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Python: The Cornerstone of Data Science in the Modern Era

Python emerges as the cornerstone of data science, empowering analysts with its open-source flexibility. From mining insights to visualizing data, Python's prowess shines. Dive deeper into its impact with Rang Technologies. Curious? Read more! Read more for insights. https://www.rangtech.com/blog/data-science/python-the-cornerstone-of-data-science-in-the-modern-era

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How To Pass Employment Background Verification Checks | How Companies Do Background Verification

The Status of your Background Verification can make or break your career. Every year 15% of offered candidates lose their offers after failing the #BGC. They have resigned from their current organization and the following organization withdraws their offer. And they end up Jobless.. #Unemployed. That's why its important to learn how to clear the #BGV or #BGC.

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Well, Cheritz already confirmed that they're using AI. Isn't More Easy to just use stock images like Teo/Harry routes? I didn't think I could have a lower opinion on Cheritz, but I guess Cheritz can always dig a bigger hole...

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And it's not like their housekeepers are being paid a decent living wage. Most housekeepers I've known make between 14-20 an hour.

They're still complicit in keeping others in poverty.

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ZS Hiring Hackathon : Patient Drug-switch Prediction

Machine Learning and Data Science have stolen the attention of the young generation who are seeking an exciting and well-paid career. With the advent of growth in data volume and technology the gap between Machine Learning Engineers and Data Scientist is narrowing leading companies to look for full-stack data scientists. The demand for such folks with Machine learning and Data Science skills is exponentially increasing and organizations are struggling to find their best fit. Good MLDS profile not only requires a strong analytical and engineering background but also needs a good understanding of the algorithms, advanced statistics, optimization approaches, coding & distributed computing skills. One of the few possible ways to master this new domain is through constant learning, practice and showcasing of skills through hackathons. This is where MachineHack is going to help. With the help of our top hackathons, you can become an expert in handling data and build a path to a successful Machine Learning and Data Science career. MachineHack and ZS Associates bring to you the “Patient Drug-switch Prediction Hackathon” Problem Description Armanik, a multinational pharmaceutical company based in Texas, USA, is one of the largest pharmaceutical companies by both market capitalization and sales. Armanik manufactures drugs across multiple therapy areas – Cardiovascular, Diabetes, HIV and Immunology therapy. Their innovative SGLT 2 is the market leader in diabetes therapy. Recently, the company announced that they have successfully completed a Phase 3 trial for an Anti-TNF drug in Rheumatoid Arthritis therapeutic. The company expects to get approval for its new drug in the next 6 months. Given the competition in the market, Prakash Vishwanathan, CEO of Armanik, has reached out to ZS to help identify the patient population in the U.S who are likely to switch any product in the RA market. ZS has proposed a machine learning-based approach using medical transactional data to first identify the factors that are most closely associated with the switching RA patients that will help predict patients who are likely to switch in the near term.  Can you help ZS in achieving the below-mentioned objectives?

Data Sets  File Descriptions : Train_data.csv – Data for training (This is a transactional data for feature creation)Train_labels.csv – Outcome flag for train patients (1/0)Test_data.csv – Data for testing (Create final features for modelling)Fitness_values.csv – Fitness values for features created with train data. Must be used to match the fitness values with the Feature created using Train Data Only.Sample Submission.csv – Sample submission Format to submit the Predictions (Don’t Shuffle the Patient_ID, Keep the sequence entact). Train_data.csv

Data Size: Train Data: 627 MBTest Data: 273 MB Packages allowed: PandasNumpyScipyNumbaCython extensionoperator Click here to participate. ABOUT ZS ZS is a professional services firm that works side by side with companies to help develop and deliver products that drive customer value and company results. We leverage our deep industry expertise, leading-edge analytics, technology and strategy to create solutions that work in the real world. With more than 35 years of experience and 6,000-plus ZS-ers in 23 offices worldwide, we are passionately committed to helping companies and their customers thrive. Our most valuable asset is our people—a fact that’s reflected in our values-driven organization in which new perspectives are integral and new ideas are celebrated. We apply our knowledge, capabilities and innovation-oriented approach in industries ranging from healthcare and life sciences to high-tech, financial services, travel and transportation, and beyond. Rules Eligibility: Hackathons are open to all registered users at www.machinehack.com, a participant must be 18 years or older.Only one account is allowed per participant; submissions from multiple accounts will lead to disqualification. This is an individual exercise.We expect that you respect the spirit of the competition and do not cheat. Privately sharing code or data is not permitted—any case of code plagiarism will result in the disqualification of all users involved.It is obligatory to submit a well-commented and reproducible source code that you would generate as part of this contest in .zip or .tar compressed archive or the submission will not be considered.The ideal candidate is expected to hold 2-6 years of experience in working as a Data Scientist or a Machine Learning EngineerThe leaderboard will be updated on the basis of the AUC score of the submitted predictions.Users must have an updated MachineHack profile and must specify their LinkedIn for final shortlisting.  Submission limits : You can make a maximum of 3 excel file submissions in a day. Timeline Hackathon will be live from 3rd January 2020 to 13th January 2020. Phase 1:  AUC evaluation of submitted predictions - 3rd January 2020 to 10th January 2020. Phase 2:  Time complexity evaluation of Submitted code files - 11th January 2020 to 13th January 2020. Submitting Your Files For Evaluation

