Big Data

Statistics                        Probability

Data Science

Artificial Intelligence           Machine Learning          Deep Learning         Singularity

Python Programming        R Programming       SQL and No-SQL      MySQL    Hadoop       MongoDB        Hive       Power BI       Tableau       AWS       Azure      

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What is the best definition for data?

In computing, data is information that has been translated into a form that is efficient for movement or processing. Relative to today’s computers and transmission media, data is information converted into binary digital form. It is acceptable for data to be used as a singular subject or a plural subject.

What is big data in simple terms?

Big Data is a phrase used to mean a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity.

What is big data used for?

Big data has been used in the industry to provide customer insights for transparent and simpler products, by analyzing and predicting customer behavior through data derived from social media, GPS-enabled devices, and CCTV footage. The big data also allows for better customer retention from insurance companies.

What is considered big data?

Big Data, while impossible to define specifically, typically refers to data storage amounts in excesses of one terabyte(TB). Big Data has three main characteristics: Volume (amount of data), Velocity (speed of data in and out), Variety (range of data types and sources).

Why do we need big data?

Big data analytics efficiently helps operations to become more effective. This helps in improving the profits of the company. Big data analytics tools like Hadoop helps in reducing the cost of storage. This further increases the efficiency of the business.

What is big data main objective?

The main objective of big data is to tell a story – with numbers. With this technology an organisation or individual can obtain, store, transform and analyse large amounts of data to solve specific problems. A data driven approach to understanding a business.

What are the types of big data?

Types of Data Used in Analytics. Data types involved in Big Data analytics are many: structured, unstructured, geographic, real-time media, natural language, time series, event, network and linked.

What is Big Data basics?

Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity.

What is Data example?

Data is defined as facts or figures, or information that’s stored in or used by a computer. An example of data is information collected for a research paper. An example of data is an email.

What are the sources of big data?

The three primary sources of  Big Data

Social data comes from the Likes, Tweets & Retweets, Comments, Video Uploads, and general media that are uploaded and shared via the world’s favorite social media platforms. This kind of data provides invaluable insights into consumer behavior and sentiment and can be enormously influential in marketing analytics. The public web is another good source of social data, and tools like Google Trends can be used to good effect to increase the volume of big data.

Machine data is defined as information which is generated by industrial equipment, sensors that are installed in machinery, and even web logs which track user behavior. This type of data is expected to grow exponentially as the internet of things grows ever more pervasive and expands around the world. Sensors such as medical devices, smart meters, road cameras, satellites, games and the rapidly growing Internet Of Things will deliver high velocity, value, volume and variety of data in the very near future.

Transactional data is generated from all the daily transactions that take place both online and offline. Invoices, payment orders, storage records, delivery receipts – all are characterized as transactional data yet data alone is almost meaningless, and most organizations struggle to make sense of the data that they are generating and how it can be put to good use.

Where is Big Data stored?

With Big Data you store schemaless as first (often referred as unstructured data) on a distributed file system. This file system splits the huge data into blocks (typically around 128 MB) and distributes them in the cluster nodes. As the blocks get replicated, nodes can also go down.

Does big data require coding?

You need to code to conduct numerical and statistical analysis with massive data sets. Some of the languages you should invest time and money in learning are Python, R, Java, and C++ among others. … Finally, being able to think like a programmer will help you become a good big data analyst.

The five V’s of big data

Volume, velocity, variety, veracity and value are the five keys to making big data a huge business.

Top 10 Best Open Source Big Data Tools in 2020

 

  • Hadoop
  • Apache Spark
  • Apache Storm
  • Cassandra
  • RapidMiner
  • MongoDB
  • R Programming Tool
What is big data advantages and disadvantages?

Following are the benefits or advantages of Big Data:

Big data analysis derives innovative solutions. Big data analysis helps in understanding and targeting customers. It helps in optimizing business processes.

It helps in improving science and research.

