Get $1 credit for every $25 spent!

The Complete Big Data Master Class Bundle

Ending In:
Add to Cart - $29
Add to Cart ($29)
$891
96% off
wishlist
(19)
Courses
9
Lessons
318
Enrolled
188

What's Included

Product Details

Access
Lifetime
Content
2 hours
Lessons
28

Learn By Example: Plotly

Create Insightful Charts & Graphs with Minimal Programming Know-How

By Loonycorn | in Online Courses

You don't need to be a programming prodigy to get started in data science. Easy to use and highly accessible, Plotly is library in Python that lets you create complex plots and graphs with minimal programming know-how. From creating basic charts to adding motion to your visualizations, this course will walk you through the Plotly essentials with hands-on examples that you can follow.

  • Access 28 lectures & 2 hours of content 24/7
  • Learn how to build line charts, bar charts, histograms, pie charts & other basic visualizations
  • Explore visualizing data in more than two dimensions
  • Discover how to add motion to your graphs
  • Work w/ plots on your local machine or share them via the Plotly Cloud

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us - 2:12
    • Course Materials
  • Introduction
    • Overview - 1:33
    • Introduction - 6:43
    • Offline Plots - Not Connected To The Cloud - 5:04
    • Online Plots - Connected To The Cloud - 3:41
  • Basics
    • Line Charts - 8:26
    • Bar Charts - 5:04
    • Histograms - 6:41
    • Pie Charts - 3:48
    • Inset Plots - 5:06
    • Box Plots - 5:56
    • Scatter Plots - 5:19
  • Advanced Plots
    • Bubble Charts - 5:25
    • Bubble Maps - 5:16
    • Time Series - 6:16
    • Heat Maps - 4:04
    • Candlestick Charts - 1:55
    • Candlestick Charts - continued - 2:59
    • SVG Path - 3:44
    • Introducing Shapes - 5:51
    • Funnel Charts - 8:01
    • Funnel Charts - continued - 2:13
  • 3D And Interactivity
    • Gantt Charts - 6:54
    • 3D Scatter Plots - 3:31
    • 3D Surface Plots - 2:41
    • Animations - 8:12
    • Summary - 2:28

View Full Curriculum


Access
Lifetime
Content
3 hours
Lessons
30

Learn By Example: Matplotlib

Build Professional Graphs & Plots with this Essential Visualization Tool

By Loonycorn | in Online Courses

Before a data scientist can properly analyze their data, they must first visualize it and understand any relationships that might exist in the information. To this end, many data professionals use Matplotlib, an industry-favorite Python library for visualizing data. Highly customizable and packed with powerful features for building graphs and plots, Matplotlib is an essential tool for any aspiring data scientist, and this course will show you how it ticks.

  • Access 30 lectures & 3 hours of content 24/7
  • Explore the anatomy of a Matplotlib figure & its customizable parts
  • Dive into figures, axes, subplots & more components
  • Learn how to draw statistical insights from data
  • Understand different ways of conveying statistical information

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us - 1:39
    • Course Materials
  • Matplotlib and Pyplot
    • Overview - 2:24
    • Importance of Visualization - 5:32
    • Object Hierarchy - 4:02
    • Anatomy Of A Figure - 1:59
    • Non-Interactive Mode - 5:48
    • Interactive Mode - 4:39
    • Getting Started - 6:24
    • Lines And Markers - 6:36
    • Figures And Axes - 10:59
    • Figures And Subplots - 9:36
    • Watermarks - 10:13
    • Putting It Together - 7:37
  • Varieties of Plots
    • Shapes - 11:32
    • Polygon and Arrows - 3:37
    • Bezier Curves - 4:38
    • Curves - 9:21
    • Annotations - 11:11
    • Scales - 7:04
    • Twin Axis - 5:01
  • Statistical Data
    • Boxplots And Violinplots - 9:43
    • Visualize Corn Data With Box And Violin Plots - 9:46
    • Histograms - 7:55
    • Pie Charts - 8:58
    • Stacked Plots - 7:31
    • Color Maps - 8:00
    • Autocorrelation - 4:02
    • Autocorrelation continued - 4:51
    • Summary - 1:55

View Full Curriculum


Access
Lifetime
Content
1 hours
Lessons
21

Learn By Example: Bokeh

Become a Data Visualization Expert

By Loonycorn | in Online Courses

Bokeh is an open-source, easy-to-use and highly accessible library in Python which allows even developers with just basic programming ability to get up and running with complex plots and graphs. Far easier to use than competing frameworks such as Matplotlib, Bokeh is especially compelling because of how easy it is to build interactivity into your visualizations. Explore relationships in your data without in-depth programming knowledge, extract insights which can be used for further analysis on your data.

