Hire the best
Statistics

Find talented Statistics

Get Started

Hire Trusted Freelancers for your project

More than 150,000 freelancers ready to tackle any kind of project

How it works

Post a job

Define your project

What you need in as much detail as possible. We will connect you with top talented ready to work freelancers best suitable for your requirement around the world, or near you.

Proposals

Find your expert

Get qualified proposals within 24 hours. Compare bids, reviews, and prior work. Interview favorites and hire the best fit. Auto proposal will help for urgent hiring

Communicate

Communicate

Use Toogit Messenger to chat, share files, and track project milestones from your desktop or mobile with realtime updates.

Payment

Pay Securely

Pay securely through Toogit's Partial/Full Payment system. Simply create invoices for project milestones, and only release the funds when you are 100% satisfied with the work completed.

Browse Our Top Rated Statistics


Rabia Aftab Mathematician, Pakistan
$10 /hr
0 Years Exp.
0 Followers
To gain employment as a Mathematics educator at one of the country’s most excellent and outstanding educational establishments known for producing stu...Read More
Rehnuma K.Web designer , Henna Artist, India
$86 /hr
1 Years Exp.
0 Followers
I am a certified web designer and c++ coder .. I M in last year of BCA .I also have knowledge of core java , DBMs .. I am also a Professional Henna Ar...Read More
Shruti B.business analyst, India
$9 /hr
0 Years Exp.
0 Followers
I have done B.Sc and M.Sc in statistics from delhi university. Have worked with EY and is currently working with Whyte as Business analyst. Also I am...Read More
Amarnath V.Manager SAS Programming , India
$40 /hr
9 Years Exp.
0 Followers
I am having more than 9 years of experience with SAS & Statistics and I completely believe that my qualifications match perfectly with your requir...Read More
Dufle A.data analyst, Kenya
$18 /hr
1 Years Exp.
0 Followers
i am mathematics and computer science graduate and i specialize in statistical analysis
I'm currently a student pursuing a bachelors degree in applied statistics with programming
Ar A.Data Analyst, India
$8 /hr
0 Years Exp.
0 Followers
I am an undergraduate student at a reputed university in Dhaka with major in Statistics.
Reading in the best business university of Bangladesh has given me an edge over multiples matters business persons want to freelance about. If you are...Read More
Sherif Typist, Oman
$15 /hr
0 Years Exp.
0 Followers
I can type on Microsoft word easily. I can do statistics for data .
Maha Data analyst , Morocco
$20 /hr
0 Years Exp.
0 Followers
Rigorous and ambitious, I am a Statistical engineer graduated from National Institute of Statistics and Applied Economics. I am an Artificial Intellig...Read More
Francis O.Expert, Kenya
$4 /hr
8 Years Exp.
0 Followers
I have vast experience over 8 years in different Areas. I can handle tutoring work in Maths, Physics, Calculus and Statistics. I have great experien...Read More
Asad Ullah Mathematics expert, Pakistan
$13 /hr
15 Years Exp.
0 Followers
I am MBA student with accounting and finance majors having 8+ years experience in freelancing. I worked as online tutor helping the students in their...Read More
Rabia - 3.Mathematical expert, Pakistan
$70 /hr
0 Years Exp.
0 Followers
I am a certified bs hons mathematics with 8 year experience.
Safna P.Data entry professional, Qatar
$3 /hr
1 Years Exp.
0 Followers
I am a data entry professional with 1 yr experience. Certified in HTML and CSS for web development. Done Indexing and quality check as well. Accuracy...Read More
Aishwarya. P. U.Biostatistician,Statistician, India
$69 /hr
0 Years Exp.
0 Followers
Skilled in Microsoft Excel, Microsoft Word.Have working knowledge of SPSS,R,SAS,STATA Strong research professional with a Master's degree focused...Read More
Willy Kiptoo statistician, Kenya
$6 /hr
5 Years Exp.
0 Followers
i am a certified statistician with 5years experience in field
Marry J.Senior High School Teacher, Philippines
$35 /hr
1 Years Exp.
0 Followers
I am a senior high school teacher who handles different positions.
Nyachieo Data analyst and Statistician, Kenya
$25 /hr
10 Years Exp.
0 Followers
I am an experienced statistician with training, exposure and experience in: - data collection, - data processing, - data mining, - statistical...Read More
Denmark C.Learner and a Teacher, Philippines
$20 /hr
1 Years Exp.
1 Followers
We learn everyday also we can teach everyday. Let me share my knowledge and make a part for better learning. Knowledge is power thus application of kn...Read More
Krishna M.Statistical analyst, India
$14 /hr
5 Years Exp.
0 Followers
I am certified M.sc statistics with 5 years experiences
Syeda Sheeza R.Data Entry, Pakistan
$100 /hr
0 Years Exp.
0 Followers
I am currently enrolled in BS program of Computational Finance from NED University of Engineering and technology in Karachi
Meshack M.DATA ANALYST, Kenya
$20 /hr
2 Years Exp.
0 Followers
I am a satisfied data analyst with knowledge in SPSS, R Programming, SAS software. I believe in the delivery of timely and first-class output within t...Read More
I am graduate in Mathematics and Statistics
To view more profile join Toogit

Get Started
 



Are you looking for Statistics Freelance Job? We’ll help you find the perfect matching job here

Top Earning Freelancers

Syed Rameez H.

