DATA SCIENCE WITH PYTHON | Feb 15-Mar 21 | Rajaneesh

Discussion in 'Big Data and Analytics' started by Sriraksha G, Feb 16, 2020.

  1. Sriraksha G

    Sriraksha G Member
    Simplilearn Support Customer

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    #1
    Naga Kishore Rao P likes this.
  2. Quamar Equbal

    Quamar Equbal Member

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  3. Mukundan KG

    Mukundan KG New Member

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    Simplilearn Team

    Please provide access to google drive
     
    #3
  4. Rajaneesh_2

    Rajaneesh_2 Member

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    #4
  5. Adewale Aderonmu

    Adewale Aderonmu New Member

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    #5
  6. Kelsey Morris_1

    Kelsey Morris_1 New Member

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    Good afternoon Rajaneesh,
    Thank for taking the time to instruct. Could you please emphasize to our fellow students to go over the modules you are going to cover prior to the live class, as well as completing the Elective offered, Python for Data Science? I am a beginner in this course as well, but as this course progresses, I see alot of questions that could be answered by either completing the modules ahead of time, and/or the elective. This would ensure the course continues to progress at your desired pace, and to keep all learners on track. As we are all professionals, and time is limited, all learners should be respectful of others time in doing their part prior to the live class. Thanks!
     
    #6
  7. Raghavendra B M

    Raghavendra B M Active Member
    Simplilearn Support

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    Hi Kelsey Morris_1,

    Greetings from Simplilearn!

    I totally understand your concern, what you're referring to is called 'Learning Path'. And some of you have Data Scientist Master Program and some of you have Artificial Intelligence Engineering Master Program.

    I will list the learning path based on the respective master program:
    Full Forms:
    OSL
    - Online Self Learning
    LVC - Live Virtual Class

    1. Learning Path - Artificial Intelligence Engineer - Master Program

    a.) Initial Stage (Very Important) :
    01. Statistics Essentials for Data Science (OSL)
    02. Data Science in Real Life (OSL) {--IBM--}
    03. Python for Data Science (OSL) {--IBM--}
    04. Data Science with Python (OSL + LVC)
    b.) Intermediate Stage :
    05. Math Refresher (OSL)
    06. Introduction to Artificial Intelligence (OSL)
    07. Machine Learning (OSL + LVC)
    08. Deep Learning Fundamentals (OSL on TensorFlow v1) {--IBM--}
    c.) Last courses to cover :
    09. Deep Learning with Keras and TensorFlow (OSL on TensorFlow v1 + LVC on latest TensorFlow v2)
    10. Accelerated Deep Learning with GPU (OSL on TensorFlow v1) {--IBM--}
    11. Natural Language Processing (OSL + LVC)
    12. IBM Watson for Chatbots (OSL) {--IBM--}
    13. R Programming for Data Science (OSL) {--IBM--}
    14. Machine Learning with R (OSL) {--IBM--}
    d.) End Course(s) for Master Program (not required to unlock the Master Certificate) :
    15. AI Capstone Project (LVC Only and No OSL Videos)

    2. Learning Path - Data Scientist - Master Program

    a.) Initial Stage (Very Important) :
    01. Statistics Essentials for Data Science (OSL)
    02. Data Science in Real Life (OSL) {--IBM--}
    03. R Programming for Data Science (OSL) {--IBM--}
    04. Data Visualization with R (OSL) {--IBM--}
    05. Data Science with R (OSL + LVC)
    06. Machine Learning with R (OSL) {--IBM--}
    b.) Intermediate Stage :
    07. Python for Data Science (OSL) {--IBM--}
    08. Data Science with Python (OSL + LVC)
    09. Math Refresher (OSL)
    10. Machine Learning (OSL + LVC)
    11. Tableau 10 (OSL + LVC)
    c.) Last courses to cover :
    12. Core Java (OSL)
    13. Simplifying data pipelines with Apache Kafka (OSL) {--IBM--}
    14. Big Data Hadoop and Spark Developers (OSL + LVC)
    15. IBM Watson for Chatbots (OSL) {--IBM--}
    16. Certified SAS Base Programmer (OSL)
    17. Data Science with SAS (OSL)
    d.) End Course(s) for Master Program (not required to unlock the Master Certificate) :
    18. Data Science Capstone (LVC Only and No OSL Videos)

    For future updates on learning path visit the below link:
    https://community.simplilearn.com/threads/learning-paths-ibm-certification-guidelines.47663/

    I hope the information would help you all. Happy learning guys!

