Real-World Data Science with Spark 2

Real-World Data Science with Spark 2, available at $19.99, has an average rating of 3.8, with 55 lectures, 7 quizzes, based on 21 reviews, and has 336 subscribers.

You will learn about An introduction to Big Data and data science Get to know the fundamentals of Spark 2 Understand Spark and its ecosystem of packages in data science Consolidate, clean, and transform your data acquired from various data sources Unlock the capabilities of various Spark components to perform efficient data processing, machine learning, and graph processing Dive deeper and explore various facets of data science with Spark This course is ideal for individuals who are This course is for anyone who wants to work with Spark on large and complex datasets. or Data analyst, data scientists, or Big Data architects interested to explore the data processing power of Apache Spark will find this course very useful. It is particularly useful for This course is for anyone who wants to work with Spark on large and complex datasets. or Data analyst, data scientists, or Big Data architects interested to explore the data processing power of Apache Spark will find this course very useful. .

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Summary

Title: Real-World Data Science with Spark 2

Price: $19.99

Average Rating: 3.8

Number of Lectures: 55

Number of Quizzes: 7

Number of Published Lectures: 55

Number of Published Quizzes: 7

Number of Curriculum Items: 62

Number of Published Curriculum Objects: 62

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

An introduction to Big Data and data science

Get to know the fundamentals of Spark 2

Understand Spark and its ecosystem of packages in data science

Consolidate, clean, and transform your data acquired from various data sources

Unlock the capabilities of various Spark components to perform efficient data processing, machine learning, and graph processing

Dive deeper and explore various facets of data science with Spark

Who Should Attend

This course is for anyone who wants to work with Spark on large and complex datasets.

Data analyst, data scientists, or Big Data architects interested to explore the data processing power of Apache Spark will find this course very useful.

Target Audiences

This course is for anyone who wants to work with Spark on large and complex datasets.

Data analyst, data scientists, or Big Data architects interested to explore the data processing power of Apache Spark will find this course very useful.

Are you looking forward to expand your knowledge of performing data science operations in Spark??Or are?you?a data scientist who wants to understand how algorithms are implemented in Spark, or a newbie with minimal development experience and want to learn about Big Data analytics? If yes, then this course is ideal you.?Let’s get on this data science?journey together.

When people want a way to process Big Data at speed, Spark is invariably the solution. With its ease of development (in comparison to the relative complexity of Hadoop), it’s unsurprising that it’s becoming popularwith data analysts and engineers everywhere. It is one of the most widely-used large-scale data processing engines and runs extremely fast.

The aim of the course is to make you comfortable and confident at performing real-time data processing using Spark.

What is included?

This course is meticulously designed and developed in order to empower you with all the right and relevant information on Spark. However, I want to highlight that the road ahead may be bumpy on occasions, and some topics may be more challenging than others, but I hope that you will embrace this opportunity and focus on the reward. Remember that throughout this course, we will add many powerful techniques to your arsenal that will help us solve the problems.

Let’s take a look at the learning journey. The course begins with the basics of Spark 2 and covers the core data processing framework and API, installation, and application development setup. Then, you’ll be introduced to the Spark programming model through real-world examples. Next, you’ll learn how to collect, clean, and visualize the data coming from Twitter with Spark streaming. Then, you will get acquainted with Spark machine learning algorithms and different machine learning techniques. You will also learn to apply statistical analysis and mining operations on your dataset. The course will ?give you ideas on how to perform analysis including graph processing. Finally, we will take up an end-to-end case study and apply all that we have learned so far.

By the end of the course, you should be able to put your learnings into practice for faster, slicker Big Data projects.

Why should I choose this course?

Packt courses are very carefully designed to make sure that they’re delivering the best learning experience possible. This course is a?blend of text, videos, code examples, and quizzes, which together makes your learning journey all the more exciting and truly rewarding. This helps you learn a range of topics at your own speed and also move towards your goal of learning the technology. We have prepared this course using extensive research and curation skills. Each section adds to the skills learned and helps you to achieve?mastery of Spark.?

This course is an?amalgamation?of sections that form a sequential flow of concepts covering a focused learning path presented in a modular manner. We have combined the best of the following Packt products:

Data Science with Spark by Eric Charles

Spark for?Data Science?by?Bikramaditya Singhal?and?Srinivas?Duvvuri

Apache Spark 2 for Beginners by Rajanarayanan Thottuvaikkatumana

Meet your?expert instructors:

For this course, we have combined the best works of these extremely esteemed authors:

Eric Charles has 10 years of experience in the field of data science and is the founder of Datalayer, a social network for data scientists. He is passionate about using software and mathematics to help companies get insights from data.

