Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

http://DataScienceSpecialization.github.io

Public repository for course materials for the Data Science at Scale Specialization at Coursera

Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes

Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.

Compiled Notes for all 9 courses in the Coursera Data Science Specialization

repository for Community Mentor content related to the Johns Hopkins University Data Science Specialization on Coursera

Repository for coursera specialization Applied Data Science with Python by University of Michigan

This repository contains Ipython notebooks of assignments and tutorials used in the course introduction to data science in python, part of Applied Data Science using Python Specialization from University of Michigan offered by Coursera

This repo has been created to share the solutions of all the quizzes and assignments of all three courses of this specialization.

Course - 5; Specialization: Applied Data Science with Python; University Of Michigan

My solution to Bioinformatics Specialization (Finding Hidden Messages in DNA; Genome Sequencing; Comparing Genes, Proteins, and Genomes; Molecular Evolution; Genomic Data Science and Clustering; Finding Mutations in DNA and Proteins; Bioinformatics Capstone: Big Data in Biology)

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

From the Specialization of Mathematics for Machine Learning and Data Science by DeepLearning.AI from Coursera.

Genomic data science specialization

Repo for the first course of the Applied Data Science with Python Specialization taught by University of Michigan hosted by Coursera

Solutions on Practical Data Science Specialization on Coursera (offered by deeplearning.ai)

A comprehensive, well-structured repository of B.Tech (Hons) CSE notes and learning resources, specializing in Artificial Intelligence and Data Science. Includes semester-wise notes, question papers, curated study guides, and indexed materials designed for efficient learning, revision, and academic reference.

Non-credit online course by DeepLearning.AI

This repo consists of the lecture PDFs and quiz solutions of all the courses under the IBM Data Science Professional Certificate specialization course of Coursera.

This repo contains the files related to the Coursera Data Science Specialization

Data Science has been ranked as one of the hottest professions and the demand for data practitioners is booming. This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Data Science or Machine Learning. This program consists of 9 courses providing you with latest job-ready skills and techniques covering a wide array of data science topics including: open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. You will practice hands-on in the IBM Cloud using real data science tools and real-world data sets. It is a myth that to become a data scientist you need a Ph.D. This Professional Certificate is suitable for anyone who has some computer skills and a passion for self-learning. No prior computer science or programming knowledge is necessary. We start small, re-enforce applied learning, and build up to more complex topics. Upon successfully completing these courses you will have done several hands-on assignments and built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in Data Science. In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Data Science.

This Capstone is the 10th (final) course in IBM Data Science Professional Certificate specialization, and it actually summarizes in the form of project all materials that have been learned during this specialization

My Code Repository for Coursera Data Science Specialization by John Hopkins University

Applied Data Science with Python Specialization: Course 4 (University of Michigan)

Coursera - Mathematics for Machine Learning and Data Science Specialization - DeepLearning.ai

Notebooks from IBM Advanced Data Science Specialization. Topics include Scalable Data Science and Machine Learning for Signal Processing.

@DeepLearning.AI Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It has helped me to develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker.

Codes from Coursera's course Algorithms for DNA sequencing, part of genomic data science specialization offered by Johns Hopkins University

My Assignment Submission made during the course 📙