    Regards,
    Raghavendra B M
     
    #7
    Last edited: Mar 5, 2020
    Prasann Prem and Bijoy_11 like this.
  8. Quamar Equbal

    Quamar Equbal Member

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    hi Sir,
    in new jupyter lab after 15 to 20 minutes i need to refresh page and the launch lab which takes too much time. i.e why i installed anaconda and jupyter in local machine. but i am not able open jupyter in localhost can u plz guide me to launch jupyter in local machine. please reply sir.
     
    #8
  9. Raghavendra B M

    Raghavendra B M Active Member
    Simplilearn Support

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    Hi Quamar Equbal,

    Greetings from Simplilearn!

    May I know the exact issue you're facing while opening the lab on the localhost?

    For example, Jupyter fails to start or Jupyter doesn't load or doesn't work in the browser.

    Note: Please use a personal laptop/pc instead of an office laptop/pc. Because office laptop/pc might have restrictions.

    Or Just uninstall the anaconda software from your laptop/pc and re-install.

    While re-installing the anaconda software please make sure to check the highlighted checkbox:

    Add Anaconda to my PATH environment variable

    upload_2020-3-5_18-28-5.png

    I hope this information will help you.

    If you are still facing the issue, please attach a screenshot for the same. So that we can assist you better.

    Regards,
    Raghavendra BM
     
    #9
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  10. Arunraddi

    Arunraddi Member

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    A self-learning video of data science with python quotes one can avail free course of python for beginners. Where should we enroll? Can anyone send link?
     
    #10
  11. Anirudh_35

    Anirudh_35 Member

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    Could you please explain how the inverse works? Didn't get it. Appreciate if you could explain it with example -->

    inv
    np.linalg.inv(inv_array)
     
    #11
  12. Vikas Kumar_18

    Vikas Kumar_18 Well-Known Member
    Simplilearn Support Alumni

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    Hi Anirudh,

    (inv) functions would give the inverse of a matrix. Inverse matrix is a matrix as:

    If A is a matrix then

    (A ). inv(A) = Identity matrix
    #The product of Matrix A and Inverse Matrix of A would be an identity Matrix.

    Example:
    import numpy as np

    x = np.array([[1,2],[3,4]])
    y = np.linalg.inv(x)print x
    print (y)print (np.dot(x,y))
     
    #12
  13. Vikas Kumar_18

    Vikas Kumar_18 Well-Known Member
    Simplilearn Support Alumni

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    Hi Arun,

    If you have taken the Master Course then kindly check the elective course lists where you would get the "Python for Data Science" course. That is the beginner course. If you have not taken the Master course then kindly raise a ticket and we would assist you.
     
    #13
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  14. Prasann Prem

    Prasann Prem Member

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    Hi Rajneesh Sir,
    Can you help me provide some links or e-books to read about statistics and probability involved in data analytics in details. I want to be somewhat known to what analytics mean in the industry and beside programming, what else is there to stand out. Your considerations will be of utmost help.

    Regards,
    Prasan
     
    #14
  15. Drajneanu Diana

    Drajneanu Diana New Member

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    I have a question regarding the recommender system for movie reviews.. I tried to use NMF sklearn and the reconstructed matrix I obtained is full of zeros. I assume it's because the initial matrix has a lot of zeros.
    From what I understand, the NMF sklearn tries to have the reconstructed matrix as close as possible to the initial and this closeness is calculated using Frobenius norm. I think is obvious that if the initial matrix has a lot of zeros, the reconstructed matrix has a lot of zeros as well.
    So now my question is, the solution is to somehow calculate this Frobenius norm only for the non-zeros values? And if yes, how to do this in sklearn?
    I searched online and didn't find anything, using NMF sklearn.
    Thank you!
     
    #15
  16. Rajesh Kurup

    Rajesh Kurup Member

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    hi
    while importing NYC 311 data i get this warning... can someone tell me what it means.. ( I am working on the local PC and not Lab)

    Syntax that i am using for importing is:
    ----------------------------------------------------
    path = "C:/workspace/Personal/Self Help/NYC311_SR.csv"
    nyc_df = pd.read_csv(path)

    Warning that i am getting is:
    ------------------------------------
    C:\Users\KurupR\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3058: DtypeWarning: Columns (48,49) have mixed types. Specify dtype option on import or set low_memory=False.
    interactivity=interactivity, compiler=compiler, result=result)
     
    #16
  17. Rajesh Kurup

    Rajesh Kurup Member

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    Also while trying to describe the data i get a display which hides several columns. How can i expand to see all the columns of the data frame:

    the syntax is am using is:
    nyc_df.describe(include="all")

    The display i get is as follows:
    upload_2020-3-16_12-56-40.png

    How do i expand and view the hidden/ collapsed columns?

