The document provides an overview of key concepts in data science and big data including: 1) It defines data science, data scientists, and their roles in extracting insights from structured, semi-structured, and unstructured data. 2) It explains different data types like structured, semi-structured, unstructured and their characteristics from a data analytics perspective. 3) It describes the data value chain involving data acquisition, analysis, curation, storage, and usage to generate value from data. 4) It introduces concepts in big data like the 3V's of volume, velocity and variety, and technologies like Hadoop and its ecosystem that are used for distributed processing of large datasets.

1. Chapter 2

2. Data

3. ● Data science is now one of the most influential topics all around.
● Companies and enterprises are focusing a lot on gathering data
science talent further creating more viable roles in the data science
industry.
● Data science is a multi-disciplinary field that uses scientific
methods, processes, algorithms, and systems to extract knowledge
and insights from structured, semi-structured and unstructured data.
● Example: The data involved in buying a box of cereal from the store or
supermarket
Introduction

4. Data Science vs Data scientist
• Data Science defined as the extraction of actionable
knowledge directly from the data through the process
of discovery, hypothesis, and analytical hypotheses
analysis.
• It is a process of effectively producing or helping to
produce some tool, method, or other product that
derives intelligence from datasets too large.

5. Data Science vs Data scientist
• A data scientist (is a job title) is a person engaging in a
systematic activity to acquire knowledge from data.
• In a more restricted sense, a data scientist may refer to
an individual who uses the scientific method on
existing data.
• Data Scientists perform research toward a more
comprehensive understanding of products, systems, or
nature, including physical, mathematical and social
realms.

6. Role of a Data Scientist
• Advance the skills of analyzing large amounts of data,
data mining, and programming skills.
• The processed and filtered data are handed to them
which are then fed to various analytics programs and
machine learning with statistical methods to generate
data which will soon be used in predictive analysis and
other fields
• Explore for more cryptic patterns to procure proper
insights.

7. Data Science
• Scientific method requires data to begin iterating
towards a more convincing hypothesis.
• Science doesn’t exist without data.
• Data scientist
• possess a strong
• Quantitative background in statistics
• Linear algebra
• Programming knowledge with focuses on data warehousing,
mining, and modeling to build and analyze algorithms

8. Algorithms
• An algorithm is a set of instructions designed to
perform a specific task.
• This can be a simple process, such as multiplying two
numbers, or a complex operation, such as playing a
compressed video file.
• Search engines use proprietary algorithms to display
the most relevant results from their search index for
specific queries.

9. Data vs. Information
• Data
• Can be defined as a representation of facts, concepts, or instructions in a
formalized manner, which should be suitable for communication,
interpretation, or processing, by human or electronic machines.
• It can be described as unprocessed facts and figures
• It is represented with the help of characters such as alphabets (A-Z, a-z), digits
(0-9) or special characters (+, -, /, *, <,>, =, etc.
• Information
• The processed data on which decisions and actions are based
• Information is interpreted data; created from organized, structured, and
processed data in a particular context

10. Data Processing Cycle
• Data processing is the conversion of raw data to meaningful
information through a process.
• Data is manipulated to produce results that lead to a resolution of a
problem or improvement of an existing situation.
• The process includes activities like data entry/input,
calculation/process, output and storage
• Input is the task where verified data is coded or converted into
machine readable form so that it can be processed through a computer.
Data entry is done through the use of a keyboard, digitizer, scanner, or
data entry from an existing source.

11. Data Processing Cycle
• Processing is when the data is subjected to various means and
methods of manipulation, the point where a computer program is
being executed, and it contains the program code and its current
activity.
• Output and interpretation is the stage where processed information is
now transmitted to the user. Output is presented to users in various
report formats like printed report, audio, video, or on monitor.
• Storage is the last stage in the data processing cycle, where data,
instruction and information are held for future use. The importance of
this cycle is that it allows quick access and retrieval of the processed
information, allowing it to be passed on to the next stage directly,
when needed.

