Data Science Interview Handbook

Description

In this course, you will learn :

• Prepare for a data science or machine learning interview.
• You will go over all of the most common data science and machine learning concepts, as well as relevant interview questions.
• Fundamentals of Python, as well as the most commonly used algorithms and data structures.
• Advanced topics such as feature engineering, unsupervised learning, neural networks, and deep learning.

Syllabus :

1. Python Basics

• Introduction to Python
• Variables
• Decision Making
• Loops
• Functions
• List and Tuple
• Dictionary
• Classes and Methods

 

2. Python Libraries

• NumPy
• SciPy
• Pandas
• Data Visualization
• Scikit-learn
• TensorFlow

 

3. More Data Science Tools

• KNIME
• R
• Orange
• Tableau
• Jupyter
• Weka
• Cloud ML Engines

 

4. Data Structures and Algorithms - I

• Why Data Structures and Algorithms are Important
• Array
• Linked List
• Stack
• Queue
• Trees
• Hash Tables

 

5. Data Structures and Algorithms - II

• Greedy Algorithms
• Divide and Conquer
• Backtracking
• Dynamic Programming

 

6. Statistics and Probability

• Data Exploration
• Correlation
• Basics of Probability
• Conditional Probability
• Random Variable
• Normal and Binomial Distribution

 

7. Feature Engineering

• The Need for Feature Engineering
• Numerical Features
• Categorical Features
• Date and Time Features
• Missing Data
• Putting Everything Together!

 

8. Basics of Machine Learning

• Types of ML Problems
• Measuring ML Model Performance
• Improving ML Model Performance

 

9. Regression

• Simple Regression
• Multiple Regression
• Regularized Regression
• Nonparametric Regression
• Regression Model Assessment

 

10. Classification

• Linear Classifiers
• Logistic Regression
• Naïve Bayes
• Decision Trees
• Random Forest
• Adaboost
• Classification Model Assessment

 

11. Unsupervised Learning

• Nearest Neighbors
• KMeans Clustering
• Probabilistic Clustering
• Hierarchical Clustering

 

12. Advanced Topics in Machine Learning

• Neural Network and Deep Learning
• Issues in Deep Learning
• Recommendation Engines
• Natural Language Processing

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