\r\n...and much more! There's also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to "big data" analyzed on a computing cluster.
\r\n\r\nIf you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's; the sample code will also run on MacOS or Linux desktop systems, but I can't provide OS-specific support for them.
\r\n\r\nEach concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference.
\r\n\r\nIf you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. I think you'll enjoy it!
\r\n","price":"3992.00","discount_percent":0,"discount_flat":"0.00","price_flat":"599.00","student_count_old_db":11,"manual_rank":0,"drive_link":null,"topics":null,"recent_payments_count":139,"language":{"id":1,"name":"English"},"student_count":{"course_id":248,"count":139},"rating_average":{"course_id":248,"average":"5.00","user":null},"basePrice":3992,"studentsCount":150,"averageRating":5,"finalPrice":599,"ratingCount":41},{"id":220,"tutor":{"id":586,"name":"Loonycorn A 4-Ppl Team;ex-Google.","email":"vitthal.srinivasan@gmail.com","is_affiliate":0,"slug":"loonycorn-a-4-ppl-teamex-google.681"},"course_category_id":28,"language_id":1,"name":"Connect the Dots: Linear and Logistic Regression","slug":"connect-the-dots-linear-and-logistic-regression","level":"high level","search_keywords":"Analytics, Business","target_audience":"Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.
\r\n\r\nThis course will teach you how to build robust linear models and do logistic regression in Excel, R and Python.
\r\n\r\nLet’s parse that.
\r\n\r\nRobust linear models : Linear Regression is a powerful method for quantifying the cause and effect relationships that affect different phenomena in the world around us. This course will teach you how to build robust linear models that will stand up to scrutiny when you apply them to real world situations.
\r\n\r\nLogistic regression: Logistic regression has many cool applications : analyzing consequences of past events, allocating resources, solving binary classification problems using machine learning and so on. This course will help you understand the intuition behind logistic regression and how to solve it using cookie-cutter techniques.
\r\n\r\nExcel, R and Python : Put what you've learnt into practice. Leverage these powerful analytical tools to build models for stock returns.
\r\n\r\nWhat's covered?
\r\n\r\nSimple Regression :
\r\n\r\nMultiple Regression :
\r\n\r\nLogistic Regression :
\r\n\r\n\r\n\r\n
\r\n\r\n
Using discussion forums
\r\n\r\nPlease use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(
\r\n\r\nWe're super small and self-funded with only 2-3 people developing technical video content. Our mission is to make high-quality courses available at super low prices.
\r\n\r\nThe only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.
\r\n\r\nWe understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.
\r\n\r\nIt is a hard trade-off.
\r\n\r\nThank you for your patience and understanding!
\r\n","price":"1996.00","discount_percent":0,"discount_flat":"0.00","price_flat":"599.00","student_count_old_db":10,"manual_rank":0,"drive_link":null,"topics":null,"recent_payments_count":16,"language":{"id":1,"name":"English"},"student_count":{"course_id":220,"count":16},"rating_average":{"course_id":220,"average":"5.00","user":null},"basePrice":1996,"studentsCount":26,"averageRating":5,"finalPrice":599,"ratingCount":6},{"id":284,"tutor":{"id":586,"name":"Loonycorn A 4-Ppl Team;ex-Google.","email":"vitthal.srinivasan@gmail.com","is_affiliate":0,"slug":"loonycorn-a-4-ppl-teamex-google.681"},"course_category_id":28,"language_id":1,"name":"Financial Risk Management in Python, R and Excel","slug":"financial-risk-management-in-python-r-and-excel","level":"high level","search_keywords":"Risk Management in Python, R, Excel","target_audience":"A financial portfolio is almost always modeled as the sum of correlated random variables. Measuring the risk of this portfolio accurately is important for all kinds of applications: the financial crisis of 2007, the failure of the famous hedge fund LTCM and many other mishaps are attributable to poor risk modeling.
\r\n\r\nIn this course, we cover the theory and practice of robust risk modeling:
\r\n\r\nThis course, The Comprehensive Statistics and Data Science with R Course, is mostly based on the authoritative documentation in the online "An Introduction to R" manual produced with each new R release by the Comprehensive R Archive Network (CRAN) development core team. These are the people who actually write, test, produce and release the R code to the general public by way of the CRAN mirrors. It is a rich and detailed 10-session course which covers much of the content in the contemporary 105-page CRAN manual. The ten sessions follow the outline in the An Introduction to R online manual and specifically instruct with respect to the following user topics:
\r\n\r\n1. Introduction to R; Inputting data into R
\r\n\r\n2. Simple manipulation of numbers and vectors
\r\n\r\n3. Objects, their modes and attributes
\r\n\r\n4. Arrays and matrices
\r\n\r\n5. Lists and data frames
\r\n\r\n6. Writing user-defined functions
\r\n\r\n7. Working with R as a statistical environment
\r\n\r\n8. Statistical models and formulae; ANOVA and regression
\r\n\r\n9. GLMs and GAMs
\r\n\r\n10. Creating statistical and other visualizations with R
\r\n\r\nIt is a comprehensive and decidedly "hands-on" course. You are taught how to actually use R and R script to create everything that you see on-screen in the course videos. Everything is included with the course materials: all software; slides; R scripts; data sets; exercises and solutions; in fact, everything that you see utilized in any of the 200+ course videos are included with the downloadable course materials.
