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Mandar fullstack developer, India
$20 /hr
9 Years Exp.
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• Having 9+ years of IT experience in designing, developing and implementing software modules for web applications. • Over 7+ years of experience i...Read More
Asish PradhanTech lead, India
$20 /hr
8 Years Exp.
0 Followers
Having 8 years experience in java development
Harsh NigamFull Stack App Developer, India
$22 /hr
5 Years Exp.
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I'm a full stack with over 5 years of experience in building and maintaining mobile apps as well as their backend servers. Technologies I'm...Read More
Ashu RaturiSr. Python Developer, India
$16 /hr
6 Years Exp.
0 Followers
I am a python developer, having 6.5 years of experience in python
Khalil Mohammad MirzaFull Stack developer, Pakistan
$15 /hr
0 Years Exp.
0 Followers
Dear Customers, This is Khalil, I'm an enthusiastic fast learner programmer who's ultimate goal is customer satisfaction. I won't s...Read More
Soufiane MaguerraBig Date Expert, Morocco
$30 /hr
0 Years Exp.
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I am a Ph. D Computer Science student, about to finish my thesis, looking for opportunities in the Big Data field and Scala programming so that I can...Read More
Gopi MallaBackend/Javascript Developer, India
$21 /hr
5 Years Exp.
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I am a techie who's working in a startup with the latest tech stack. Worked in LAMP and MEAN stacks.
Ajay YadavSoftware Engineer, India
$20 /hr
0 Years Exp.
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Experienced software engineer with a demonstrated history of working in the computer software industry. Skilled in Big Data, Spark, Hive, Hbase and Ka...Read More
Gaurav JadwaniFull stack developera, India
$10 /hr
2 Years Exp.
0 Followers
I am full stack developer with industry experience of 2 years
I have over 8 years of experience in the IT field which includes linux server administration, AWS and over 4 years in Application Development I posses...Read More
Kautilya BhardwajFull-stack web developer, India
$0 /hr
2 Years Exp.
0 Followers
Full-stack web developer with 2+ years of working experience with Google App Engine Python, Google Cloud Datastore, Angular, AngularJS, and RESTful AP...Read More
Abhijit SealDeveloper / Software Architect, India
$2 /hr
12 Years Exp.
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i am Software Architect with extensive experience developing software in an agile environment. 12+ years experience with server side Java development....Read More
Anubhav SinghFull Stack Developer, India
$22 /hr
2 Years Exp.
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As a part of small paced software development team, I have experience in taking an idea to MVP, and then reiterating to get the best product both in t...Read More
***** Current Availability: 40 Hrs/week I am available 18 hrs/day online ***** • I have 17 years of Information Technology experience in software...Read More
Michael LeeSenior Mobile&Web developer, China
$50 /hr
9 Years Exp.
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Thank you very much to visit my profile. As a senior Mobile and web developer that 7 + years of long history and experiences, I am very strong skills...Read More
Nitesh MishraBusiness Analyst, India
$30 /hr
6 Years Exp.
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I am a certified Business Analyst with 6+ years of experience in MS SQL, MS Excel and reporting. I also code in R and python. I use Tableau as my visu...Read More
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Optimization deals with selecting the simplest option among a number of possible choices that are feasible or do not violate constraints. Python is used to optimize parameters in a model to best fit data, increase profitability of a possible engineering style, or meet another form of objective which will be described mathematically with variables and equations.

 

pyOpt is a Python-based package for formulating and solving nonlinear constrained optimization problems in an efficient, reusable and portable manner. Python programming uses object-oriented concepts, such as class inheritance and operator overloading, to maintain a distinct separation between the problem formulation and the optimization approach used to solve the problem.

 

All optimisation downside solvers inherit from the Optimizer abstract category. The category attributes include the solver name (name), an optimizer kind symbol (category), and dictionaries that contain the solver setup parameters (options) and message output settings (informs). The class provides ways to check and alter default solver parameters (getOption, setOption), as well as a method that runs the solver for a given optimisation problem (solve).

 

Optimization solver

A number of constrained optimization solvers are designed to solve the general nonlinear optimization problem.

