Friday, June 29, 2018

Cloud Computing Management System

It is the responsibility of cloud provider to manage resources and their performance. Management of resources includes several aspects of cloud computing such as load balancing, performance, storage, backups, capacity, deployment, etc. The management is essential to access full functionality of resources in the cloud.

Cloud Management Tasks
The cloud provider performs a number of tasks to ensure efficient use of cloud resources. Here, we will discuss some of them:

Cloud Management Tasks
Audit System Backups
It is required to audit the backups timely to ensure restoring of randomly selected files of different users. Backups can be performed in following ways:

Backing up files by the company, from on-site computers to the disks that reside within the cloud.

Backing up files by the cloud provider.

It is necessary to know if cloud provider has encrypted the data, who has access to that data and if the backup is taken at different locations then the user must know the details of those locations.

Data Flow of the System
The managers are responsible to develop a diagram describing a detailed process flow. This process flow describes the movement of data belonging to an organization throughout the cloud solution.

Vendor Lock-In Awareness and Solutions
The managers must know the procedure to exit from services of a particular cloud provider. The procedures must be defined to enable the cloud managers to export data of an organization from their system to another cloud provider.

Knowing Provider’s Security Procedures
The managers should know the security plans of the provider for the following services:

Multitenant use
E-commerce processing
Employee screening
Encryption policy
Monitoring Capacity Planning and Scaling Capabilities
The managers must know the capacity planning in order to ensure whether the cloud provider is meeting the future capacity requirement for his business or not.

The managers must manage the scaling capabilities in order to ensure services can be scaled up or down as per the user need.

Monitor Audit Log Use
In order to identify errors in the system, managers must audit the logs on a regular basis.

Solution Testing and Validation
When the cloud provider offers a solution, it is essential to test it in order to ensure that it gives the correct result and it is error-free. This is necessary for a system to be robust and reliable.

Introduction To Python Data Science

Data science is the process of deriving knowledge and insights from a huge and diverse set of data through organizing, processing and analysing the data. It involves many different disciplines like mathematical and statistical modelling, extracting data from it source and applying data visualization techniques. Often it also involves handling big data technologies to gather both structured and unstructured data. Below we will see some example scenarios where Data science is used.

Recommendation systems
As online shopping becomes more prevalent, the e-commerce platforms are able to capture users shopping preferences as well as the performance of various products in the market. This leads to creation of recommendation systems which create models predicting the shoppers needs and show the products the shopper is most likely to buy.

Financial Risk management
The financial risk involving loans and credits are better analysed by using the customers past spend habits, past defaults, other financial commitments and many socio-economic indicators. These data is gathered from various sources in different formats. Organising them together and getting insight into customers profile needs the help of Data science. The outcome is minimizing loss for the financial organization by avoiding bad debt.

Improvement in Health Care services
The health care industry deals with a variety of data which can be classified into technical data, financial data, patient information, drug information and legal rules. All this data need to be analysed in a coordinated manner to produce insights that will save cost both for the health care provider and care receiver while remaining legally compliant.

Computer Vision
The advancement in recognizing an image by a computer involves processing large sets of image data from multiple objects of same category. For example, Face recognition. These data sets are modelled, and algorithms are created to apply the model to newer images to get a satisfactory result. Processing of these huge data sets and creation of models need various tools used in Data science.

Efficient Management of Energy
As the demand for energy consumption soars, the energy producing companies need to manage the various phases of the energy production and distribution more efficiently. This involves optimizing the production methods, the storage and distribution mechanisms as well as studying the customers consumption patterns. Linking the data from all these sources and deriving insight seems a daunting task. This is made easier by using the tools of data science.

Python in Data Science
The programming requirements of data science demands a very versatile yet flexible language which is simple to write the code but can handle highly complex mathematical processing. Python is most suited for such requirements as it has already established itself both as a language for general computing as well as scientific computing. More over it is being continuously upgraded in form of new addition to its plethora of libraries aimed at different programming requirements. Below we will discuss such features of python which makes it the preferred language for data science.

