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Senior Financial Accountant / Analyst
Reethi

Senior Financial Accountant / Analyst  


NetSuite Accounting Financial Reporting 
$6 /hr
India
Content and creative writer
Dakshaa Singhvi

Content and creative writer  


Content Writers Creative Writing 
$17 /hr
India
content writer
Blogger Yourself

content writer  


Content Writers Blog Writing Physical Fitness 
$12 /hr
India
Ghost writer
Danny

Ghost writer  


Content Writers Creative Writers Copywriters 
$15 /hr
Nigeria
Content writer
Sahil Sharma

Content writer  


Content Writers English Writers & Translators 
$12 /hr
India
Web research
Abilash

Web research  


Content Writers Website Development Web Crawler 
$4 /hr
India
English Teacher
Aayushi Nanda

English Teacher  


Content Writers 
$3 /hr
India
Corporate Communication Specialist
Surjeet Kumar

Corporate Communication Specialist  


Content Writers Content Writing Content Creation 
$9 /hr
India
Content Analyst
Anisha Paul

Content Analyst  


Content Writers Content Editing Content Creation 
$6 /hr
India
Digital marketer
Sindhu Jhaveri

Digital marketer  


Content Writers Content Writing Content Marketing 
$9 /hr
India
Curriculum developing
Geetika Pawar

Curriculum developing  


Content Writers Curriculum Development Social Media Training 
$17 /hr
India
Content writer and Proofreader
Richa Rajput

Content writer and Proofreader  


Content Writers Proofreading 
$2 /hr
India
Content Creator
Riya Banik

Content Creator  


Content Writers Content Writing Copywriting 
$9 /hr
India
Content Writer, Manager and Marketer
Ahana Chatterjee

Content Writer, Manager and Marketer  


Content Writers Content Writing Content Strategy 
$3 /hr
India
content writing
Pooja

content writing  


Content Writers Content Writing Copyediting 
$17 /hr
India
Content Developer
Aishwarya Holla

Content Developer  


Content Writers Content Writing Content Development 
$3 /hr
India
Typing master
Prabhjit Singh

Typing master  


Content Writers Content Writing Typing Agents 
$2 /hr
India
Content Writer
Samia Arshan

Content Writer  


Content Writers Content Writing Article Writing 
$14 /hr
India
Content Marketing
Alisha Pamnani

Content Marketing  


Content Writers Content Writing Blog Writing 
$0 /hr
India
Content Writer
Lima

Content Writer  


Content Writers Content Editing Content Marketing 
$7 /hr
India
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Vb Vishal

Content writer  


Content Writers Technical Writers Internet Researchers 
$5 /hr
India
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Chetan Bhutani

Journalist  


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$10 /hr
India
Freelancer
Manisha Gupta

Freelancer  


Content Writers Translation Hindi English English Proofreading 
$5 /hr
India
Medical Doctor
Dany Baby

Medical Doctor  


Content Writers Content Writing Blog Writing 
$20 /hr
India
Data Analyst
Rajat

Data Analyst  


Content Writers Content Writing Blog Writing 
$9 /hr
India
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Amita Bhattacharya

Content editing & writing  


Content Writers Content Editing 
$34 /hr
India
French language Translator
Sandra Sudarsanan

French language Translator  


Content Writers Translation 
$9 /hr
India
Business Development & Analytics
Anand Shukla

Business Development & Analytics  


Content Writers Content Management Business Strategy 
$15 /hr
India
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Lubna Beg

Content writer  


Content Writers Content Writing Blog Writing 
$3 /hr
India
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Varsha Parashar

Content Creator  


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$2 /hr
India
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Jyoti Kaul

Freelance content writer  


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Popular How-To's in Not Quite C category


 
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Articles Related To Not Quite C


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.

With such a huge number of choices it may be hard to find the right designer for your specific needs. Toogit is always there to help you make the right choice when choosing a logo designer. These points could also be useful when hiring a web designer, graphic designer, or any designer for that matter.

I don’t want this post to come across as self promotional, however, I’ve linked to my own examples to show you how I personally communicate to potential clients the value of my design work. For other designers, I hope this in turn, gives you an idea of how you too can communicate the value of your work.

In no particular order:

  1. Experience : Previous identity projects will give you a good idea of what skill level your designer is at and what you can expect though this is not to say that a new designer can not produce top quality results – this point has to be considered with all of the other points mentioned below, in which case a strong portfolio is probably the best indicator.
  2. Positive Testimonials : Have they got positive testimonials from past clients and colleagues? Ensure you check the testimonials validity which can be done by looking for a web address or even by emailing the company. It’s a good idea to check if the company even exists.
  3. A Thorough Design Process : Do they have a logo design process in which they follow or are they simply producing logos like fast food? A typical process does not take under 24 hours to complete.
  4. Awards Won / Published Work : Have they won any awards for their work? Is their work published in any books or magazines? How recognised are they in the industry?
  5. A Strong Portfolio : How strong is their portfolio?   What is the make up of real to ‘fake’ logo designs? When I say ‘fake logos’ I refer to the logos made for fictional companies, rather than for real clients.
  6. Price : The price of the service is usually quite evident of what he is going to deliver. In most cases, freelancer should deliver what you pay for but don’t take price as the only indication. How much does a logo design actually cost? In my experience, this is the most frequently asked question and the hardest to answer. This is because every company has different requirements the best approach is to draw up a customised quote for each individual client.
  7. Design Affiliations : Are they affiliated with any design associations or publications? This is a good indication of how dedicated they are to their craft though is not at all essential.
  8. Great Customer Service : Do they respond to your emails quickly? How do they communicate & present themselves? A designer should provide great customer service throughout the whole process, from the initial email right through to after sales support.
  9. Business Professionalism : Attention to detail, trustworthiness, strong communication skills and time management are all vital and go hand in hand with great customer service.
  10. Appropriate Questions : A designer should ask a no of questions to find out your needs in relation to your business goals. Questions should revolve around the companies history, target market, competitors, company goals, etc.