12. Data types
• A data type is way to tell compiler as to which data (integer, character, float,
etc.) is supposed to be stored and what amount of memory consequently to
allocate.
• A data type is way to tell the compiler that at a cell x in a memory space, a
bit value of some range y is only supposed to be stored. It restricts the
compiler to store anything else other than that value range
• Common data types include
• Integers(int)- is used to store whole numbers, mathematically known as integers
• Booleans(bool)- is used to represent restricted to one of two values: true or false
• Characters(char)- is used to store a single character
• Floating-point numbers(float)- is used to store real numbers
• Alphanumeric strings(string)- used to store a combination of characters and numbers

13. Data representation
• Types are an abstraction letting us model things in categories
and it is largely a mental construct.
• All computer represent data nothing more than a string of
ones and zeroes.
• In order for said ones and zeroes to convey any meaning, they
need to be contextualized.
• Data types provide that context.
• E.g. 01100001

14. Data types from Data Analytics perspective
• Data analytics (DA) is that the method of examining
knowledge sets to conclude the data they contain,
progressively with the help of specialized systems and
software package
• From a data analytics point of view, it is important to
understand that there are three common types of data types or
structures:
• Structured,
• Semi-structured, and
• Unstructured data types

15. Structured Data
• Structured data is data that adheres to a pre-defined data model and is
therefore straightforward to analyze.
• Structured data concerns all data which can be stored in database SQL
in table with rows and columns. They have relational key and can be
easily mapped into pre-designed fields.
• Structured data is highly organized information that uploads neatly
into a relational database
• Structured data is relatively simple to enter, store, query, and analyze,
but it must be strictly defined in terms of field name and type

16. Unstructured Data
• Unstructured data is information that either does not have a predefined
data model or is not organized in a pre-defined manner.
• Unstructured data may have its own internal structure, but does not
conform neatly into a spreadsheet or database.
• Most business interactions, in fact, are unstructured in nature.
• Today more than 80% of the data generated is unstructured.
• The fundamental challenge of unstructured data sources is that they
are difficult for nontechnical business users and data analysts alike to
unbox, understand, and prepare for analytic use.

17. Semi structured Data
• Semi-structured data is a form of structured data that does not conform
with the formal structure of data models associated with relational
databases or other forms of data tables, but nonetheless, contains tags
or other markers to separate semantic elements and enforce hierarchies
of records and fields within the data.
• Semi-structured data is information that doesn’t reside in a relational
database but that does have some organizational properties that make
it easier to analyze.
• Examples of semi-structured : CSV but XML and JSON documents
are semi structured documents, NoSQL databases are considered as
semi structured.

18. Metadata – Data about Data
• Metadata is data about data. Data that describes other data.
• It provides additional information about a specific set of data.
• Metadata summarizes basic information about data, which can make
finding and working with particular instances of data easier.
• For example, author, date created and date modified and file size are
examples of very basic document metadata.
• Having the ability to filter through that metadata makes it much easier
for someone to locate a specific document.
• In context of databases, metadata would be info on tables, views,
columns, arguments etc.

19. Data value chain
• The Data Value Chain is introduced to describe the
information flow within a big data system as a series of steps
needed to generate value and useful insights from data.
• Data acquisition, data analysis, data curation, data storage, data
usage
• Data acquisition is the process of digitizing data from the
world around us so it can be displayed, analyzed, and stored
in a computer. It is the processes for bringing data that has
been created by a source outside the organization, into the
organization, for production use.

20. Data value chain
• Data analysis is the process of evaluating data using
analytical and logical reasoning to examine each component
of the data provided. Data from various sources is gathered,
reviewed, and then analyzed to form some sort of finding or
conclusion.
• Data analytics is process of finding information from data to
make a decision and subsequently act on it.

21. Data value chain
• Data curation is about managing data throughout its lifecycle.
Collecting, organizing, cleaning and much more are included in data
curation. Data curators manage the data through various stages and
make the data usable for data analysts and scientists.
• Data storage is defined as a way of keeping information in the
memory storage for use by a computer. An example of data storage is
a folder for storing Microsoft Word documents.
• Data usage is the amount of data (things like images, movies, photos,
videos, and other files) that you send, receive, download and/or
upload.

