Why this course?

Description

This module serves as a comprehensive guide to the most commonly encountered interview questions in the field of data science. It is structured to help learners systematically prepare for technical interviews, combining theoretical concepts, practical coding challenges, and case-based problem-solving scenarios.

Learning Objectives

• Identify and understand the core concepts commonly tested in data science interviews.
• Demonstrate proficiency in answering both technical and non-technical questions.
• Analyze and respond to scenario-based interview problems effectively.
• Prepare for coding and algorithmic challenges using Python and related libraries.
• Develop personalized responses to behavioral questions and project discussions.

Module Content
Section 1: Python and Data Structures

• Data types and type conversions
• List comprehensions vs. loops
• Dictionary and set operations
• File handling and exception management
• Object-Oriented Programming in Python

Section 2: Data Manipulation and Analysis

• Pandas: DataFrames, merging, grouping, reshaping
• NumPy: Arrays, indexing, broadcasting, vectorized operations
• Data cleaning and transformation techniques

Section 3: Data Visualization

• Matplotlib and Seaborn usage
• Best practices for visual storytelling
• Plot selection for different data types and purposes

Section 4: Statistical Concepts

• Probability distributions and sampling
• Descriptive and inferential statistics
• Hypothesis testing and p-values
• Correlation and causation

Section 5: Machine Learning Algorithms

• Supervised vs. Unsupervised learning
• Model evaluation metrics (Accuracy, Precision, Recall, F1, ROC-AUC)
• Linear and logistic regression
• Decision Trees, Random Forests, KNN, SVM
• Clustering: K-means, Hierarchical clustering

Section 6: Data Preprocessing and Feature Engineering

• Handling missing data
• Encoding categorical variables
• Feature scaling and normalization
• Feature selection techniques

Section 7: Case-Based and Scenario Questions

• Model selection strategies
• Interpreting model outputs for stakeholders
• Business problem solving using data

Section 8: Behavioral and Situational Questions

• Describing past projects and challenges
• Conflict resolution and teamwork
• Communication of results to non-technical audiences

Supplementary Materials

• Downloadable Resource: Top 100 Data Science Interview Questions (PDF)
• Templates: Mock Interview Guide and Answer Writing Practice Sheet
• Interactive Quiz: Interview Readiness Assessment

Assessment and Practice

• Quiz: A multiple-choice quiz to assess familiarity with core concepts.
• Short Answer Practice: Write answers to selected questions to simulate real interviews.
• Mock Interview Template: Structured framework for peer or self-led mock interviews.