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. You’ll even put what you’ve learnt into practice by leveraging Excel, R, and Python to build a model for stock returns.
- Access 40 lectures & 5 hours of content 24/7
- Cover method of least squares, explaining variance, & forecasting an outcome
- Explore residuals & assumptions about residuals
- Implement simple & multiple regression in Excel, R, & Python
- Interpret regression results & avoid common pitfalls
- Introduce a categorical variable
Factor analysis helps to cut through the clutter when you have a lot of correlated variables to explain a single effect. This course will help you understand factor analysis and its link to linear regression. You’ll explore how Principal Components Analysis (PCA) is a cookie cutter technique to solve factor extraction, and how it relates to machine learning.
- Access 19 lectures & 1.5 hours of content 24/7
- Understand principal components
- Discuss Eigen values & Eigen vectors
- Perform Eigenvalue decomposition
- Use principal components for dimensionality reduction & exploratory factor analysis
- Apply PCA to explain the returns of a technology stock like Apple
- Find the principal components & use them to build a regression model
This course is an introduction to the R programming language. R has its own set of data structures that take some getting used to, and this course will help you familiarize yourself with the intricacies of data manipulation in R. You’ll dive into data analysis with R, visualizing a variety of plots and graphs, descriptive statistics, and much more.
- Access 59 lectures & 5.5 hours of content 24/7
- Harness R & R packages to read, process, & visualize data
- Understand the intricacies of all the different data structures in R
- Use descriptive statistics to perform a quick study of some data & present results
- Discuss data analysis & visualization w/ R
Hive helps you leverage the power of distributed computing and Hadoop for analytical processing. Its interface, HiveQL, is very similar to SQL, making it an especially convenient tool to know. This course will help you take advantage of Hive features that help you tune performance and perform complex transformations.
- Access 50 lectures & 6 hours of content 24/7
- Write complex analytical queries on data in Hive & uncover insights
- Leverage ideas of partitioning & bucketing to optimize queries in Hive
- Understand what goes on under the hood of Hive w/ HDFS & MapReduce
- Explore subqueries, table generating functions, windowing, & more
A Qlikview app is like an in-memory database. It is a single tool that you can use to transform, summarize, and visualize data. The interactive nature of Qlikview allows you to explore and iterate data very quickly to develop an intuitive feel. In this course, you’ll use real-life, practical examples to learn how to work with this tool.
- Access 26 lectures & 2.5 hours of content 24/7
- Use list boxes, table boxes, & chart boxes to query data
- Load data into a QV app from CSV & databases, avoiding synthetic keys & circular references
- Transform & add new fields in a load script
- Present your insights effectively using elements like charts, drill downs, & triggers
- Perform nested aggregations in charts
Storm is to real-time stream processing what Hadoop is to batch processing. Using Storm, you can build applications that let you be highly responsive to the latest data and react within seconds and minutes – like finding the latest trending topics on Twitter, or monitoring spikes in payment gateway failures. From simple data transformations to applying machine learning algorithms on the fly, Storm can do it all.
- Access 36 lectures & 4 hours of content 24/7
- Understand Spouts & Bolts, which are the building blocks of every Storm topology
- Run a Storm topology in the local mode & in the remote mode
- Parallelize data processing within a topology using different grouping strategies
- Manage reliability & fault-tolerance within Spouts & Bolts
- Perform complex transformations on the fly using the Trident topology
- Apply ML algorithms on the fly using libraries like Trident-ML & Storm-R
The best way to learn is by example, and in this course you’ll get the lowdown on Scala with 65 comprehensive, hands-on examples. Scala is a general-purpose programming language that is highly scalable, making it incredibly useful in building programs. Over this immersive course, you’ll explore just how Scala can help your programming skill set, and how you can set yourself apart from other programmers by knowing this efficient tool.
- Access 67 lectures & 6.5 hours of content 24/7
- Use Scala w/ an intermediate level of proficiency
- Read & understand Scala programs, including those w/ highly functional forms
- Identify the similarities & differences between Java & Scala to use each to their advantages
The functional programming nature and the availability of a REPL environment make Scala particularly well suited for a distributed computing framework like Spark. Using these two technologies in tandem can allow you to effectively analyze and explore data in an interactive environment with extremely fast feedback. This course will teach you how to best combine Spark and Scala, making it perfect for aspiring data analysts and Big Data engineers.
- Access 51 lectures & 8.5 hours of content 24/7
- Use Spark for a variety of analytics & machine learning tasks
- Understand functional programming constructs in Scala
- Implement complex algorithms like PageRank & Music Recommendations
- Work w/ a variety of datasets from airline delays to Twitter, web graphs, & Product Ratings
- Use the different features & libraries of Spark, like RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming, & GraphX
- Write code in Scala REPL environments & build Scala applications w/ an IDE
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