23. Big Data Definition
• No single standard definition…
“Big Data” is data whose scale, diversity, and complexity require new
architecture, techniques, algorithms, and analytics to manage it and
extract value and hidden knowledge from it…

24. Big data
• Big data is the term for a collection of data sets so large and complex
that it becomes difficult to process using on-hand database
management tools or traditional data processing applications.
• In other words, data that is the range of 100s of TBs or PB comes into
Big Data.
• But it doesn't mean the amount of data, the thing matters is what
organization do with data.
• Big Data is analyzed for insights that lead to better decisions.

26. Big data
• Big Data is associated with the concept of 3 V that is
volume, velocity, and variety. Big data is
characterized by 3V and more:
• Volume: large amounts of data Zeta bytes/Massive
datasets
• Velocity: Data is live streaming or in motion
• Variety: data comes in many different forms from diverse
sources
• Veracity: can we trust the data? How accurate is it? etc.

27. Clustered Computing
• Cluster Computing addresses the latest results in these fields that
support High Performance Distributed Computing .
• The Clustering methods have identified as- HPC IAAS, HPC PAAS,
that are more expensive and difficult to setup and maintain than a
single computer.
• In HPDC environments, parallel and/or distributed computing
techniques are applied to the solution of computationally intensive
applications across networks of computers.

28. Clustered Computing
• “Computer cluster” basically refers to a set of connected
computer working together.
• The cluster represents one system and the objective is to
improve performance.
• The computers are generally connected in a LAN (Local Area
Network).
• So, when this cluster of computers works to perform some
tasks and gives an impression of only a single entity, it is
called “cluster computing”.

29. Clustered Computing
• Big data clustering software combines the resources of many
smaller machines, seeking to provide a number of benefits:
• Resource Pooling:
• Combining the available storage space to hold data is a clear benefit, but
CPU and memory pooling are also extremely important. Processing large
datasets requires large amounts of all three of these resources.
• Object Pooling is a way which enable storing of group of object(called
pool storage) in memory.
• Whenever new object is needs to be created, it is first checked in pool
storage and if available it is reused and like this it provide reusability of
object and system resources, improves the scalability of program.

30. Clustered Computing
• High Availability: In computing, the term availability is used to describe
the period of time when a service is available, as well as the time required
by a system to respond to a request made by a user. High availability is a
quality of a system or component that assures a high level of operational
performance for a given period of time.
• Clusters can provide varying levels of fault tolerance and availability
guarantees to prevent hardware or software failures from affecting access to
data and processing. This becomes increasingly important as we continue to
emphasize the importance of real-time analytics.
• Easy Scalability: Clusters make it easy to scale horizontally by adding
additional machines to the group. This means the system can react to
changes in resource requirements without expanding the physical resources
on a machine.

31. Hadoop and its Ecosystem
• Hadoop is an open-source framework intended to make interaction with big
data easier. It is a framework that allows for the distributed processing of
large datasets across clusters of computers using simple programming
models.
• The four key characteristics of Hadoop are:
• Economical: Its systems are highly economical as ordinary computers can be used
for data processing.
• Reliable: It is reliable as it stores copies of the data on different machines and is
resistant to hardware failure.
• Scalable: It is easily scalable both, horizontally and vertically. A few extra nodes help
in scaling up the framework.
• Flexible: It is flexible and you can store as much structured and unstructured data as
you need to and decide to use them later.

32. ● Hadoop has an ecosystem that has evolved from its four core components: data management, access,
processing, and storage.
● It is continuously growing to meet the needs of Big Data.
● It comprises the following components and many others:
○ HDFS: Hadoop Distributed File System
○ YARN: Yet Another Resource Negotiator
○ MapReduce: Programming based Data Processing
○ Spark: In-Memory data processing
Hadoop and its Ecosystem
○ PIG, HIVE: Query-based processing of
data services
○ HBase: NoSQL Database
○ Mahout, Spark MLLib: Machine Learning
algorithm libraries
○ Solar, Lucene: Searching and Indexing
○ Zookeeper: Managing cluster
○ Oozie: Job Scheduling

33. Big Data Life Cycle

34. End of chapter 2
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