Data Science Training can be inexpensive and free

IS it TOUGH to be a DATA SCIENTIST? NO , it is not

Data Science is Not Rocket Science. But once a data scientist you have to keep learning every day.

Master R and Python basics along with statistics basics.

Then learn Machine Learning.

  • Text Mining Basic and Topic Modeling.
  • Time Series.

  • Then learn Deep Learning, ANN, CNN, RNN , LSTM.

  • Computer Vision.

  • Speech Recognition.

  • Chatbots.

  • Blockchain.

you can learn this from internet for free. Dont get confused or insecure to pay lacs of rupees or thousands of dollars to institutes that give you certificates that are not recognized by corporations

 

Intro to R https://lnkd.in/e65KfmX

Intro to Python https://lnkd.in/eJ-F3c7

Intro to Machine Learning https://lnkd.in/eeHdi_t

Here is one more free “kernel”, but in colab format:  https://github.com/noahgift/functional_intro_to_python#safari-online-training–essential-machine-learning-and-exploratory-data-analysis-with-python-and-jupyter-notebook

Is KAGGLE a website only for super human data scientists? NO NO NO

You can be a kaggler very easily-

1) Understand how kernels function especially input file and output submission- The best is to use Notebook method not script method of using code

2) Have basic knowledge of EDA and Data Viz in either R or Python ( if you dont know that EDA means exploratory data analysis you can start learning – from Kaggle KERNELS itself

3) Have basic knowledge of Machine Learning Algorithms (and how to apply ) and how to compare Area under Curve (AUC)

4) Deep Learning is advanced and for Python preferably

5) Practice one hour a day. Kaggle is like a gym for the brain if you do this for a year, see where your career zooms.

And one more thing- cross port your code on Github

I am sure there are better kernels, but you can find them out yourself, and best of all they are free. tip- Number of votes often points out to a better more popular kernel

 

R Basics Here https://www.statmethods.net/ and  https://www.datacamp.com/courses/free-introduction-to-r

basic statistics https://www.slideshare.net/ajayohri/statistics-for-data-scientists

and free SAS learning from SAS itself https://www.sas.com/en_us/learn/academic-programs/resources/free-how-to-videos.html 

Interview Questions https://www.edureka.co/blog/interview-questions/top-data-science-interview-questions-for-budding-data-scientists/

Python basics here https://cognitiveclass.ai/courses/python-for-data-science/ and http://www.statisticsviews.com/details/feature/8868901/A-Tutorial-on-Python.html

 

(Free )Kaggle kernel + IBM Cognitive + edx + Kaggle contest + hackathon > certificate from paid private company ???

40 hours to gain a certificate for X dollars versus 40 hours on Kaggle for free. Which will give you better skills. What will get you a job – skills or certificates. 

When we interview data scientist freshers we always have  a coding round as the first step. Certificates from private institutes dont matter regardless of how long or how expensive they are

I have been asked why I write these articles on free resources on data science, what is my agenda and why not let things be.

Well, short answer, if you charge thousands of dollars for content which can be free, and force young people in debt and indulge in predatory pricing, then someone needs to expose these merchants of data science certificates

Someone asked why I charge for my 3 data science books. I write books, publisher sells them and gives me 13% of royalty.  The books are 1/10th of price of a course.

Most importantly I write books for academic credentials and because I love writing (as seen by my extensive blogging on https://decisionstats.com/decisionstats-org/ (writing books is a great way to share knowledge in my opinion but takes a long time so writing a blog tutorial or kaggle kernel or github code is faster  

I still do guest lectures- but in all cases I am not responsible for students paying too much and I balance this by my evangelizing free resources that would be students are completely unaware of.

