How to crack Data Science Interview #shorts #datascience #interviewquestions

Analytics Vidhya · Intermediate ·🏗️ Systems Design & Architecture ·3y ago

Key Takeaways

The video provides a 7-step framework for cracking data science interviews, with a focus on resume building and job search strategies, highlighting the importance of tailoring resumes to specific roles and leveraging social networks for job referrals.

Full Transcript

I'll be giving you a seven step framework for cracking your next data science interview if I had to pick the toughest step in the data science hiring process this would be it and mind you I'm putting this even ahead of in-person technical interviews make sure your resume reflects the relevant technical skills you will need for the job you might be a PowerPoint wizard but that should not be your key skill for a data engineering role right create separate resume for different roles a startup usually have very different hand Zone expectations as compared to an established corporate firm so make sure you make two separate versions of your resume emphasizing these two aspects once your resume is ready expand your job search remember Joe portals are not the only way to apply for data science roles in fact they are the least effective manner of searching for a job you may leverage your social network in form of job referrals or participate in hackathons data science competitions to get noticed

Original Description

Full video link: https://youtu.be/Jz_575po2FQ
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from Analytics Vidhya · Analytics Vidhya · 0 of 60

← Previous Next →
1 The DataHour: Data Science in Retail
The DataHour: Data Science in Retail
Analytics Vidhya
2 The DataHour: Anomaly detection using NLP and Predictive Modeling
The DataHour: Anomaly detection using NLP and Predictive Modeling
Analytics Vidhya
3 The DataHour: Energy Data Science Project from Scratch
The DataHour: Energy Data Science Project from Scratch
Analytics Vidhya
4 The DataHour: Explainable AI Need and Implementation
The DataHour: Explainable AI Need and Implementation
Analytics Vidhya
5 The DataHour: Google Cloud AI/ML
The DataHour: Google Cloud AI/ML
Analytics Vidhya
6 Prediction to Production in Machine Learning #machinelearning #prediction
Prediction to Production in Machine Learning #machinelearning #prediction
Analytics Vidhya
7 Practical Applications of Data science in Ecommerce
Practical Applications of Data science in Ecommerce
Analytics Vidhya
8 How to tackle Overfitting?#machinelearning #overfitting
How to tackle Overfitting?#machinelearning #overfitting
Analytics Vidhya
9 Building Data Pipelines on GCP #googlecloud #datapipelines #data
Building Data Pipelines on GCP #googlecloud #datapipelines #data
Analytics Vidhya
10 Hands-on with A/B Testing #abtesting #datascience
Hands-on with A/B Testing #abtesting #datascience
Analytics Vidhya
11 Efficient Implementations of Transformers #transformers #cnn  #machinelearning
Efficient Implementations of Transformers #transformers #cnn #machinelearning
Analytics Vidhya
12 Modern Deep Learning Architecture #deeplearning  #architecture #deeplearningtutorial
Modern Deep Learning Architecture #deeplearning #architecture #deeplearningtutorial
Analytics Vidhya
13 Key steps for Designing Artificial Neural Network (ANN) for Image classification #machinelearning
Key steps for Designing Artificial Neural Network (ANN) for Image classification #machinelearning
Analytics Vidhya
14 5 things you should know about Azure SQL #azure #sql #datahour #datascience
5 things you should know about Azure SQL #azure #sql #datahour #datascience
Analytics Vidhya
15 AI & ML in the Automotive Industry #machinelearning #ai
AI & ML in the Automotive Industry #machinelearning #ai
Analytics Vidhya
16 Building Machine Learning Models in BigQuery
Building Machine Learning Models in BigQuery
Analytics Vidhya
17 NLP aspects in Telecommunication Industry
NLP aspects in Telecommunication Industry
Analytics Vidhya
18 Practical Time Series Analysis
Practical Time Series Analysis
Analytics Vidhya
19 Fundamentals of Quantum Computing
Fundamentals of Quantum Computing
Analytics Vidhya
20 A DAY IN THE LIFE of a Data Scientist (From waking up to working on algorithms)
A DAY IN THE LIFE of a Data Scientist (From waking up to working on algorithms)
Analytics Vidhya
21 Classification Machine Learning Model from Scratch
Classification Machine Learning Model from Scratch
Analytics Vidhya
22 Knowledge Graph Solutions using Neo4j
Knowledge Graph Solutions using Neo4j
Analytics Vidhya
23 Model Guesstimation (MLOps)
Model Guesstimation (MLOps)
Analytics Vidhya
24 ETL Pipelines in Google Cloud Platform
ETL Pipelines in Google Cloud Platform
Analytics Vidhya
25 Key steps for Designing Convolutional Neural Network(CNN) for Image Classification
Key steps for Designing Convolutional Neural Network(CNN) for Image Classification
Analytics Vidhya