Bikramaditya Singhal is a data scientist with about 7 years of industry experience. He is an expert in statistical analysis, predictive analytics, machine learning, Bitcoin, Blockchain, and programming in C, R, and Python. He has extensive experience in building scalable data analytics solutions in many industry sectors.

Srinivas Duvvuri is currently the?senior vice president development, heading the development teams for fixed income suite of products at Broadridge Financial Solutions (India) Pvt Ltd. In addition, he also leads the Big Data and Data Science COE and is the principal member of the Broadridge India Technology Council.

Rajanarayanan Thottuvaikkatumana, Raj, is a seasoned technologist with more than 23 years of software development experience at various multinational companies.?He has worked on various technologies including major databases, application development platforms, web technologies, and Big Data technologies.

Course Curriculum

Chapter 1: Big Data and Data Science

Lecture 1: Course Introduction

Lecture 2: An introduction to Big Data

Chapter 2: The Spark Programming Model

Lecture 1: An overview of Apache Hadoop

Lecture 2: Understanding Apache Spark

Lecture 3: Install Spark on your laptop with Docker, or scale fast in the cloud

Lecture 4: Apache Zeppelin, a web-based notebook for Spark with matplotlib and ggplot2

Lecture 5: The RDD API

Chapter 3: Spark SQL and DataFrames

Lecture 1: Understanding the structure of data and the need of Spark SQL

Lecture 2: The DataFrame API and its operations

Chapter 4: Data Analysis on Spark

Lecture 1: Data analytics life cycle

Lecture 2: Basics of statistics

Lecture 3: Descriptive statistics

Lecture 4: Inferential statistics

Chapter 5: First Step with Spark Visualization

Lecture 1: Data visualization

Lecture 2: Manipulating data with the core RDD API

Lecture 3: Using DataFrame, dataset, and SQL – natural and easy!

Lecture 4: Manipulating rows and columns

Lecture 5: Dealing with file format

Lecture 6: Visualizing more – ggplot2, matplotlib, and Angular.js at the rescue

Lecture 7: References

Chapter 6: The Spark Machine Learning Algorithms

Lecture 1: An introduction to machine learning

Lecture 2: Discovering spark.ml and spark.mllib – and other libraries

Lecture 3: Wrapping up basic statistics and linear algebra

Lecture 4: Cleansing data and engineering the features

Lecture 5: Reducing the dimensionality

Lecture 6: Pipeline for a life

Lecture 7: References

Chapter 7: Collecting and Cleansing the Dirty Tweets

Lecture 1: Streaming tweets to disk

Lecture 2: Streaming tweets on a map

Lecture 3: Cleansing and building your reference dataset

Lecture 4: Querying and visualizing tweets with SQL

Chapter 8: Statistical Analysis on Tweets

Lecture 1: Indicators, correlations, and sampling

Lecture 2: Validating statistical relevance

Lecture 3: Running SVD and PCA

Lecture 4: Extending the basic statistics to your needs

Chapter 9: Extracting Features from the Tweets

Lecture 1: Analyzing free text from the tweets

Lecture 2: Dealing with stemming, syntax, idioms, and hashtags

Lecture 3: Detecting tweet sentiment

Lecture 4: Identifying topics with LDA

Chapter 10: Mine Data and Share Results

Lecture 1: Word cloudify your dataset

Lecture 2: Locating users and displaying heatmaps with GeoHash

Lecture 3: Collaborating on the same note with peers

Lecture 4: Create visual dashboards for your business stakeholders

Chapter 11: Classifying the Tweets

Lecture 1: Building the training and test datasets

Lecture 2: Training a logistic regression model

Lecture 3: Evaluating your classifier

Lecture 4: Selection your model

Chapter 12: Clustering Users

Lecture 1: Clustering users by followers and friends

Lecture 2: Clustering users by location

Lecture 3: Running k-means on a stream

Chapter 13: Putting It All Together

Lecture 1: Case study

Chapter 14: Data Science Applications

Lecture 1: Building data science applications

Chapter 15: Your Next Data Challenges

Lecture 1: Recommending similar users

Lecture 2: Analyzing mentions with GraphX

Lecture 3: Where to go from here

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 1 votes

2 stars: 2 votes

3 stars: 4 votes

4 stars: 6 votes

5 stars: 8 votes

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