kandi X-RAY | DS-Career-Resources Summary
kandi X-RAY | DS-Career-Resources Summary
Data science can seem like an intimidating field to get into. I know this first hand. Throughout my journey, I've learned a lot. I've also documented a lot. Through this process, I’ve accumulated a bunch of useful resources that helped me with learning new concepts, doing impactful work, interviewing at top tech companies, and more. This repo is an attempt to ‘open-source’ my experience and insights becoming a data scientist. Enjoy! For more on me and what I'm up to, you can head over to my website. With this by your side, you should have more than enough material at your disposal the next time you’re prepping for a big interview or suring up fundamental concepts. Being updated and improved on constantly. This list is a compilation of over 200+ undergraduate intern roles from Summer 2018 that were explicitly centered around data science and software engineering. You can use this as a jumping off point for your next job search. This post was designed to make it a little easier for aspiring data scientists to find all of the excellent advice out there from experts in the field. The majority of the ideas are condensed from 6 posts that I found especially helpful. A reflection of lessons and advice from my time at a Data Science Intern working at Unity Technologies in San Francisco, CA. My goal is to share a handful of actionable lessons, takeaways, thoughts, and advice from the memorable experience. Have you ever wanted to start a new project but you can’t decide what to do? First, you spend a couple hours brainstorming ideas. Then days. Before you know it, weeks have gone by without shipping anything new. In this post, my intention is provide some useful resources to springboard you into your next data science project. Learn how to implement 8 fundamental machine learning algorithms in Python over the course of 8 minutes or less by leveraging the power of scikit-learn and Python for data science. If you’ve ever found yourself looking up the same question, concept, or syntax over and over again when programming, you’re not alone. Here’s the stuff that I’m always forgetting when working with Python, NumPy, and Pandas. Think of your newsletter subscriptions as an elite force of smart, specialized people working to bring you the latest and most valuable information well worth your time. Data science moves fast, you should too. Data science isn't entirely about machine learning. Here I make the argument for the value provided by skills and actions associated with the often overlooked and under-appreciated, Type A Data Scientist. As Data Scientists, there is very little that is black and white. We do our work in a world of grey. We need to do a better job of consistently reminding ourselves that our primary focus should be to drive impact. This post is designed to help you achieve an edge in data science interviews by laying out a multi-step system to product knowledge and ideation that I’ve used with a lot of success.
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Trending Discussions on Data Science
I am getting the following error while trying to upload a dataset to Hub (dataset format for AI)
S3SetError: Connection was closed before we received a valid response from endpoint URL: "<...>".
So, I tried to delete the dataset and it is throwing this error below.
CorruptedMetaError: 'boxes/tensor_meta.json' and 'boxes/chunks_index/unsharded' have a record of different numbers of samples. Got 0 and 6103 respectively.
Using Hub version: v2.3.1...
ANSWERAnswered 2022-Mar-24 at 01:06
Seems like when you were uploading the dataset the runtime got interrupted which led to the corruption of the data you were trying to upload. Using
force=True while deleting should allow you to delete it.
For more information feel free to check out the Hub API basics docs for details on how to delete datasets in Hub.
If you stop uploading a Hub dataset midway through your dataset will be only partially uploaded to Hub. So, you will need to restart the upload. If you would like to re-create the dataset, you can use the
overwrite = True flag in
hub.empty(overwrite = True). If you are making updates to an existing dataset, you should use version control to checkpoint the states that are in good shape.
I was taking a look at Hub—the dataset format for AI—and noticed that hub integrates with GCP and AWS. I was wondering if it also supported integrations with MinIO.
I know that Hub allows you to directly stream datasets from cloud storage to ML workflows but I’m not sure which ML workflows it integrates with.
I would like to use MinIO over S3 since my team has a self-hosted MinIO instance (aka it's free)....
ANSWERAnswered 2022-Mar-19 at 16:28
Hub allows you to load data from anywhere. Hub works locally, on Google Cloud, MinIO, AWS as well as Activeloop storage (no servers needed!). So, it allows you to load data and directly stream datasets from cloud storage to ML workflows.
