soudipd/cracking-the-data-science-interview-in-7-days

cracking-the-data-science-interview-in-7-days

Here are the sections:

• Data Science Cheatsheets
• Data Science Case Studies
• Data Science Portfolio
• Data Journalism Portfolio
• Downloadable Cheatsheets

Data Science Cheatsheets

This section contains cheatsheets of basic concepts in data science that will be asked in interviews:

• SQL
• Statistics and Probability
• Mathematics
• Machine Learning Concepts
• Deep Learning Concepts
• Supervised Learning
• Unsupervised Learning
• Computer Vision
• Natural Language Processing
• Stanford Materials

Data Science Case Studies

This section contains case study questions that concern designing machine learning systems to solve practical problems.

Data Science Portfolio

This section contains portfolio of data science projects completed by me for academic, self learning, and hobby purposes.

For a more visually pleasant experience for browsing the portfolio, check out piyush pathak

• Computer Vision

  • Dog Breed Classification: Designed a Convolutional Neural Network that identifies dog breed.

  • Road Segmentation: Implemented a Fully-Convolutional Network for semantic segmentation task in the Kitty Road Dataset.

  Tools: TensorFlow, Keras, Pandas, NumPy, Matplotlib, Scikit-Learn, TensorBoard

• Natural Language Processing

  • Classifying Tweets with Weights & Biases: Developed 3 different neural network models that classify tweets on a crowdsourced dataset in Figure Eight.

Data Journalism Portfolio

This section contains portfolio of data journalism articles completed by me for freelance clients and self-learning purposes.

For a more visually pleasant experience for browsing the portfolio, check out Piyush Pathak

• Statistics

  • The 10 Statistical Techniques Data Scientists Need to Master

  • Logistic Regression Tutorial

  • Decision Trees Tutorial

  • Support Vector Machines Tutorial

  • A Friendly Introduction to Data-Driven Marketing for Business Leaders

• Machine Learning

  • The 10 Algorithms Machine Learning Engineers Need to Know

  • 12 Useful Things to Know About Machine Learning

  • A Tour of The Top 10 Algorithms for Machine Learning Newbie

  • The 10 Data Mining Techniques Data Scientists Need For Their Toolbox

  • Clustering and Classification in E-Commerce

  • The ABCs of Learning to Rank

  • 6 Ways to Debug a Machine Learning Model

• Deep Learning

  • The 10 Deep Learning Methods AI Practitioners Need to Apply

  • The 8 Neural Network Architectures ML Researchers Need to Learn

  • The 5 Deep Learning Frameworks Every Serious Machine Learner Should Be Familiar With

  • The 5 Computer Vision Techniques That Will Change How You See The World

  • Convolutional Neural Networks: The Biologically-Inspired Model

  • Recurrent Neural Networks: The Powerhouse of Language Modeling

  • The 7 NLP Techniques That Will Change How You Communicate in the Future

  • The 5 Trends Dominating Computer Vision in 2018

  • The 3 Deep Learning Frameworks For End-to-End Speech Recognition That Power Your Devices

  • The 5 Algorithms for Efficient Deep Learning Inference on Small Devices

  • The 4 Research Techniques to Train Deep Neural Network Models More Efficiently

  • The 2 Hardware Architectures for Efficient Training and Inference of Deep Nets

  • 10 Deep Learning Best Practices to Keep in Mind in 2020

Downloadable Cheatsheets

These PDF cheatsheets come from BecomingHuman.AI.

1 - Neural Network Basics

2 - Neural Network Graphs

3 - Machine Learning with Emojis

4 - Scikit-Learn With Python

5 - Python Basics

6 - NumPy Basics

7 - Pandas Basics

8 - Data Wrangling With Pandas

9 - SciPy Linear Algebra

10 - Matplotlib Basics

11 - Keras

12 - Big-O

About me

Piyush Pathak

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