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BigData Assignment Help

Realcode4you have an excellent team of BigData experts offer assistance for BigData Assignment Help & BigData Homework Help.

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Big Data Assignment Help

‘Big Data’ is a term that defines the huge volume of data that is growing exponentially. In machine Learning BigData analytics help to manage big data, sometimes data is huge number of records then big data use to analyze the data, the speed to big data analytics techniques is more than normal techniques

Big Data Assignment Help | Big Data Homework Help

Are you looking for an expert help to complete your Big Data programming assignment? Then, seek the help of our Programming Assignment Help experts who possesses immense knowledge in Big Data Programming and can complete the assignment on any programming topic irrespective of its level of complexity.

Struggling to complete Big Data assignments on your own? No need to worry any further!  We have a team of skilled Big Data assignment help programmers who can help you complete an Big Data assignment with ease. Our programming experts leverage their in-depth programming experience to provide the best-in-class help in Big Data coding. 

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Who Are The Experts and Who Help Me Do My Big Data Assignment?

Our cohesive team of Big Data assignment experts consists of:

  • Experienced web developers, programmers and software engineers working with leading IT companies that provide top rated Big Data Assignment Help, Big Data Homework Help and Big Data Project Help and get complete support in data analysis projects.

  • PhD qualified experts who have several years of experience so you will get quality of work in your Big Data Programming Assignment and Homework.

  • Former professors of acclaimed universities including National University of Singapore, Columbia University, University of Melbourne, Australian National University, etc

Our scholars can provide you any kind of Big Data assignment related support. Therefore, you should stop wondering, “Who can help me do my Big Data Programming assignment” and seek assistance from our seasoned writers.

If you are dealing with a complicated topic and thinking, “Can anyone solve my Big Data Programming assignment”, then you can also consult our experts. No matter how complex your topic is, they can assist you.

If you have the query, “Can realcode4you.com experts write or draft my all types of Big Data assignments”, then the answer is yes. Our writers can provide Big Data assignment help for all types of academic papers. Most importantly, our tutors are well-acquainted with all the assignment related guidelines provided by top universities across the world.

Need Big Data Assignment Help?

Do you want to search person who can help you to do your Big Data Assignment? Then realcode4you.com is the right place.  Realcode4you provides provided top rated online platform that students who are struggling with this area due to lack to time, lots of work in short time frame. We offer our services at  affordable prices then the other services for all students and professionals. Realcode4you team covers all requirements which is given by your professor or industries and also provided the code assistance with low price so you can understand the code flow easily.

Big Data Assignment Help

Our Machine Learning Expert Provide Big Data Assignment help & Big Data homework help. Our expert are able to do your Big Data homework assignments at bachelors , masters & the research level. Here you can get top quality code and report at any basic to advanced level. We are solve lots of projects and papers related to Big Data and Machine Learning research paper so you can get code with more experienced expert.

Machine Learning and Data Science Engineer – Scope of Work In Future

 

Realcode4you Machine Learning Experts and Data Scientists can help develop the best ML models by creating a winning AI strategy for your company. Below the description of Machine Learning engineer jobs include various tasks and responsibilities.

  • Design the solution architectures for ML Applications

  • Research and implementation of ML algorithms and thesis without any plagiarism

  • Develop Machine Learning applications as per customer need

  • Data Analysis with right and clean visuals

  • Identify and fix the issues

  • Help to deploy machine Learning models

Get Help In Big Data PySpark

Apache Spark is written in Scala programming language. PySpark has been released in order to support the collaboration of Apache Spark and Python, it actually is a Python API for Spark.

Our expert provide all PySpark related help; Big Data PySpark Coding Help, Big Data PySpark Assignment Help, Big Data PySpark Homework Help, Big Data PySpark Coursework Help, etc. 

