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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 Handbook
 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

1. Data Science and YOU!

2. Data Science Process Pipeline

3. Advancements in Data Science

1. Introduction to Python

2. Variables

3. Decision Making

4. Loops

5. Functions

6. List and Tuple

7. Dictionary

8. Classes and Methods

1. NumPy

2. SciPy

3. Pandas

4. Data Visualization

5. Scikit-learn

6. TensorFlow

1. KNIME

2. R

3. Orange

4. Tableau

5. Jupyter

6. Weka

7. Cloud ML Engines

1. Why Data Structures and Algorithms are Important

2. Array

3. Linked List

4. Stack

5. Queue

6. Trees

7. Hash Tables

1. Greedy Algorithms

2. Divide and Conquer

3. Backtracking

4. Dynamic Programming

1. Data Exploration

2. Correlation

3. Basics of Probability

4. Conditional Probability

5. Random Variable

6. Normal and Binomial Distribution

1. The Need for Feature Engineering

2. Numerical Features

3. Categorical Features

4. Date and Time Features

5. Missing Data

6. Putting Everything Together!

1. Types of ML Problems

2. Measuring ML Model Performance

3. Improving ML Model Performance

1. Simple Regression

2. Multiple Regression

3. Regularized Regression

4. Nonparametric Regression

5. Regression Model Assessment

1. Linear Classifiers

2. Logistic Regression

3. Naïve Bayes

4. Decision Trees

5. Random Forest

6. Adaboost

7. Classification Model Assessment

1. Nearest Neighbors

2. KMeans Clustering

3. Probabilistic Clustering

4. Hierarchical Clustering

1. Neural Network and Deep Learning

2. Issues in Deep Learning

3. Recommendation Engines

4. Natural Language Processing

1. This is The Beginning!

2. Mega Quiz

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Careertail
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 Handbook
 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

1. Data Science and YOU!

2. Data Science Process Pipeline

3. Advancements in Data Science

1. Introduction to Python

2. Variables

3. Decision Making

4. Loops

5. Functions

6. List and Tuple

7. Dictionary

8. Classes and Methods

1. NumPy

2. SciPy

3. Pandas

4. Data Visualization

5. Scikit-learn

6. TensorFlow

1. KNIME

2. R

3. Orange

4. Tableau

5. Jupyter

6. Weka

7. Cloud ML Engines

1. Why Data Structures and Algorithms are Important

2. Array

3. Linked List

4. Stack

5. Queue

6. Trees

7. Hash Tables

1. Greedy Algorithms

2. Divide and Conquer

3. Backtracking

4. Dynamic Programming

1. Data Exploration

2. Correlation

3. Basics of Probability

4. Conditional Probability

5. Random Variable

6. Normal and Binomial Distribution

1. The Need for Feature Engineering

2. Numerical Features

3. Categorical Features

4. Date and Time Features

5. Missing Data

6. Putting Everything Together!

1. Types of ML Problems

2. Measuring ML Model Performance

3. Improving ML Model Performance

1. Simple Regression

2. Multiple Regression

3. Regularized Regression

4. Nonparametric Regression

5. Regression Model Assessment

1. Linear Classifiers

2. Logistic Regression

3. Naïve Bayes

4. Decision Trees

5. Random Forest

6. Adaboost

7. Classification Model Assessment

1. Nearest Neighbors

2. KMeans Clustering

3. Probabilistic Clustering

4. Hierarchical Clustering

1. Neural Network and Deep Learning

2. Issues in Deep Learning

3. Recommendation Engines

4. Natural Language Processing

1. This is The Beginning!

2. Mega Quiz

Similar courses
Opportunities
Make the most out of your online education
Careertail
Copyright © 2021 Careertail.
All rights reserved
Quick Links
Get StartedLog InAbout UsCourses
Company
BlogContactsPrivacy PolicyCookie PolicyTerms and Conditions
Stay up to date
Trustpilot