    Thanks in advance for the replies..
    Rajesh Kurup
     

    Attached Files:

    #17
  18. BIMAL SEN

    BIMAL SEN Member

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    Hi,
    Regarding project "Project NYC 311". I can't able to formulate the structure of the data which fits perfectly in the Scify
    f_oneway()
    method. Please help if you already resolve that.

    ## 7. Declaring Null Hypothesis and Alternate Hypothesis for the average response time across complaint types is similar or not¶
    ## 8. Statistical test for the average response time across complaint types is similar or not


    Thanks,
    Bimal
     
    #18
  19. A Divya

    A Divya Member

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    the file path of your nyc_311 project should be same as your local python interface. so go to your nyc_311 excel file and while saving the file change the file path and save it as read.csv. Hope this will help you out
     
    #19
  20. A Divya

    A Divya Member

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    Rajaneesh sir,

    I'm stuck at 7th step in the NYC_311 project i.e.,
    (Declaring Null Hypothesis and Alternate Hypothesis for the average response time across complaint types is similar or not) as it is not present in self-learning and as well as in labs.

    Please help me out.
     
    #20
  21. Srinivasulu Tippireddy

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    Hi Raj,
    Can you please provide some hints for below steps regarding "NYC311" project.
    I did groupby and mean as below:
    nyc_df_groupby = nyc_df.groupby(['Location_Type','Complaint_Type'])['Request_Closing_Time'].mean()
    7. Declaring Null Hypothesis or Alternate Hypothesis for the average response time across complaint types is similar or not
    8. Statistical test for the average response time across complaint types is similar or not
    9. Declaring Null Hypothesis or Alternate Hypothesis for the type of complaint or service requested and location related
    10. Statistical test for the type of complaint or service requested and location related

    Thanks
    Srini
     
    #21
  22. Anirudh_35

    Anirudh_35 Member

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    How to take an average of datetime datatype.? Suppose, I have calculated the closing time as (Closed datetime - Created datetime). But now, I need to take an average closing time per complaint type. How can I do that. This is with reference to NYC311 project only.
     
    #22
  23. Sneha I

    Sneha I Member

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    While working on project Q6- Ordering the complaint types based on the average ‘Request_Closing_Time’, and grouping them for different locations.

    I am getting below error. Please can someone help.

    df['RequestClosingTime'].fillna(value=df['RequestClosingTime'].mean(),inplace=True).
    df['RequestClosingTime'].dtypes

    dtype('<m8[ns]')

    data_grp = df.groupby('City')['RequestClosingTime'].mean()

    ---------------------------------------------------------------------------
    DataError Traceback (most recent call last)
    <ipython-input-82-e1b8bd9c84f1> in <module>
    ----> 1 data_grp = df.groupby('City')['RequestClosingTime'].mean()

    /usr/local/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in mean(self, *args, **kwargs)
    1230 nv.validate_groupby_func("mean", args, kwargs, ["numeric_only"])
    1231 return self._cython_agg_general(
    -> 1232 "mean", alt=lambda x, axis: Series(x).mean(**kwargs), **kwargs
    1233 )
    1234

    /usr/local/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _cython_agg_general(self, how, alt, numeric_only, min_count)
    905
    906 if len(output) == 0:
    --> 907 raise DataError("No numeric types to aggregate")
    908
    909 return self._wrap_aggregated_output(output)

    DataError: No numeric types to aggregate
     
    #23
  24. Shashi kumar_16

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    #24
  25. Shashi kumar_16

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    I am not able to download hadoop quick start for VM.
    please help....
     
    #25
  26. Anirudh_35

    Anirudh_35 Member

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    Does any one has prepared Movi Lens project? How to create train and test split in that project, if any one can share the steps, it would be of great help. I am not getting more than 20% in test score, I think I am doing something wrong there?
    Thanks in advance.
     
    #26
  27. Rufai Ahmad

    Rufai Ahmad Member

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    Please guys my elapsed time is not given me the number of days only hours, any idea how to fix this? I have attached my code below.
     

    Attached Files:

    #27
  28. ZNOX TECHNOLGIES

    ZNOX TECHNOLGIES New Member

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    Hi,

    convert the column df['Request_Closing_Time] = df['Request_Closing_Time].astype(int) and perform all the operations. this will change dtype tp flaot64.

    Thanks
     
    #28

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