Hire the best
Sol Consultants

Top 33 Sol Consultants on 24 Apr 2019 on Toogit. Sol Consultants on Toogit are highly skilled and talented. Hiring Sol Consultants is quite affordable as compared to a full-time employee and you can save upto 50% in business cost by hiring Sol Consultants. Hiring Sol Consultants is 100% safe as the money is released to the Sol Consultants only after you are 100% satisfied with the work.

Get Started

Explore Toogit’s top Sol Consultants

 
 
 
Wordpress Security Experts
Manisekaran Chandra

Wordpress Security Experts  


SSL mysql Wordpress 
$30 /hr
India
Data Analyst
Arun

Data Analyst  


sql SQL Programming Microsoft Excel 
$3 /hr
India
senior software engineer
Raviteja Damera

senior software engineer  


sql Oracle PLSQL "Extract, Transform and Load (ETL)" 
$4 /hr
India
Business Analyst
Ashish Parmar

Business Analyst  


sql SAS MS Office 
$9 /hr
India
Software Engineer
Gurusiddesh Katti

Software Engineer  


sql CSS3 Microsoft SQL Server 
$10 /hr
India
Freelance Developer
Keerthana Viyasarayan

Freelance Developer  


sql Robotic Process Automation ASP.NET Core 
$20 /hr
Czech Republic
Software Developer
Nikhil

Software Developer  


sql ASP.NET AngularJS 
$18 /hr
India
Java & Scala developer
Madhu

Java & Scala developer  


sql Scala AngularJS 
$10 /hr
Belgium
Data Analyst
Manish Dawar

Data Analyst  


sql Microsoft Visio Microsoft Power BI Data Visualization 
$10 /hr
India
Senior software developer
Abhishek Sinha

Senior software developer  


sql Microsoft Excel Data Science & Analytics 
$9 /hr
India
Senior Software engineer
Vivek

Senior Software engineer  


sql CSS Oracle PL/SQL 
$12 /hr
India
Freelance developer
Haris Khan

Freelance developer  


sql Java 
$2 /hr
India
Full stack developer
Denys Popov

Full stack developer  


sql Web Services Social Networking 
$40 /hr
Germany
Trainer
Lakhan

Trainer  


sql Data Structures C Programming 
$9 /hr
India
Tableau and Power BI Developer
Karthik Venkat

Tableau and Power BI Developer  


sql Business Intelligence Data Visualization 
$20 /hr
India
Backend developer With experience in Python, C, Oracle, API, Hive, Presto
Sudhanshu Patel

Backend developer With experience in Python, C, Oracle, API, Hive, Presto  


sql mysql Oracle PLSQL 
$10 /hr
India
Sr. Investigation Specialist
Varshith Shetty

Sr. Investigation Specialist  


sql Excel Microsoft Excel 
$6 /hr
India
Consultant
Ankush Gulati

Consultant  


sql Microsoft SQL Server Microsoft Excel 
$5 /hr
India
Sr. Tableau Developer
Nageswara Rao

Sr. Tableau Developer  


sql Tableau Software 
$20 /hr
India
Software Engineer
Suresh

Software Engineer  


sql CSS3 HTML5 
$4 /hr
India
Lead Enginner
Siva

Lead Enginner  


sql JasperReports Database Design 
$10 /hr
India
Full Stack Developer
Rekha

Full Stack Developer  


sql HTML NodeJS 
$9 /hr
India
Software developer
Kokila

Software developer  


sql mysql Oracle PLSQL 
$9 /hr
India
Bachelors of Business Administration – (Computer Application)
Saylee Khetale

Bachelors of Business Administration – (Computer Application)  


sql HTML MS Office 
$6 /hr
India
Software testing engineer
Tarun Sharma

Software testing engineer  


sql Selenium Atlassian Jira 
$9 /hr
India
Application Developer
Jawad Khan

Application Developer  


sql React.js javascript 
$15 /hr
Pakistan
Business Analyst
Priya Bhatia

Business Analyst  


sql SAS Excel 
$26 /hr
India
ETL,BI REPORT,SQL,PL/SQL Developer
Prabhat Mishra

ETL,BI REPORT,SQL,PL/SQL Developer  


sql Oracle PL/SQL Unix Shell 
$50 /hr
India
Data Scientist
Sumit Maan

Data Scientist  


sql Time Series Analysis Data Visualization 
$20 /hr
India
Senior Python Developer
Venkatesh Km

Senior Python Developer  


sql Selenium WebDriver Excel VBA 
$50 /hr
India
STUDENT (big data analytics)
Shriti Gupta

STUDENT (big data analytics)  


sql mysql Microsoft SQL Server 
/hr
India
Technical support specialist
Junaid Shaikh

Technical support specialist  


SSL DNS Customer Support 
$17 /hr
India
Support Engineer
Subhasri Pradhan

Support Engineer  


sql Shell Script Linux 
$3 /hr
India
Sign-up
to view more profiles

Get Started
 

How it works

Post a job

Post a Job

List your project requirement with us. Anything you want to get developed or want to add to your business. Toogit connects you to Top freelancers around the world.