\r\n\r\nThe course is structured for both the novice R user, as well as for the more experienced R user who seeks a refresher course in the benefits, tools and capabilities that exist in R as a software suite appropriate for statistical analysis and manipulation. The first half of the course is suited for novice R users and guides one through "hands-on" practice to master the input and output of data, as well as all of the major and important objects and data structures that are used within the R environment. The second half of the course is a detailed "hands-on" transcript for using R for statistical analysis including detailed data-driven examples of ANOVA, regression, and generalized linear and additive models. Finally, the course concludes with a multitude of "hands-on" instructional videos on how to create elegant and elaborate statistical (and other) graphics visualizations using both the base and gglot visualization packages in R.
\r\n\r\nThe course is very useful for any quantitative analysis professional who wishes to "come up to speed" on the use of R quickly. It would also be useful for any graduate student or college or university faculty member who also seeks to master these data analysis skills using the popular R package.
\r\n","price":"3743.00","discount_percent":0,"discount_flat":"0.00","price_flat":"599.00","student_count_old_db":9,"manual_rank":0,"drive_link":null,"topics":null,"recent_payments_count":10,"language":{"id":1,"name":"English"},"student_count":{"course_id":257,"count":10},"rating_average":{"course_id":257,"average":"5.00","user":null},"basePrice":3743,"studentsCount":19,"averageRating":5,"finalPrice":599,"ratingCount":7},{"id":215,"tutor":{"id":586,"name":"Loonycorn A 4-Ppl Team;ex-Google.","email":"vitthal.srinivasan@gmail.com","is_affiliate":0,"slug":"loonycorn-a-4-ppl-teamex-google.681"},"course_category_id":28,"language_id":1,"name":"Connect the Dots: Factor Analysis","slug":"connect-the-dots-factor-analysis","level":"high level","search_keywords":"Data Ananlytics, Business","target_audience":"Factor analysis helps to cut through the clutter when you have a lot of correlated variables to explain a single effect.
\r\n\r\nThis course will help you understand Factor analysis and it’s link to linear regression. See how Principal Components Analysis is a cookie cutter technique to solve factor extraction and how it relates to Machine learning .
\r\n\r\nWhat's covered?
\r\n\r\nPrincipal Components Analysis
\r\n\r\nImplementing PCA in Excel, R and Python
\r\n\r\n\r\n\r\n\r\n\r\n","price":"1996.00","discount_percent":0,"discount_flat":"0.00","price_flat":"599.00","student_count_old_db":12,"manual_rank":0,"drive_link":null,"topics":null,"recent_payments_count":8,"language":{"id":1,"name":"English"},"student_count":{"course_id":215,"count":8},"rating_average":{"course_id":215,"average":"5.00","user":null},"basePrice":1996,"studentsCount":20,"averageRating":5,"finalPrice":599,"ratingCount":3},{"id":181,"tutor":{"id":586,"name":"Loonycorn A 4-Ppl Team;ex-Google.","email":"vitthal.srinivasan@gmail.com","is_affiliate":0,"slug":"loonycorn-a-4-ppl-teamex-google.681"},"course_category_id":28,"language_id":1,"name":"Learn By Example : Qlikview","slug":"learn-by-example-qlikview","level":"high level","search_keywords":"Qlikview","target_audience":"
Qlikview : One tool to Transform, Summarize and Visualize Data
\r\n\r\nA Qlikview app is like an in-memory database. The interactive nature of Qlikview allows you to explore and iterate very quickly to develop an intuitive feel for your data.
\r\n\r\nWhat's covered?
\r\n\r\n1) The Qlikview In-memory data model
\r\n\r\n2) Use List boxes, Table boxes and Chart boxes to query data
\r\n\r\n3) Load data into a QV app from CSV and Databases, avoiding Synthetic keys and Circular references
\r\n\r\n4) Transform and adding new fields in a Load script
\r\n\r\n5) Transform tables with Join, Keep
\r\n\r\n6) Effectively present your insights using elements like charts, drill downs, triggers.
\r\n\r\n7) Nested aggregations in charts
\r\n\r\n8) Generic Loads, Mapping Loads, Crosstable
\r\n\r\n\r\n\r\n
Anyone looking to use optimization - in finance, or elsewhere
\r\n","pre_requisites":"Basic understanding of math and statistics at a high school level. No real programming background required. Some understanding of finance would help
\r\n","image":"https://d17xyaa0yka9gz.cloudfront.net/tutor_documents/course_images/unanth_1503919074_59a3fbe2bce34.jpg","intro_video":284,"summary":"Optimization techniques are used everywhere, but until recently they were not that important in software. With the rising importance of machine learning that is changing, because training ML models requires optimization in parameter training.
\r\n\r\nThis course focuses on the theory and implementation of optimization in Python, R and Excel.
\r\n\r\nWhat Will I Learn?
\r\n\r\nSikuliX is very unusual - a scripting/automation technology that relies on pattern matching, and is available for use via Python or Java. Developed at the User Interface Design Group at MIT, is a powerful and easy-to-use technology that uses image recognition to automate just about anything that appears on-screen.
\r\n
\r\nSikuli is rather hard to slot - it offers all of the functionality of an automation or scripting tool, but it also offers some powerful and very novel image-matching functionality for truly novel use-cases that revolve around image search. In addition it has an OCR-mode, in which image matches are performed after converting those image patterns to text. This gives rise to some pretty new applications.
\r\n
\r\nThe OCR-functionality is powered by Tesseract, an open-source optical character recognition engine whose development is sponsored by Google.