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Hi all,

Today I will discuss about three basics and very core concepts of BPC, and then will share some definition and step by step process to create all these three objects, this document is for those who are beginner in BPC.

* Environment
* Dimension
* Model

Environment

* Environment can be defined as the base components which doesn’t contain physical data. It can be considered as INFOAREA from BW perspective, which acts as a folder.
* Environment can’t be created by our own, we have to take copy from existing environment only.
* SAP provides one standard environment which is called as “ENVIRONMENTSHELL”.
* As an example, we can consider MouriTech as one environment

NOTE: – It is highly recommended not to do modification and changes directly to “ENVIRONMENTSHELL”.

Steps to create Environment in BPC.

* Logon to BPC with username and password.

 

2) In Library folder, go to bottom(Right) of the page in which existing name of the Environment is displayed which is always delivered by SAP, like ENVIRONMENT: ENVIRONMET_COMPANYNAME, or in most of case termed as ENVIRONMENTSHELL

3) Single click on Environment will bring one popup screen for you, then go to manage all environment, here we can get list of Environment which is delivered by SAP along with the list of Environment which is being copied by user.

4)To create copy from standard Environment, simply click on the SAP delivered Environment, when you will click on the Environment, “COPY” icon will get enable, click on “COPY”, a popup will appear which ask you for ENIRONMENT ID: and description.

Provide check marks to copy a)Data in Models b) Web Documents c) Document to the newly created Environment as per your requirements.

NOTE: Copying Environment will take some time to copy.

MODELS

Models :- A model is built inside an Environment , It is collection of several dimensions and are designed to meet the planning and consolidation requirements of an organization, For instance in a company, we might have model for “ANALYTICS” , “TECHNICAL”, “HR” etc. It can be compared with InfoCube in BW terminology.

* Model will be used to store transaction data.
* There are two types of models
* Standard Model

2)      Embedded Model

STANDARD MODEL : This model is designed by SAP to provide a protected area for customers who to plan and consolidate in their own department without wanting to share or coordinate the resources with other Business Warehouse community users.

It has protected namespace starting with  “/CPMB/”

EMBEDDED MODEL : This model is different from Standard model and includes data access to all community users. In short, you can say that in embedded model, any number of InfoProviders can be assigned to a single BPC model/application.

 

Steps to create Standard Model

1)      After login into BPC application

2) Go to Administration page.

3) Click on Models and you will get list of all standard and Embedded models which is being already created, to create a new model click on new.

4) Provide name and description of the model click on next.

5) Under source model, click on blank model , since we are creating this for first time and do not want to copy from the existing models then click on next.

 

6)Now choose standard radio button from the list, in data entry mode we have default entry as Periodic for standard models. Click on next.

7) In this step, choose all mandatory dimensions which are A,C,E,T along with dimensions you wish to add in your model, click on next

8) Now in copy from you will find blank, since we started from blank model, click on next.

9)      In the final step review and create, we can see name of the model with their dimensions, click on create, you will get a message “Model has been created. You can close the window”

10) You can see your model created in the model list.

DIMENSIONS: It is used to store master data.

In BW language it can be compared with Infoobject.

SAP has given ten dimension type (9- standard,1-user defined)

Steps to create Dimension

1) After login into BPC application

2)Go to Administration page.

3) Click on dimensions and you will get list of all dimensions which is being already created, to create a new dimension click on new.

4)Provide your Dimension ID and description

5) Once created, it will show structure of dimension which is automatically created by system with some specific properties and can’t be changed.

The system generated properties will be highlighted in light blue list of rows.

6) Here you can add new dimensions and also can create hierarchies for the existing dimensions, in this case I have created one hierarchies with dimension  GL_ACCOUNT

7) You can give referential integrity if you want to give reference with the existing dimension, in my case I have chosen none.

8) Click on save to create your dimension

9) Here in dimension list, you can see your dimension created

To understand relation between Environment, models and dimensions in simple language, see below diagram.

 

NOTE- one dimension can be reused in multiple models but all these models belongs to same environment only.

  http://bit.ly/2FzyfMp #SAP #SAPCloud #AI

Machine Learning is guiding Artificial Intelligence capabilities.