Here you can get help in:

  • PySpark Installation

  • PySpark SQL Related Assignment

  • PySpark Context

  • And More Other

Configure PySpark

!apt-get install openjdk-8-jdk-headless -qq > /dev/null
!wget -q http://www-us.apache.org/dist/spark/spark-2.4.0/spark-2.4.0-bin-hadoop2.7.tgz
!tar xf spark-2.4.0-bin-hadoop2.7.tgz
!pip install -q findspark

import os
os.environ["JAVA_HOME"] = "C:\work\Java\jre1.8.0_301"
os.environ["SPARK_HOME"] = "C:\work\Spark\spark-3.1.2-bin-hadoop2.7"
os.environ["HADOOP_HOME"] = "C:\work\Spark\spark-3.1.2-bin-hadoop2.7"

import findspark
findspark.init()

from pyspark import SparkContext
from pyspark.sql import SQLContext
sc = SparkContext.getOrCreate()
sqlContext = SQLContext(sc)

Get Help In Big Data Map-Reduce

In a MapReduce program, Map() and Reduce() are two functions. The Map function performs actions like filtering, grouping and sorting. While Reduce function aggregates and summarizes the result produced by map function. The result generated by the Map function is a key value pair (K, V) which acts as the input for Reduce function.

Basic Steps To Perform Map Reduce:

Input Splits:

It is a fixed-size pieces called input splits Input split is a chunk of the input that is consumed by a single map

 

Mapping

In this phase data in each split is passed to a mapping function to produce output values. In our example, a job of mapping phase is to count a number of occurrences of each word from input splits (more details about input-split is given below) and prepare a list in the form of <word, frequency>

 

Shuffling

This phase consumes the output of Mapping phase. Its task is to consolidate the relevant records from Mapping phase output. In our example, the same words are clubed together along with their respective frequency.

 

Reducing

In this phase, output values from the Shuffling phase are aggregated. This phase combines values from Shuffling phase and returns a single output value. In short, this phase summarizes the complete dataset.

Big Data Topics In Which You can Get Help

  1. Big Data Revolution

  2. Hadoop Architecture and Ecosystem

  3. Setting up Hadoop

  4. Hadoop Distributed File System (HDFS) Architecture

  5. Hadoop Distributed File System (HDFS) Programming Basics

  6. Hadoop Distributed File System (HDFS) Programming Advanced

  7. YARN and MapReduce Architecture

  8. MapReduce Programming Basics

  9. MapReduce Programming Intermediate

  10. MapReduce Programming Advanced

  11. Data Analysis using Hive

  12. Data Analysis using Pig

  13. Hadoop NOSQL Database HBase

  14. Spark

  15. Miscellaneous Hadoop Topics

 

Here You Get

  • Understand the trends that is fueling the modern Big Data Revolution.

  • Gain a solid understanding of the Apache Hadoop Architecture including HDFS and MapReduce.

  • Apply the HDFS Programming model and the ability to author HDFS Programs using Apache Hadoop HDFS API for importing and exporting data into Hadoop.

  • Apply the Distributed Storage and Distributed Programming model for distributed processing.

  • Best practices for Hadoop development, debugging, and implementation of work

  • How to leverage Hive and Pig for big data processing, and a look at related Hadoop projects.

 

Required Software

Oracle VM VirtualBox

You will need to install VirtualBox Oracle VM. This is open source software. Information on how to install and configure you can get from us.

 

Ubuntu Linux

You will need to install CentOS Linux as your own VM on VirtualBox. This is open source software. Information on how to install and configure you can get from us.

 

Apache Hadoop

You will need to install Apache Hadoop inside your CentOS Linux VM. Hadoop is open source software. Get help in how to install and configure it also when start this.

 

Apache Hive

You will need to install Apache Hive inside your CentOS Linux VM. Hadoop is open source software. Get help in how to install and configure it also when start this.

 

Apache Pig

You will need to install Apache Pig inside your CentOS Linux VM. Hadoop is open source software. Get help in how to install and configure it also when start this.

 

Apache HBase

You will need to install Apache Pig inside your CentOS Linux VM. Hadoop is open source software. Get help in how to install and configure it also when start this.