A simple and easy to learn language which achieves result in fewer lines of code than other similar languages like R. Its simplicity also makes it robust to handle complex scenarios with minimal code and much less confusion on the general flow of the program.
It is cross platform, so the same code works in multiple environments without needing any change. That makes it perfect to be used in a multi-environment setup easily.
It executes faster than other similar languages used for data analysis like R and MATLAB.
Its excellent memory management capability, especially garbage collection makes it versatile in gracefully managing very large volume of data transformation, slicing, dicing and visualization.
Most importantly Python has got a very large collection of libraries which serve as special purpose analysis tools. For example – the NumPy package deals with scientific computing and its array needs much less memory than the conventional python list for managing numeric data. And the number of such packages is continuously growing.
Python has packages which can directly use the code from other languages like Java or C. This helps in optimizing the code performance by using existing code of other languages, whenever it gives a better result.
In the subsequent chapters we will see how we can leverage these features of python to accomplish all the tasks needed in the different areas of Data Science.

International Business management Country Attractiveness

The International business environment includes various factors like social, political, regulatory, cultural, legal and technological factors that surround a business entity in various sovereign nations. There are exogenous factors relative to the home environment of the organization in the international environment. These factors influence the decision-making process on the use of resources and capabilities. They also make a nation either more or less attractive to an international business firm.

We will take up the most important factors and see how they affect the operational process of a business.

Adapting to Changing Needs
Firms do not have any control over the external business environment. Therefore, the success of an international company depends upon its ability to adapt to the overall environment.

Its success also depends on the ability to adjust and manage the company’s internal variables to leverage on the opportunities of the external environment. Moreover, the company’s capability to control various threats produced by the same environment, also determines its success.

A term called ‘country attractiveness’ is often discussed in the international business fraternity. It is important to consider attractiveness before we move on to discuss environmental factors.

Changing Needs
Country Attractiveness
Country attractiveness is a measure of a country’s attractiveness to the international investors. In international business, investment in foreign countries is the most important aspect and hence firms want to determine how suitable a country is in terms of its external business environments.

International business firms judge the risks and profitability of doing business in a particular country before investing and starting a business there. This judgment includes studying the environmental factors to arrive at a decision.

It is pretty clear that businesses prefer a country that is less costly, more profitable, and has fewer risks. Cost considerations are related with investment. Profitability is dependent on resources. Risks are associated with the environment and hence it is of prime concern.

Risks may be of various types. However, the general consensus is that a country that is more stable in terms of political, social, legal, and economic conditions is more attractive for starting a business.

Business Environments
There are numerous types of business environments, however the political, the cultural, and the economic environments are the prime ones. These factors influence the decision-making process of an international business firm. It is important to note that the types of environments we discuss here are interlinked; meaning one’s state affects the others in varying dimensions.

The Political Factors
The political environment of a nation affects the legal aspects and government rules which a foreign firm has to experience and follow while doing business in that nation. There are definite legal rules and governance terms in every country in the world. A foreign company that operates within a particular country has to abide by the country’s laws for the duration it operates there.

Political environment can affect other environmental factors −

Political decisions regarding economy can affect economic environment.
Political decisions may affect the socio-cultural environment of a nation.
Politicians may affect the rate of emergence of new technologies.
Politicians can exert influence in the acceptance of emerging technologies.
There are four major effects of political environment on business organizations −

Impact on Economy − The political conditions of a nation have a bearing on its economic status. For example, Democratic and Republican policies in the US are different and it influences various norms, such as taxes and government spending.

Changes in Regulation − Governments often alter their decisions related to business control. For example, accounting scandals in the beginning of the 21st century prompted the US SEC turn more mindful on the issues of corporate compliance. Sarbanes-Oxley compliance regulations (2002) were social reactions. The social environment demanded the public companies to be more responsible.

Political Stability − Political stability effects business operations of international companies. An aggressive takeover overthrowing the government could lead to a disordered environment, disrupting business operations. For example, Sri Lanka’s civil war and Egypt and Syria disturbances were overwhelming for businesses operating there.

Mitigation of Risk − There are political risk insurance policies that can mitigate risk. Companies with international operations leverage such insurances to reduce their risk exposure.

Note − You can check The Index of Economic Freedom. It ranks and compares the countries depending on how politics impacts business-decisions in those locations.

The Economic Factors
Economic factors exert a huge impact on international business firms. The economic environment includes the factors that influence a country's attractiveness for international business firms.