Image Classification, Recommendation Systems, and AI in Gaming, are popular uses of Machine Learning capabilities in our everyday lives. If we breakdown machine learning further, we find that these 3 Machine Learning examples are powered by different types of machine learning:

* Image classification comes from Supervised Learning.
* Recommendation systems comes from Unsupervised Learning.
* Gaming AI comes from Reinforcement Learning.

How can we better understand Supervised, Unsupervised, and Reinforcement Learning?

Let’s start with Supervised Learning, which makes up most of the uses for Machine Learning today. In Supervised Learning, the machine already knows the output of the algorithm before it starts working on it. The algorithm is taught through a training data set that guides the machine, and the machine works out the steps from input to output. Supervised learning is used for image classification or identity fraud detection, and for weather forecasting. But how is Unsupervised Learning different?

Well first off, with Unsupervised Learning, the system does not have any concrete data sets, and the outcomes are also mostly unknown. Unsupervised Learning has the ability to interpret and find solutions to a limitless amount of data. Now when you log onto Hulu or Netflix, you have personalized recommendations because of Unsupervised Learning.

Lastly, there is Reinforcement Learning. Reinforcement Learning is different, because it gives a high degree of control to software agents and machines, which are determining what the behavior within a context should be. People are helping the machine to grow by maximizing performance, providing feedback to the machine, helping it to learn its behavior.

Reinforcement Learning requires the use of tons of different algorithms, giving control to the agent as they decide the best action based on the current results. When you are gaming on PC, Xbox, Playstation, or Nintendo, and you witness AI in Gaming, this is because of Reinforcement Learning. https://goo.gl/tkz4SA #DataScience #Cloud

Sat across from the interviewer for your dream job, you may start to feel the pressure. A sure-fire way to quash the interview jitters is to prepare as much as possible. Typically, you can segment the types of questions you’ll get asked in a data science interview; things such as statistics, programming and technical ability, business acumen, and culture fit assessment. Studying up on these will help you prepare as best you can. 

Here are some examples of what you could expect when interviewing for a data science role. Tailor these in accordance with what the job description asks for, read it thoroughly and get clued up on the desired points!

“What ML techniques do you work with? / Are these research level or production level techniques?”

What techniques and knowledge are required for the role? Your experience should match up with what is being asked of in the description if you’re at interview level so make sure you go in with examples of your experience with these.

Try memorizing 3 different examples of where you have used particular techniques and the effect that they have had. For example, if the role requires convolutional neural network experience, prepare 3 examples of projects where you have worked with CNN and the impact they had on the business or research you’ve contributed to.

“Tell me about an in-depth example of projects you have worked on from inception to completion. What was the project, how did you approach the problem, what was the end result etc.”?

Be prepared to explain your experience and impact in granular detail!

-Why the project existed.

-Your part vs other people’s role.

– Provide a step by step walk-through of what you did, what tools and techniques you used.

-End product and what it meant to the business.

Know your own cv inside out, don’t be caught off guard by questions on experience or a project that you cannot dive into and explain thoroughly!

“What’s your favourite algorithm?”

This is a tough one and which algorithms and tools you use will be totally dependent on the job you’re working on. The best approach to a question like this, is to have an answer ready before going in, that is fitting to the role you’re going for rather than trying to think of a ‘favourite’, think of the most relevant and be able to talk about it – show that you’re able to make a decision (this is also what they could be trying to figure out!), and communicate your reasons for your choice, all the while framing it to what they will desire in a candidate.

“What level of experience do you have with [programming language]?  What do you do daily with [programming language] and what was your hardest challenges with this?”

This is a great way for interviewers to measure you up alongside other candidates in terms of technical ability. The programming language they will more than likely ask you about will have been named as a requirement in the job description so make sure you go in with your answer on this ready to go. Have an example up your sleeve and be able to frame your use of the programming language in terms of how you could use it similarly in this role. If you’re not well versed in what they’re asking for, be honest and show your willingness to learn.

“What is the largest data set that you have processed? How did you approach this, and what was the end result?”

Again, with questions like this, interviewers will be looking for a deep dive into your successes with processing large data sets, your understanding of the approach and techniques used, and how the results have benefited the company. Can you quantify your results in terms of costs, revenue and time saved? If you can, make sure these are front and centre in describing the impact you had.

 

There is, of course, no one size fits all when it comes to data science interviews, questions, and tasks but hopefully, this guide can go some way in helping you know what to expect broadly speaking.  https://goo.gl/s2S9wm #DataScience #Cloud