  • Access 21 lectures & 1 hour of content 24/7
  • Structure your visuals in the right format
  • Customize the look & feel of basic line plots
  • Plot stacked graphs & bar charts w/ multi-level data
  • Visualize nodes & edges in network graphs
  • Use Bokeh's built-in libraries to view maps & plot geo-location data
  • Add interactivity to legends, tooltips, & toolbars, and use plot tools to play w/ and modify data
  • Use the model-view-controller paradigm to separate data & visualization to build custom, interactive plots

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us - 2:03
    • Course Materials
  • Bokeh Glyphs
    • Overview - 2:12
    • Visualization - 5:29
    • Basics - 1:45
    • Internals - 4:32
    • Introduction - 6:37
    • Markers And Lines - 7:26
    • Shapes - 7:02
    • Layouts - 4:04
    • Axes - 6:42
  • Bokeh Plots
    • Categorical Data - 10:53
    • Network Graph - 5:25
    • Geographical Data - 8:20
    • Annotations - 6:37
  • Bokeh Server
    • Legends - 4:18
    • Plot Tools - 2:06
    • Tooltips And Toolbar - 6:29
    • The Bokeh Server - 3:10
    • Implementation - 12:59
    • Summary - 1:44

View Full Curriculum


Access
Lifetime
Content
1 hours
Lessons
16

Learn By Example: Seaborn

Go From Mounds of Data to Detailed Insights Using This Powerful Visualization Tool

By Loonycorn | in Online Courses

From tech to medicine and finance, data plays a pivotal role in guiding today's businesses. But, it needs to be properly broken down and visualized before you can get any sort of actionable insights. That's where Seaborn comes into play. Designed for enhanced data visualization, this Python-based library helps bridge the gap between vast swathes of data and the valuable insights they contain. This course acts as your Seaborne guide, walking you through what it can do and how you can use it to display information, find relationships, and much more.

  • Access 16 lectures & 1 hour of content 24/7
  • Familiarize yourself w/ Seaborn via hands-on examples
  • Discover Seaborn's enhanced data visualization capabilities
  • Explore histograms, linear relationships & more visualization concepts

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us - 1:21
    • Course Materials
  • Introduction
    • Overview - 2:15
    • Installing Seaborn And Exploring Pokemon Dataset - 3:32
    • Matplotlib and Seaborn - 6:31
  • Distributions And Relationships
    • Kernal Density Estimation (KDE) - 5:08
    • Visualizing Distribution To Find Patterns - 14:47
    • Linear Relationships - 6:31
    • Categorical Data And Multipanel Data - 11:47
  • Trellis Plots
    • The FacetGrid - 10:28
    • Customizing The FacetGrid - 4:07
    • The PairGrid - 4:59
  • Aesthetics, Styles, Colors
    • Themes And Figure Styles - 3:51
    • Color Palettes - 9:03
    • Figure Aesthetics - 2:40
    • Summary - 1:24

View Full Curriculum


Access
Lifetime
Content
2 hours
Lessons
27

Learn By Example: NumPy

Start Working with Multidimensional Data as You Dive Into This Python Library

By Loonycorn | in Online Courses

Today's companies collect and utilize a staggering amount of data to guide their business decisions. But, it needs to be properly cleaned and organized before it can be put to use. Enter NumPy, a core library in the Python data science stack used by data science gurus to wrangle vast amounts of multidimensional data. This course will take you through NumPy's basic operations, universal functions, and more as you learn from hands-on examples.