Phase 1 Submission window: January 3rd to January 10th 2020 The hackathon assignment requires participants to submit an excel file (Only .xlsx files are allowed) containing the unique identifier  ‘patient_id’ and the corresponding prediction classes ‘outcome_flag’. The submissions are evaluated on AUC score and the leaderboard is updated. Phase 2 Submission window: January 11th to January 13th 2020 The phase 2 submission window (FINAL SUBMISSION) will be enabled to share the following files as a zip  post 10th January : The phase 1 submission file with the best AUC score on the leaderboard. (Best Score.xlsx)A csv file containing a list of all the features along with their fitness value. (Fitness_Score.csv)A python script file that can be executed on a machine with RAM of 16 GB to recreate the features and fitness values. (Feature_Pipeline.py)A well commented and reproducible source code(python script) for the best AUC which will be evaluated for time complexity. (Model.py)Fully documented approach in a PPT. Note: Participants must upload the above files in a zip archive to a submission portal which will be enabled on January 11th 2020. Bounty

The best submission will receive a Macbook and a free pass to Machine Learning Developers Summit 2020.The shortlisted participants will get an opportunity to present their approach at MLDS 2020 to a panel of experienced leadership of ZS-ers and participants of the summit.  The top 15 participants will get an opportunity to be interviewed for the role of Data Scientist/ Machine Learning Engineer at ZS Associates (The role can be chosen based on your expertise area between the two roles) Click here to participate. Evaluations THE CANDIDATES WILL HAVE TO MAKE SUBMISSIONS AS PER THE GUIDELINES MENTIONED BELOW: Objective 1: Auto Feature Engineering A CSV file containing a list of all the features along with their fitness value.

Once the set of mandatory features is created using the training data, evaluate the fitness of these features using the following methodology. (A starter python notebook is available here) Any submission with >1% of error – sum of % errors across fitness values of all features (~40000 features), will be considered as Invalid. Objective 2: Patient Switching Probabilities Test Set Predictions: The test set predictions are evaluated using AUC. An excel file (.xlsx only) containing the prediction probability for all the patients in the test data. The top submissions will be decided based on the leaderboard score and evaluation of other submission documents (code file, .csv file, supporting notebooks, model pipeline- .py file or .ipynb format). All the good submissions will be eligible to receive the bounties Participate Read the full article

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Indian Sex Site:  Best  Mоney  Generаting  Рrоfessiоn

Indian sex site аnd  best Indian sex sites аre  beсоming рорulаr.  аnd  in this  раrt,  I will  be  mаking yоu  understаnd  the imроrtаnсe  оf  саll bоys,  hоw  it сhаnges  the  lives оf  mаny  рeорle.

Everyоne  wаnts mоney  аnd  tо live  а  luxuriоus life  whiсh  he dreаms  оf  it. аnd  fоr  then  call boy company in Tamilnadu is  the  best сhаnсe.  Рeорle  whо enter  intо  the аre  seen  enjоying their  life  аlоng with  trаveling  аnd оther  benefits.

The reаl  meаning  оf саll  bоy-

Рlаybоy  jоbs аre  оne  оf the  rising  сhаnсes in  this  mоdern wоrld.  Nоwаdаys  yоu’ll аlsо  find  mаny рeорle  whо  lооk fоr  а  gigоlо jоb  vасаnсy  in Indiа.  They  аre  hired  by рeорle  whо  wаnt sаtisfying  sex  in their  life.

Рrосess  оf registrаtiоn  in  sоme reрuted  sites  -

There аre  mаny  рeорle whо  wаnt  tо sрend  time  with wоmen  seek  men саtegоry  рeорle  but mаny  times  they fаil  оr  аre сheаted  in  sоme wаys  when  they register  .sо  here we  аre  shаring steрs  оn  hоw yоu  саn  register in  оne  оf the  reрuted  sites.

Fоllоw these  steрs  fоr eаsy  registrаtiоn.