  1. PSQP: This optimizer is a preconditioned sequential quadratic programming algorithm. This optimizer implements a sequential quadratic programming method with a BFGS variable metric update.
  2. SLSQP: This optimizer is a sequential least squares programming algorithm. SLSQP uses the Han–Powell quasi-Newton method with a BFGS update of the B-matrix and an L1-test function in the step-length algorithm. The optimizer uses a slightly modified version of Lawson and Hanson’s NNLS nonlinear least-squares solver.
  3. CONMIN: This optimizer implements the method of feasible directions. CONMIN solves the nonlinear programming problem by moving from one feasible point to an improved one by choosing at each iteration a feasible direction and step size that improves the objective function.
  4. COBYLA: It is an implementation of Powell’s nonlinear derivative–free constrained optimization that uses a linear approximation approach. The algorithm is a sequential trust–region algorithm that employs linear approximations to the objective and constraint functions.
  5. SOLVOPT: SOLVOPT is a modified version of Shor’s r–algorithm with space dilation to find a local minimum of nonlinear and non–smooth problems.
  6. KSOPT: This code reformulates the constrained problem into an unconstrained one using a composite Kreisselmeier–Steinhauser objective function to create an envelope of the objective function and set of constraints. The envelope function is then optimized using a sequential unconstrained minimization technique.
  7. NSGA2: This optimizer is a non-dominating sorting genetic algorithm that solves non-convex and non-smooth single and multiobjective optimization problems.
  8. ALGENCAN: It solves the general non-linear constrained optimization problem without resorting to the use of matrix manipulations. It uses instead an Augmented Lagrangian approach which is able to solve extremely large problems with moderate computer time.
  9. FILTERSD: It use of a Ritz values approach Linear Constraint Problem solver. Second derivatives and storage of an approximate reduced Hessian matrix is avoided using a limited memory spectral gradient approach based on Ritz values.

 

To solve an optimization problem with pyOpt an optimizer must be initialized. The initialization of one or more optimizers is independent of the initialization of any number of optimization problems. To initialize SLSQP, which is an open-source, sequential least squares programming algorithm that comes as part of the pyOpt package, use:

>>> slsqp = pyOpt.SLSQP()

This initializes an instance of SLSQP with the default options. The setOption method can be used to change any optimizer specific option, for example the internal output flag of SLSQP:

>>> slsqp.setOption('IPRINT', -1)

Now Schittkowski’s constrained problem can be solved using SLSQP and for example, pyOpt’s automatic finite difference for the gradients:

>>> [fstr, xstr, inform] = slsqp(opt_prob,sens_type='FD')

By default, the solution information of an optimizer is also stored in the specific optimization problem. To output solution to the screen one can use:

>>> print opt_prob.solution(0)

 

Example:

The problem is taken from the set of nonlinear programming examples by Hock and Schittkowski and it is defined as

=======================================================================

      min            − x1x2x3

     x1,x2,x3

 

subjected to     x1 + 2x2 + 2x3 − 72 ≤ 0

                        − x1 − 2x2 − 2x3 ≤ 0

 

                        0 ≤ x1 ≤ 42

                        0 ≤ x2 ≤ 42

                        0 ≤ x3 ≤ 42

 

The optimum of this problem is at (x1∗ , x2∗ , x3* ) = (24, 12, 12), with an objective function value of f ∗ = −3456, and constraint values g (x∗ ) = (0, −72).

 

#======================================================================

# Standard Python modules

#======================================================================

import os, sys, time

import pdb

#======================================================================

# Extension modules

#======================================================================

#from pyOpt import *

from pyOpt import Optimization

from pyOpt import PSQP

from pyOpt import SLSQP

from pyOpt import CONMIN

from pyOpt import COBYLA

from pyOpt import SOLVOPT

from pyOpt import KSOPT

from pyOpt import NSGA2

from pyOpt import ALGENCAN

from pyOpt import FILTERSD

 

#======================================================================

def objfunc(x):

   

    f = -x[0]*x[1]*x[2]

    g = [0.0]*2

    g[0] = x[0] + 2.*x[1] + 2.*x[2] - 72.0

    g[1] = -x[0] - 2.*x[1] - 2.*x[2]

   

    fail = 0

    return f,g, fail  

 

#======================================================================

# Instantiate Optimization Problem

opt_prob = Optimization('Hock and Schittkowski Constrained Problem',objfunc)

opt_prob.addVar('x1','c',lower=0.0,upper=42.0,value=10.0)

opt_prob.addVar('x2','c',lower=0.0,upper=42.0,value=10.0)

opt_prob.addVar('x3','c',lower=0.0,upper=42.0,value=10.0)

opt_prob.addObj('f')

opt_prob.addCon('g1','i')

opt_prob.addCon('g2','i')

print opt_prob

 

# Instantiate Optimizer (PSQP) & Solve Problem

psqp = PSQP()

psqp.setOption('IPRINT',0)

psqp(opt_prob,sens_type='FD')

print opt_prob.solution(0)

 