  • Access 27 lectures & 2 hours of content 24/7
  • Familiarize yourself w/ NumPy's basic operations & universal functions
  • Learn how to properly manage data w/ hands-on examples
  • Validate your training w/ a certificate of completion

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us - 1:48
    • Course Materials
  • NumPy for Multi-dimensional Arrays
    • Overview - 4:31
    • Array Creation - 10:06
    • Printing Arrays - 4:34
    • Basic Operations - 10:42
    • Universal Functions - 9:36
    • Iterating Over Arrays - 5:35
    • Reshaping Arrays - 6:57
    • Indexing And Slicing - 7:02
    • Splitting Arrays - 8:02
    • Automatic Reshaping - 4:41
    • Copying Arrays - 3:21
  • Complex Indexing
    • Indexing Arrays Using Other Arrays - 3:50
    • Fancy Indexing - 2:58
    • Conditional Evaluation - 4:09
    • Structured Data In Arrays - 6:25
    • Broadcasting - 8:19
    • Array Broadcasting - 3:27
    • Images As 3D Arrays - 6:21
    • Image Manipulation - 10:48
  • Miscellaneous Operations
    • Vector Stacking - 5:35
    • Useful Functions - 5:52
    • Vectorization - 2:23
    • SciPy Integration - 5:12
    • Pandas Integration - 5:27
    • Summary - 1:38

View Full Curriculum


Access
Lifetime
Content
2.5 hours
Lessons
23

Learn By Example: Pandas

Complete Your Data Science Toolbox with this Essential Python Library

By Loonycorn | in Online Courses

It's no secret that data scientists stand to make a pretty penny in today's data-driven world; but if you're keen on becoming one, you'll need to master the appropriate tools. Pandas is one of the most popular of the Python data science libraries for working with mounds of data. By expressing data in a tabular format, Pandas makes it easy to perform data cleaning, aggregations and other analyses. Built around hands-on demos, this course will walk you through using Pandas and what it can do as you take on series, data frames, importing/exporting data, and more.

  • Access 23 lectures & 2.5 hours of content 24/7
  • Explore Panda's built-in functions for common data manipulation techniques
  • Learn how to work with data frames & manage data
  • Deepen your understanding w/ example-driven lessons

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us - 2:02
    • Course Materials
  • Introduction
    • Introduction To Pandas - 7:23
    • Series - 4:54
    • Series - continued - 12:49
    • Dataframes - 10:53
    • Creating Dataframes - 6:18
    • Addition And Deletion - 10:00
  • Selecting, Indexing, Reshaping And Other Operations
    • Selection And Indexing - 9:39
    • Selection And Indexing - continued - 3:58
    • Iterating Over Dataframes - 3:56
    • Reshaping Using Pivot - 12:01
    • Export To CSV Excel Text - 4:44
    • Stack Unstack - 4:25
    • Sorting - 5:17
  • Missing Data, MultiIndex, Group By, Concat And Other Operations
    • Handling Missing Data - 11:36
    • MultiIndex DataFrames - 4:47
    • GroupBy - 7:16
    • Concat And Merge - 12:18
    • SQL - 7:17
    • Explore Data Using Pandas - 10:57
    • Pandas For TimeSeries Data - 4:58
    • Summary - 1:18

View Full Curriculum


Access
Lifetime
Content
13 hours
Lessons
72

Learn By Example: Hadoop & MapReduce for Big Data Problems

Discover Mass Data Processing Methods by Using the Leading Data Frameworks

By Loonycorn | in Online Courses

Big Data sounds pretty daunting doesn't it? Well, this course aims to make it a lot simpler for you. Using Hadoop and MapReduce, you'll learn how to process and manage enormous amounts of data efficiently. Any company that collects mass amounts of data, from startups to Fortune 500, need people fluent in Hadoop and MapReduce, making this course a must for anybody interested in data science.