Syed Rameez H.

Mobile Developer
Shilpi G.

Shilpi G.

Full stack frontend developer
Shital S.

Shital S.

QA Engineer
Pratik

Pratik

Web and Mobile Developer

Popular How-To's in Statistics category


How to Integrate Websites with YouTube’s API using PHP
How to Integrate Websites with YouTube’s API using...
Web Development

YouTube is currently world’s most popular video sharing web site. Over 1 billion hours of videos are watched every day and over 300 hours of video content is uploaded every minute....

Read More

Skills related to Statistics

Articles Related To Statistics


The importance of extracting information from the web is becoming increasingly loud and clear. Every few weeks, I realize myself in a situation where we need to extract information from the web to create a machine learning model. We have to pull or extract a large amount of information from websites and we would like to do it as quickly as possible. How would we do it without manually going to every web site and getting the data? Web Scraping simply makes this job easier and faster.

Why is web scraping needed?

Web scraping is used to collect large information from websites. But why does someone have to collect such large data from websites? Let’s look at the applications of web scraping: 

  1. Price Comparison: Services such as ParseHub use web scraping to collect data from online shopping websites and use it to compare the prices of products.
  2. Social Media Scraping: Web scraping is used to collect data from Social Media websites such as Twitter to find out what’s trending.
  3. Email address gathering: Many companies that use email as a medium for marketing, use web scraping to collect email ID and then send bulk emails.
  4. Research and Development: Web scraping is used to collect a large set of data (Statistics, General Information, Temperature, etc.) from websites, which are analyzed and used to carry out Surveys or for R&D.
  5. Job listings: Details regarding job openings, interviews are collected from different websites and then listed in one place so that it is easily accessible to the user.

 

Web scraping is an automated method used to extract large amounts of data from websites. The data on the websites are unstructured. Web scraping helps collect these unstructured data and store it in a structured form. There are different ways to scrape websites such as online Services, APIs or writing your own code.

Why Python is best for Web Scraping

Features of Python which makes it more suitable for web scraping:

  1. Ease of Use: Python is simple to code. You do not have to add semi-colons “;” or curly-braces “{}” anywhere. This makes it less messy and easy to use.
  2. Large Collection of Libraries: Python has a huge collection of libraries such as Numpy, Matlplotlib, Pandas etc., which provides methods and services for various purposes. Hence, it is suitable for web scraping and for further manipulation of extracted data.
  3. Dynamically typed: In Python, you don’t have to define datatypes for variables, you can directly use the variables wherever required. This saves time and makes your job faster.
  4. Easily Understandable Syntax: Python syntax is easily understandable mainly because reading a Python code is very similar to reading a statement in English. It is expressive and easily readable, and the indentation used in Python also helps the user to differentiate between different scope/blocks in the code.
  5. Small code, large task: Web scraping is used to save time. But what’s the use if you spend more time writing the code? Well, you don’t have to. In Python, you can write small codes to do large tasks. Hence, you save time even while writing the code.
  6. Community: What if you get stuck while writing the code? You don’t have to worry. Python community has one of the biggest and most active communities, where you can seek help from.

How does web scraping work

To extract data using web scraping with python, you need to follow these basic steps:

  1. Find the URL that you want to scrape
  2. Inspecting the Page
  3. Find the data you want to extract
  4. Write the code
  5. Run the code and extract the data
  6. Store the data in the required format

Example: Scraping a website to get product details

Pre-requisite:

  • Python 2.x or Python 3.x
  • Selenium Library
  • BeautifulSoup Library
  • Pandas Library
  1. We are going scrape online shopping website to extract the Price, Name, and rating of products, go to products URL
  2. The data is usually nested in tags. So, we inspect the page to examine, under which tag the information we would like to scrape is nested. To inspect the page, just right click on the element and click on “Inspect”. When you click on the “Inspect” tab, you will see a “Browser Inspector Box” open.
  3. Let’s extract the Price, Name, and Rating which is nested in the “div” tag respectively.
  4. Write code:

#Let us import all the necessary libraries

from selenium import webdriver

from BeautifulSoup import BeautifulSoup

import pandas as pd

driver = webdriver.Chrome("/usr/lib/chromium-browser/chromedriver")

products=[] #List to store name of the product

prices=[] #List to store price of the product

ratings=[] #List to store rating of the product

driver.get("Product_URL")

content = driver.page_source

soup = BeautifulSoup(content)

for a in soup.findAll('a',href=True, attrs={'class':'.…'}):

name=a.find('div', attrs={'class': '….'})

price=a.find('div', attrs={'class':'….'})

rating=a.find('div', attrs={'class':'….'})

products.append(name.text)

ratings.append(rating.text)

df = pd.DataFrame({'Product Name':products,'Price':prices,'Rating':ratings})

df.to_csv('products.csv', index=False, encoding='utf-8')

 

To run the code, a file name “products.csv” is created and this file contains the extracted data.

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.

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 Statistics


 
Learn Web Scraping using Python
Web Development

The importance of extracting information from the web is becoming increasingly loud and clear. Every few weeks, I realize myself in a situation where we need to extract information...

Read More
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
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

What our users are discussing about Statistics