Business firms seek predictable, risk-free, and stable mechanisms. Monetary systems that acknowledge the relative dependence of countries and their economies are good for a firm. If an economy fosters growth, stability, and fairness for prosperity, it has a positive effect on the growth of companies.

Inflation contributes hugely to a country's attractiveness. High rate of inflation increases the cost of borrowing and makes the revenue contract in domestic currency. It exposes the international firms to foreign-exchange risks.

Absolute purchasing power parity is also an important consideration. The ratio of exchange rate between two particular countries is identical to the ratio of the price levels. The law of one price states that the real price of a product is same across all nations.

Relative purchasing power parity (PPP) is valuable for foreign firms. It asks how much money is needed to buy the same goods and services in two particular countries. PPP rates prompt international comparisons of income.

The Cultural Factors
Cultural environments include educational, religious, family, and social systems within the marketing system. Knowledge of foreign culture is important for international firms. Marketers who ignore cultural differences risk failure.

Language − There are nearly 3,000 languages in the world. Language differences are important in designing advertising campaigns and product labels. If a country has several languages, it may be problematic.

Colors − It is important to know how people associate with colors. For example, purple is unacceptable in Hispanic nations because it is associated with death.

Customs and Taboos − It is important for marketers to know the customs and taboos to learn what is acceptable and what is not for the marketing programs.

Values − Values stem from moral or religious beliefs and are acquired through experiences. For example, in India, the Hindus don’t consume beef, and fast-food restaurants such as McDonald's and Burger King need to modify the offerings.

Aesthetics − There are differences in aesthetics in different cultures. Americans like suntans, the Japanese do not.

Time − Punctuality and deadlines are routine business practices in the U.S. However, Middle East and Latin American people are far less bound by time constraints.

Religious Beliefs − Religion can affect a product’s labelling, designs, and items purchased. It also affects the consumers' values.

Cultural Differences

Ireland’s evening meal is called tea, not dinner.

If you nod in Bulgaria, it means "no" and moving the head from one side to the other means "yes".

Pepsodent toothpaste did not sell well in Southeast Asia, as it promised white teeth. Black or yellow teeth are symbols of prestige there.

Introduction to Data Structures In Python Programming language

Pandas deals with the following three data structures −

Series
DataFrame
Panel
These data structures are built on top of Numpy array, which means they are fast.

Dimension & Description
The best way to think of these data structures is that the higher dimensional data structure is a container of its lower dimensional data structure. For example, DataFrame is a container of Series, Panel is a container of DataFrame.

Data Structure Dimensions Description
Series 1 1D labeled homogeneous array, sizeimmutable.
Data Frames 2 General 2D labeled, size-mutable tabular structure with potentially heterogeneously typed columns.
Panel 3 General 3D labeled, size-mutable array.
Building and handling two or more dimensional arrays is a tedious task, burden is placed on the user to consider the orientation of the data set when writing functions. But using Pandas data structures, the mental effort of the user is reduced.

For example, with tabular data (DataFrame) it is more semantically helpful to think of the index (the rows) and the columns rather than axis 0 and axis 1.

Mutability
All Pandas data structures are value mutable (can be changed) and except Series all are size mutable. Series is size immutable.

Note − DataFrame is widely used and one of the most important data structures. Panel is used much less.

Series
Series is a one-dimensional array like structure with homogeneous data. For example, the following series is a collection of integers 10, 23, 56, …

10 23 56 17 52 61 73 90 26 72
Key Points
Homogeneous data
Size Immutable
Values of Data Mutable
DataFrame
DataFrame is a two-dimensional array with heterogeneous data. For example,

Name Age Gender Rating
Steve 32 Male 3.45
Lia 28 Female 4.6
Vin 45 Male 3.9
Katie 38 Female 2.78
The table represents the data of a sales team of an organization with their overall performance rating. The data is represented in rows and columns. Each column represents an attribute and each row represents a person.

Data Type of Columns
The data types of the four columns are as follows −

Column Type
Name String
Age         Integer
Gender String
Rating Float
Key Points
Heterogeneous data
Size Mutable
Data Mutable
Panel
Panel is a three-dimensional data structure with heterogeneous data. It is hard to represent the panel in graphical representation. But a panel can be illustrated as a container of DataFrame.

Key Points
Heterogeneous data
Size Mutable
Data Mutable