 

Apache Spark

You will need to install Apache Spark inside your CentOS Linux VM. Hadoop is open source software. Get help in how to install and configure it also when start this.

 

IDE (NetBeans or Eclipse)

You will need to install NetBeans or Eclipse inside your CentOS VM. NetBeans or Eclipse is your standard open source IDE software.

Perform Map Reduce For Integer Number

Problem statement:

You are given a large number of files containing positive integers. Design the MapReduce process to compute the number of even integers across all files.

Before answering the question we will creating MapReduce job using positive integers with, <key, value>

Using MapReduce, determine how many odd and even numbers of jobs in positive integer jobs.

map reduce image integer example.JPG
Map Reduce For Reduce Class.JPG
Get Help In Big Data Hadoop/HDFS

The Hadoop Distributed File System (HDFS) is the primary data storage system used by Hadoop applications. HDFS employs a NameNode and DataNode architecture to implement a distributed file system that provides high-performance access to data across highly scalable Hadoop clusters.

Here you can get good quality code from our expert. Our expert focus to deliver code without any Plagiarism and within your due. 

Big Data can be characterized by:
  • Volume - the amount and scale of data

  • Velocity - the speed at which data travels in and out

  • Variety - the range and complexity of data types, structures, and sources

Examples:

  • Financial markets - 7 billion shares change hands every day in the U.S. markets

  • Google, Twitter, GPS data, Facebook, YouTube, etc.

MapReduce is a framework for processing large-scale data using parallel and distributed computing technologies with a large number of computers. Apache Hadoop is an open-source implementation of MapReduce.

Big Data Analytics using Spark SQL

PySpark SQL is a module in Spark which integrates relational processing with Spark's functional programming API. We can extract the data by using an SQL query language. We can use the queries same as the SQL language. If you have basic understanding related to RDBMS, SQL, and Data Analytics then you can easily work with this. 

Feature of PySpark SQL: Consistence Data Access, Incorporation with Spark, Standard Connectivity, User-Defined Functions, Hive Compatibility

Visualization of big data

Big data visualization is the process of displaying data in charts, graphs, maps, and other visual forms. It is used to help people easily understand and interpret their data at a glance, and to clearly show trends and patterns that arise from this data.

Realcode4you also covers all big data visualization help which is related to any programming languages; Python, R, Java, C# etc. If you are looking to hire big data visualization expert then we are the right choice for you. Our expert coverts all types of visualizations like: Bar Plot, Line Plot, Pie Plot, Scatter Plot , Histogram, Heat MapDashboardsAnd more others.

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Data Visualization Using Tableau.JPG

WE ARE EXPERTISE IN BELOW TYPES OF VISUALIZATIONS

Related to Big Data Analysis

Below the list of python machine learning visualizations in which you can also get help to analyze the data. It makes easy to understand the data for any non technical persons. There are many types of visualizations used in data science and machine learning

Below the list of machine learning visualizations in which you can also get help to analyze the data. It makes easy to understand the data for any non technical persons. There are many types of visualizations used in data science and machine learning:

  • Parallel Coordinates chart: Parallel coordinates is a visualization technique used to plot individual data elements across many performance measures. Each of the measures corresponds to a vertical axis and each data element is displayed as a series of connected points along the measure/axes.

  • Density Plot: Density Plot is a type of data visualization tool. It is a variation of the histogram that uses ‘kernel smoothing’ while plotting the values. It is a continuous and smooth version of a histogram inferred from a data.

  • Column Chart: A column chart is a data visualization where each category is represented by a rectangle, with the height of the rectangle being proportional to the values being plotted. Column charts are also known as vertical bar charts.

  • Bar Graph: A bar plot or bar chart is a graph that represents the category of data with rectangular bars with lengths and heights that is proportional to the values which they represent. The bar plots can be plotted horizontally or vertically

  • Stacked Bar Graph: A stacked bar chart is also known as a stacked bar graph. It is a graph that is used to compare parts of a whole. In a stacked bar chart each bar represents the whole, and the segments or parts in the bar represent categories of that whole

  • Grouped Bar Chart: A grouped barplot is used when you have several groups, and subgroups of these groups. The example in this post shows how to build a grouped barplor using the bar() function of matplotlib library.

