" What We’re Watching In 2020 Ruhul Kader Insight January 1, 2020 0 Dhaka’s tech scene has seen some increased activities in 2019 compared to past years. Overall deal flow increased driven by an increased angel and seed-stage activities, although growth stage deals remain far scarce. 2020 offers mixed signals with a lot happening in …
Category: ব্লগ । Blog
ব্লগ । Blog
Jan 05
100+ Startups We’re Watching In 2020
" 100+ Startups We’re Watching In 2020 Future Startup Insight January 2, 2020 0 Bangladesh’s startup scene is growing fast. Bangladesh may lack in necessary infrastructures and a VC ecosystem but the country does not lack entrepreneurial initiatives and interest from international investors who want to be part of a fast-growing economy. We have high …
Jan 05
A Founder’s Manifesto For 2020
"A Founder’s Manifesto For 2020 Ruhul Kader Insight January 4, 2020 0 The new year, above all, comes with hope and promise of a new beginning. It offers an opportunity to turn a new page and start over anew. Traditionally, people begin with writing new year resolutions. Of course, hardly a few manage to keep …
Jan 05
Landknock Expands, Launches Logistics Management Software
"The Dhaka-based SaaS startup Landknock, that offers a field force management software, has introduced a new software product targeting logistics and home delivery services companies. The new software called Home Delivery Management Software can work as a back-end for logistics companies to manage merchants’ orders, delivery personnel, payment, order, location history, invoice, bill sharing, and …
Jan 01
Social and Cultural Awareness for IT jobs
Automating Inequalityhttps://virginia-eubanks.com/books/ Weapons of Math Destruction https://weaponsofmathdestructionbook.com/ ******************* Sayed Ahmed BSc. Eng. in Comp. Sc. & Eng. (BUET) MSc. in Comp. Sc. (U of Manitoba, Canada) MSc. in Data Science and Analytics (Ryerson University, Canada) Linkedin: https://ca.linkedin.com/in/sayedjustetc Blog: http://Bangla.SaLearningSchool.com, http://SitesTree.com Online and Offline Training: http://Training.SitesTree.com FB Group on Learning/Teaching: https://www.facebook.com/banglasalearningschool Our free or paid events …
Dec 31
Part 4: Some Basic Math/Stat Concepts for the wanna be Data Scientists
Part 4: Some Basic Math/Stat Concepts for the wanna be Data Scientists Also for the Engineers in General Quadratic form “In multivariate statistics, if is a vector of random variables, and is an -dimensional symmetric matrix, then the scalar quantity is known as a quadratic form in . ” Ref: https://en.wikipedia.org/wiki/Quadratic_form_(statistics) Please also check matrix …
Dec 30
Part 3: Some Basic Math/Stat Concepts for the wanna be Data Scientists
Conditional Probability and PDF “The conditional probability of an event B is the probability that the event will occur given the knowledge that an event A has already occurred. This probability is written P(B|A), notation for the probability of B given A. “ “In the case where events A and B are independent (where event …
Dec 29
The Team
4 teamwork lessons you can learn from the military https://www.hrzone.com/community/blogs/sophie-henderson/4-teamwork-lessons-you-can-learn-from-the-military The Myth of the Top Management Team https://hbr.org/1997/11/the-myth-of-the-top-management-team
Dec 29
Part 2: Some basic Math/Statistics concepts that Data Scientists (the true ones) will usually know/use
Part 2: Some basic Math/Statistics concepts that Data Scientists (the true ones) will usually know/use (came across, studied, learned, used) Covariance and Correlation “Covariance is a measure of how two variables change together, but its magnitude is unbounded, so it is difficult to interpret. By dividing covariance by the product of the two standard deviations, …
Dec 28
Part 1: Some Math/Stat Background that (true) Data Scientists will know/use: from the internet
Chebyshev’s inequality “In probability theory, Chebyshev’s inequality (also called the Bienaymé–Chebyshev inequality) guarantees that, for a wide class of probability distributions, no more than a certain fraction of values can be more than a certain distance from the mean. Specifically, no more than 1/k2 of the distribution’s values can be more than k standard deviations …