It improves healthcare and public health with availability of record of patients.

It helps in financial trading’s, sports, polling, security/law enforcement etc.

Anyone can access vast information via surveys and deliver answer of any query.

Every second additions are made.

One platform carries unlimited information.

Drawbacks or disadvantages of Big Data

 

Following are the drawbacks or disadvantages of Big Data:

Traditional storage can cost lot of money to store big data.

Lots of big data is unstructured.

Big data analysis violates principles of privacy.

It can be used for manipulation of customer records.

It may increase social stratification.

Big data analysis is not useful in short run. It needs to be analyzed for longer duration to leverage its benefits.

Big data analysis results are misleading sometimes.

Speedy updates in big data can mismatch real figures.

What is big data in research?

Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. … Big data can be analyzed for insights that lead to better decisions and strategic business moves.

Who generates big data?

Big Data is torrent of information generated by machines or humans which is so huge that traditional database failed to process it. To understand the scope of Big Data, let us consider this example: Twitter processes 1 Petabyte (100 Terabyte) of data daily while Google processes 100 Petabyte data.

How is big data used in business?

The use of big data allows businesses to observe various customer related patterns and trends. Observing customer behaviour is important to trigger loyalty. Theoretically, the more data that a business collects the more patterns and trends the business can be able to identify.

Is Google Big Data?

As a cloud platform-based big data analytics service, Google BigQuery was designed to perform analytics of read-only data from billions of data source rows using an SQL-like syntax. As a service, it runs on the Google Cloud Storage platform and can be invoked through REST-based API framework.

How do you classify big data?

Types of Big Data:

Classification is essential for the study of any subject. So Big Data is widely classified into three main types, which are-

  • Structured
  • Unstructured
  • Semi-structured
What are big data techniques?

According to IDC Canada, a Toronto-based IT research firm, Big Data is one of the top three things that will matter in 2013. With that in mind, there are 7 widely used Big Data analysis techniques that we’ll be seeing more of over the next 12 months:

  • Association rule learning
  • Classification tree analysis
  • Genetic algorithms
  • Machine learning
  • Regression analysis
  • Sentiment analysis
  • Social network analysis
What is big data stack?

When we say “big data”, many think of the Hadoop technology stack. … Cloud-based data warehouses which can hold petabyte-scale data with blazing fast performance. Even traditional databases store big data—for example, Facebook uses a sharded MySQL architecture to store over 10 petabytes of data.

What are the challenges of big data?
Some of the most common of those big data challenges include the following:
  1. Dealing with data growth. …
  2. Generating insights in a timely manner. …
  3. Recruiting and retaining big data talent. …
  4. Integrating disparate data sources. …
  5. Validating data. …
  6. Securing big data. …
  7. Organizational resistance.
Do I need big data?

This makes it ideal processing platform for typically set of heterogeneous inputs. Big Data is often processed with parallel cluster based computing using Apache Hadoop and MapReduce. Yes we need Big Data, because bigger the sample, the better is the accuracy in the results.

What is big data risk?
Data storage and retention

This is one of the most obvious risks associated with big data. When data gets accumulated at such a rapid pace and in such huge volumes, the first concern is its storage. Traditional data storage methods and technology are just not enough to store big data and retain it well.

Why do we use data?

Web data is important because it’s one of the major ways businesses can access information that isn’t generated by themselves. … Web data can be used to monitor competitors, track potential customers, keep track of channel partners, generate leads, build apps, and much more.

What are the main components of big data?

Variety refers to the ever increasing different forms that data can come in such as text, images, voice. Velocity refers to the speed at which data is being generated and the pace at which data moves from one point to the next. Volume, variety, and velocity are the three main dimensions that characterize big data.

What are the four characteristics of big data?

The general consensus of the day is that there are specific attributes that define big data. In most big data circles, these are called the four V’s: volume, variety, velocity, and veracity.

What is big data and examples?