Now a days, the popularity of scientific computing environments such as IDL, Maple, Mathematica, Matlab and R has increased considerably. Engineer simply feel more productive in such environments. One reason is the simple and clean syntax of command languages in these environments. Another factor is tight integration of simulation and visualization in Maple, R and similar environments you can quickly and conveniently visualize what you just have computed. One problem with the mentioned environments is that they do not work, at least not in an easy way, with other types of numerical software and visualization systems. Many of the environment specific programming languages are also quite simple or primitive. At this point scripting in Python comes in.

 

Python offers the clean and simple syntax of the popular scientific computing environments, the language is very powerful, and there are lots of tools for simulation, visualization, and data analysis programs. Python allows you to build your own Matlab like scientific computing environment, tailored to your specific needs and based on your favorite high performance FORTRAN, C, or C++ codes.

 

Scientific Computing Is More Than Number Crunching: Many computational scientists work with their own numerical software development and realize that much of the work is not only writing computationally intensive number-crunching loops. Very often programming is about shuffling data in and out of different tools, converting one data format to another, extracting numerical data from a text, and administering numerical experiments involving a large number of data files and directories. Such tasks are much faster to accomplish in a language like Python than in FORTRAN, C, C++, and C#.

 

Scripting is particularly attractive for building demos related to teaching or project presentations. Such demos benefit greatly from a GUI, which offers input data specification, calls up a simulation code, and visualizes the results. The simple and intuitive syntax of Python encourages users to modify and extend demos on their own, even if you are newcomers to Python.

 

Python has some clear advantageous over Matlab and similar environments:

  • The Python programming language is more powerful.
  • The Python environment is completely open and made for integration with external tools.
  • A complete toolbox/module with lots of functions and classes can be contained in a single file.
  • Transferring functions as arguments to functions is simpler.
  • Nested, heterogeneous data structures are simple to construct and use.
  • Object-oriented programming is more convenient.
  • Interfacing C, C++, and FORTRAN code is better supported and therefore simpler.
  • Scalar functions work with array arguments to a larger extent (without modifications of arithmetic operators).
  • The source is free and runs on more platforms.

 

How to run Python script

One of the most important skills you need to build as a Python developer is to be able to run Python scripts and code. This is going to be the only way for you to know if your code works as you planned. It’s even the only way of knowing if your code works at all!

 

A Python script is a reusable set of code which is essentially a Python program or a sequence of Python instructions contained in a file. You can run the program by specifying the name of the script file to the interpreter. 

 

This step-by-step will guide you through a series of ways to run Python scripts, depending on your environment, platform, needs, and skills as a programmer. When you try to run Python scripts, a multi-step process begins. 

 

  1. Run Python Scripts Using the Command-Line: A Python interactive session will allow you to write a lot of lines of code, but once you close the session, you lose everything you’ve written. That’s why the usual way of writing Python programs is by using plain text files. By convention, those files will use the .py extension. Open a command-line and type in the word ‘python’ followed by the path to script file and press enter. You’ll see output on your screen.
  2. Run Python Scripts Interactively: It is also possible to run Python scripts and modules from an interactive session. This option offers you a variety of possibilities.
    • Taking advantage of import
    • Use importlib and imp
    • Use runpy.run_module()
    • Hacking exec()
    • Use execfile()
  3. Run Python Scripts from an IDE or a Text Editor: IDE offer the possibility of running your scripts from inside the environment itself. It is common for them to include a Run or Build command, which is usually available from the tool bar or from the main menu.
  4. Run Python Scripts From a File Manager: Running a script by double-clicking on its icon in a file manager is another possible way to run your Python scripts. This option may not be widely used in the development stage, but it may be used when you release your code for production.

 

After you play around with Python on your own or in an online tutorial, I highly recommend to you to write small scripts to strengthen your knowledge. To stay motivated, choose a program that is in some way useful to you, so you can gain insight while figuring out Python. Below are a few ways you can begin to build your expert level in Python script:

 

  • Python Documentation
  • Google and stackoverflow
  • Ask an experience person

 

First, create a very basic version end-to-end. It is much less frustrating than trying to build a super-duper version from scratch. A big plus is that you will have something you can use very fast. Then iterate and add more complex functionality one by one.

 

Second, decompose large problems to smaller ones by introducing functions. Small, cohesive functions are easy to understand, test and debug.

 

Last, but probably the most important thing to keep in mind, is practice makes perfect. Start small, be patient and practice. Happy coding!

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