Gerenral insurance Accounts IT and Investment

Accounts, IT and Investment
Durbadal Mukherjee
02-Dec-2023

Accounts, IT and Investment
Bifurcation observed in questions:
1. Investment : 10 to 12 questions
2. IT : 4 to 6 questions
3. ALSM : 2 to 3 questions
4. Accounting Ratios : 3 to 4 questions
5. Preperation of Financial Accounts: 8 to 10 questions

Information Technology
1. Storage Units – bit, byte, kilobyte, megabyte,
gigabyte, petabyte....
2. Bandwidth units – bps, Bps, KBps, MBps,
GBps,....
3. Network Types – LAN, MAN, WAN, ....
4. Network Topologies - Point to Point Topology ,
Mesh Topology , Star Topology , Bus Topology ,
Ring Topology , Tree Topology , Hybrid Topology

Some common protocals:
Address Resolution Protocol (ARP)
Border Gateway Protocol (BGP)
Domain name system (DNS)
Dynamic Host Configuration Protocol (DHCP)
File Transfer Protocol (FTP)
Hypertext Transfer Protocol (HTTP)
Internet Protocol (IP)
Open Shortest Path First (OSPF)
Simple Mail Transfer Protocol (SMTP)
Telnet
Transmission Control Protocol (TCP)
User Datagram Protocol (UDP)

List of Major Programming Langiages:
Java, Python, JavaScript
Ruby, C++, SQL, Perl
Swift, Scala, Rust
Kotlin, TypeScript, Objective-C
C, Fortran, COBOL
Ada, Haskell, Dart
MATLAB, C#, Go, Lua, PHP

What is a database?
Every organization has information that it must store and manage to meet its
requirements. For example, a corporation must collect and maintain human
resources records for its employees. This information must be available to
those who need it.
An information system is a formal system for storing and processing
information. An information system could be a set of cardboard boxes
containing manila folders along with rules for how to store and retrieve the
folders. However, most companies today use a database to automate their
information systems. A database is an organized collection of information
treated as a unit. The purpose of a database is to collect, store, and
retrieve related information for use by database applications.

What is a Spread-Mart?
Left to their own devices, business users will fend for themselves. More times
than not, we see a chasm between data and information; a chasm filled by
books and books full of spreadsheets. On their own, spreadsheets are not
the issue. There is simply to too much reliance on spreadsheets as a form
of Swiss army knife.
Though it may work in the short-term, calling this approach a “process” seems
to be a stretch, at best. Spreadsheets are fantastic personal productivity
tools; unfortunately, everyone tends to overuse them.
More to the point, the spreadsheets are not really being used properly. Time
and time again, analysts and business users create massive workbooks,
filled with dozens - if not hundreds - of sheets turning them into “reporting
applications”. So a spread-mart is really a data mart built using a series of
spreadsheet workbooks.

What is a Data Mart?
A data mart serves the same role as a data warehouse, but it is intentionally
limited in scope. It may serve one particular department or line of business.
The advantage of a data mart versus a data warehouse is that it can be
created much faster due to its limited coverage. However, data marts also
create problems with inconsistency.
It takes tight discipline to keep data and calculation definitions consistent
across data marts. This problem has been widely recognized, so data
marts exist in two styles. Independent data marts are those which are fed
directly from source data. They can turn into islands of inconsistent
information. Dependent data marts are fed from an existing data
warehouse. Dependent data marts can avoid the problems of
inconsistency, but they require that an enterprise-level data warehouse
already exist.
Data marts can be physically instantiated or implemented purely logically
though views. Furthermore, data marts can be co-located with the
enterprise data warehouse or built as separate systems.

What is an Operational Data Store?
Operational data stores exist to support daily operations. The ODS data is
cleaned and validated, but it is not historically deep: it may be just the data
for the current day. Rather than support the historically rich queries that a
data warehouse can handle, the ODS gives data warehouses a place to
get access to the most current data, which has not yet been loaded into the
data warehouse.
The ODS may also be used as a source to load the data warehouse. As data
warehousing loading techniques have become more advanced, data
warehouses may have less need for ODS as a source for loading data.
Instead, constant trickle-feed systems can load the data warehouse in near
real time.

What is a Data Warehouse?
A data warehouse is a database designed to enable business intelligence
activities: it exists to help users understand and enhance their
organization's performance. It is designed for query and analysis rather
than for transaction processing, and usually contains historical data derived
from transaction data, but can include data from other sources. Data
warehouses separate analysis workload from transaction workload and
enable an organization to consolidate data from several sources.
This helps in:
Maintaining historical records
Analyzing the data to gain a better understanding of the business and to
improve the business

What is the difference between a Data Warehouse vs. OLTP System?
Data warehouses are distinct from online transaction processing (OLTP)
systems. With a data warehouse you separate analysis workload from
transaction workload. Thus data warehouses are very much read-oriented
systems. They have a far higher amount of data reading versus writing and
updating.
This enables far better analytical performance and avoids impacting your
transaction systems. A data warehouse system can be optimized to
consolidate data from many sources to achieve a key goal: it becomes your
organization's "single source of truth".
There is great value in having a consistent source of data that all users can
look to; it prevents many disputes and enhances decision-making
efficiency.

