What are different types of analytics? Describe different types of analytics.

Analytics: 

The term analytics is used to characterize a vast array of methods that use data to help make better business decisions. And there are many ways to organize them into subcategories. The simplest way is to distinguish analytics into three large classes

• Descriptive Analytics
• Predictive Analytics, and 
• Prescriptive Analytics

Descriptive Analytics: The most basic form of analytics are descriptive analytics. The simplest way to define descriptive analytics is that it answers the question what has happened. Descriptive analytics typically condenses large amounts of historical or real-time data into smaller, more meaningful nuggets of information.

For example, in an internet marketing context, descriptive analytics could be used to summarize a large number of search, display and social media advertising campaigns into a smaller set of metrics that shows the average click-through rate, conversion rate, and the return on investment of each of these three advertising channels.

The main objective of descriptive analytics is to find out the reasons behind success or failure in the past. The vast majority of big data analytics used by organizations falls into the category of descriptive analytics.

Predictive Analytics: The next class of analytics, predictive analytics, uses data from the past to predict what will happen in the future.

For example, suppose we would like to predict the likelihood that a new prospective customer will respond to a promotional email campaign. By analyzing past data that includes situations where prospects responded and did not respond to similar campaigns in the past, analytics can help identify what distinguishes those prospects who responded from those who did not. On the basis of this data, a model can be built that can help assess the probability that the new prospect will respond to a future campaign. 

As we can see, predictive analytics is based on a solid understanding of what happened in the past. So most organizations are deploying them after they have mastered the art and science of descriptive analytics.

Prescriptive Analytics: Armed with models of the past and forecasts of the future, organizations can then venture to the more advanced level of analytics, prescriptive analytics.

This class of method is using optimization algorithms to determine the set of actions that optimize a desirable objective, given predictions of what is likely to happen. 

Referring to the previous example, assuming that we have built predictive models for a number of different campaigns, a prescriptive analytics model could be used to determine which campaign should be sent to which prospects in order to maximize our expected sales and stay within our marketing budget.

As organizations gain experience and skills with data-driven business decision-making, they typically progress from descriptive, to predictive, and finally to using prescriptive analytics to inform decisions and actions in a growing number of functions.

Roles in Business Intelligence and Data Analytics Team

Most data science projects, especially the ones of sufficient scale and complexity, are carried out by teams that consists of roles of different skill sets. Different organizations give different names to these roles. However, in most cases, these roles boil down to three broad categories that broadly correspond
to the steps of the analytics workflow.

Data engineers: are primarily responsible for the collection, transformation, and organization of data that is required for an analytics project.

Their skill set grows upon computer science and software engineering, and includes programming, data modeling, and database skills.

Data scientists: also known as statisticians, are responsible for developing and executing the analytical models that process data to derive the desired insights.

Such individuals are well-versed in statistical analysis, and increasingly, in the newer spectrum of powerful analytics techniques, such as machine learning, natural language processing, and social network analysis.

Data translators: are the interface between the analytics team and the rest of the business.

They understand the business side very well and have a broad, though not necessarily very deep, understanding of the technical steps of the analytics process.

They are responsible for defining the business questions that motivate the project, helping translate these into data questions, and developing a strategy for obtaining the right data.

At the other end, they are responsible for translating the results of the analysis into business insights and helping the business stakeholders use these insights to make decisions.

Manager/Leader: Analytics teams usually also include the manager role, who oversees the entire process and manages the relationships among the team members, as well as with the business stakeholders.

Data analysts: are typically more junior employees who help people from across an organization
answer relatively straightforward business questions by creating databases and data warehouses, and using business intelligence and data visualization software to construct charts and reports.

 

What is project management? What are main characteristics of a project? What are the steps of project management life cycle?

 

What is project management: When people think about project management, they often have an image of large construction projects, buildings, and machinery worth millions of dollars. These type of projects represent project management at a very high level. But in reality, project management can be used in almost any setting.
 

Everyone, from teachers, students, wedding planners, through to office workers and do it your selfers
can employ the core skills of project management to help their day to day work run more smoothly,
and ensure a greater chance of success. Almost every job requires a level of planning, understanding of what and who is involved, how long it should take, and what it's going to cost.

Carefully managing each of these aspects and understanding what success looks like is exactly what project management is all about. This course has been designed to break the stereotypical boundaries of project management, to walk you through the core project management activities, and give you the tools and confidence needed to manage, lead, and communicate effectively throughout the project life cycle.

Whether you are involved in construction, information technology, corporate, or small business, or simply just running your own home project, a sound knowledge of essential project management skills
will ensure the result is far more successful, and reduce surprises along the way.

 

Project management transcends all industries. Projects can be large or small. They can be fairly simple and straightforward, or quite complex and time consuming.  

 

Characteristics of a project: Generally, there are four main characteristics of a project, regardless of its size and complexity.

1. Start and End Date: The first characteristic is that a project has a definite start and end date. It is a temporary undertaking within a fixed period of time, whether this is one week or six years. A project manager has to complete the project within the specified amount of time.

2. Goal or Outcome: The second key characteristic of a project is that it achieves a goal or an outcome. In other words, something is completed or achieved by the end of the project life cycle. In our examples, we saw that the end points were the wedding, the workplace system, and the tiling of a kitchen.

3. Benefit or Value: The third characteristic to consider is that a project provides benefit or value to the recipient. In other words, what will the recipient gain from the completed project? 

4. Allocation of Resources: Finally, a project requires an allocation of resources that need to be skillfully used. These resources will vary depending on the size and complexity of the project. And all projects will, in one way or another, allocate and use resources to achieve their goals or outcomes.
 

Project Management Life Cycle: Project management follows a distinct linear process or journey, which is known as the project management life cycle. The life cycle has four phases.
 

Initiation: The life cycle begins with initiation, which is the starting point of any project. It is usually the shortest phase but the most important, because it sets up the foundation of the project. It is in this phase that you flesh out the project objectives, success criteria, and high-level plan. Is also in this phase that you identify risks, stakeholders, and your team.

Planning: The second phase of the life cycle is planning. Planning is the primary function of any project manager and requires you to undertake a rigorous process of developing plans to ensure you achieve your project objectives. The area that you will focus on in this phase of the life cycle are scope, scheduling, and costing.

Execution: Phase three is project execution. This phase is about completing specific deliverables that are required to meet the scope and objectives of the project. In this phase, assessing risk and implementing strategies to reduce or mitigate risk is paramount. This phase also focuses on being an effective leader for your team and engaging your stakeholders in the execution of the project.

Closure: The final phase of the project management life cycle is the closure of the project. It is in this phase that the outcomes are achieved and the benefits of the completed project are experienced by the stakeholders. The closure of a project requires you to obtain feedback from your stakeholders and your team.
 

 

How to write dynamic pivot query in SQL Server

This video show how to write dynamic pivot query in sql server. Dynamic pivot query is useful when you want to display data in columns instead of rows where you are not confirm you much row will be converted to columns.


Pivot query in sql is used to convert rows to columns where generally you need to explicitly declare which row value will be converted to which column which is possible when you know all the values of the row
values to pivot. When you have a variable number of pivot values then dynamic pivot query might be very helpful.