1. Lооk  fоr  оriginаl   genuine  websites

2.Visit  а demаnd  site  nаmed desireрlаybоys.соm

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4. Get  а  саll fоr  рrоviding  serviсe

5. Аfter  the serviсe  соlleсts  the саsh  аnd  leаve.

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This blосk  will  рrоvide yоu  the  benefits аnd  will  сleаr the  sоul  reаsоn why  рeорle  wаnt tо  jоin  gigolo pvt ltd company in tamilnadu

The best  sоurсe  оf generаting  mоney-Eаring  а gооd  аmоunt  оf mоney  саn  fulfill аll  yоur  neсessаry neсessities  аnd  desires. there  аre  mаny wоmen  lооking  fоr men  whо  lоve tо  рrоvide  mоney if  yоu  give them  gооd  serviсe

Enjоy yоur  life  with beаutiful  lаdies-in  tоdаy’s wоrld  there  аre mаny  wоmen  seeking men  fоr  sex аre  there  аnd if  yоu  enter the  рrоfessiоn  аlоng with  рrоviding  serviсes yоu  саn  enjоy with  the  beаutiful girls

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Whо аre  the  buyers оf  Gigоlо?

In different  аreаs,  the demаnd  fоr  esсоrts in Indian sex site аre  different if  yоu  trаvel tо  sоme  big сities  yоu  саn find  mаny  wоmen seeking  men  аnd there  the  demаnd is  quite  high but  still  there аre  mаny  саtegоries оf  рeорle  whо  аre seen  hirirng  them like

High Рrоfile  lаdies,  divоrсed рeорle,  unmаrried  wоmen, hоrny  рeорle.  Whо seаrсh  sоmeоne  whо соuld  understаnd  their wild  desire  аnd sаtisfy  them.  In gigоlо  website  Indiаn оnly  they  eаsily  get рeорle  аnd  hire them  

The mаin  reаsоn  they wаnt  tо  hire them  is  beсаuse

Аre nоt  sаtisfied  with their  life  раrtners

Аre either  divоrсed  оr seраrаted

Wаnt sаtisfying  sex  аheаd  

Minimum  sаlаry оf  а  mаle  esсоrt.

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If yоu  get  а gigоlо  jоb  vасаnсy in  Delhi  then yоu  shоuld  nоt miss  the  орроrtunities аs  in  these сities  the  demаnd fоr  this  саtegоry оf  рeорle  аre very  muсh  high sо  never  miss аn  uрdаte  if yоu  get  the сhаnсe.

In this  blоg,  I hаve  given  yоu аn  аmрle  аmоunt оf  knоwledge  regаrding, best Indian sex sites, their  inсоme  sоurсes them  аnd  hоw саn  а  рersоn jоin  this  рlаtfоrm.

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Solugenix Hirirng Freshers For Trainee Position- Apply Now !!

Solugenix Hirirng Freshers For Trainee Position- Apply Now !!

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Staff Nurses with OMAN -MOH Licensea

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[analyticsindiam]

ZS Hiring Hackathon : Patient Drug-switch Prediction

Machine Learning and Data Science have stolen the attention of the young generation who are seeking an exciting and well-paid career. With the advent of growth in data volume and technology the gap between Machine Learning Engineers and Data Scientist is narrowing leading companies to look for full-stack data scientists. The demand for such folks with Machine learning and Data Science skills is exponentially increasing and organizations are struggling to find their best fit. Good MLDS profile not only requires a strong analytical and engineering background but also needs a good understanding of the algorithms, advanced statistics, optimization approaches, coding & distributed computing skills. One of the few possible ways to master this new domain is through constant learning, practice and showcasing of skills through hackathons. This is where MachineHack is going to help. With the help of our top hackathons, you can become an expert in handling data and build a path to a successful Machine Learning and Data Science career. MachineHack and ZS Associates bring to you the “Patient Drug-switch Prediction Hackathon” Problem Description Armanik, a multinational pharmaceutical company based in Texas, USA, is one of the largest pharmaceutical companies by both market capitalization and sales. Armanik manufactures drugs across multiple therapy areas – Cardiovascular, Diabetes, HIV and Immunology therapy. Their innovative SGLT 2 is the market leader in diabetes therapy. Recently, the company announced that they have successfully completed a Phase 3 trial for an Anti-TNF drug in Rheumatoid Arthritis therapeutic. The company expects to get approval for its new drug in the next 6 months. Given the competition in the market, Prakash Vishwanathan, CEO of Armanik, has reached out to ZS to help identify the patient population in the U.S who are likely to switch any product in the RA market. ZS has proposed a machine learning-based approach using medical transactional data to first identify the factors that are most closely associated with the switching RA patients that will help predict patients who are likely to switch in the near term.  Can you help ZS in achieving the below-mentioned objectives?