  • Access 72 lectures & 13 hours of content 24/7
  • Set up your own Hadoop cluster using virtual machines (VMs) & the Cloud
  • Understand HDFS, MapReduce & YARN & their interaction
  • Use MapReduce to recommend friends in a social network, build search engines & generate bigrams
  • Chain multiple MapReduce jobs together
  • Write your own customized partitioner
  • Learn to globally sort a large amount of data by sampling input files

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Requirements

  • Internet required

Course Outline

  • Introduction
    • You, this course and Us - 1:52
  • Why is Big Data a Big Deal
    • The Big Data Paradigm - 14:20
    • Serial vs Distributed Computing - 8:37
    • What is Hadoop? - 7:25
    • HDFS or the Hadoop Distributed File System - 11:01
    • MapReduce Introduced - 11:39
    • YARN or Yet Another Resource Negotiator - 4:00
  • Installing Hadoop in a Local Environment
    • Hadoop Install Modes - 8:32
    • Setup a Virtual Linux Instance (For Windows users) - 15:31
    • Hadoop Standalone mode Install - 9:33
    • Hadoop Pseudo-Distributed mode Install - 14:25
    • [For Linux/Mac OS Shell Newbies] Path and other Environment Variables - 8:25
  • The MapReduce "Hello World"
    • The basic philosophy underlying MapReduce - 8:49
    • MapReduce - Visualized And Explained - 9:03
    • MapReduce - Digging a little deeper at every step - 10:21
    • "Hello World" in MapReduce - 10:29
    • The Mapper - 9:48
    • The Reducer - 7:46
    • The Job - 12:28
  • Run a MapReduce Job
    • Get comfortable with HDFS - 10:59
    • Run your first MapReduce Job - 14:30
  • Juicing your MapReduce - Combiners, Shuffle and Sort and The Streaming API
    • Parallelize the reduce phase - use the Combiner - 14:40
    • Not all Reducers are Combiners - 14:31
    • How many mappers and reducers does your MapReduce have? - 8:23
    • Parallelizing reduce using Shuffle And Sort - 14:55
    • MapReduce is not limited to the Java language - Introducing the Streaming API - 5:05
    • Python for MapReduce - 12:19
  • HDFS and Yarn
    • HDFS - Protecting against data loss using replication - 15:32
    • HDFS - Name nodes and why they're critical - 6:48
    • HDFS - Checkpointing to backup name node information - 11:10
    • Yarn - Basic components - 8:33
    • Yarn - Submitting a job to Yarn - 13:10
    • Yarn - Plug in scheduling policies - 14:21
    • Yarn - Configure the scheduler - 12:26
  • Setting up a Hadoop Cluster
    • Manually configuring a Hadoop cluster (Linux VMs) - 13:50
    • Getting started with Amazon Web Servicies - 6:25
    • Start a Hadoop Cluster with Cloudera Manager on AWS - 13:04
  • MapReduce Customizations For Finer Grained Control
    • Setting up your MapReduce to accept command line arguments - 13:47
    • The Tool, ToolRunner and GenericOptionsParser - 12:36
    • Configuring properties of the Job object - 10:41
    • Customizing the Partitioner, Sort Comparator, and Group Comparator - 15:16
  • The Inverted Index, Custom Data Types for Keys, Bigram Counts and Unit Tests!
    • The heart of search engines - The Inverted Index - 14:41
    • Generating the inverted index using MapReduce - 10:25
    • Custom data types for keys - The Writable Interface - 10:23
    • Represent a Bigram using a WritableComparable - 13:13
    • MapReduce to count the Bigrams in input text - 8:26
    • Test your MapReduce job using MRUnit - 13:41
  • Input and Output Formats and Customized Partitioning
    • Introducing the File Input Format - 12:48
    • Text And Sequence File Formats - 10:21
    • Data partitioning using a custom partitioner - 7:11
    • Make the custom partitioner real in code - 10:25
    • Total Order Partitioning - 10:10
    • Input Sampling, Distribution, Partitioning and configuring these - 9:04
    • Secondary Sort - 14:34
  • Recommendation Systems using Collaborative Filtering
    • Introduction to Collaborative Filtering - 7:25
    • Friend recommendations using chained MR jobs - 17:15
    • Get common friends for every pair of users - the first MapReduce - 14:50
    • Top 10 friend recommendation for every user - the second MapReduce - 13:46
  • Hadoop as a Database
    • Structured data in Hadoop - 14:08
    • Running an SQL Select with MapReduce - 15:31
    • Running an SQL Group By with MapReduce - 14:02
    • A MapReduce Join - The Map Side - 14:20
    • A MapReduce Join - The Reduce Side - 13:08
    • A MapReduce Join - Sorting and Partitioning - 8:49
    • A MapReduce Join - Putting it all together - 13:46
  • K-Means Clustering
    • What is K-Means Clustering? - 14:04
    • A MapReduce job for K-Means Clustering - 16:33
    • K-Means Clustering - Measuring the distance between points - 13:52
    • K-Means Clustering - Custom Writables for Input/Output - 8:26
    • K-Means Clustering - Configuring the Job - 10:50
    • K-Means Clustering - The Mapper and Reducer - 11:23
    • K-Means Clustering : The Iterative MapReduce Job - 3:39