Hire

Hire

Invite and interview your preferred talent to get work done. Toogit Instant Connect helps you if you need your project started immediately.

Work

Work

Define Tasks, use Toogit's powerful project management tool, stay updated with real time activity logs

Payment

Pay

Review work, track working hours. Pay freelancers only if you are 100% satisfied with the work done.

Popular How-To's in Sol category


 
How to create a solver in python
Scripts & Utilities

Python scipy provides a good number of optimizers/solvers. You can use these optimizers to solve various non-linear and linear equations. However, sometimes things might get tricky...

Read More

Reviews From Our Users

Articles Related To Sol


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.

What is a web scraping?

Web scraping, web harvesting, or web data extraction is data scraping used for extracting data from websites. Web scraping software may access the World Wide Web directly using the Hypertext Transfer Protocol, or through a web browser. While web scraping can be done manually by a software user, the term typically refers to automated processes implemented using a bot or web crawler. It is a form of copying, in which specific data is gathered and copied from the web, typically into a central local database or spreadsheet, for later retrieval or analysis.

Web scraping a web page involves fetching it and extracting from it. Fetching is the downloading of a page (which a browser does when you view the page). Therefore, web crawling is a main component of web scraping, to fetch pages for later processing. Once fetched, then extraction can take place. The content of a page may be parsed, searched, reformatted, its data copied into a spreadsheet, and so on. Web scrapers typically take something out of a page, to make use of it for another purpose somewhere else. An example would be to find and copy names and phone numbers, or companies and their URLs, to a list (contact scraping).

 

What you can do with data scraping?

Web scraping is used for content scraping, and as a component of applications used for web indexing, web mining and data mining, online price change monitoring and price comparison, product review scraping (to watch the competition), gathering real estate listings, weather data monitoring, website change detection, research, tracking online presence and reputation, web mashup and, web data integration.

Using data scraping you can build sitemaps that will navigate the site and extract the data. Using different type selectors you will navigate the site and extract multiple types of data - text, tables, images, links and more.

 

What role scraper should play for you?

Web scraping is the process of automatically mining data or collecting information from the World Wide Web. It is a field with active developments sharing a common goal with the semantic web vision, an ambitious initiative that still requires breakthroughs in text processing, semantic understanding, artificial intelligence and human-computer interactions. Current web scraping solutions range from the ad-hoc, requiring human effort, to fully automated systems that are able to convert entire web sites into structured information, with limitations.

 

Below are the ways for scraping data:

  • Human Copy Paste : Sometimes even the best web-scraping technology cannot replace a human’s manual examination and copy-and-paste, and sometimes this may be the only workable solution when the websites for scraping explicitly set up barriers to prevent machine automation.
  • Text Pattern Matching : A simple yet powerful approach to extract information from web pages can be based on the UNIX grep command or regular expression-matching facilities of programming languages
  • HTTP programming : Static and dynamic web pages can be retrieved by posting HTTP requests to the remote web server using socket programming.
  • HTML parsing : Many websites have large collections of pages generated dynamically from an underlying structured source like a database. Data of the same category are typically encoded into similar pages by a common script or template. In data mining, a program that detects such templates in a particular information source, extracts its content and translates it into a relational form, is called a wrapper. Wrapper generation algorithms assume that input pages of a wrapper induction system conform to a common template and that they can be easily identified in terms of a URL common scheme.Moreover, some semi-structured data query languages, such as Xquery and the HTQL, can be used to parse HTML pages and to retrieve and transform page content.
  • DOM parsing: By embedding a full-fledged web browser, such as the Internet Explorer or the Mozilla browser control, programs can retrieve the dynamic content generated by client-side scripts. These browser controls also parse web pages into a DOM tree, based on which programs can retrieve parts of the pages.
  • Vertical aggregation : There are several companies that have developed vertical specific harvesting platforms. These platforms create and monitor a multitude of “bots” for specific verticals with no "man in the loop" (no direct human involvement), and no work related to a specific target site. The preparation involves establishing the knowledge base for the entire vertical and then the platform creates the bots automatically. The platform's robustness is measured by the quality of the information it retrieves (usually number of fields) and its scalability (how quick it can scale up to hundreds or thousands of sites). This scalability is mostly used to target the Long Tail of sites that common aggregators find complicated or too labor-intensive to harvest content from.
  • Semantic annotation recognizing : The pages being scraped may embrace metadata or semantic markups and annotations, which can be used to locate specific data snippets. If the annotations are embedded in the pages, as Microformat does, this technique can be viewed as a special case of DOM parsing. In another case, the annotations, organized into a semantic layer,are stored and managed separately from the web pages, so the scrapers can retrieve data schema and instructions from this layer before scraping the pages.
  • Computer vision web-page analysis : There are efforts using machine learning and computer vision that attempt to identify and extract information from web pages by interpreting pages visually as a human being would.

