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Nikhil IssarLegal Associate, India
$10 /hr
1 Years Exp.
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I am a corporate attorney at a prestigious Delhi based law firm. I work on private equity, drafting investment contracts, general commercial drafting,...Read More
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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.”

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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