26 Getting Started with AWS EC2 #amazon #aws
Getting Started with AWS EC2 #amazon #aws
Analytics Vidhya
27 How to Use Azure NLP and Graph Databases for Intelligent Knowledge Mining
How to Use Azure NLP and Graph Databases for Intelligent Knowledge Mining
Analytics Vidhya
28 Certified AI & ML BlackBelt Plus Program #shorts
Certified AI & ML BlackBelt Plus Program #shorts
Analytics Vidhya
29 Visualizing Data using Python #machinelearning #visualization #python
Visualizing Data using Python #machinelearning #visualization #python
Analytics Vidhya
30 DCNN for Machine RUL Prediction using Time-series Data #timeseries #machinelearning #datascience
DCNN for Machine RUL Prediction using Time-series Data #timeseries #machinelearning #datascience
Analytics Vidhya
31 M in ML stands for Math & Magic
M in ML stands for Math & Magic
Analytics Vidhya
32 An Unsupervised ML approach using Clustering
An Unsupervised ML approach using Clustering
Analytics Vidhya
33 Customizing Large Language Models GPT3 for Real-life Use Cases #gpt3 #datascience
Customizing Large Language Models GPT3 for Real-life Use Cases #gpt3 #datascience
Analytics Vidhya
34 Model Parameters vs Hyperparameters - Techniques in ML Engineering #machinelearning
Model Parameters vs Hyperparameters - Techniques in ML Engineering #machinelearning
Analytics Vidhya
35 Practical MLOps #mlops #datascience
Practical MLOps #mlops #datascience
Analytics Vidhya
36 Data Engineering with Databricks #dataengineering #databricks
Data Engineering with Databricks #dataengineering #databricks
Analytics Vidhya
37 Multi-Objective Optimisation
Multi-Objective Optimisation
Analytics Vidhya
38 When Airflow Meets Kubernetes
When Airflow Meets Kubernetes
Analytics Vidhya
39 AI in Banking
AI in Banking
Analytics Vidhya
40 Learn Convolutional Neural Network for Image Recognition
Learn Convolutional Neural Network for Image Recognition
Analytics Vidhya
41 Extracting Value from Data
Extracting Value from Data
Analytics Vidhya
42 How to measure Marketing Channel Effectiveness
How to measure Marketing Channel Effectiveness
Analytics Vidhya
43 Transforming Lives | Data Science Immersive Bootcamp
Transforming Lives | Data Science Immersive Bootcamp
Analytics Vidhya
44 Stock Market Analysis - AI driven approach
Stock Market Analysis - AI driven approach
Analytics Vidhya
45 Become a Data Engineering Professional in 2022 | Future Trends + Skills Required
Become a Data Engineering Professional in 2022 | Future Trends + Skills Required
Analytics Vidhya
46 Ensemble Techniques in Machine Learning #machinelearning #ensemble #datascience
Ensemble Techniques in Machine Learning #machinelearning #ensemble #datascience
Analytics Vidhya
47 The Power of Visualization | Tableau Full Course | Analytics Vidhya
The Power of Visualization | Tableau Full Course | Analytics Vidhya
Analytics Vidhya
48 Demand for Data Engineers is on the Rise | Data Engineer | Analytics Vidhya
Demand for Data Engineers is on the Rise | Data Engineer | Analytics Vidhya
Analytics Vidhya
49 Data Visualization in Data Science | DataHour | Analytics Vidhya
Data Visualization in Data Science | DataHour | Analytics Vidhya
Analytics Vidhya
50 Role of Optimization in Machine Learning & Deep Learning | DataHour | Analytics Vidhya
Role of Optimization in Machine Learning & Deep Learning | DataHour | Analytics Vidhya
Analytics Vidhya
51 Solving any Machine Learning Problem | Approach and Steps Involved
Solving any Machine Learning Problem | Approach and Steps Involved
Analytics Vidhya
52 Topic Modeling Explained with Implementation | Using LDA in Python | DataHour by Arpendu Ganguly
Topic Modeling Explained with Implementation | Using LDA in Python | DataHour by Arpendu Ganguly
Analytics Vidhya
53 Data Engineering in E-Commerce | The Best Case Study
Data Engineering in E-Commerce | The Best Case Study
Analytics Vidhya
54 Introduction to Classification using Azure Machine Learning | DataHour | Analytics Vidhya
Introduction to Classification using Azure Machine Learning | DataHour | Analytics Vidhya
Analytics Vidhya
55 Introduction to Federated Learning | DataHour | Analytics Vidhya
Introduction to Federated Learning | DataHour | Analytics Vidhya
Analytics Vidhya
56 Diffusion Models for Generative Arts | DataHour | Analytics Vidhya
Diffusion Models for Generative Arts | DataHour | Analytics Vidhya
Analytics Vidhya
57 Master Google Analytics in 1 Hour | DataHour | Analytics Vidhya
Master Google Analytics in 1 Hour | DataHour | Analytics Vidhya
Analytics Vidhya
58 Learn Hypothesis Testing | DataHour | Analytics Vidhya
Learn Hypothesis Testing | DataHour | Analytics Vidhya
Analytics Vidhya
59 A Practical Approach to Kaggle Competition | DataHour | Analytics Vidhya
A Practical Approach to Kaggle Competition | DataHour | Analytics Vidhya
Analytics Vidhya
60 Making AI work for Business | DataHour | Analytics Vidhya
Making AI work for Business | DataHour | Analytics Vidhya
Analytics Vidhya