Big Data is defined as data that is huge in size. Bigdata is a term used to describe a collection of data that is huge in size and yet growing exponentially with time. Examples of Big Data generation includes stock exchanges, social media sites, jet engines, etc.

The 13 Types Of Data

1 – Big data. Today In: Tech.

2 – Structured, unstructured, semi-structured data.

3 – Time-stamped data.

4 – Machine data. …

5 – Spatiotemporal data. …

6 – Open data. …

7 – Dark data. …

8 – Real time data.

9 – Genomics data

10 – Operational data

11 – High-dimensional data

12 – Unverified outdated data

13 – Translytic Data

What skills do you need for big data?
Following skills are essential to crack a Big Data job:
  • Apache Hadoop. …
  • Apache Spark. …
  • NoSQL. …
  • Machine learning and Data Mining. …
  • Statistical and Quantitative Analysis. …
  • SQL. …
  • Data Visualization. …
  • General Purpose Programming language.
How do I start big data?
  1. Start by Learning a Programming Language: If you want to tackle Big data you should know Python/Java. …
  2. Learn about a Big Data Platform: Once you feel that you could solve basic problems using Python/Java, you are ready for the next step. …
  3. Learn a Little Bit of Bash Scripting: …
  4. Learn Spark.
Is Hadoop open source?

Apache Hadoop is an open source software framework for storage and large scale processing of data-sets on clusters of commodity hardware. Hadoop is an Apache top-level project being built and used by a global community of contributors and users. It is licensed under the Apache License 2.0.

What do u mean by variable?

In programming, a variable is a value that can change, depending on conditions or on information passed to the program. Typically, a program consists of instruction s that tell the computer what to do and data that the program uses when it is running.

What are the types of data items?
Types of Data Items
  • Integer.
  • 64-bit integer.
  • Integer filter.
  • Character-string.
  • String filter.
  • Byte string.
  • Byte string filter.
  • Bag handle.
How is data converted to information?

Use tools that help you analyze the information and data you have. Export the data from your system if necessary and load it into Excel. Use Excel’s pivot table tool to analyze data and convert it into information. You can use other software or enterprise systems that are designed for data analysis as well.

What is biggest source of big data?

Two of the largest sources of data in large quantities are transactional data, including everything from stock prices to bank data to individual merchants’ purchase histories; and sensor data, much of it coming from what is commonly referred to as the Internet of Things (IoT).

Is MongoDB Big Data?

MongoDB: The Database for Big Data Processing. MongoDB is a document database that provides high performance, high availability, and easy scalability. Because of its features, MongoDB is The database for Big Data processing.

Which database is used in big data?
NoSQL databases
NoSQL databases store unstructured data with no particular schema. Each row can have its own set of column values. NoSQL gives better performance in storing massive amount of data. There are many open-source NoSQL DBs available to analyse big Data.
Is Big Data difficult to learn?

No Learning Hadoop is not very difficult. Hadoop is a framework of java. Java is not a compulsory prerequisite for learning hadoop. … Hadoop is an open source software platform for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware.

Is python required for big data?

Python is popular in Data industry because it easy, flexible and concentrates on code readability and productivity. Python is just another language that can be used in writing Hadoop projects and Data Science. So, to get in to Big Data you should start learning Hadoop and Java.

Which language is best for big data?
Top 3 Big Data Programming Languages
  • Java – The Ultimate Big Data Programming Language.
  • Python – The Importance is on the Rise.
  • Scala: Go on a Hybrid Language Way for Big Data.
Which language is required for big data?
Python
Python is the most popular language used by data scientists to explore Big Data, thanks to its slew of useful tools and libraries, such as pandas and matplotlib.
What is big data SQL?

Extends Oracle SQL to Hadoop and NoSQL and the security of Oracle Database to all your data. It also includes a unique Smart Scan service that minimizes data movement and maximizes performance, by parsing and intelligently filtering data where it resides.