You can find more information about storage authentication in the Hub docs.
Then, Hub allows you to stream data to PyTorch or TensorFlow with simple dataset integrations as if the data were local since you can connect Hub datasets to ML frameworks.
I am trying to achieve a calculation involving geometric progression (split). Is there any effective/efficient way of doing it. The data set has millions of rows. I need the column "Traded_quantity"Marker Action Traded_quantity 2019-11-05 09:25 0 0 09:35 2 BUY 3 09:45 0 0 09:55 1 BUY 4 10:05 0 0 10:15 3 BUY 56 10:24 6 BUY 8128
turtle = 2 (User defined)
base_quantity = 1 (User defined)...
ANSWERAnswered 2022-Jan-22 at 10:09
This should work
I have a multi index df, with column "Turtle"...
ANSWERAnswered 2022-Jan-21 at 01:02
There is a simple formula that maps
Net Pos. The calculation can be expressed as a sum of geometric series times base_quantity, yielding the function
n_level = range(1, steps + 2)
ANSWERAnswered 2022-Jan-16 at 14:22
this can be achieved easily using binary search, there are many ways to apply that(NumPy, bisect). I would recommend the library bisect.
Added Buu for the Crest and See for the Trough, so that code and differentiate the segments. You can choose anything
I have a scenario in which I have a peptide frame having 9 AA. I want to generate all possible peptides by replacing a maximum of 3 AA on this frame ie by replacing only 1 or 2 or 3 AA.
The frame is CKASGFTFS and I want to see all the mutants by replacing a maximum of 3 AA from the pool of 20 AA.
we have a pool of 20 different AA (A,R,N,D,E,G,C,Q,H,I,L,K,M,F,P,S,T,W,Y,V).
I am new to coding so Can someone help me out with how to code for this in Python or Biopython.
output is supposed to be a list of unique sequences like below:
CKASGFTFT, CTTSGFTFS, CTASGKTFS, CTASAFTWS, CTRSGFTFS, CKASEFTFS ....so on so forth getting 1, 2, or 3 substitutions from the pool of AA without changing the existing frame....
ANSWERAnswered 2021-Dec-01 at 07:07
Ok, so after my code finished, I worked the calculations backwards,
Case1, is 9c1 x 19 = 171
Case2, is 9c2 x 19 x 19 = 12,996
Case3, is 9c3 x 19 x 19 x 19 = 576,156
That's a total of 589,323 combinations.
Here is the code for all 3 cases, you can run them sequentially.
You also requested to join the array into a single string, I have updated my code to reflect that.
I created one notebook using Google AI platform. I was able to start it and work but suddenly it stopped and I am not able to start it now. I tried building and restarting the jupyterlab, but of no use. I have checked my disk usages as well, which is only 12%.
but didn't fix it.
Thanks in advance....
ANSWERAnswered 2021-Aug-20 at 14:00
You should try this Google Notebook trouble shooting section about 524 errors : https://cloud.google.com/notebooks/docs/troubleshooting?hl=ja#opening_a_notebook_results_in_a_524_a_timeout_occurred_error
I am trying to work with Featuretools to develop an automated feature engineering workflow for the customer churn dataset. The end outcome is a function that takes in a dataset and label times for customers and builds a feature matrix that can be used to train a machine learning model.
As part of this exercise I am trying to execute the below code for plotting a histogram and got "TypeError: import_optional_dependency() got an unexpected keyword argument 'errors' ". Please help resolve this TypeError....
ANSWERAnswered 2021-Sep-14 at 20:32
Try to upgrade
I am working on Fine-Tuning Pretrained Model on custom (using HuggingFace) dataset I will copy all code correctly from the one youtube video everything is ok but in this cell/code:...
ANSWERAnswered 2021-Sep-12 at 16:55
Seems to be an issue with the new version of transformers.
Installing version 4.6.0 worked for me.
I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.
Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?
ANSWERAnswered 2021-Aug-11 at 15:55
No vulnerabilities reported
You can use DS-Career-Resources like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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