# Instantiate Optimizer (SLSQP) & Solve Problem

slsqp = SLSQP()

slsqp.setOption('IPRINT',-1)

slsqp(opt_prob,sens_type='FD')

print opt_prob.solution(1)

 

# Instantiate Optimizer (CONMIN) & Solve Problem

conmin = CONMIN()

conmin.setOption('IPRINT',0)

conmin(opt_prob,sens_type='CS')

print opt_prob.solution(2)

 

# Instantiate Optimizer (COBYLA) & Solve Problem

cobyla = COBYLA()

cobyla.setOption('IPRINT',0)

cobyla(opt_prob)

print opt_prob.solution(3)

 

# Instantiate Optimizer (SOLVOPT) & Solve Problem

solvopt = SOLVOPT()

solvopt.setOption('iprint',-1)

solvopt(opt_prob,sens_type='FD')

print opt_prob.solution(4)

 

# Instantiate Optimizer (KSOPT) & Solve Problem

ksopt = KSOPT()

ksopt.setOption('IPRINT',0)

ksopt(opt_prob,sens_type='FD')

print opt_prob.solution(5)

 

# Instantiate Optimizer (NSGA2) & Solve Problem

nsga2 = NSGA2()

nsga2.setOption('PrintOut',0)

nsga2(opt_prob)

print opt_prob.solution(6)

 

# Instantiate Optimizer (ALGENCAN) & Solve Problem

algencan = ALGENCAN()

algencan.setOption('iprint',0)

algencan(opt_prob)

print opt_prob.solution(7)

 

# Instantiate Optimizer (FILTERSD) & Solve Problem

filtersd = FILTERSD()

filtersd.setOption('iprint',0)

filtersd(opt_prob)

print opt_prob.solution(8)

 

Solving non-linear global optimization problems could be tedious task sometimes. If the problem is not that complex then general purpose solvers could work. However, as the complexity of problem increases, general purpose global optimizers start to take time. That is when need to create your problem specific fast and direct global optimizer’s need arises.

 

We have an specialized team with PHD holders and coders to design and develop customized global optimizers. If you need help with one, please feel free to send your queries to us.

 

We first understand the problem and data by visualizing it. After that we create a solution to your needs.

 

Please do read to understand what a solver is and how it works - If you want to create your own simple solver. This is not exactly how every solver works, however, this will give you a pretty solid idea of what is a solver and how it is supposed to work.

Applying for a data scientist job can be an intimidating task as there can be many things to take care in an interview process — right from justifying the practical knowledge to showcasing the coding skills. While we have earlier discussed articles on how to crack data science interview and what are the things to keep in mind while appearing for an interview for data science-related roles. This article deals with some of the things that you might be doing wrong if ever you are rejected in a data science interview.

 

Here are five things you may have been doing wrong:

 

Not focusing on the job description: The definition of data science jobs is not always the same and may mean different roles and responsibilities for different companies. Some of the commonly required skills may be a PhD in statistics, Excel skills, machine learning generalist, Hadoop skills, Spark skills, among others. The job description largely varies for every company and it is important to thoroughly dig it and carefully look for specific skills, tools and languages. It is important to display the skills that the potential recruiter is looking for so that they can shortlist you easily.

 

No specific distinction of technical skills: The technical skills in data science and analytics industry is quite wide and not mentioning your strengths correctly might jeopardise your chances of cracking the interview. For instance, it might not be apt to just say machine learning skills as it might include a whole spectrum of things ranging from linear regression to neural networks. And these sub-areas might further require knowledge of specific tools and software such as Python, Keras, R or Pandas. It is always advisable to give specific skills that you master than describing generic skills as might confuse recruiters of the exact skills that you pose.

 

Incorrect information and rephrasing work experience: To suit the data science job roles, many a times candidates rephrase their previous work experiences such as in the IT or software domains to present it as data science job roles, which might disguise your abilities initially but expose the depth and understanding of the skills later. You might have included job description aligning in a way that suits data science job roles but you might not have a deeper experience in it, which may get noticeable by recruiters during a one-to-one interaction. Mentioning of incorrect or misleading facts may also lead to recruiters rejecting you. For instance, the resume may state achieved an accuracy of say 90% on the test run, but what are the baseline and state-of-the-art score for this dataset to claim these numbers?