  • Area Chart: An area chart or area graph displays graphically quantitative data. It is based on the line chart. The area between axis and line are commonly emphasized with colors, textures and hatchings. Commonly one compares two or more quantities with an area chart.

  • Dual Axis Chart: A dual axis chart (also called a multiple axes chart) uses two axes to easily illustrate the relationships between two variables with different magnitudes and scales of measurement. The relationship between two variables is referred to as correlation.

  • Line Graph: A line chart or line plot or line graph or curve chart is a type of chart which displays information as a series of data points called 'markers' connected by straight line segments. It is a basic type of chart common in many fields. 

  • Candle set chart: A typical candlestick chart is composed of a series of bars, known as candles, which vary in height and color. Candlestick charts are one of the most popular chart types for day traders. Learn how to read these charts and apply them to your trading

  • Box and whisker plot: A Box and Whisker Plot (or Box Plot) is a convenient way of visually displaying the data distribution through their quartiles.

  • Mekko Chart: A Mekko chart (sometimes also called marimekko chart) is a two-dimensional stacked chart. In addition to the varying segment heights of a regular stacked chart, a Mekko chart also has varying column widths. Column widths are scaled such that the total width matches the desired chart width. 

  • Pie Chart: A pie chart, sometimes called a circle chart, is a way of summarizing a set of nominal data or displaying the different values of a given variable (e.g. percentage distribution).

  • Bubble Chart: Bubble chart displaying the relationship between poverty and violent and property crime rates by state. Larger bubbles indicate higher percentage of state residents at or below the poverty level. 

  • Scatter Plot Chart: A scatter plot (aka scatter chart, scatter graph) uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point.

  • Grouped Scatter Chart: Display scatter plot of two variables. Adding a grouping variable to the scatter plot is possible. In this we group more than two variable that called group scatter chart.

  • Scatter Plot Matrix: A scatter plot matrix is a grid (or matrix) of scatter plots used to visualize bivariate relationships between combinations of variables. Each scatter plot in the matrix visualizes the relationship between a pair of variables, allowing many relationships to be explored in one chart.

  • Radar Chart: A radar chart is a way of showing multiple data points and the variation between them. They are often useful for comparing the points of two or more different data sets.

  • Radial Bar Chart: A Radial/Circular bar chart is a bar chart displayed on a polar coordinate system. The difference between radial column chart is that base axis of series is y axis of a radar chart making columns circular. You can easily adjust start/end angles of a chart by setting startAngle and endAngle of your RadarChart component.

  • Donut chart: A donut chart is essentially a Pie Chart with an area of the centre cut out. A donut chart (also spelled doughnut) is functionally identical to a pie chart, with the exception of a blank center and the ability to support multiple statistics at once.

  • Bullet Graph: A bullet graph is a variation of a bar graph developed to replace dashboard gauges and meters. A bullet graph is useful for comparing the performance of a primary measure to one or more other measures

  • Funnel Chart: A funnel chart is a specialized chart type that demonstrates the flow of users through a business or sales process. Funnel charts show values across multiple stages in a process. For example, you could use a funnel chart to show the number of sales prospects at each stage.

  • TreeMap: A treemap chart provides a hierarchical view of your data and makes it easy to spot patterns, such as which items are a store's best sellers. Treemapping is a data visualization technique that is used to display hierarchical data using nested rectangles;

  • Dendo gram: A dendrogram (or tree diagram) is a network structure. It is constituted of a root node that gives birth to several nodes connected by edges or branches.

  • Heat Map: A heat map is a two-dimensional representation of data in which values are represented by colors. A simple heat map provides an immediate visual summary of information. More elaborate heat maps allow the viewer to understand complex data sets.

