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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

6 Key Points you Should Focus on for your Next Data Science Interview

k
khyati Last Updated : 24 Sep, 2020
7 min read

Overview

  • Preparing for your next data science interview? You need to ensure you’re covering your basics
  • Here are 6 key points we’ve taken from our data science interview experience that you should focus on

 

Introduction

You’ve finally done it! You have landed an interview for a data science role. Now, a day before your interview, you’re not sure what to study. The day is almost here but there is so much to cover!

Sound familiar?

Interviews can be daunting – I completely get that. Add in Data Science, and you’ve got yourself a nerve-racking cocktail. Data Science professionals need to combine their technical skills with their soft skills. It’s a tough landscape to navigate.

Landing the interview is great – but cracking it? That’s where things get really interesting. What should you study? What should you leave out? Is there any cheat code you can apply and simply plug and play it in during the interview?

If you are in a similar situation – you’ve come to the right place!

In this article, I will focus on 6 key things to do a day before your big Data Science Interview, apart from the obvious revision of concepts, to make sure you absolutely nail this opportunity. I will not cover the entire preparation process which should ideally begin months in advance of the actual interview.

There is nothing quite like real-world hands-on industry experience. And trust me, that will hold you in high regard in your data science interview. If you’re looking for something similar, the Ascend Pro program is made for you!  It combines data science knowledge with practical industry experience by industry leaders and experts – a one-in-a-lifetime opportunity to prepare yourself for your dream data science role.

 

1. Be Thorough with your Data Science Resume

The absolute basics of any interview, and especially a data science one. You should be able to explain everything listed on your resume. Anything that you could possibly reference, you should be able to speak about it.

If you’ve listed an NLP project for example, and are unable to explain the details – that’s a MAJOR red flag for the interviewer.

Use the day before the interview to edit and revise your resume. Cut details that are not required and add new ones if required. Think about each experience and project that you list – does it add something relevant?

That means your experience with a marketing firm as a non-technical person might not be very relevant for a data science role. You should consider keeping details like that off your resume. Mentioning it will just give the interviewer a sense that you are not clear about what you want from the job.

Also, think of how you will go about explaining your work experience. Your account should depict your skills and how they led to progress. Consider the following statements:

  • “Used LSTM’s to predict the company’s stock prices.”
  • “Used LSTM’s to predict the company’s stock prices with 40% more accuracy than the historical average.”

Doesn’t the second statement sound way more impressive than the first?

Make sure to make your achievements are measurable and quantifiable. This will leave a better impression on the minds of your data science interviewer(s).

I recommend reading our guide to building an effective Data Science Resume. It mentions the 4 key aspects that will make or break your data science application.

 

2. Study up on your Data Science Projects

Much like the other details on your resume, deciding what projects to talk about in your interview is also crucial. If there are any projects irrelevant to the role you’ve applied to, adding it in anyway isn’t a great practice. This just shows your interviewer that you cannot prioritize well.

Source: DataOptimal

Shortlist 3 to 4 projects which showcase your best work and prepare yourself to talk about them. These projects could be from your current organization, internships, from some coursework or even independent projects using datasets from Analytics Vidhya or Kaggle. Also, keep in mind that these projects should be relevant to your job profile.

I keep reiterating this because it is THAT important.

Let me give you my own example. I had listed a research project on my resume which I had done two years back. In hindsight, I should have left it off since it had nothing relevant to the internship role I was interviewing for – a data analytics intern.

As I went on explaining what I did in this project, I made the mistake of mentioning the term ‘cubic splines’. The interviewer immediately wanted me to elaborate on cubic splines and I realized that I had dug myself into a hole. And no, I did not get the internship.

There’s a lesson there for all of you data science enthusiasts! If you are looking for projects, refer to our list of 24 Ultimate Data Science Projects to boost your Knowledge and Skills.

 

3. Practice Solving Puzzles – A Key Data Science Skill

Puzzles are a fairly popular way of evaluating a candidate’s quick thinking and analytical acumen. You need to be logical, creative and good with numbers to solve puzzles.