 

No mention about the projects that you have worked on from the scratch: Many times the only projects that a candidate mention in a resume are the ones they have done on Kaggle. While Kaggle is a platform for a lot of researchers to explore avenues in data science, it also serves as a source of practice for people who aren’t a pro in data science field and are trying to make a transition, mentions a recruiter in one of the forums. There are different kinds of the audience at Kaggle such as those who are playing around with the dataset or getting to know how problem-solving in data science works like, without having actual experience in solving or creating a new data science problems. So, listing just Kaggle project might be good but not definitive of how good your data science skills are. Even if it a Kaggle project, it is better if it is done from scratch. Other than that, it is important to mention the projects that you have worked on. It gives recruiters a chance to understand the problems you faced and the way you approached the problem, thereby giving them a glance at your problem-solving abilities.

 

The resume is full of buzzwords and no concrete proof of your skills: While the resume may suit the job description, but there are chances that you are rejected if there are too many buzzwords in the resume and no concrete way to prove that you actually pose those skills. You may mention in the resume that you have had experience with Hadoop, Excel or certain areas, but if you have showcased it real-time on platforms such as GitHub, it convinces the potential employers of the skills you have. They can look through various projects you have been a part of and see how you have dealt with real data. Hiring managers like to see the time that a candidate has spent from start to finish. Having a portfolio gives recruiters just that. There may be fancy sounding terms in the resume, but if you don’t have a proof to showcase it, you might be rejected for a potential data science job role.

A large number of people think that “freelancing” is something you do when you cannot get a real job. On the other hand, “freelancers” know that there is nothing more real than that to be the owner, director, and the financial manager at the same time.

 

Freelancing is basically being self-employed and not committed to any one company or firm. You’ve heard those seemingly perfect freelance stories. Some designer quits his jobs and starts freelancing and now he’s making more money than he was while at a firm. All the while travelling the world and working for himself. Not to mention he gets to choose what kind of work he does.

 

When I say “full-time job” I mean one that’s 30-hours per week or more. Basically, you’ve hit the threshold for wherever you’ve started to receive benefits for the time you work each week. Generally, over 30-hours is considered regular, and 40-hours is that the “traditional” hours for full time, however many jobs will go over that mark.

 

When you work as a freelancer, you’re not permanently employed by any one company. You may have a long-term contract, however freelancers are usually working with totally different employers at any given time and should have a spread of tasks that they'll be employed for.

 

“I choose to be in freelance because I’m able to work my own hours, determine my own salary, and be creative in my work.”

Freelance work offers tremendous advantages and can represent an attractive alternative to a traditional job. If you are considering a freelance career, you should explore the benefits of freelancing. 

  1. Working from home: Working from home is a perfect resolution for balancing work and family or personal life, during which you can with success make for a living and support yourself and your family. Engaging from home and thereby carve out a comfortable life, it's fully possible. But, as long as you're willing to work hard.
  2. Flexibility of hours: Working from home or from a remote workplace as a freelancer allows you to dictate your own hours and work on times most convenient to you. Freelancers with young kids, for instance, will work when the children are sleeping; freelancers with traditional employment or part-time jobs will perform their freelance work around their regular work hours.
  3. Perform multiple task as same time: Large Scale Company engaged in one activity or an entrepreneur who knows how to do five things at once? Freelancers are themselves in their work. That speaks to that they constantly further educate, constantly wide network of contacts and work hard at acquiring new skills that can make them more competitive in the work they are dealing with.
  4. Lower Cost: Utility costs, equipment, insurance, and running the business from the office building has become too costly. If the profit is insufficient, jobs will fail because of the buildup of these costs. Freelancers, on the other hand, almost don't have any additional cost, then will get started by simply registering at premium freelancing sites like Toogit.
  5. Freedom: As a freelancer, you can choose the clients you wish to work with and the projects on which you work, particularly if you have an excess of work. You can drop high maintenance or slow-paying clients or turn down undesirable projects if you desire.
  6. Income Control: Your income is the direct results of your own efforts instead of being set by the law firm or company. In most cases, the harder you work, the greater the reward. Your paycheck or bonus will not be capped, reduced or eliminated by your leader, though it will vary month to month, depending on your efforts and business.
  7. Learning through Work: Do not think about work like at a company wherever you work twenty years the same thing, you'll change jobs and employers on a weekly basis, and lots of additional can learn what is going to be helpful for future jobs.
  8. Full credit: When you work as a freelancer, you receive full credit for your work. You don't have to worry about the blunders of other employees, compromising your work product for the sake of the team or others taking credit for your work.
  9. Opportunity for all: Increasing employment of vulnerable teams like mothers and fathers with young kids, people with mobility problems and people living in remote areas.

 

“I prefer the freedom to choose what sort of work I do without my schedule being controlled and my choices being commanded by someone else. I can express myself and be appreciated for it as well as bring beauty to the world by way of my work. It also is less stressful than an office environment and allows me the time necessary to take care of my farm.”

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