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Are you a combination of an amazing analyst, awesome writer and thought leader in Analytics? Then, you have come at the right place. We encourage original contributions from experts in  analytics & data science industry. As a writer on our website blog, you benefit from exposure to our traffic and our readership (we are one of the leading knowledge portal for Analytics in India).

If you are passionate about analytics and are interested in publishing your articles solely with us (we don’t publish duplicate content), please review the following guest author guidelines.

Kindly go through the guidelines mentioned below for publishing articles on Analytics Vidhya

  1. The content of the guest blog must fit in with the theme of Analytics Vidhya’s blog – which is to impart useful knowledge to the analytics and data science community
  2. The main purpose behind writing and publishing an article on Analytics Vidhya should be to add value to the audience. The audience should learn something new and the article should add value to their knowledge of analytics / data science. This unique feature could be something you have learnt over the years, a trick you might have discovered / invented, tips you have learnt through your experience or real-life application of analytics to solve business problems.
  3. The blog post is not an Advertorial. We do not charge guest authors nor accept payment from guest writers for publishing their blog posts. We therefore do not accept marketing collateral, customer testimonials or posts that visibly promote a product/service/company/individual.
  4. Articles submitted to Analytics Vidhya’s blog must be detailed and unique – duplicate content will not be published.
  5. All articles published on Analytics Vidhya’s blog are owned by Analytics Vidhya. If the author would like to republish their article that has already been posted on Analytics Vidhya, they may request Analytics Vidhya for permission to reprint elsewhere.
  6. Outgoing links – we permit the author to link to their profile on LinkedIn/Twitter handle or on their company’s website in the About the Author section at the end of the blog. We do not encourage outgoing links within the blog article unless it significantly benefits our community and their knowledge.
  7. We expect articles to be well written and be free of spelling and grammatical errors.

 Analytics Vidhya exercises full editorial rights on all content published

Guidelines for writing an interesting blog

  • Remember the old saying “A picture speaks a thousand word”. Same applies to blogging. Visuals, charts and diagrams make your article more readable.
  • Any article published should have a direct call to action. How exactly can it benefit the reader? Theory is good, but practical is must.
  • The more specific your article is, the better it would be. For example, 5 tips to improve your predictive models is better than How to improve predictive models? is much better than basics of predictive modeling.
  • The tone of voice on Analytics Vidhya is polite, down to earth and informative. Please keep the same in your article.
  • Try and keep the article interactive. Invite comments at places, ask questions and take opinions wherever possible.

 

Analytics Vidhya owns all the content on its website. It reserves the right to publish, re-publish or remove the content on its discretion. Thanks.

5 Comments

  • Prem says:

    Hi

    This is Prem here , Working as Quality data Analyst , Which revolves around key business decisions in terms of quality function and RCCA are done . I am looking to expand my skills and explore new oppurtunities in data analytics . If you could guide me course of action i plan such as i can start about my new career analytics and its features . i have overall 8 yrs exp in field of quality domain related to service based and manufacturing industry.

    Regards
    Prem 9901999041

    • Kunal Jain says:

      Hi Prem,

      I think that you should start learning analytics in parallel to your current work profile. This can be done by online courses provided by the likes of Jigsaw, Edvancer, Edureka. You can then apply these concepts in your current role.

      Once you have gained some more experience, you can switch into a complete analytics role.

      Regards,
      Kunal

  • Hakeem says:

    Hi,

    Firstly, a big thank you and lots of appreciation for running this exceptionally informative blog.

    I have a few stupid questions to ask you!! I am certified SAP HCM Consultant but haven’t really got a chance to work in that field, I have got an MBA (systems) from Madras University.

    I am currently working as an HR Analyst in Saudi Arabia since the past 4 years. My job has basically requires me to work heavily on excel. So I can say i am fairly good in using excel.

    I wish to change my job and work as data analyst. I seek expert’s guidance. I shall be indebted to you.

    Expecting a reply.

    Thanks.

  • Chitransu Prasad says:

    Hi,

    I am working in SQL for last 2 yrs,At the same time,i have to use EXCEL also.

    .I have learnt R and Basic Statistics i.e. Regression,Annova

    Kindly Suggest what approach shall I take to enter in analytics field.

  • sagar says:

    from scipy.stats import mode
    mode(df[‘Gender’])

    C:\Anaconda3\lib\site-packages\scipy\stats\stats.py:257: RuntimeWarning: The input array could not be properly checked for nan values. nan values will be ignored.
    “values. nan values will be ignored.”, RuntimeWarning)

    —————————————————————————
    TypeError Traceback (most recent call last)
    in ()
    —-> 1 mode(df[‘Gender’])

    C:\Anaconda3\lib\site-packages\scipy\stats\stats.py in mode(a, axis, nan_policy)
    642 return mstats_basic.mode(a, axis)
    643
    –> 644 scores = np.unique(np.ravel(a)) # get ALL unique values
    645 testshape = list(a.shape)
    646 testshape[axis] = 1

    C:\Anaconda3\lib\site-packages\numpy\lib\arraysetops.py in unique(ar, return_index, return_inverse, return_counts)
    196 aux = ar[perm]
    197 else:
    –> 198 ar.sort()
    199 aux = ar
    200 flag = np.concatenate(([True], aux[1:] != aux[:-1]))

    TypeError: unorderable types: str() > float()

    can anybody help me on python..new in python..what should I do for this error

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