Topics discussed in the post the following questionnaire :
Q1) What are some predominant features of No-SQL databases ?
Q2) Why NoSQL is called "SQL with a No " with "No" as the preceding string on SQL ?
Q3) How do organisations leverage NoSQL data for their businesses ?
Q4) Where are NoSQL databases useful ?
Q5) What do you mean by the statement , "The constraints of a relational database are relaxed ? "
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Topics for Python - Basics
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1) Syntax
2)
Variables
3)
Operators
* One can explore
different mathematical operators and
calculations that can happen using
the available operators in Python
4) Data Structures
1)
Decision Making Statements
:
If , If-else , Nested If else , chained conditionals of the else if type of statements , single statement conditions all come under the category of decision-making statements . Along with earning , one may also have to practice 10+ coding problems on a daily basis which would help someone get familiar with the manner in which one can scale up on the level of complexity of the programs and their creation
2)
Loops :
Loops are the structures that repeat a sequence of instructions . Python deals with while loops , for loops and nested loops . Along with these one may also learn loop control statements which show the manner in which loops can be initialised , implemented , interjected and put to end in the desired way of the programmer .
3)
Functions in Python
We will just put a small recap over Functions in Python :
User Defined functions are those functions which a user / programmer writes himself which takes programming constructs and statements to write a function which would achieve a desired result at the end of the function which may include the use of some built-in functions into the overall function code construct .
Built-In functions are those functions which are created as a part of the programme package at the time of creation of the Program package and its default libraries which come together with the program at the beginning of the program package .
Lambda functions are also some form of user defined function , however the construct is limited to only one line and this usually does the work of iteration over the elements of the function , access the individual elements , or use the functions to iteratively append , retrieve or delete some elements over an existing structure or a dummy/temporary structure .
* Along with the the basic concepts
of the scripting part of the Python Language one also needs to have a good understanding
of implementation
as well as creation of features in Object Oriented Language features of Python
:
1)
Polymorphism
2) Inheritance
3) Encapsulation
4) Abstraction
All these aspects of Programming in Python and how the same are going to be used in Python for Data Science and Machine Learning Modelling would be
discussed in further blogs . As I am getting disarrayed due to a some intermittent problems over my laptop I am not able to implement some of the important Algorithms and Modellling Processes . But soon , I hope to get these started on a better level .
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modified: 21:32
When should one use dictionaries and when should one use lists in Python
* Example where a List is used for data storage is an example of a shopping cart where any stored item object's storage does not matter .
* Example where a Dictionary Item object is used as a storage object is that of a an examination roll / rank number sheet ( parikhya patra ) where people are arranged on the basis of their marks obtained in some examination from Rank 1 to the last rank in the exam . Usually the person who obtains the highest marks in a given exam is attributted the 1st rank and the first roll number for that examination is also alloted to the first rank holder after the results are out , the successive role numbers are also attributed according the net marks obtained by an examiner in the considered case of the examination till the last rank/roll number of the examiner .
* A dictionary is very useful data storage object where one may need to access a given data based on the item that is present over a given index position within the storage object of the data structure .
To sum up once again for Lists and Dictionaries
* Lists are ordered data structure objects that means that the program has control over the
order in which the data is stored . This data is also sortable that means that the program can sort the data into whatever order when the data is needed to be fetched and retrieved , the list is also containing a list of iterable items i,e items that can be accessed one item at a time when the list object is iterated or
executed within a loop .
* Dictionaries are like the partially ordered data structures that
can control the order or the manner in which the data items are inserted and stored within the
object in the form of keys and attributes . Dictionaries are non-sortable
and iterable in pairs of both keys
and associated value objects .
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modified: 18:49
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Why is Artificial
Intelligence a big fab hype in the IT and technology world
=====================================================
* The most comprehensible perception about artificial Intelligence in the current technological biome is a distopian future coupled with human-like looking and performing robots and realistic looking holograms that throw a completely realistic idea of a certain picture or scenario in which non-human like androids and droids would be acting and talking like real people and having human- level or even superhuman intelligence and capablities for performing a task which would be much complex and strenuous to humans . This concept is still beyond the aegis of reality as of now and this term is called as "Artificial General Intelligence(AGI)" and this does not exist anywhere on earth till now as depicted by some high-level CGI based movies in their content .
* But what is actually existing as of
now is that of a fractional level implementation of Deep Learning which is shortly known as DL which can do some small level specific
tasks which are better than people doing the computing task .. one needs
to keep in mind over here is that it
is only restricted to computing
activity and factory work . Still is believed that there are multiple fundamental level
limitations that does not properly
get developed into AGI - Artificial General Intelligence (AGI) . So the next level of algorithmic data science and machine learning development is that of innovating the present technology to come
up with better networks and better methods for shaping into an artificial Intelligence product .
* So
inorder to get a bird's eye view of where the development stands at , one might
need to look at the following persisting scenarios :
4) A possible architecture to achieve artificial general Intelligence
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Machine Learning Algorithms used in solving some of the popular
real world problems
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* In the real world machine learning environment , deep learning also plays a very shallow and limited role in the field of machine learning and therefore it is considered as one of the smallest factors that go into building of applications over machine learning
* Therefore , gradient boosters like XGBoost are considered as the gold
standard for modelling over
structured data problems used in data competitions .Again it needs to be re-iterated
and kept in memory eternally that
almost all of the data problems use XGBoost and other gradient boosters for working over any data problem .
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modified: 14:20
Technical Data Analyst posted at HP @Bangalore on 07-06-21
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* The secondary requirement for the fitting criteria is that the person should have a Master's degree in computer science or Information systems
* A candidate for the job should be able to leverage and scale open source tools .This aspect is currently I am also not sure and I am also not yet sure which open source tools are being referred to in this , since collaboration tools are the most requisite open source tools that are common to all the people albeit their technological skills
* Proficiency in Public Cloud
Providers like Amazon AWS , Google GCP , Microsoft
Azure etc is also a must { I have some experience over AWS cloud and
am aware of the manner in which some of the applications work over AWS but I am
yet to explore all the greater details and aspects of AWS and how big scale
projects are deployed at AWS }
*
Experience over Industry - standard BI platforms { this necessitates a candidate to have some good amount of experience
over working over Business Intelligence and Reporting Applications like Microsoft Power Bi and Tableau where one might be able to carry out and find out report generation and deployment
activities in order to find out the necessary actionable items required for the specific work }
* Knowledge of SAAS products and their multitenant cloud implementation { I this area I am just aware of the fundamental theorems that are under work and implementation is still yet to be done and scaled up , so this is one area that I shall again need to brush up and get my hands upon. I shall try to learn from the available online resources and try online cloud platforms over where such projects and modules are deployed}
* Analyse Sales and Financial
Data with Cloud and Operational data to present
over to the clients to view
* Data Warehousing and Reporting solutions to address the growing needs for reporting , analytics , and data requirements
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