Many organizations use puzzles for testing their candidates on their problem-solving skills. They want to know about your thought process and how you approach a problem.

I cannot give you a complete guide to solving each puzzle, but I do have a few tips for you to proceed towards puzzle-solving:

  • Approach the problem slowly and understand all the details. Ask for any assumptions if they are not explicitly mentioned
  • These are meant to showcase your thought process. So make sure to walk your interviewer through your solution while you think
  • Do not stick with an approach for too long. Take cues from your interviewer and modify your approach accordingly
  • Realize that it is okay if you were not able to completely solve the puzzle. Different puzzles have different levels of difficulty and not all of them are meant to be solved in one sitting

Try solving the puzzles in our list of 20 Hard Data Science Interview Puzzles that every analyst should solve at least once.

 

4. Prepare to Face Case Studies for Data Science Roles

Organizations use case studies as a means of evaluating candidates on how they approach real-life problems. Case studies are the closest thing to the problems that you would be encountering in your role later on. I have seen freshers struggle the most with this part of the data science interview process.

The tricky aspect of a case study is that it might not be directly related to data science. For example, I got a case study around how to predict the number of black cars in Delhi NCR right now. It’s a tricky one – but if you have a structured mindset – you’ll knock it out of the park!

Approaching a case study can appear hard since there is no fixed formula to solve them. But you can use the below points to guide yourself through them:

  • Ask a lot of questions. Whatever questions pop in your head, ask away! It will help you uncover a lot of details that you will require for the solution
  • Structure the problem. This could be organizing all available data into a table. Structuring might unveil some hidden patterns in the data
  • Practice! Try case studies from different domains like retail, healthcare, business, etc. The more you practice, the easier a new problem will feel
  • Remember what is important is good brainstorming and a great discussion. The goal is not to reach a fixed or pre-defined solution, but rather to find a path to it and show your thought process

Have a look at some of the case studies on Analytics Vidhya (practice each of them and you’ll be interview-ready in a jiffy):

 

5. Research the Job Profile and the Organization

Researching the job profile has obvious benefits. You would be able to streamline your preparation based on what is required from the role.

Sometimes, employers may even ask candidates a question or use a keyword to make sure they read the job description carefully:

  • “What technologies do we work with?”
  • “What are you expecting from this role?”
  • “Can you tell us the latest project our data science team open-sourced?”

These questions will be dreadful if you didn’t read up on the company and the role.

I highly recommend spending some time reading about the company’s mission, vision and core values. Find out about their key achievements. Try and find the data science set up that they have and what kind of projects they work on. If possible, find out about the hierarchy of the organization and how the data science team fits into it.

Studying the organization and its structure will help you frame better questions for your interviewers. This shows your enthusiasm and curiosity towards the organization and leaves your interviewers impressed.

 

6. Review Confusing Data Science Terms

Are there any data science terms that have bamboozled you before? I’m sure there are a few – this is true for even experienced data scientists.

A few confusing terms or concepts that I encourage you to read up on a day before your interview:

  • Type I and Type II errors
  • Precision and Recall
  • False Positive Rate and True Negative Rate
  • Business metrics v STatistical metric
  • Model deployment

I frequently have to look up the difference between these terms and I am sure most of you do as well. These can stump you if asked in an interview. You know the answer, but the slight differences just aren’t coming to you.

Source: AB Tasty

Make sure to revise such terms a day before the interview. Refer to our glossary of common machine learning and data science terms for a quick idea around these concepts.

 

End Notes

These are just some last-minute tips. The entire data science interview preparation is a long process. You need to start months in advance and build your profile. There are also multiple rounds in a data science hiring process, including:

  1. Telephonic Screening
  2. Assignments
  3. On-site interview, which has several rounds like technical, case studies, puzzles, guesstimate, and more.

The ‘Ace Data Science Interviews‘ course covers all of these rounds in detail. The course also has a rich collection of Interview Questions along with many helpful tips and tricks. This could significantly increase your chances of acing your next Data Science Interview. So make sure to check it out!

k
khyati

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A very easy and simple way to remember false +ve/-ve

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