Itqan Analytics & Software Limited, Dhaka (2026)

30/05/2022

Ethics and Data Science
New technologies frequently pose new ethical concerns. The advent of nuclear weapons, for example, put a strain on the distinction between combatants and non-combatants that had been crucial to the just war theory since the Middle Ages. In the nuclear age, new theories were required to reinterpret the meaning of this differentiation. With the advent of new machine learning techniques and the ability to use algorithms to accomplish activities previously performed by humans, as well as to develop new knowledge, a new set of ethical problems has arisen.These concerns include not only the risk of harm from data misuse, but also how to protect privacy when sensitive data is involved, how to minimize bias in data selection, how to prevent data disruption and "hacking," and issues of transparency in data collecting, research, and dissemination. Many of these problems are rooted in a larger debate over who owns the data, who gets access to it, and under what conditions.

These questions presently have no agreed-upon answers. Nonetheless, confronting them and attempting to figure out shared ethical principles is critical. When consensus isn't attainable, it's critical to pay attention to conflicting values and to define the underlying assumptions that underpin different models. The dispute about fairness in models estimating the probability of recidivism among black and white defendants in Broward County, Florida, is an interesting example.Should a risk score be: equally accurate in predicting recidivism for members of diverse racial groups; presume that members of different groups have the same possibility of being incorrectly predicted to recidivate; or assume that failure to forecast recidivism occurs at the same rate across groups? According to recent research, completing all three criteria at the same time is unachievable in most cases; meeting two will result in failure to meet the third. As a result, we must determine which components of fairness are most critical.

Collaboration amongst programmers, statisticians, legal experts, and philosophers will be required to develop a shared framework.

28/05/2022

Data Science for Humanity
Massive amounts of data chronicling the activities of individuals, groups, companies, cultures, and even entire societies characterize our modern period. This massive digitization of historical data, both textual and numeric, in the form of historic newspapers, literary and linguistic corpora, economic data, censuses, and other government data, gathered and preserved over centuries, and newly digitized, acquired, and provisioned by libraries, scholars, and commercial entities, is accompanied by a wealth of data on modern humanity.

Scholars can use data science methods and approaches to better understand humanity in all of its forms and activities, across time and space: from studying individual human behavior to better modeling human communities, organizations, countries, and societies; and from understanding both our current moment and the histories that have brought us here. Data science can help answer long-standing problems like what factors influence economic progress and well-being. What factors contribute to democratic societies? What causes violence, repression, and strife in some societies?
What factors contribute to democratic societies? Data science provides tools to interpret the beliefs and behaviors of individuals, groups, and organizations; it can be used to interpret the cultural and linguistic output of entire cultural periods and peoples; and it can assist us in reasoning about causal relationships and the complex motivations of societal and state actors. Data, as well as a science to interpret it, are essential for progress in the social sciences, humanities, business, education, and law.

Data science for humanity is a thoroughly interdisciplinary endeavour, not only for academic study of the humanities and social sciences, but also for the betterment of humanity. Machine learning, statistical reasoning, natural language processing, categorization, textual analysis, and other data science methodologies developed primarily (but not entirely) in the computer science professions have all become indispensable tools for academics and students across disciplines. This interdisciplinarity has benefited data science in both directions: it has benefited from the complex, important, and consequential research questions focused on both the rich history of human cultures and societies, as well as the current state of humanity — in both its triumphs and its difficulties. Data science can assist in diagnosing problems and suggesting remedies.

27/05/2022

Data Science for Human Health
Data science will undoubtedly play a key role in shifting the world's healthcare systems from reactive "sick-based" to proactive, preventive care.

First and foremost, data science has the potential to empower consumers by giving them greater control over their own health care. People can make better, more informed decisions if their health-care providers can make more data-driven recommendations. Imagine your healthcare provider having access to your genetic data in a proactive healthcare system, being able to assess your genetic risk for disease—not just as an individual, but as a member of a wider population—and then assisting you in managing that risk throughout your life.

This is the kind of individualized, patient-centered medicine that today's reactive healthcare systems can't support since they're built to wait until something goes wrong with the human body before intervening, and each individual is held accountable for his or her own health and risk. Public education might enlighten people about what it means to have varying levels of risk in a data-driven proactive healthcare system. Individuals could be informed of their individual and communal health risks early on, strengthening control over their own health at every stage of their lives, because we all carry some level of risk (some more than others for specific diseases).

Third, data science technologies have the potential to improve patient outcomes and conditions with mixed results. When fighting diseases like brain or other neurological tumors, they can record data inputs, w**d out subtypes, and extract effective methods.

Finally, by leveraging continuous data capture, analytics, and new critical insights to notify physicians and clinicians when things have gone wrong in the human body before patients become ill, data science technologies can restructure the costs associated with care delivery. This knowledge might then be incorporated into a new model of care, allowing for early intervention and avoiding the need for the individual to visit the hospital. Recent Stanford research has begun to look into the possibility of monitoring cardiomyopathy patients at home as well as monitoring youngsters in the ER and ICU: we believe these studies will lead to a future of proactive, consumer-based care.

We acknowledge that technology advancements and the extraordinary proliferation of biomedical data have generated tremendous potential, but they have also created significant hurdles in terms of ensuring the privacy and security of patient and other research data. We must collaborate with private-sector stakeholders and experts, as well as federal agencies such as the National Institutes of Health, to promote and implement robust and proactive information-security procedures that ensure appropriate stewardship of patient and research-participant data while enabling scientific and medical progress.

