Automated proposal submission



Automated proposal submission

‘Automated freelancing’, read this term twice and try thinking how automated freelancing would feel like?

Freelancing is complicated for beginners. Not only for beginners but for experienced freelancers as well. It is complicated not because of the project’s difficulty, but because of the time consumed in ‘getting’ the projects. Let’s put it in this way you will have to search for a project that matches your skills, then you will create a good proposal, fix your price, and make the proposal or bid on the project. In a normal case scenario, at least 15-20 people also would have submitted their bids(proposals) on the same project. Now, the normal probability of you getting this project is 1/20 or 5%. Means you will have to bid on 20 projects to get one. Let say one proposal take one hour(Without copy paste), You have wasted around 20 hours, almost 3 days of productive time and you did not work yet. This conversion percentage goes even lower to .5-1% for beginners. It is no exaggeration saying 60% freelancers waste their 70% time in hunting for work. It is equally painful from client’s point of view as well. Some projects require immediate assistance and are urgent in nature, but they get delayed because of unavailability of ‘right’ freelancer. Moreover, the hiring process gets delayed most of the times.

The solution, If there is way to come over this hurdle what would that be? Yes, the answer is Automated freelancing. Toogit has started the automated freelancing and the first step in automated freelancing is Auto Proposals submission.

You can activate auto proposal submission in your Toogit profile by following these simple steps:

Step 1

Login your toogit profile, click your name at top right and then settings.

 

 

Step 2

Now click proposal template, then add new template, and set up a template that will be used in auto proposals in the preselected skills and budget.

 

 

Step 3

Click Auto proposals at left, check the box for Enable\Disable auto proposals. You are all set to go now.

 

 

How it works

Toogit does not submit all the matching profiles to client in one shot. Instead it uses a complex algorithm to decide which profiles should be submitted to client first. Toogit submits the auto-proposals in a bunch of 10 at a time. Once client has reviewed the top 10 next 10 are submitted to client for review on request and below are the conditions for auto submissions to work: Client is willing to accept auto-proposals. At the time of posting a job client needs to decide if he wants to receive auto-proposals or not.

  1. Freelancer has defined a proposal template for one or more of the skills mentioned in “Expertise Required” section of job postings
  2. Budget of job-posting is equal to or more than the minimum budget specified by freelancer at the time of enabling auto-proposal.
  3. Budget of job-posting is equal to or more than the minimum budget specified by freelancer at the time of enabling auto-proposal.

 

Note: Automated proposal submission is completely free while it is in beta phase. If you have suggestions to improve this feature, please write us at support@toogit.com.

 

Dushyant Tyagi

Dushyant is a seasoned freelance writer, developer and start-up enthusiast. Apart from front and backend development, his passion for writing makes him an expert in blogging, content writing and generating quality web traffic.

Dushyant Tyagi | Freelance Writer


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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.

The global demand for data Science professionals is extremely high because of increasing relevance across various sectors. Data Science has become the most-sought skill because the data is piling along with a surge in different tech fields like Artificial Intelligence, machine learning and data Analytics. Hiring data scientist is being carried across numerous domains like e-commerce, education, retail, telecommunication and much more.

 

In the past years, analysts used excel tools to analyze data. Things are changing now! In this modern world, data-driven decision making is sparkling and technology is advanced in the data industry. The tools and technologies that modern Data Scientists employ are a combination of statistical and Machine Learning algorithms. They are used to discover patterns using predictive models. The future of Data Science is bright and the options for its implementation are extensive.

 

Data Scientists must consistently evolve at the edge of innovation and creativity. They must be aware of the types of models they create. These innovations will allow them to spend time discovering new things that may be of value. Subsequently, the advances in Data Science tools will help leverage existing Data Science talent to a greater extent.

 

So what does a Data Scientist do?

Data Scientists influence a pile of data in an innovative way to discover valuable trends and insights. This approach helps to identify opportunities by implementing research and management tools to optimize business processes by reducing the risks. Data Scientists are also responsible for designing and implementing processes for data mining, research and modeling purposes.

 

Data scientist performs research and analyses data and help companies flourish by predicting growth, trends and business insights based on a large amount of data. Basically, data scientists are massive data wranglers. They take a vast data and use their skills in mathematics, statistics and programming to scrub and organize the information. All their analysis combined with industrial knowledge helps to uncover hidden solutions to business challenges.

 

Generally, a data scientist needs to know what could be the output of the big data he/she is analyzing. He/she also needs to have a clearly defined plan on how the output can be achieved with the available resources and time. Most of all the data scientists must know the reason behind his attempt to analyze the big data.

 

To achieve all of the above, a data scientist may be required to:

 

Every organization has unique data problems with its own complexities. Solving different Data Science problems requires different skill sets. Data Science teams are groups of professionals with varied skill sets. They, as a team, solve some of the hardest data problems an organization might face. Each member contributes distinctive skill set required to complete a Data Science project from start to finish.

 

The Career Opportunities:

The careers associated with data science are generally categorized into five.

