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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

Beware – interviewer for analytics job is observing you closely!

Kunal Jain
Kunal Jain Last Updated : 26 Feb, 2019
3 min read

Introduction

Analysts are people with high attention to details! This trait is visible across any endeavor they are involved in.

I once went to a Go-Karting event with a bunch of highly competitive analysts. A week after that evening, we had (apart from multiple discussions) an Excel (VBA based) simulation showing details on what happened on each and every turn, when someone overtook the other person, when did people take a pit stop, who couldn’t accelerate as much as they could and what not! We almost ended up creating personalized recommendations for each driver.

As an analyst, getting into details and studying them carefully, almost becomes your second nature. In an interview, you are likely being interviewed by someone who has been an analyst for a longer duration that you have been. Hence, you should expect a through and close examination of minute details.

This, and many more details are covered in our comprehensive ‘Ace Data Science Interviews‘ course. Make sure you check that out before you head for your next interview!

useful interview tips, analytics and data science

Many people new to the industry or who is trying to enter this industry are typically not aware and hence ignore these finer aspects. Hence, I thought I’ll create a list of these small things, which are typically overlooked by people coming for an interview. Please see that this is not a list of competencies which you are being judged upon (if you want them, you can read them here). You can almost treat this list as a checklist of things you need to avoid while you are being interviewed.

 

Hygiene factors:

  1. You reach late for an interview.
  2. The folder containing your documents is all messed up. When someone asks you for the CV, it takes you more than 10 seconds to pull it out.
  3. During the case study round (which normally happens for analytics interviews), not writing your solution in a structured fashion. This is one point which so many people overlook. The idea is that whatever you write during the case-study should be re-usable at a later point. Typically, case studies evolve as you go through them. So you would need the same structure, multiple times during the same interview. You cannot be fiddling with papers when asked to re-calculate profits in the new scenario!
  4. If it is a coding assignment, not indenting and commenting your code. Same principle as the last point. It also shows, how much of your work would be re-usable by others.
  5. If you have done something wrong, defending it rather than accepting it and correcting it.
  6. Error / typo in a CV or a non-updated CV

 

Structured thinking:

  1. Not putting a structure / framework to your answers is a big red flag. An ideal analyst would always have his thoughts structured – starting from when he / she is telling about himself / herself to when they are solving a case study.
  2. Not stepping back to see the bigger picture. Quite often, being good with details can back fire. If there is a business problem / case you are solving, you should always take a step back and think about the bigger picture. Don’t get into the details unless you are clear about the business problem and its impact.

 

Numerical abilities:

  1. Using calculators / excel too often – even when they are not needed. 33.33 times 6 is 200, you don’t need a calculator for this.
  2. Not taking shortcuts when they are available. If two factors compensate each other, you can directly knock them off.
  3. Not triangulating numbers or making sense out of numbers once they have been calculated. Do they tie back to the original problem?

 

Curiosity / Motivation:

  1. Not asking enough questions or good questions during the interview rounds or outside.
  2. Not asking for clarity in an ambiguous / amorphous situation
  3. Lack of energy at any point in time during the process.
  4. Discomfort in doing white board discussions during the interview.

 

Problem solving:

  1. Not suggesting a few crazy (read out of the box) solutions during the case studies
  2. Suggesting too complex solutions to simple business problems
  3. Thinking about the problem on a single track (usually data driven solutions) rather than holistically (e.g. Marketing, Operations, etc.)

 

There were some of the behaviors to avoid in an analytics interview I know of. If you have more to add to this list, please feel free to add them through comments below. These are part of our comprehensive Ace Data Science Interviews course. That’s a guide you don’t want to be without for your next data science interview!

photo credit: andercismo via photopincc

 

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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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