Scala for Data Science introduces the use of Scala for data analysis and machine learning. It covers Scala basics, data processing, statistical calculations, visualization, and machine learning techniques. The subject develops analytical and problem-solving skills through practical applications and provides a foundation for data-driven solutions.
Dr. Mahendra Kanojia
Author
Dr. Mahendra Kanojia (Ph.D., M.Phil., Computer Science)
Dr. Kanojia is an AI research scientist and academic leader with 18years of experience. He serves as Principal and HOD of Computer Science at Sheth L.U.J. & Sir M.V. College, Mumbai, and founded the e-learning platform rocktheit.com., He specializes in AI, Machine Learning, chatbots, and advanced applications like autonomous oncology. His hands-on, accessible approach to technical training is driven by a single belief: “If humans can, technology can.”
Simran Shaikh
Instructor Assistant
Simran Shaikh is a B.Sc. Computer Science student with a strong academic and professional interest in Artificial Intelligence, Machine Learning, Web Development, Software Engineering, and Data Analytics. She currently serves as an Instructor Assistant for this course, supporting students in their academic and practical learning. She is also engaged in developing technology-driven projects using Python, React, JavaScript, databases, and APIs, with a focus on building practical and effective software solutions.
Aditya Rana
Instructor Assistant
I am a Computer Science student focused on programming, data science, and software development. I work with Python, Scala, and Java, and enjoy building practical projects involving data analysis, machine learning, and application development. As an Instructor Assistant for the Scala for Data Science course, I contribute to coding, technical documentation, and practical learning resources.
Lesson1:Introduction to Scala Environment Setup and Program Development
Introduction to Scala Environment Setup and Program Development
Lesson 1 of 1 within section Lesson1:Introduction to Scala Environment Setup and Program Development .
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Lesson 2: Calculate mean, median, and mode of a list of numbers. Implement basic statistical calculations using Scala collections
Calculate mean, median, and mode of a list of numbers. Implement basic statistical calculations using Scala collections
Lesson 1 of 1 within section Lesson 2: Calculate mean, median, and mode of a list of numbers. Implement basic statistical calculations using Scala collections .
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Lesson 3: Calculate variance and standard deviation of a list of numbers. Implement basic statistical calculations using Scala
Calculate variance and standard deviation of a list of numbers. Implement basic statistical calculations using Scala
Lesson 1 of 1 within section Lesson 3: Calculate variance and standard deviation of a list of numbers. Implement basic statistical calculations using Scala.
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Lesson 4: Create a dense vector using Breeze and calculate its sum, mean, and dot product with another vector
Create a dense vector using Breeze and calculate its sum, mean, and dot product with another vector
Lesson 1 of 1 within section Lesson 4: Create a dense vector using Breeze and calculate its sum, mean, and dot product with another vector.
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Lesson 5: Generate a random matrix using Breeze and compute its transpose and determinant
Generate a random matrix using Breeze and compute its transpose and determinant
Lesson 1 of 1 within section Lesson 5: Generate a random matrix using Breeze and compute its transpose and determinant.
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Lesson 17: Find the correlation between two lists of numbers. Implement the formula for Pearson correlation coefficient.
Find the correlation between two lists of numbers. Implement the formula for Pearson correlation coefficient.
Lesson 1 of 1 within section Lesson 17: Find the correlation between two lists of numbers. Implement the formula for Pearson correlation coefficient..
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Lesson 18: Calculate the moving average of a time series data using Scala collections.
Calculate the moving average of a time series data using Scala collections.
Lesson 1 of 1 within section Lesson 18: Calculate the moving average of a time series data using Scala collections..
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Lesson 19: Write a program to compute frequency distribution and cumulative frequency of a dataset.
Write a program to compute frequency distribution and cumulative frequency of a dataset.
Lesson 1 of 1 within section Lesson 19: Write a program to compute frequency distribution and cumulative frequency of a dataset..
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Lesson 21: Implement linear regression using Breeze. Fit a model to a small dataset and predict a value.
Implement linear regression using Breeze. Fit a model to a small dataset and predict a value.
Lesson 1 of 1 within section Lesson 21: Implement linear regression using Breeze. Fit a model to a small dataset and predict a value. .
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Lesson 22: Perform logistic regression using Breeze. Classify a dataset with binary labels.
Perform logistic regression using Breeze. Classify a dataset with binary labels.
Lesson 1 of 1 within section Lesson 22: Perform logistic regression using Breeze. Classify a dataset with binary labels..
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Lesson 23: Compute the Euclidean distance between two Breeze vectors. Use it for nearest neighbor classification.
Compute the Euclidean distance between two Breeze vectors. Use it for nearest neighbor classification.
Lesson 1 of 1 within section Lesson 23: Compute the Euclidean distance between two Breeze vectors. Use it for nearest neighbor classification. .
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