 

  1. Statisticians: Statisticians work usually for national governments, marketing research firms and research institutes. Extracting information from massive databases through numerous statistical procedures is what they do.
  2. Data Analyst: Telecommunication companies, manufacturing companies, financial companies etc. hire data scientists to analyze their data. A data analyst keeps track of various factors affecting company operation and make visual graphics.
  3. Big Data and Data Mining Engineer: Tech companies, retail companies and recreation companies use data scientists as data mining engineers. They have to gather and analyze huge amounts of data, typically from unstructured information.
  4. Business Intelligence Reporting Professional: They work for tech companies, financial companies, and consulting companies etc. Market research is the primary objective of this job. They also generate various reports from the structured data to improve the business.
  5. Project Manager: A project manager evaluates data and insights fetched from the operational departments and influences the business decisions. They have to plan the work and make sure everything goes in accordance with the plan.

Are you planing to hire a CSS developer—how can you find a top developer for your front-end or PSD to CSS project?

 

CSS has been in use for more than 20 years and has become an integral part of any front-end development. Therefore, there is no shortage of developers with CSS listed on their resumes. Locating CSS developers is fairly easy, but choosing the perfect one is that much more of a challenge. This article offers a sampling of effective questions to help you identify the best CSS developers who are experts in their field.

 

What is CSS?

CSS stands for Cascading Style Sheets, CSS is a programming language that describes the style of a HTML document. If you want to customize your website background image, text colors or border then you need CSS design. Alongside HTML (responsible for structure) and JavaScript (responsible for interactivity), CSS (responsible for style) is one of the big three core components of the web.

 

Next, we learn about what a CSS developer does, and provide you with a general framework for writing a CSS project description to help you find the right developer for your requirements. How to hire a top css developer to get work done.

 

What kind of work a CSS developer should deliver to you? A front-end developer uses a combination of HTML, CSS, and JavaScript to build everything a user sees and interacts with on a website—everything from front-end features like fonts and sliders, to the overall manner in which web content like photos, videos, and articles are displayed in your web browser. A CSS developer who specializes in CSS, taking .psd files and mockups and writing the CSS code that incorporates all of the colors, padding, margins, and more that comprise those designs. Beyond the fundamentals, they can work magic with raw CSS, are well versed in preprocessors like LESS/Sass, and may even use a front-end CSS framework like Bootstrap or Foundation.

 

Hire the best CSS Developers Work with the world's best talent on Toogit — the top growing freelancing website trusted by over 150,000 users.

 

Writing A CSS Development Project Description

After you get a firm idea of your project deliverables, it’s time to write a project description. The way you write a description will determine the quality of developer that you’ll attract. It’s important to be concise yet detailed enough so developers interested in your project can submit proposals with fairly accurate cost and time estimates. Here our recommendation to use Toogit’s auto-proposal to speed up your hiring procedure and feel the power of AI in freelancing.

 

The title of your project description can include the type of development that you need. You know that you need a CSS developer, but why specifically a front-end developer specialized in CSS? The title should attract CSS developers with the specific technologies or skills you require for your project.

 

Next is the project overview. Describe what you’re planning to build or what you’ll need the developer to do. Be as detailed as possible, and include any wireframes or mockups that can help you attract the talented developer for your needs. 

 

Part of your description should also define the deliverables including any designs, documentation, or source code. 

 

Sample CSS Project Description

Below sample will help you to write a perfect project description. 

 

Project Title:

CSS Developer for a Fashion design website 

 

Description: 

We’re looking for an expert CSS developer to help us build an exciting new fashion design website template. The project is based on the (MongoDB, AngularJS, and Node.js) stack, so familiarity using Bootstrap with AngularJS is required.

The right developer will be able to provide us with the following skills and services:

  • Translation of designer mock-ups and wireframes into front-end code
  • Front-end integration with a MEAN back-end
  • Unit testing
  • Bootstrap, LESS, AngularJS
  • Familiarity with API Creation and RESTful services

 

Project Scope & Deliverables:

While much of the project has already been completed, we still need additional support to help us polish our website and meet our launch deadline in 4 months (mm/dd/yyyy). We will need the following three deliverables:

Deliverable #1 by (date) 

Deliverable #2 by (date) 

Deliverable #3 by (date)

Hire a CSS Designer

On Toogit.com you can hire CSS coders and designers to make your web design and custom CSS project shine. Get started today.

 

Conclusion

For a top CSS developer, read our css interview question and answer section this might come off as a bit basic. However, It cover most of the core CSS concepts and principles, and provide a starting point for evaluating individuals. Being able to discuss CSS principles and concepts in a clear and coherent manner will demonstrate candidate’s communication skills as well as their theoretical and peripheral subject knowledge. Finding true CSS expert is a challenge. We hope you find the interview questions to be a useful foundation in your quest for the elite few among CSS developers. Finding such candidates is well worth the effort, as they will undoubtedly have a significant positive impact on your team’s productivity and results.

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