How is big data used in marketing?
Here are five ways to pull Big Data into your marketing strategy:
  1. Monitor Google Trends to Inform Your Global/Local Strategy. …
  2. Use Digital Information to More Clearly Define Your ICP. …
  3. Create Real-Time Personalization to Buyers. …
  4. Identify the Specific Content that Moves Buyers Down the Sales Funnel.
Is BigQuery a data warehouse?

The data in a warehouse may be from different sources. BigQuery is Google’s offering for data warehousing. It is designed to store and query petabytes of data without requiring you to setup and manage any operational infrastructure. It is, however, not a transactional database.

Is Big Data a good career?

Big Data is one of the most rewarding careers with a number of opportunities in the field. Organisations today are looking for data analysts, data engineers, and professionals with Big Data expertise in a big number. The need for analytics professionals and big data architects is also increasing.

Is Hadoop outdated?

No, Hadoop is not outdated. There is still no replacement for Hadoop ecosystem. HDFS is still the most reliable storage system in world and more than 50% of the world’s Data has been moved to Hadoop.

Is Hadoop good for freshers?
More Job Opportunities with Apache Hadoop:

Hence, the job trend or Market is not a short lived phenomenon as Big Data and its technologies are here to stay. Hadoop has the potential to improve job prospects whether you are a fresher or an experienced professional.

Does Hadoop use SQL?

Using Hive SQL professionals can use Hadoop like a data warehouse. Hive allows professionals with SQL skills to query the data using a SQL like syntax making it an ideal big data tool for integrating Hadoop and other BI tools.

Is Hadoop a database?

Hadoop is not a type of database, but rather a software ecosystem that allows for massively parallel computing. It is an enabler of certain types NoSQL distributed databases (such as HBase), which can allow for data to be spread across thousands of servers with little reduction in performance.

Is Hadoop a data lake?

A data lake is an architecture, while Hadoop is a component of that architecture. In other words, Hadoop is the platform for data lakes. … For example, in addition to Hadoop, your data lake can include cloud object stores like Amazon S3 or Microsoft Azure Data Lake Store (ADLS) for economical storage of large files.

Which software is used for Hadoop?

Best Hadoop-Related Software include: Cloudera Manager, Amazon EMR, IBM Analytics Engine, MapR, Apache Spark, and Hadoop.

What are the steps to learn big data?
  1. Step 1: Basics. The basic knowledge you need to have are:
  2. Step 2: Foundation. Your foundation needs to be song and in order to do so, the following needs to be strong. …
  3. Step 3: Application on your own computer. …
  4. Step 4: Processing Big Data. …
  5. Step 5: Processing Real Time Data & Streaming Data. …
  6. Step 6: Big Data Analytics.
What are data technologies?

Data technology (may be shortened to DataTech or DT) is the technology connected to areas such as martech or adtech. Data technology sector includes solutions for data management, and products or services that are based on data generated by both human and machines.

How do you say data?

Data‘ is pronounced: day-taa, not daa-taa. It is the plural of ‘datum’ (day-tum).

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What is the difference between qualitative and quantitative data?

Quantitative data are measures of values or counts and are expressed as numbers. Quantitative data are data about numeric variables (e.g. how many; how much; or how often). Qualitative data are measures of ‘types’ and may be represented by a name, symbol, or a number code.

What is data application?

Data applications are a big part of where our data-driven world is headed. They’re how data science gets operationalized. They are how end-users – whether they’re subject matter experts, business decision makers, or consumers – interact with data, big and small.

Data Science

What is data science in simple words?

Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured.

What exactly is a data scientist?

“More generally, a data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as being human. She spends a lot of time in the process of collecting, cleaning, and munging data, because data is never clean.

Does data science require coding?

You need to have the knowledge of programming languages like Python, Perl, C/C++, SQL, and Java—with Python being the most common coding language required in data science roles. Programming languages help you clean, massage, and organize an unstructured set of data.

How is Python used in data science?

