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Courses>log>Data Science Interview Handbook

Data Science Interview Handbook

Price:Paid

Length:9 hours

Content type:text

level:intermediate

Language:English

Updated:21 August 2022

Published:29 March 2022

Similar courses

Opportunities

Courses>>Data Science Interview Handbook

Data Science Interview HandbookPaid

Educative English

AI Learner Hub

DescriptionThis 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 prep, in that it focuses on data science fundamentals instead of open-ended questions.
In all, this course will get you ready for data science interviews. By the time you finish this course, you will have reviewed all the major concepts in data science and will have a good idea of what interview questions you can expect.

Syllabus

Are You Ready to Become a Data Scientist?

1. Data Science and YOU!

2. Data Science Process Pipeline

3. Advancements in Data Science

Python Basics

1. Introduction to Python

2. Variables

3. Decision Making

4. Loops

5. Functions

6. List and Tuple

7. Dictionary

8. Classes and Methods

Python Libraries

1. NumPy

2. SciPy

3. Pandas

4. Data Visualization

5. Scikit-learn

6. TensorFlow

More Data Science Tools

1. KNIME

2. R

3. Orange

4. Tableau

5. Jupyter

6. Weka

7. Cloud ML Engines

Data Structures and Algorithms - I

1. Why Data Structures and Algorithms are Important

2. Array

3. Linked List

4. Stack

5. Queue

6. Trees

7. Hash Tables

Data Structures and Algorithms - II

1. Greedy Algorithms

2. Divide and Conquer

3. Backtracking

4. Dynamic Programming

Statistics and Probability

1. Data Exploration

2. Correlation

3. Basics of Probability

4. Conditional Probability

5. Random Variable

6. Normal and Binomial Distribution

Feature Engineering

1. The Need for Feature Engineering

2. Numerical Features

3. Categorical Features

4. Date and Time Features

5. Missing Data

6. Putting Everything Together!

Basics of Machine Learning

1. Types of ML Problems

2. Measuring ML Model Performance

3. Improving ML Model Performance

Regression

1. Simple Regression

2. Multiple Regression

3. Regularized Regression

4. Nonparametric Regression

5. Regression Model Assessment

Classification

1. Linear Classifiers

2. Logistic Regression

3. Naïve Bayes

4. Decision Trees

5. Random Forest

6. Adaboost

7. Classification Model Assessment

Unsupervised Learning

1. Nearest Neighbors

2. KMeans Clustering

3. Probabilistic Clustering

4. Hierarchical Clustering

Advanced Topics in Machine Learning

1. Neural Network and Deep Learning

2. Issues in Deep Learning

3. Recommendation Engines

4. Natural Language Processing

Conclusion

1. This is The Beginning!

2. Mega Quiz

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Make the most out of your online education

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Courses>log>Data Science Interview Handbook

Data Science Interview Handbook

Price:Paid

Length:9 hours

Content type:text

level:intermediate

Language:English

Updated:21 August 2022

Published:29 March 2022

Similar courses

Opportunities

Courses>>Data Science Interview Handbook

Data Science Interview HandbookPaid

Educative English

AI Learner Hub

DescriptionThis 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 prep, in that it focuses on data science fundamentals instead of open-ended questions.
In all, this course will get you ready for data science interviews. By the time you finish this course, you will have reviewed all the major concepts in data science and will have a good idea of what interview questions you can expect.

Syllabus

Are You Ready to Become a Data Scientist?

1. Data Science and YOU!

2. Data Science Process Pipeline

3. Advancements in Data Science

Python Basics

1. Introduction to Python

2. Variables

3. Decision Making

4. Loops

5. Functions

6. List and Tuple

7. Dictionary

8. Classes and Methods

Python Libraries

1. NumPy

2. SciPy

3. Pandas

4. Data Visualization

5. Scikit-learn

6. TensorFlow

More Data Science Tools

1. KNIME

2. R

3. Orange

4. Tableau

5. Jupyter

6. Weka

7. Cloud ML Engines

Data Structures and Algorithms - I

1. Why Data Structures and Algorithms are Important

2. Array

3. Linked List

4. Stack

5. Queue

6. Trees

7. Hash Tables

Data Structures and Algorithms - II

1. Greedy Algorithms

2. Divide and Conquer

3. Backtracking

4. Dynamic Programming

Statistics and Probability

1. Data Exploration

2. Correlation

3. Basics of Probability

4. Conditional Probability

5. Random Variable

6. Normal and Binomial Distribution

Feature Engineering

1. The Need for Feature Engineering

2. Numerical Features

3. Categorical Features

4. Date and Time Features

5. Missing Data

6. Putting Everything Together!

Basics of Machine Learning

1. Types of ML Problems

2. Measuring ML Model Performance

3. Improving ML Model Performance

Regression

1. Simple Regression

2. Multiple Regression

3. Regularized Regression

4. Nonparametric Regression

5. Regression Model Assessment

Classification

1. Linear Classifiers

2. Logistic Regression

3. Naïve Bayes

4. Decision Trees

5. Random Forest

6. Adaboost

7. Classification Model Assessment

Unsupervised Learning

1. Nearest Neighbors

2. KMeans Clustering

3. Probabilistic Clustering

4. Hierarchical Clustering

Advanced Topics in Machine Learning

1. Neural Network and Deep Learning

2. Issues in Deep Learning

3. Recommendation Engines

4. Natural Language Processing

Conclusion

1. This is The Beginning!

2. Mega Quiz

Similar courses

Opportunities

Make the most out of your online education

Copyright © 2021 Careertail.

All rights reserved

All rights reserved

Quick Links

Get StartedLog InAbout UsCourses

Company

BlogContactsPrivacy PolicyCookie PolicyTerms and Conditions

Stay up to date

Trustpilot