View Full Curriculum


Access
Lifetime
Content
13 hours
Lessons
73

Harnessing Hive

Get an End-to-End Hive Education In HQL, Partitioning, Bucketing, & Much More

By Loonycorn | in Online Courses

Hive helps you leverage the power of distributed computing and Hadoop for Analytical processing. Its interface is similar to SQL and this course will help you fill in all the gaps between SQL and what you need to use Hive. It's an end-to-end guide for using Hive: whether you're an analyst who wants to process data or an engineer who needs to build custom functionality or optimize performance, everything you need is right here.

  • Access 73 lectures & 13 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
  • Customize Hive w/ user-defined functions in Java & Python
  • Understand what goes on under the hood of Hive w/ HDFS & MapReduce

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Requirements

  • Internet required

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web and mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Requirements

  • Internet required

Course Outline

  • You, Us & This Course
    • You, Us & This Course - 2:02
  • Introducing Hive
    • Hive: An Open-Source Data Warehouse - 12:59
    • Hive and Hadoop - 9:19
    • Hive vs Traditional Relational DBMS - 13:52
    • HiveQL and SQL - 7:20
  • Hadoop and Hive Install
    • Hadoop Install Modes - 8:32
    • Hadoop Install Step 1 : Standalone Mode - 15:46
    • Hadoop Install Step 2 : Pseudo-Distributed Mode - 11:44
    • Hive install - 12:05
    • Code-Along: Getting started - 6:24
  • Hive Basics
    • Primitive Datatypes - 17:07
    • CollectionsArraysMaps - 9:28
    • Structs and Unions - 5:57
    • Create Table - 13:15
    • Insert Into Table - 12:05
    • Insert into Table 2 - 6:51
    • Alter Table - 7:22
    • HDFS - 9:25
    • HDFS CLI - Interacting with HDFS - 10:59
    • Code-Along: Create Table - 9:54
    • Code-Along : Hive CLI - 3:06
  • Built-in Functions
    • Three types of Hive functions - 6:45
    • The Case-When statement, the Size function, the Cast function - 10:09
    • The Explode function - 13:06
    • Code-Along : Hive Built - in functions - 4:28
  • Sub-Queries
    • Quirky Sub-Queries - 7:13
    • More on subqueries: Exists and In - 15:13
    • Inserting via subqueries - 5:23
    • Code-Along : Use Subqueries to work with Collection Datatypes - 5:57
    • Views - 12:18
  • Windowing
    • Windowing Introduced - 12:59
    • Windowing - A Simple Example: Cumulative Sum - 9:39
    • Windowing - A More Involved Example: Partitioning - 11:55
    • Windowing - Special Aggregation Functions - 15:08
  • Understanding MapReduce
    • The basic philosophy underlying MapReduce - 8:49
    • MapReduce - Visualized and Explained - 9:03
    • MapReduce - Digging a little deeper at every step - 10:21
  • MapReduce logic for queries: Behind the scenes
    • MapReduce Overview: Basic Select-From-Where - 11:33
    • MapReduce Overview: Group-By and Having - 9:12
    • MapReduce Overview: Joins - 14:17
  • Join Optimizations in Hive
    • Improving Join performance with tables of different sizes - 13:12
    • The Where clause in Joins - 4:52
    • The Left Semi Join - 12:11
    • Map Side Joins: The Inner Join - 9:41
    • Map Side Joins: The Left, Right and Full Outer Joins - 11:36
    • Map Side Joins: The Bucketed Map Join and the Sorted Merge Join - 7:52
  • Custom Functions in Python
    • Custom functions in Python - 10:40
    • Code-Along : Custom Function in Python - 5:45
  • Custom functions in Java
    • Introducing UDFs - you're not limited by what Hive offers - 4:38
    • The Simple UDF: The standard function for primitive types - 7:03
    • The Simple UDF: Java implementation for replacetext() - 8:34
    • Generic UDFs, the Object Inspector and DeferredObjects - 13:50
    • The Generic UDF: Java implementation for containsstring() - 9:11
    • The UDAF: Custom aggregate functions can get pretty complex - 14:09
    • The UDAF: Java implementation for max() - 9:21
    • The UDAF: Java implementation for Standard Deviation - 10:47
    • The Generic UDTF: Custom table generating functions - 7:38
    • The Generic UDTF: Java implementation for namesplit() - 10:21
  • SQL Primer - Select Statemets
    • Select Statements - 11:46
    • Select Statements 2 - 14:11
    • Operator Functions - 6:55
  • SQL Primer - Group By, Order By and Having
    • Aggregation Operators Introduced - 18:15
    • The Group By Clause - 17:19
    • More Group By Examples - 19:46
    • Order By - 16:15
    • Having - 19:52
  • SQL Primer - Joins
    • Introduction to SQL Joins - 9:54
    • Cross Joins aka Cartesian Joins - 17:02
    • Inner Joins - 19:52
    • Left Outer Joins - 15:31
    • RIght, Full Outer Joins, Natural Joins, Self Joins - 16:08
  • Appendix
    • [For Linux/Mac OS Shell Newbies] Path and other Environment Variables - 8:25
    • Setting up a Virtual Linux Instance - For Windows Users - 15:58

