Published 7/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 656.20 MB | Duration: 1h 15m
This course covers real-life data science interview questions with Python
What you’ll learn
Categorical variables and how to include them into model
Missing values and how to handle them?
What is a Correlation Matrix’s role?
How to check relationship between variables?
How to interpret the regression analysis?
How to use polynomial model?
What is an overfitting? How to prevent it?
Requirements
Basic knowledge of Python is required Particularly Pandas library
Description
In this course, we aim to provide you with a focused and efficient approach to preparing for data science interviews. We understand that your time is valuable, so we have carefully curated the content to cut out any unnecessary noise and provide you with the most relevant materials. Moving beyond theory, the course will dive into a wide range of practical data science interview questions. These questions have been carefully selected to represent the types of problems frequently encountered in real-world data science roles. By practicing these questions, you will develop the skills and intuition necessary to tackle similar problems during interviews.Throughout the course, we have filtered out any extraneous materials and focused solely on the core topics and questions that are most likely to come up in data science interviews. This approach will save you time and allow you to focus your efforts on what truly matters.Index:Missing values and how to handle them? (Python)What are categorical variables and how to include them into model (Python)What is a Correlation Matrix’s role? (Python)How to check relationship between variables? (Python)How to interpret the regression analysis? (Python)How to improve the regression model results with logarithmic transformation? (Python)How to use polynomial model? (Python)What is an overfitting? How to prevent it? (Theory)Supervised vs Unsupervised Learning (Theory)Parametric and Unparametric model (Theory)
Overview
Section 1: Introduction and Environment Setup
Lecture 1 Introduction
Lecture 2 Setting up Colab
Lecture 3 Setting up working Environment
Section 2: Data Analyst Questions
Lecture 4 Categorical variables and how to include them into model
Lecture 5 Missing values and how to handle them?
Lecture 6 What is a Correlation Matrix’s role?
Lecture 7 How to check relationship between variables?
Section 3: Data Scientist Questions
Lecture 8 Supervised vs Unsupervised Learning & Parametric vs non-parametric Models
Lecture 9 How to interpret the regression analysis?
Lecture 10 How to improve the regression model results with logarithmic transformation?
Lecture 11 How to use polynomial model in Python
Lecture 12 Comparing the results of models
Beginners in Machine Learning and Python,Students who are searching to land their first job as a data scientist
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