What is a Data Discovery Lab?
The data discovery lab is a separate environment built to allow your analysts
and data scientists to figure out the value hidden in your data. The data lab
helps you find the right questions to ask and, of course, put those answers
to work for your business. It also referred to as a “sandbox”.
The lab is not the end result. Rather, it’s a way to generate new insights that
can be put to productive use. It’s important to figure out upfront how you’re
going to turn insight into value. And if you’re starting a data lab project for
the first time, you want that value to be visible quickly to maintain or gain
organizational support for the work

What is Big Data?
Put simply, big data is larger, more complex data sets, especially from new
data sources. These data sets are so voluminous that traditional data
processing software just can’t manage them. But these massive volumes of
data can be used to address business problems you wouldn’t have been
able to tackle before.

What is a Data Lake?
A data lake is a place to store your structured and unstructured data, as well
as a method for organizing large volumes of highly diverse data from
diverse sources. Watch this video to go a bit deeper.
Data lakes are becoming increasingly important as people, especially in
business and technology, want to perform broad data exploration and
discovery. Bringing data together into a single place or most of it in a single
place can be useful for that.
The key difference between a data lake and a data warehouse is that the data
lake tends to ingest data very quickly and prepare it later on the fly as
people access it. With a data warehouse, on the other hand, you prepare
the data very carefully upfront before you ever let it in the data warehouse.

What Is Artificial Intelligence?
Artificial intelligence as an academic discipline was founded in 1956. The goal
then, as now, was to get computers to perform tasks regarded as uniquely
human: things that required intelligence. Initially, researchers worked on
problems like playing checkers and solving logic problems.
Artificial intelligence, then, refers to the output of a computer. The computer is
doing something intelligent, so it’s exhibiting intelligence that is artificial.

What is Machine Learning?
Machine learning is the subset of artificial intelligence (AI) that focuses on
building systems that learn—or improve performance—based on the data
they consume.
Artificial intelligence is a broad term that refers to systems or machines that
mimic human intelligence. Machine learning and AI are often discussed
together, and the terms are sometimes used interchangeably, but they don’t
mean the same thing. An important distinction is that although all machine
learning is AI, not all AI is machine learning.
Today, machine learning is at work all around us. When we interact with
banks, shop online, or use social media, machine learning algorithms come
into play to make our experience efficient, smooth, and secure. Machine
learning and the technology around it are developing rapidly, and we're just
beginning to scratch the surface of its capabilities.

What is Deep Learning?
Put simply, deep learning is all about using neural networks with more
neurons, layers, and interconnectivity. We’re still a long way off from
mimicking the human brain in all its complexity, but we’re moving in that
direction. And when you read about advances in computing from
autonomous cars to Go-playing supercomputers to speech recognition,
that’s deep learning under the covers.
You experience some form of artificial intelligence. Behind the scenes, that AI
is powered by some form of deep learning.

What is a Subject Area?
A subject area is a single-topic-centric slice through an entire data warehouse
data model. A data mart or departmental mart is typically used to analyze a
single subject area such as finance, or sales, or HR. Within a database a
subject area groups all tables together that cover a specific (logical)
concept, business process or question. A data warehouse and enterprise
data warehouse will typically contain multiple subject areas, creating what
is sometimes referred to as a 360-degree view of the business.

What is a Schema?
A schema is a collection of database objects, including tables, views, indexes,
and synonyms. You can arrange schema objects in the schema models
designed for data warehousing in a variety of ways.
The model of your source data and the requirements of your users help you
design the data warehouse schema. You can sometimes get the source
model from your company's enterprise data model and reverse-engineer
the logical data model for the data warehouse from this. The physical
implementation of the logical data warehouse model may require some
changes to adapt it to your system parameters—size of computer, number
of users, storage capacity, type of network, and software.

What is a Star Schema?
Star schemas are often found in data warehousing systems with embedded
logical or physical data marts. The term star schema is another way of
referring to a "dimensional modeling" approach to defining your data model.
Most descriptions of dimensional modeling use terminology drawn from the
work of Ralph Kimball, the pioneering consultant and writer in this field.
Dimensional modeling creates multiple star schemas, each based on a
business process such as sales tracking or shipments.
Each star schema can be considered a data mart, and perhaps as few as 20
data marts can cover the business intelligence needs of an enterprise.

What is a Snowflake Schema?
The snowflake schema is a more complex data warehouse model than a star
schema, and is a type of star schema. It is called a snowflake schema
because the diagram of the schema resembles a snowflake. Snowflake
schemas normalize dimensions to eliminate redundancy. That is, the
dimension data has been grouped into multiple tables instead of one large
table.

What is a Dimension Table
Dimension tables provide category data to give context to the fact data. For
instance, a star schema for sales data will have dimension tables for
product, date, sales location, promotion and more. Dimension tables act as
lookup or reference tables because their information lets you choose the
values used to constrain your queries.
The values in many dimension tables may change infrequently. As an
example, a dimension of geographies showing cities may be fairly static.
But when dimension values do change, it is vital to update them fast and
reliably. Of course, there are situations where data warehouse dimension
values change frequently. The customer dimension for an enterprise will
certainly be subject to a frequent stream of updates and deletions.

What is a Fact Table
Fact tables have measurement data. They have many rows but typically not
many columns. Fact tables for a large enterprise can easily hold billions of
rows. For many star schemas, the fact table will represent well over 90
percent of the total storage space. A fact table has a composite key made
up of the primary keys of the dimension tables of the schema.
A fact table contains either detail-level facts or facts that have been
aggregated. Fact tables that contain aggregated facts are often called
summary tables. A fact table usually contains facts with the same level of
aggregation. Though most facts are additive, they can also be semi-
additive or non-additive. Additive facts can be aggregated by simple
arithmetical addition. A common example of this is sales. Non-additive facts
cannot be added at all

Gerenral insurance Accounts IT and Investment

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