 

Key Features of Web Scraping

In order to remain competitive, businesses must be able to act quickly and assuredly in the markets. Web Scraping plays a big role in the development of various business organizations that use the services. 

The benefits of these services are: 

  1. Low Cost: Web Scraping service saves hundreds of thousands of man-hours and money as the use of scraping service completely avoids manual work.
  2. Less Time: Scraping solution not only helps to lower the cost, it also reduces the time involved in data extraction task. This tool ensures and gathers fast results required by people.
  3. Accurate Results: Web Scraping solutions help to get the most accurate and fast results that cannot be collected by human beings. It generates correct product pricing data, sales leads, duplication of online database, captures real estate data, financial data, job postings, auction information and many more.
  4. Time to Market Advantage: Fast and accurate results help businesses to save time, money and labor and get an obvious time-tomarket advantage over the competitors.
  5. High Quality: A Web Scraping solution provides access to clean, structured and high quality data through scraping APIs so that the fresh data can be integrated into the systems.

Finding and hiring expert scraper/crawler

It’s important to note that not all scraper will be ideal fits for every project. For example, those with highly analytical backgrounds in software engineering would be ideal for developing algorithms but may not be the right fit for a data scraping project. That’s why it’s so important to understand what type of scraping expert will bring the most benefit to your company and business goals.

Here are some questions to consider:

What is the overall learning you hope to find? 

By including your goal in the project description, it allows professionals to better understand what type of work is required.

 

What core skills will scraping experts need to complete the project? 

The answer will revolve around your current data infrastructure and the processes used to extract information.

 

Would you benefit from someone with highly specialized skills in a few areas of data scraping, or would a well-rounded expert serve you better?

 

Are there any time constraints to consider with this project?

Let professionals know the amount of hours of work that might be involved.

 

What kind of budget will this project have? 

The more experience and expertise a data scraper has, the higher they expect to be compensated. Higher budgets will more likely give top-tier experts a reason to submit a proposal.

 

Web scraping project template

Below is a sample of how a project description may look. Keep in mind that many people use the term “job description,” but a full job description is only needed for employees. When engaging a freelancer as an independent contractor, you typically just need a statement of work, job post, or any other document that describes the work to be done.

<Job/Project Title>

ABC Company is looking for a web scraping expert to help us study our website traffic patterns and find areas of improvement. This project is estimated to require approximately 20-25 hours per week for the next few months to achieve the following goals

  • Reporting findings in a weekly summary
  • Split testing underperforming pages and recording results
  • Discovering which pages currently perform best
  • Organizing site data into spreadsheets

The following skills are required:

The ideal freelancer will be a creative problem solver with an excellent work history on Toogit. To submit a proposal, please send a short summary of similar projects you’ve completed and why we should consider you for this project.

  • Excellent technical abilities
  • Knowledge of quantitative split testing
  • Experience with WordPress and Google Analytics
  • A thorough understanding of MySQL databases
  • Expertise or extensive experience with Python

 

Hiring the right Web Scraping talent

Remember that technical ability is only a small portion of what makes an excellent web scraper. Great web scrapers are inquisitive—they want to ensure that they’re seeking the right types of answers, plus they’ll take an interest in your business to better understand it. The ideal professional will also be able to advise you on additional metrics to analyze and compare in order to help you meet your goals.

Also, keep in mind that communication is always a key consideration in the data science field. A brief interview can allow you to gauge how strong each professional is in expressing ideas and explaining their process. The more you speak to each professional by phone, email, or chat, the better you’ll be able to gauge their professionalism and communication skills and determine whether they’re right for your project.

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.

Articles Related To Sol


5 Reasons Why You May Have Been Rejected In A Data Science Interview
5 Reasons Why You May Have Been Rejected In A Data...
Data Mining & Management

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 sh...

Read More
How to write/compose a Job description for web scraping to achieve your goal with minimal line of code?
How to write/compose a Job description for web scr...
Data Extraction / ETL

What is a web scraping?Web scraping, web harvesting, or web data extraction is data scraping used for extracting data from websites. Web scraping software may access the World Wide...

Read More
Scope and Career Opportunities of Data Science
Scope and Career Opportunities of Data Science
Data Extraction / ETL

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...

Read More

Other Freelancers In Similar Categories

Vinit Agarwal


I am certified mechanical engineer working as a design engineer in my company. I am well versed with both AutoCAD a...

Ankur Garg


I am a solidworks designer certifucation from cetpa info tech and having 2 years of experience in designing

Niraj Issrani


I am an mechanical engineering student , I've completed my diploma in it. And I am professionally trained for...

Aditya Pandharp...


1. I am a product designer with an experience of designing medical ozonation products and designed a portable water...

What our users are discussing about Sol