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Data Science Interview Handbook

The ultimate guide to data science interviews. Developed by FAANG engineers, it covers Python, algorithms, ML concepts, and interview questions. Get interview-ready in just a few hours.

Intermediate

72 Lessons

9h

Certificate of Completion

The ultimate guide to data science interviews. Developed by FAANG engineers, it covers Python, algorithms, ML concepts, and interview questions. Get interview-ready in just a few hours.
AI-POWERED

Mock Interview

Explanations

AI-POWERED

Mock Interview

Explanations

This course includes

140 Playgrounds
205 Quizzes
Course Overview
Course Content
Apply Your Skills
Recommendations

Course Overview

This course will increase your skills to crack the data science or machine learning interview. You will cover all the most common data science and ML concepts coupled with relevant interview questions. You will start by covering Python basics as well as the most widely used algorithms and data structures. From there, you will move on to more advanced topics like feature engineering, unsupervised learning, as well as neural networks and deep learning. This course takes a non-traditional approach to interview...Show More
This course will increase your skills to crack the data science or machine learning interview. You will cover all the most common data science and ML concepts coupled with relevant interview questions. You will start by covering Python basics as well as th...Show More

Course Content

1.

Are You Ready to Become a Data Scientist?

3 Lessons

Learn how to use data science principles, processes, and advancements for solving real-world problems.
Data Science and YOU!
Data Science Process Pipeline
Advancements in Data Science

2.

Python Basics

8 Lessons

Look at Python basics, including variables, decision-making, loops, functions, lists, dictionaries, and classes.
Introduction to Python
Variables
Decision Making
Loops
Functions
List and Tuple
Dictionary
Classes and Methods

3.

Python Libraries

6 Lessons

Examine essential Python libraries for data science, from NumPy to TensorFlow.
NumPy
SciPy
Pandas
Data Visualization
Scikit-learn
TensorFlow

4.

More Data Science Tools

7 Lessons

Grasp the fundamentals of essential data science tools for analysis, visualization, and model deployment.
KNIME
R
Orange
Tableau
Jupyter
Weka
Cloud ML Engines

5.

Data Structures and Algorithms - I

7 Lessons

Deepen your knowledge of essential data structures and algorithms for effective problem-solving.
Why Data Structures and Algorithms are Important
Array
Linked List
Stack
Queue
Trees
Hash Tables

6.

Data Structures and Algorithms - II

4 Lessons

Follow the process of solving problems with Greedy, Divide and Conquer, Backtracking, and Dynamic Programming.
Greedy Algorithms
Divide and Conquer
Backtracking
Dynamic Programming

7.

Statistics and Probability

6 Lessons

Piece together the parts of statistics, correlation, probability, random variables, and distributions.
Data Exploration
Correlation
Basics of Probability
Conditional Probability
Random Variable
Normal and Binomial Distribution

8.

Feature Engineering

6 Lessons

Try out feature engineering techniques to enhance data quality and improve model performance.
The Need for Feature Engineering
Numerical Features
Categorical Features
Date and Time Features
Missing Data
Putting Everything Together!

9.

Basics of Machine Learning

3 Lessons

Unpack the core of machine learning problems, model performance evaluation, and enhancement strategies.
Types of ML Problems
Measuring ML Model Performance
Improving ML Model Performance

10.

Regression

5 Lessons

Break apart regression principles, from simple to nonparametric, while mastering model assessment metrics.
Simple Regression
Multiple Regression
Regularized Regression
Nonparametric Regression
Regression Model Assessment

11.

Classification

7 Lessons

Find out about classification techniques, including linear classifiers, logistic regression, and decision trees.
Linear Classifiers
Logistic Regression
Naïve Bayes
Decision Trees
Random Forest
Adaboost
Classification Model Assessment

12.

Unsupervised Learning

4 Lessons

Take a closer look at various clustering methods and nearest neighbor search in unsupervised learning.
Nearest Neighbors
KMeans Clustering
Probabilistic Clustering
Hierarchical Clustering

13.

Advanced Topics in Machine Learning

4 Lessons

Tackle Neural Networks, Deep Learning issues, Recommendation Engines, and Natural Language Processing advancements.
Neural Network and Deep Learning
Issues in Deep Learning
Recommendation Engines
Natural Language Processing

14.

Conclusion

2 Lessons

Master the steps to begin your data science career, with emphasis on continuous learning.
This is The Beginning!
Mega Quiz

Course Author

Practice and apply your skills

Project
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