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Data Science MOOCS (John Hopkins Data Science Specialization (Practical…
Data Science MOOCS
John Hopkins
Data Science Specialization
Practical Machine Learning
Regression Models
Statistical Inference
Reproducible Research
Exploratory Data Analysis
Getting and Cleaning Data
R Programming
The Data Scientist’s Toolbox
Developing Data Products
Higher School of Economics
Advanced Machine Learning Specialization
Bayesian Methods for Machine Learning
Introduction to Bayesian methods & Conjugate priors
Expectation-Maximization algorithm
Variational Inference & Latent Dirichlet Allocation
Markov chain Monte Carlo
Variational Autoencoder
Gaussian processes & Bayesian optimization
Introduction to Reinforcement Learning
Deep Learning in Computer Vision
Natural Language Processing
Introduction to Deep Learning
Introduction to neural networks
Introduction to optimization
Deep Learning for images
Unsupervised representation learning
Deep learning for sequences
How to Win a Data Science Competition
: Learn from Top Kagglers
Feature Preprocessing and Generation with Respect to Models
Exploratory Data Analysis
Metrics Optimization
Advanced Feature Engineering I
Ensembling
University of Michigan
Applied Data Science with Python Specialization
Introduction to Data Science in Python
Applied Plotting, Charting & Data Representation in Python
Applied Machine Learning in Python
Applied Text Mining in Python
Applied Social Network Analysis in Python
UC San Diego
Big Data Specialization
Introduction to Big Data
Big Data Modeling and Management Systems
Big Data Integration and Processing
Machine Learning With Big Data
Graph Analytics for Big Data
Yandex
Big Data for Data Engineers Specialization
Big Data Essentials: HDFS, MapReduce and Spark RDD
Big Data Analysis: Hive, Spark SQL, DataFrames and GraphFrames
Big Data Applications: Machine Learning at Scale
Big Data Applications: Real-Time Streaming
Google Cloud Platform
Data Engineering on Google Cloud Platform
Big Data and Machine Learning Fundamentals
Introduction to Google Cloud Platform and its Big Data Products
Foundations of GCP Compute and Storage
Data Analysis on the Cloud
Scaling Data Analysis: Compute with GCP
Data Processing Architectures: Scalable Ingest, Transform and Load
Leveraging Unstructured Data with Cloud Dataproc
Introduction to Cloud Dataproc
Running Dataproc jobs
Analyzing Unstructured Data
Serverless Data Analysis with Google BigQuery and Cloud Dataflow
Serverless Data Analysis with Google BigQuery and Cloud Dataflow
Autoscaling Data Processing Pipelines with Dataflow
Serverless Machine Learning with Tensorflow
Scaling ML models with Cloud ML Engine
Building ML models with Tensorflow
Feature Engineering
Building Resilient Streaming Systems
Architecture of Streaming Analytics Pipelines
Ingesting Variable Volumes
Implementing Streaming Pipelines
Streaming analytics and dashboards
Handling Throughput and Latency Requirements
John Hopkins
Executive Data Science Specialization
Building a Data Science Team
Data Engineer:
Data Scientist
Data Science Manager
· Embedded Teams vs. Dedicated Groups
Managing Data Analysis
Data Analysis Iteration
Stages of Data Analysis
Exploratory Data Analysis Goals & Expectations
Using Statistical Models to Explore Your Data
Making Inferences from Data
Inference vs. Prediction
Interpreting Your Results
Routine Communication in Data Analysis
Data Science in Real Life
Machine Learning vs. Traditional Statistics
Managing the Data Pull
Effect size, significance, & modeling
Comparison with benchmark effects
A Crash Course in Data Science
Machine learning
Statistics by example activities
What is Data Science?
What is Software Engineering for Data Science?
The Structure of a Data Science Project
The outputs of a data science experiment
Data Scientist Toolbox
University of Washington
Data Science at Scale Specialization
Data Manipulation at Scale: Systems and Algorithms
Data Science Context and Concepts
Relational Databases and the Relational Algebra
MapReduce and Parallel Dataflow Programming
NoSQL: Systems and Concepts
Graph Analytics
Practical Predictive Analytics: Models and Methods
Practical Statistical Inference
Supervised Learning
Optimization
Unsupervised Learning
Communicating Data Science Results
Visualization
Privacy and Ethics
Reproducibility and Cloud Computing