Databricks Courses

Introduction to Bayesian Statistics

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeThe objective of this course is to introduce Computational Statistics to aspiring or new data scientists. The attendees will start off by learning the basics of probability, Bayesian modeling and inference. This will be the first course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.The instructors for this course will be Dr. Srijith Rajamohan and Dr. Robert Settlage.

Data Science Fundamentals for Data Analysts

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn this course we're going to guide you through the fundamental building blocks of data science, one of the fastest-growing fields in the world! With the help of our industry-leading data scientists, we’ve designed this course to build ready-to-apply data science skills in just 15 hours of learning. First, we’ll give you a quick introduction to data science - what it is and how it is used to solve real-world problems. For the rest of the course, we'll teach you the skills you need to apply foundational data science concepts and techniques to solve these real-world problems. By the end of this course, you'll be able to leverage your existing data analysis skills to design, execute, assess, and communicate the results of your very own data science projects.

Data Science with Databricks for Data Analysts

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeThis specialization is intended for data analysts looking to expand their toolbox for working with data. Traditionally, data analysts have used tools like relational databases, CSV files, and SQL programming, among others, to perform their daily workflows. In this specialization, you will leverage existing skills to learn new ones that will allow you to utilize advanced technologies not traditionally linked to this role - technologies like Databricks and Apache Spark. By the end of this specialization, you'll be able to solve real-world business problems with Databricks and the most popular machine learning techniques.

Applied Data Science for Data Analysts

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn this course, you will develop your data science skills while solving real-world problems. You'll work through the data science process to and use unsupervised learning to explore data, engineer and select meaningful features, and solve complex supervised learning problems using tree-based models. You will also learn to apply hyperparameter tuning and cross-validation strategies to improve model performance.NOTE: This is the third and final course in the Data Science with Databricks for Data Analysts Coursera specialization. To be successful in this course we highly recommend taking the first two courses in that specialization prior to taking this course. These courses are: Apache Spark for Data Analysts and Data Science Fundamentals for Data Analysts.

Introduction to PyMC3 for Bayesian Modeling and Inference

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeThe objective of this course is to introduce PyMC3 for Bayesian Modeling and Inference,The attendees will start off by learning the the basics of PyMC3 and learn how to perform scalable inference for a variety of problems. This will be the final course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling with PyMC3.. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.The instructor for this course will be Dr. Srijith Rajamohan.

Bayesian Inference with MCMC

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeThe objective of this course is to introduce Markov Chain Monte Carlo Methods for Bayesian modeling and inference,The attendees will start off by learning the the basics of Monte Carlo methods. This will be augmented by hands-on examples in Python that will be used to illustrate how these algorithms work. This will be the second course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling with PyMC3. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.The instructor for this course will be Dr. Srijith Rajamohan.

Apache Spark (TM) SQL for Data Analysts

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeApache Spark is one of the most widely used technologies in big data analytics. In this course, you will learn how to leverage your existing SQL skills to start working with Spark immediately. You will also learn how to work with Delta Lake, a highly performant, open-source storage layer that brings reliability to data lakes. By the end of this course, you will be able to use Spark SQL and Delta Lake to ingest, transform, and query data to extract valuable insights that can be shared with your team.