Data Sets  File Descriptions : Train_data.csv – Data for training (This is a transactional data for feature creation)Train_labels.csv – Outcome flag for train patients (1/0)Test_data.csv – Data for testing (Create final features for modelling)Fitness_values.csv – Fitness values for features created with train data. Must be used to match the fitness values with the Feature created using Train Data Only.Sample Submission.csv – Sample submission Format to submit the Predictions (Don’t Shuffle the Patient_ID, Keep the sequence entact). Train_data.csv

Data Size: Train Data: 627 MBTest Data: 273 MB Packages allowed: PandasNumpyScipyNumbaCython extensionoperator Click here to participate. ABOUT ZS ZS is a professional services firm that works side by side with companies to help develop and deliver products that drive customer value and company results. We leverage our deep industry expertise, leading-edge analytics, technology and strategy to create solutions that work in the real world. With more than 35 years of experience and 6,000-plus ZS-ers in 23 offices worldwide, we are passionately committed to helping companies and their customers thrive. Our most valuable asset is our people—a fact that’s reflected in our values-driven organization in which new perspectives are integral and new ideas are celebrated. We apply our knowledge, capabilities and innovation-oriented approach in industries ranging from healthcare and life sciences to high-tech, financial services, travel and transportation, and beyond. Rules Eligibility: Hackathons are open to all registered users at www.machinehack.com, a participant must be 18 years or older.Only one account is allowed per participant; submissions from multiple accounts will lead to disqualification. This is an individual exercise.We expect that you respect the spirit of the competition and do not cheat. Privately sharing code or data is not permitted—any case of code plagiarism will result in the disqualification of all users involved.It is obligatory to submit a well-commented and reproducible source code that you would generate as part of this contest in .zip or .tar compressed archive or the submission will not be considered.The ideal candidate is expected to hold 2-6 years of experience in working as a Data Scientist or a Machine Learning EngineerThe leaderboard will be updated on the basis of the AUC score of the submitted predictions.Users must have an updated MachineHack profile and must specify their LinkedIn for final shortlisting.  Submission limits : You can make a maximum of 3 excel file submissions in a day. Timeline Hackathon will be live from 3rd January 2020 to 13th January 2020. Phase 1:  AUC evaluation of submitted predictions - 3rd January 2020 to 10th January 2020. Phase 2:  Time complexity evaluation of Submitted code files - 11th January 2020 to 13th January 2020. Submitting Your Files For Evaluation

Phase 1 Submission window: January 3rd to January 10th 2020 The hackathon assignment requires participants to submit an excel file (Only .xlsx files are allowed) containing the unique identifier  ‘patient_id’ and the corresponding prediction classes ‘outcome_flag’. The submissions are evaluated on AUC score and the leaderboard is updated. Phase 2 Submission window: January 11th to January 13th 2020 The phase 2 submission window (FINAL SUBMISSION) will be enabled to share the following files as a zip  post 10th January : The phase 1 submission file with the best AUC score on the leaderboard. (Best Score.xlsx)A csv file containing a list of all the features along with their fitness value. (Fitness_Score.csv)A python script file that can be executed on a machine with RAM of 16 GB to recreate the features and fitness values. (Feature_Pipeline.py)A well commented and reproducible source code(python script) for the best AUC which will be evaluated for time complexity. (Model.py)Fully documented approach in a PPT. Note: Participants must upload the above files in a zip archive to a submission portal which will be enabled on January 11th 2020. Bounty

The best submission will receive a Macbook and a free pass to Machine Learning Developers Summit 2020.The shortlisted participants will get an opportunity to present their approach at MLDS 2020 to a panel of experienced leadership of ZS-ers and participants of the summit.  The top 15 participants will get an opportunity to be interviewed for the role of Data Scientist/ Machine Learning Engineer at ZS Associates (The role can be chosen based on your expertise area between the two roles) Click here to participate. Evaluations THE CANDIDATES WILL HAVE TO MAKE SUBMISSIONS AS PER THE GUIDELINES MENTIONED BELOW: Objective 1: Auto Feature Engineering A CSV file containing a list of all the features along with their fitness value.

Once the set of mandatory features is created using the training data, evaluate the fitness of these features using the following methodology. (A starter python notebook is available here) Any submission with >1% of error – sum of % errors across fitness values of all features (~40000 features), will be considered as Invalid. Objective 2: Patient Switching Probabilities Test Set Predictions: The test set predictions are evaluated using AUC. An excel file (.xlsx only) containing the prediction probability for all the patients in the test data. The top submissions will be decided based on the leaderboard score and evaluation of other submission documents (code file, .csv file, supporting notebooks, model pipeline- .py file or .ipynb format). All the good submissions will be eligible to receive the bounties Participate Read the full article