The video teaches viewers how to crack data science interviews by building effective resumes and leveraging social networks for job referrals, emphasizing the importance of tailoring resumes to specific roles and highlighting relevant technical skills.

Key Takeaways
  1. Create a separate resume for different data science roles
  2. Emphasize relevant technical skills for each role
  3. Leverage social networks for job referrals
  4. Participate in hackathons and data science competitions
  5. Expand job search beyond job portals
  6. Tailor resume to startup or corporate firm expectations
  7. Highlight key skills for data engineering roles
💡 A well-crafted resume and effective job search strategy are crucial for cracking data science interviews, and tailoring resumes to specific roles and highlighting relevant technical skills can significantly improve chances of success.

Related Reads

📰
SvelteKit 2 Complete Guide: From Zero to Production (2026)
Learn how to build a full-stack application with SvelteKit 2 and Svelte 5's rune system, from setup to production, and boost your productivity
Dev.to · Carlos Oliva Pascual
📰
Implementing a Game Character Evolution System Backend: A 4-Stage System Design
Learn to design a 4-stage game character evolution system backend to integrate with point shops and inventory systems
Dev.to · 박준희
📰
High Level System Design Day-16 — Websockets (Part 4)
Learn to design scalable systems using WebSockets for real-time communication, a crucial skill for backend engineers
Medium · Programming
📰
The Template Method Pattern: Reusing Structure, Not Just Code
Learn the Template Method pattern to reuse algorithm structures and customize varying steps in subclasses
Medium · Programming
Up next
How To Install iOS 27 Beta on iPhone for FREE! (Step-by-Step)
Ksk Royal
Watch →