Last answered:

03 May 2022

Posted on:

29 Apr 2022

0

Resolved: Unstructured Data tools and skills required

I'm really confused on what tools and skills are required to  store, manage, clean, process and analyse unstructured data, and create insights from unstructured data like videos, images, text etc?
is this course provide learning for these skills?
and what is the job title of someone who works with unstructured data and analyze to create impactful insights? is it data analyst or data scientist?
Thanks

1 answers ( 1 marked as helpful)
Posted on:

03 May 2022

0

Hi Inderjeet,
thanks for reaching out! To answer your question, you can work with unstructured data using different technologies - for example NoSql solutions can store unstructured data. Or you could also use Python and analyse, clean and manipulate unstructured data. We have two courses on data cleaning and preprocessing with Python in NumPy and pandas, however, the focus isn't on unstructured data, still you're welcome to check them out:
https://learn.365datascience.com/courses/data-preprocessing-numpy/course-introduction/
https://learn.365datascience.com/courses/data-cleaning-preprocessing-pandas/introduction-to-the-pandas-library/
When it comes to working with images, we have a deep learning course on CNNs, which features a practical example of categorizing images of clothing items using a neural network. Keep in mind, we don't focus to heavily on the preprocessing part here, instead we delve into the theory and practical applications behind CNNs. Here is the link to the course:
https://learn.365datascience.com/courses/convolutional-neural-networks-in-python/what-does-the-course-cover/

So, to answer your second question as to who works with unstructured data - the answer is both a data analyst and a data scientist can work with unstructured data.
Your job might include either cleaning and preprocessing unstructured data, which is typically associated with a data analyst profession, or work with predicting based on unstructured data which is more the data science or ML engineer domain. Both are available options and would depend mostly on what type of data your company has and what are they data needs.
Hope this helps!

Best,
365 Eli

Submit an answer