Python is open source, interpreted, high level language and provides great approach for object-oriented programming. It is one of the best language used by data scientist for various data science projects/application. Python provide great functionality to deal with mathematics, statistics and scientific function.

Is Data Science hard?

Because learning data science is hard. It’s a combination of hard skills (like learning Python and SQL) and soft skills (like business skills or communication skills) and more. This is an entry limit that not many students can pass. They got fed up with statistics, or coding, or too many business decisions, and quit.

Do data scientists work from home?

Absolutely, you can work from home or remotely as a data scientist as all of the work happens on your system or on a distributed system that you can access remotely.

Can you learn data science on your own?

Yes, you can become a self taught data scientist. It is harder than a formal education, but so far as I know data science programs are brand new. The field is interdisciplinary, so you have to learn at least one field on your own. If you can‘t self teach, pick another field.

Where can data scientists work?
4 Types of Data Science Jobs
  • The Data Analyst. There are some companies where being a data scientist is synonymous with being a data analyst. …
  • The Data Engineer. …
  • The Machine Learning Engineer. …
  • The Data Science Generalist.
Where can I study data science?
Data scientists are in demand, but a master’s degree in the field may not open as many doors as you think.
Best undergraduate data science programs
  • Harvard.
  • University of Chicago.
  • Princeton University.
  • Cambridge University.
  • Yale University.
  • Columbia University.
  • MIT.
  • Stanford University.
Can non IT person learn data science?

It is absolutely possible to learn Data Science without a computer science or mathematics background. It is also possible to get a job. There are three main Data Science skills that one must be required these are programming, statistics and business knowledge.

How would you define a data scientist and data science?

A data scientist is a professional responsible for collecting, analyzing and interpreting extremely large amounts of data. The data scientist role is an offshoot of several traditional technical roles, including mathematician, scientist, statistician and computer professional.

What is data science salary?

Despite a recent influx of early-career professionals, the median starting salary for a data scientist remains high at $95,000. Mid-level data scientist salary. The median salary for a mid-level data scientist is $128,750. If this data scientist is also in a managerial role, the median salary rises to $185,000.

Why do data scientists quit?

In my opinion, the fact that expectation does not match reality is the ultimate reason why many data scientists leave. … The company then get frustrated because they don’t see value being driven quickly enough and all of this leads to the data scientist being unhappy in their role.

Who is eligible for data science?

What is the minimum qualification for data science training? College Degree: The applicant should have a Bachelor’s degree in science/engineering/business administration/commerce/mathematics or masters in mathematics/statistics with 50% or equivalent passing marks.

Are data scientists smart?

Challenge to being a data scientist isn’t necessarily being smart in one field, but being highly competent in a few different areas. … Good data scientists combine these skills into one role and that is why they are so valuable. Each of the skills can be mastered on their own in different ways.

Who is the father of data science?

The term “Data Science” was coined at the beginning of the 21st Century. It is attributed to William S.

What tools do data scientists use?
  1. SAS. It is one of those data science tools which are specifically designed for statistical operations.
  2. Apache Spark.
  3. BigML.
  4. D3. 
  5. MATLAB. 
  6. Excel.
  7. ggplot2. 
  8. Tableau.
Does data science require math?

The truth is, practical data science doesn’t require very much math at all. It requires some (which we’ll get to in a moment) but a great deal of practical data science only requires skill in using the right tools. Data science does not necessarily require you to understand the mathematical details of those tools.

Is Python hard to learn?

Python is in fact comparatively very easy to learn and build rapport with than other languages, but achieving expertise in it not a game. Python is actually know for being easy to code and fun. I have prepared some steps which you can follow to learn Python Easily and effectively.

Open source vs Closed source

Open source vs Closed source

Open source software (OSS) refers to the software which uses the code freely available on the Internet. … Closed source software (CSS) is opposite to OSS and means the software which uses the proprietary and closely guarded code. Only the original authors of software can access, copy, and alter that software.