First interview of Hiral Chandrana
#MASTEK
Adding 40+ customers every quarter from an year
650+ active clients
Met 150+ customers/partners virtually in last 40days
Focus to accelerate US growth-have strategy in place
960cr cash on books
Looking for organic+inorganic growth
#CNBCTV18Exclusive | Catch @_anujsinghal, @_soniashenoy & @SurabhiUpadhyay in conversation with @HiralChandrana who was appointed as global CEO of #Mastek last month. He says that a big part of their strategy is to grow in the US. @Reematendulkar pic.twitter.com/nRnfGoGHTp
— CNBC-TV18 (@CNBCTV18News) August 18, 2021
More from Shreenidhi P
Cosmo films
Investor ppt
Splty side:3yr target
Masterbatches-10% rev,20% RoCE
Textile chem-10% rev,25% RoCE
FMCG-Fabricizer launched
30% RoE,22% RoCE-fy21
1% shift in splty adds 5cr EBITDA
Target-62% to 80%
Net d/e 0.5x
D/EBITDA 1x
Petcare-plans 2 demerge in2 seperate entity https://t.co/Iq4VT6tG50
Investor ppt
Splty side:3yr target
Masterbatches-10% rev,20% RoCE
Textile chem-10% rev,25% RoCE
FMCG-Fabricizer launched
30% RoE,22% RoCE-fy21
1% shift in splty adds 5cr EBITDA
Target-62% to 80%
Net d/e 0.5x
D/EBITDA 1x
Petcare-plans 2 demerge in2 seperate entity https://t.co/Iq4VT6tG50

Cosmo films#cosmofilms
— Shreenidhi P (@nid_rockz) August 4, 2021
Probably the best results of the day
Glimpse of last 5qtr
Rev,EBITDA,pbt n PAT growth every qtr
Highest OPM 19%
Q1 EBITDA 132cr n PBT 116cr
Last 5yrs
OPM 7% to 17%\U0001f44c
80% pf-speciality-fy23
Superb debt management
Liberal dividend n buyback
Solid OCF\U0001f44c pic.twitter.com/6ulhmHZf24
More from Mastek
#MASTEK Update
TSL hit 📉
Though at CMP; Time to stack in 📈 for targets of 2620 followed by 2720.
#StockMarket https://t.co/IHHpv2bU2w
TSL hit 📉
Though at CMP; Time to stack in 📈 for targets of 2620 followed by 2720.
#StockMarket https://t.co/IHHpv2bU2w

#MASTEK Update
— Gurleen (@GurleenKaur_19) July 23, 2021
2800 Hit; 70% Booked and rest Holding for a target of 2900.
#StockMarket #StockToWatch https://t.co/gQTyzf4IUS pic.twitter.com/fp9hrZyv7d
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Nano Course On Python For Trading
==========================
Module 1
Python makes it very easy to analyze and visualize time series data when you’re a beginner. It's easier when you don't have to install python on your PC (that's why it's a nano course, you'll learn python...
... on the go). You will not be required to install python in your PC but you will be using an amazing python editor, Google Colab Visit https://t.co/EZt0agsdlV
This course is for anyone out there who is confused, frustrated, and just wants this python/finance thing to work!
In Module 1 of this Nano course, we will learn about :
# Using Google Colab
# Importing libraries
# Making a Random Time Series of Black Field Research Stock (fictional)
# Using Google Colab
Intro link is here on YT: https://t.co/MqMSDBaQri
Create a new Notebook at https://t.co/EZt0agsdlV and name it AnythingOfYourChoice.ipynb
You got your notebook ready and now the game is on!
You can add code in these cells and add as many cells as you want
# Importing Libraries
Imports are pretty standard, with a few exceptions.
For the most part, you can import your libraries by running the import.
Type this in the first cell you see. You need not worry about what each of these does, we will understand it later.
==========================
Module 1
Python makes it very easy to analyze and visualize time series data when you’re a beginner. It's easier when you don't have to install python on your PC (that's why it's a nano course, you'll learn python...
... on the go). You will not be required to install python in your PC but you will be using an amazing python editor, Google Colab Visit https://t.co/EZt0agsdlV
This course is for anyone out there who is confused, frustrated, and just wants this python/finance thing to work!
In Module 1 of this Nano course, we will learn about :
# Using Google Colab
# Importing libraries
# Making a Random Time Series of Black Field Research Stock (fictional)
# Using Google Colab
Intro link is here on YT: https://t.co/MqMSDBaQri
Create a new Notebook at https://t.co/EZt0agsdlV and name it AnythingOfYourChoice.ipynb
You got your notebook ready and now the game is on!
You can add code in these cells and add as many cells as you want
# Importing Libraries
Imports are pretty standard, with a few exceptions.
For the most part, you can import your libraries by running the import.
Type this in the first cell you see. You need not worry about what each of these does, we will understand it later.

1/OK, data mystery time.
This New York Times feature shows China with a Gini Index of less than 30, which would make it more equal than Canada, France, or the Netherlands. https://t.co/g3Sv6DZTDE
That's weird. Income inequality in China is legendary.
Let's check this number.
2/The New York Times cites the World Bank's recent report, "Fair Progress? Economic Mobility across Generations Around the World".
The report is available here:
3/The World Bank report has a graph in which it appears to show the same value for China's Gini - under 0.3.
The graph cites the World Development Indicators as its source for the income inequality data.
4/The World Development Indicators are available at the World Bank's website.
Here's the Gini index: https://t.co/MvylQzpX6A
It looks as if the latest estimate for China's Gini is 42.2.
That estimate is from 2012.
5/A Gini of 42.2 would put China in the same neighborhood as the U.S., whose Gini was estimated at 41 in 2013.
I can't find the <30 number anywhere. The only other estimate in the tables for China is from 2008, when it was estimated at 42.8.
This New York Times feature shows China with a Gini Index of less than 30, which would make it more equal than Canada, France, or the Netherlands. https://t.co/g3Sv6DZTDE
That's weird. Income inequality in China is legendary.
Let's check this number.
2/The New York Times cites the World Bank's recent report, "Fair Progress? Economic Mobility across Generations Around the World".
The report is available here:
3/The World Bank report has a graph in which it appears to show the same value for China's Gini - under 0.3.
The graph cites the World Development Indicators as its source for the income inequality data.

4/The World Development Indicators are available at the World Bank's website.
Here's the Gini index: https://t.co/MvylQzpX6A
It looks as if the latest estimate for China's Gini is 42.2.
That estimate is from 2012.
5/A Gini of 42.2 would put China in the same neighborhood as the U.S., whose Gini was estimated at 41 in 2013.
I can't find the <30 number anywhere. The only other estimate in the tables for China is from 2008, when it was estimated at 42.8.