  • Violin Chart: A violin plot is a method of plotting numeric data. It is similar to a box plot, with the addition of a rotated kernel density plot on each side.

  • Area graph: An area chart or area graph displays graphically quantitative data. It is based on the line chart. The area between axis and line are commonly emphasized with colors, textures and hatchings. Commonly one compares two or more quantities with an area chart.

  • stacked Area graph: Stacked Area Graphs work in the same way as simple Area Graphs do, except for the use of multiple data series that start each point from the point left by the previous data series. 

Best Machine Learning Sample Projects 2023

Project 1(OpenCV): Domain- Entertainment

Company X owns a movie application and repository which caters movie streaming to millions of users who on subscription basis. Company wants to automate the process of cast and crew information in each scene from a movie such that when a user pauses on the movie and clicks on cast information button, the app will show details of the actor in the scene. Company has an in-house computer vision and multimedia experts who need to detect faces from screen shots from the movie scene. The data labelling is already done. Since there higher time complexity is involved in the

OpenCV Assignment Help, Face Detection Using OpenCV.jpg

Project 2: Statistical Analysis to Reducing Gender Inequality in Wages and Employment

Germany’s government is interested in reducing gender inequality, especially gender wage gaps and gender gaps in employment. They are considering the introduction of a set of
policies that incentivize firms to shrink gender inequality in working conditions. First, the policy forces firms to internally publish salaries of all workers, so discrepancies in salaries can be detected by workers themselves. Second, firms are incentivized to encourage salary negotiations. Third, firms are incentivized to offer childcare where needed for their employees to fulfill their duties.
The government has been made aware that, in parts of the United States, exactly these policies have been introduced and now asks you to evaluate the effectiveness of these policies in reducing gender inequality in wages and employment. The dataset genderinequality (provided in RData and csv formats) contains data on individuals in the U.S., some working in firms to which the new policies apply (these are the treated workers) and some working at firms to which the new policies do not apply (these are the untreated workers). The policies have been introduced in 2007 and we have panel data on workers for the years 2005 and 2010, i.e. before and after the introduction of the new policies.

Statistical Analysis to Reducing Gender Inequality in Wages and Employment.jpg

Project 3: ReneWind

Renewable energy sources play an important role in the global energy mix, as the effort to reduce the environmental impact of energy production increases. Wind energy is one of the most developed technologies worldwide and the U.S Department of Energy has put together a guide to achieving operational efficiency using predictive maintenance practices. Predictive maintenance means failure patterns are predictable and if component failure can be predicted accurately and the component is replaced before it fails, the costs of operation and maintenance will be much lower. ReneWind is a company working on improving the machinery/processes involved in the production of wind energy using machine learning and has collected data of generator failure of wind turbines using sensors. ReneWind is a company working on improving the machinery/processes involved in the production of wind energy using machine learning and has collected data of generator failure of wind turbines using sensors.

Hire Machine Learning Expert, Get Help In Machine Learning Assignment, Best Machine learni

Project 4: TRAIN&AHEAD PROJECT

Trade&Ahead is a financial consultancy firm who provide their customers with personalized investment strategies. They have hired you as a Data Scientist and provided you with data comprising stock price and some financial indicators for a few companies listed under the New York Stock Exchange. They have assigned you the tasks of analyzing the data, grouping the stocks based on the attributes provided, and sharing insights about the characteristics of each group. 

Tread & Ahead.jpg

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Realcode4you online tutors are ready to help you with the Big Data topics in which you are struggling. You can choose or hire our one-on-one tutoring or homework assistance to get the academic support or any industrial support. You can request live or through mail directly for online tutoring session to get personalized academic support. Our tutors provide one-to-one live session in which all doubts are clear easily. We are also provide coursework or document which help you to practice related topics in Data Analysis.

There are many doubts in your mind when you start machine learning as a programming expert. Our Tutors easily explain your all doubts in live session and provide better help in your coursework and homework.

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If you not have proper resource which is related to your task then here you can get all support and also get resources which is related to your programming skills.

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