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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

How to identify a good (and bad) Business Analyst?

Kunal Jain
Kunal Jain Last Updated : 26 Aug, 2021
4 min read

Over last 6 years, I have come across more than hundreds of analysts and have conducted almost equal number of interviews. Over this time, I have developed a knack of differentiating best analysts from good and good analysts from bad. If you face this challenge regularly, this post might help you.

 

So how do you differentiate between good and bad analysts?

how to identify good and bad analyst

 

Thankfully, it’s not that difficult. I have put a framework around how to judge an analyst. You can use the same to make your life simpler. This framework has it’s genesis in hiring guidelines at Capital One. I have modified it to adopt it in Indian Analytics industry.

 

[stextbox id=”section”]Structured thinking [/stextbox]

This is the most important attribute that distinguishes a good analyst from bad. This attribute is not only required to be a successful analyst, it becomes absolutely critical for a person managing Business Analysts.

So what is Structured thinking?

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.

How do I test for structured thinking?

Typically I test this by throwing a open business problem at an analyst and then observing closely how he / she is solving it.

An example is asking a question like: “You have been appointed as CEO of a loss making restaurant at Delhi / London Airport and you are expected to join the company in a week. What would you want to do as a CEO of the company as soon as you join them?”

If the person lays out a nice structure about where the problems could be, he has already ticked one box. If he starts giving you answers out of his hat (e.g. I would be looking at what marketing are we doing?), you should consider it as a red flag. He will not be able to sail through the world of Analytics.

[stextbox id=”section”]Business understanding and problem solving [/stextbox]

There is a reason why Business Analysts are called so and not just Analysts. Until a person understands what he is trying to solve and the business owners are confident that he can solve problems in meaningful manner, he is a dead analyst.

So, how do you test for business understanding?

For an experienced analyst, I typically start judging this by asking about business context for the projects he might have worked on. If he can explain that clearly, it’s a good start. If he can’t, you can almost make your hiring decision here. Next, you can look at the answers a person gives in response to question asked for judging structured thinking. If he gives answers based on numbers only, you need to probe him further. He needs to put a business thinking hat and provide some out of box suggestions.

[stextbox id=”section”]Attention to details [/stextbox]

If a person is not detail oriented, he can never be a good analyst. Every analyst should have the ability to understand business at high level, but he should be able to get down to nuts and bolts of all the levers you might have.

So how do you judge for attention to details?

Start by looking at the CV of a person, has he spent time choosing words carefully? Has he mentioned impact of the projects he might have worked on?

For an experienced analyst, probe on the projects he might have worked on before. Did he consider all the aspects and possibilities? How much time does take to explain his previous projects?

Another way to judge it is by asking the candidate to  a guess estimate, something like “Estimate the number of smartphones used in India” and looking at how the candidate answers them. How many factors does he consider to come up with answers? How many segments does he consider to  arrive at sizing? These aspects should give you a good read on how detail oriented a person is.

[stextbox id=”section”]Ability to triangulate numbers & do back of the envelope calculations[/stextbox]

While the first three characteristics help you identify a better than average analyst, this characteristic and the next differentiates best of analysts from good analyst. This is an activity I love to do and something I know every good analyst loves. This is the ability to set up equations on page and then do back of the envelope calculations to answer 80% of questions without touching any excel / calculator or laptop. It is also the ability to arrive at a number through various sources and then validating them.

How to judge ability to triangulate numbers and do back of the envelope calculations?

Guess estimate comes to your rescue here. Just ask the candidate to perform the guess estimate on a paper and then ask him to validate the number through an alternate approach.

[stextbox id=”section”]Communication skills – Ability to tell stories based on numbers[/stextbox]

Any analyst is only as good as he can communicate. If a person can not take the complex world of numbers and create a meaningful story out of it, he will always be looked upon as a nerd. He can be a good analyst, but not the best one. Ability to create a story and present it almost has an equal, if not higher influence on your customers and hence increases the chances of success of any analytics project.

How do you judge communication skills?

You can get a sense on this through the entire interview. If this is very critical to the role you are evaluating for, you can provide datasets in excel and ask the person to present some open ended questions

Hopefully, this framework will help you for any analyst hiring in future. In case you have some suggestions, do let me know.

 

Image Source: 123rf.com

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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

vamshi
vamshi

Sir, I am an undergraduate student pursuing my last semester in a renowned IIT. In my placement, I ended up screwing up 6 interviews related to business analyst profile. I never got a chance to get a feed back from my interviewer. Could you please help me out? Thank you

123

Kunal Jain
Kunal Jain

Vamshi, I will be happy to help in whatever manner I can, but your best bet is to reach out to your interviewers and check if you can still get the feedback. Do let me know in case you want any specific help from me. Thanks, Kunal

123
Justina
Justina

These are some really good tips for both interviewer and and also the interviewee. It provides interviewees some idea of what questions can be asked in an interview for an analyst. I know I have been asked such questions before in the first round of interviews (written test - no calculators/excel allowed). Could be nice to have a detailed answer to the questions you post to what might be a model answer.

123

Kunal Jain
Kunal Jain

Justina, You might find this article of some help. While this article does not provide model answers - it does provide a lot of nifty tips to crack any interview. Regards, Kunal

123
Akshay Deshpande
Akshay Deshpande

Hello sir, I have completed my masters in MCA (master of computer application). currently i'm working in IT company as a programmer (software programmer). I'm not at all interested to do the code, while my domain or interest or focus is on the Business Analysis. I'm ready to learn the things as a trainee analyst, so I want to ask your suggestions. Is i'm on the right path? there is another thing that , if I'm not getting chance to become business analyst in same organization then can I leave the organization and try to get the job in different organization? please guide me. Thank you.

123

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