In this hands-on guided project you will learn how to use the knowledge base app Obsidian. With its powerful interface, Obsidian makes it easy for anyone to structure note taking dynamics suitable for a variety of purposes: from personal journaling, to study or work notes. On top of that, one can establish links across notes and quite literally build a digital brain based on all of these connections, powered by Obsidian's Graph View.

In this course, the instructor will discuss the fundamental analysis of investment using R programming. The course will cover investment analysis topics, but at the same time, make you practice it using R programming. This course's focus is to train you to do the elemental analysis for investment management that you might need to do in your job every day. Additionally, the study note to do using Python programming will be provided. The course is designed with the assumption that most students already have a little bit of knowledge in financial economics. Students are expected to have heard about stocks and bonds and balance sheets, earnings, etc., and know the introductory statistics level, such as mean, median, distribution, regression, etc. The instructor will explain the detail of R programming for beginners. It will be an excellent course for you to improve your programming skills. If you are very good at R programming, it will provide you an excellent opportunity to practice again with finance and investment examples. Professor Youngju Nielsen creates the course with the assistants of Keonwoo Lim and Jeeun Yuen. =========================================================================================== Coursera Course recommendations before this course for those who are not familiar with basic R programming: <Getting Started with R> https://www.coursera.org/projects/getting-started-with-r <Introduction to Business Analytics with R> https://www.coursera.org/learn/business-analytics-r <Statistics with Python > https://www.coursera.org/specializations/statistics-with-python

We have all heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question and more! Over a period of 5 weeks, you will learn how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods. Learners will have the opportunity to apply these methods to example data in R (free statistical software environment). At the end of the course, learners should be able to: 1. Define causal effects using potential outcomes 2. Describe the difference between association and causation 3. Express assumptions with causal graphs 4. Implement several types of causal inference methods (e.g. matching, instrumental variables, inverse probability of treatment weighting) 5. Identify which causal assumptions are necessary for each type of statistical method So join us.... and discover for yourself why modern statistical methods for estimating causal effects are indispensable in so many fields of study!

In this 1 hour long project, you will build an attractive and eye-catching sales dashboard using Power BI in a black and blue theme that will make your audience go "wow". We will begin this guided project by importing data. We will then create bar charts and pie charts to visualize the sales data and then position the graphs on the dashboard. In the final tasks, we will create interactive maps to visualize sales data by countries and markets. By the end of this course, you will be confident in creating beautiful dashboards with many different kinds of visualizations.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will use Vertex AI to train and serve a model with tabular data. You will build a fraud detection model to determine whether a particular credit card transaction should be classified as fraudulent.

Prepare yourself for interviewing and landing a job in the DS/AI field. In this course, we will discuss what needs to be done before, during, and after the interview process. We will also provide tips and tricks on how to practice for a major component of data science interviews: the technical interview. Finally, this course will cover best practices for accepting or declining a job offer, salary negotiations, and how to create a career development plan. By the end of this course, students will be able to: • Recall what actions need to be done before, during, and after an interview. • Discuss a technical interview preparation plan. • Identify job offer acceptance or refusal best practices. • Create a career development plan.

In this 1.5-hour long project-based course, you will learn to Visualize the data of UK accidents using Plotly Express. This project gives detailed insights into United Kingdom (UK) long-term road accident trends between 2005 - 2014. We are going to visualize: 1. What is the rate of road accidents (i.e. the number of casualties) in the UK between 2005 - 2014? 2. What is the rate of road accidents based on weekdays? 3. How is the distribution of accident severity in the UK, from 2005 - 2014? 5. Which speed limit is closely associated with road accidents in the UK, from 2005 - 2014? 6. Which road type has the highest rate of road accidents between 2005 - 2014? By the end of this project, you will learn to set up Google Colab. You'll be able to download the UK accidents dataset directly from the Kaggle Platform on the Colab using Kaggle API. You'll visualize potential casualties due to road accidents, distribution of accident severity that may be either a serious accident, fatal accident, or a slight accident type. You will also visualize how speed limit is associated with the road accidents and see which road type has the highest rate of road accidents. You must have a basic knowledge of Python Programming Language. You'll need a free Gmail account to complete this project. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

In this course, we will cover the basics of visualization and how it fits into the Data Science workflow. We will focus on the main concepts behind the purpose of visualization and the design principles for creating effective, easy-to-communicate results. You will also set up your Tableau environment, practice data loading, and perform univariate descriptive analysis of the S&P 500 stock sectors.

Science is undergoing a data explosion, and astronomy is leading the way. Modern telescopes produce terabytes of data per observation, and the simulations required to model our observable Universe push supercomputers to their limits. To analyse this data scientists need to be able to think computationally to solve problems. In this course you will investigate the challenges of working with large datasets: how to implement algorithms that work; how to use databases to manage your data; and how to learn from your data with machine learning tools. The focus is on practical skills - all the activities will be done in Python 3, a modern programming language used throughout astronomy. Regardless of whether you’re already a scientist, studying to become one, or just interested in how modern astronomy works ‘under the bonnet’, this course will help you explore astronomy: from planets, to pulsars to black holes. Course outline: Week 1: Thinking about data - Principles of computational thinking - Discovering pulsars in radio images Week 2: Big data makes things slow - How to work out the time complexity of algorithms - Exploring the black holes at the centres of massive galaxies Week 3: Querying data using SQL - How to use databases to analyse your data - Investigating exoplanets in other solar systems Week 4: Managing your data - How to set up databases to manage your data - Exploring the lifecycle of stars in our Galaxy Week 5: Learning from data: regression - Using machine learning tools to investigate your data - Calculating the redshifts of distant galaxies Week 6: Learning from data: classification - Using machine learning tools to classify your data - Investigating different types of galaxies Each week will also have an interview with a data-driven astronomy expert. Note that some knowledge of Python is assumed, including variables, control structures, data structures, functions, and working with files.

This course covers a wide range of SQL techniques, beyond basic CRUD (Create, Read, Update, and Delete) operations in PostgreSQL. You will learn the specifics of aggregation, transactions, reading and parsing CSV files and inserting data into a database. You’ll also take a look at how PostgreSQL handles and indexes text data. Specifically, students will do assignments that alter table schemas, create stored procedures, construct advanced queries, explore sorting and grouping query data, and techniques for working with text in databases including regular expressions.