25/05/2022

Some more R Language Pro Tips :

41.Find index of a value nearest to 1.5 in a vector
n

23/05/2022

Some R Language Pro tips :

36. Create a vector, append values

> a is.vector(a)

[1] TRUE

> is.character(a)

[1] TRUE

> as.numeric(a)

[1] NA 1 2 3 NA

Warning message: NAs introduced by coercion

This shows that an easy way to create a vector is with the concatenate function, which is used so commonly it is known only as “c”. As long as all of your input is of the same type, you don’t hit any snags. Note the is.vector() function will say this is a vector:

> v is.vector(v)

[1] TRUE

> is.numeric(v)

[1] TRUE

The worst case scenario is that it manufactures a list. The double brackets [[]] are the big signal that you really have a list:

> bb is.vector(bb)

[1] TRUE

> bb

[[1]]

[1] 1

[[2]]
Primitive("c")

[[3]]

[1] 4

> is.character(bb)

[1] FALSE

> is.integer(bb)

[1] FALSE

> is.list(bb)

[1] TRUE

To make sure you get what you want, you can use the vector() function, and then tell it what type of elements you want in your vector. Please note it puts a “false” value in each position, not a missing.

> vector(mode="integer",10)

[1] 0 0 0 0 0 0 0 0 0 0

> vector(mode="double",10)

[1] 0 0 0 0 0 0 0 0 0 0

> vector(mode="logical",10)

[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

37.How to create an identity matrix?

diag(n)

Or, go the long way:

n

21/05/2022

Hello there, data science aficionados!
To help you improve your R Language skills, here are some more pro javascript tips.

31. Grab an item from each of several matrices in a List
Let Z denote the list of matrices. All matrices have the same order. Suppose you need to take element [1,2] from each.
lapply(Z, function(x) x[1,2])
should do this, giving a list. Use sapply if you want a vector.

32 Get vector showing values in a dataset
xlevels

18/05/2022

Here are some more pro tips of R Languauge :

26.Rank within subgroups defined by a factor
Read the help for by()
> by(x[2], x$group, rank)
x$group: A
[1] 4.0 1.5 1.5 3.0
------------------------------------------------------------
x$group: B
[1] 3 2 1

> c(by(x[2], x$group, rank), recursive=TRUE)
A1 A2 A3 A4 B1 B2 B3
4.0 1.5 1.5 3.0 3.0 2.0 1.0

26. Work with missing values (na.omit, is.na, etc)
NA is a “symbol”, not just a pair of letters. Values for any type of variable, numeric or character, may be set to NA. Thus, in a sense, management of NA values is just like any other recoding exercise. Find the NAs and change them, or find suspicious values and set them to NA. this example, which inserts some NA’s, and then converts them all to 0.
> temp1 temp1[temp1 < 0.1] temp1[is.na(temp1)]

16/05/2022

More R programming tips:

21. Delete first observation for each element in a cluster of observations
Given data like:
1 ABK 19910711 11.1867461 0.0000000 108
2 ABK 19910712 11.5298979 11.1867461 111
6 CSCO 19910102 0.1553819 0.0000000 106
7 CSCO 19910103 0.1527778 0.1458333 166
remove the first observation for each value of the “sym” variable (the one coded ABK,CSCO, etc). . If you just need to remove rows 1, 6, and 13, do:
newhilodata jm

14/05/2022

Here are some more pro R Language tips to assist you enhance your skills.

16.Recode one column, output values into another column
Please read the documentation for transform() and replace() and also learn how to use the magic of R vectors.
The transform() function works only for data frames. Suppose a data frame is called “mdf” and you want to add a new variable “newV” that is a function of var1 and var2:
mdf x xf model.matrix( xf-1 )
xf2 xf3 xf4 xf5 xf6
1 1 0 0 0 0
2 1 0 0 0 0
3 0 0 0 1 0
4 0 1 0 0 0
5 0 0 0 0 1
6 0 0 0 1 0
attr(,"assign")
[1] 1 1 1 1 1
(from Peter Dalgaard)

18.Create lagged values of variables for time series regression .
yshft

11/05/2022

Here are some additional pro R Language tips to assist you enhance your skills.

11. Use rep to manufacture a weighted data set
> x w rep(x,w)
[1] 10 10 40 50 50 50 100 100
(from P. Malewski)
That expands a single variable, but we can expand all of the columns in a dataset one at a time to represent weighted data
Thomas Lumley provided an example: Most of the functions that have weights have frequency weights rather than probability weights: that is, setting a weight equal to 2 has exactly the same effect as replicating the observation.
expanded.data

09/05/2022

Good evening, data science lovers! Here is the second part of R laguaage pro tips.

6. Work on one row at a time.
Question: I would like to create an (empty) data frame with “headings” for every column (column titles) and then put data row-by-row into this data frame (one row for every computation I will be doing), i.e.
no. time temp pressure

Itqan Analytics & Software Limited

30/05/2022

28/05/2022

27/05/2022

25/05/2022

23/05/2022

21/05/2022

18/05/2022

16/05/2022

14/05/2022

11/05/2022

09/05/2022

Address

Website

Alerts

Shortcuts

Share

Category