on various parameters. Techniques like decision tree and clustering can help you get good understanding * Go back to customers with you findings on segments and sub-segments, validate the findings and agree on a segmentation which works for them. Hope this answers the question. If I have mis-understood, please let me know. Kunal

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Anvesh
Anvesh

Nice article! Interesting tidbit about the travel website!!

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kumar
kumar

Hi, very useful post indeed; looking for a separate post on "triangulating numbers" with some real life examples. please share the link if its already there. thanks,

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