View Full Curriculum


Access
Lifetime
Content
3 hours
Lessons
28

An Easy Introduction to Python

Become a Python Programmer in Just a Few Hours

By Loonycorn | in Online Courses

Python is a general-purpose programming language which can be used to solve a wide variety of problems, be they in data analysis, machine learning, or web development. This course lays a foundation to start using Python, which considered one of the best first programming languages to learn. Even if you've never even thought about coding, this course will serve as your diving board to jump right in.

  • Access 28 lectures & 3 hours of content 24/7
  • Gain a fundamental understanding of Python loops, data structures, functions, classes, & more
  • Learn how to solve basic programming tasks
  • Apply your skills confidently to solve real problems
Loonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: beginner

Requirements

  • Internet required

Course Outline

  • You, This Course and Us
    • You, This Course and Us (1:56)
    • Source Code
  • Getting Set Up
    • Install Anaconda (2:21)
  • Introducing Python
    • Saying Hello World in Python (5:23)
    • The If-Else Statement (10:32)
    • For Loops (9:45)
  • Data Structures
    • Lists: An Introduction (8:46)
    • Lists: Manipulating Lists with Slicing (9:58)
    • Dictionaries: Storing Key-Value Pairs (6:05)
    • Dictionaries: The setdefault Method, Dictionary of Dictionaries (6:40)
    • Sets and Tuples (4:36)
  • Define your own Functions, Modules and Classes
    • Functions (9:49)
    • Modules: Wrap your Functions into a Module (9:05)
    • Classes: The init Method and Class Variables (7:54)
    • Classes: Enhancing our Class with Decorators and Operator Overloading (7:57)
  • Getting Real - Writing a Web App
    • Build a Simple Web App using the Flask Web Framework (5:13)
    • Extending our Web App to use Web Templates (5:56)
    • Integrating our Web App with our Custom Module (6:57)
  • Common Programming Tasks
    • Parsing JSON Data (7:18)
    • Files and Exception Handling (10:46)
    • Regular Expressions (8:33)
    • Iterators (8:30)
  • Popular Python Libraries
    • Web Scraping with BeautifulSoup (3:57)
    • Pandas: An Introduction to Data Analysis (7:03)
    • Pandas: Transforming JSON Data into a Pandas Data Frame (4:13)
  • Logging
    • Log File: Logging Requests on our Web App to a file (4:37)
    • Databases: Setting up MariaDB to Store Log Data (6:31)
    • Databases: Logging Requests on our Web App to MariaDB (5:30)
    • Using the With Keyword to Manage our Database Connection (10:12)

View Full Curriculum



Terms

  • Unredeemed licenses can be returned for store credit within 15 days of purchase. Once your license is redeemed, all sales are final.