These free resources are often updated more than the curriculum of courses by institutes and they are often easy to understand

As the man said- Money for nothing and my MTV

http://www.youtube.com/watch?v=lAD6Obi7Cag

Interview Hyreo.com

1) What prompted you to make Hyreo.com

The concept of Hyreo took shape in our mind as an outcome of the recruiting challenges we faced on a daily basis. All aspects of recruiting are very human labor intense and predictability of outcome at each stage was quite limited. The amount of time spend in sourcing, validating and assessing candidates was very high and hence pretty expensive. The same challenges existed in companies of all sizes. Hyreo took shape in our mind as a possible solution to address some of the recruiting challenges we saw around. We are trying to leverage smart technology and automation to improve the way candidate sourcing, assessment and engagement is carried out. We also felt that the opportunity was quite large since globally the recruitment model and process is fairly standard with limited or minor changes. Availability of technologies including Open NLP and others also helped us decide on building Hyreo as a potential solution to these recruiting problems.

2) In your two year journey as an entrepreneur with  Hyreo, name some
learnings and some turning points.

A few learnings from our entrepreneurial journey:

  1. Customers are the most important factor impacting everything – employees, investors & partners
  2. Partner as much as possible than build everything in-house and create ‘win-win’ for all parties
  3. Be prepared for rejection, it is unavoidable
  4. Hire slow but fire fast
  5. Entrepreneur knows more about the product than investor, customer or media
  6. Marketing is more important than one might think. Place it early in the lifecycle and use it effectively
  7. Create evangelists and supporters of the cause early in the game, but never on equity

3) Specifically which need is Hyreo.com trying to address and solve

Hyreo is disrupting the way companies ‘Discover’ and ‘Engage’ with talent. Hyreo leverages smart technology to automate the process of job information dissemination to prospect candidates, understand their interest level and subject proficiency and keep the candidates engaged and up-to date on the latest status 24/7. Build as a SaaS solution with chatbot technology, the platform is able to integrate with legacy systems or exist as a stand-alone system. Hyreo is built in a modular fashion such that customers can choose the product based on specific needs. By using the platform, companies are able to reduce 50% overall effort in recruiting and 40% overall cost with substantial improvement in candidate experience and hence talent brand.

4) What are some of the other innovations you see in the HR space

All aspect of HR and human capital management areas is getting disrupted with legacy processes being challenged by newer technology including Machine learning and AI based systems. Some of the areas that we see interesting innovation and proved merit include:

  1. Employee engagement: Be it answering employee queries or addressing issues of the employees, innovative technology solutions including Chatbots are being deployed
  2. Candidate reference checks are being automated to ensure the cycle time and the overall effort is reduced considerably
  3. Digital Learning platforms including micro learning platforms
  4. Intelligent interviewing platforms

 

5) What are some of the obstacles you see to HR innovations.

The journey has just begun and the initial inertia opposing the change has drastically reduced. There is a lot of exciting new technology in the market now, and it will take time for all stakeholders to evaluate options and adopt best practices. Some of the areas we should look at:

  1. HR should be a CEOs function and there should be focus on not just improving process but the mindset should be to invest in success
  2. There is a need for re-branding HR as a growth catalyst rather than a growth support function
  3. Need more investments in HR Tech space

About Hyreo-

 

Missing Value Imputation and Dealing With Outliers

Missing Value Imputation and Dealing With Outliers

These are an important part of data pre-processing and these are rarely taught in DONKEY ACADEMY who charge you a lot to give you a certificate that doesn’t give you a job.

So okay after that violence and double talk (from Dire Straits) here is how you deal with outliers

1) Replace outliers or missing values them with mean or median – based on distribution -which you see if age< 20 or age>80 then age=median(age)

2) Replace them by capping upper and lower limits. eg an age distribution of 1-120 for bank customers can be capped like if age<20 then age=20 if age>80 then age=80

3) Use MICE package for Imputation (in R) or pandas-mice for Python (https://lnkd.in/f6Z3jj5) eg if males have median age of 50 and females have median age 0f 45, replace all male age missing values with 50 and all female missing values with 45

4) Use OutlierTest in car package in R This is barely the tip of iceberg in missing value and outliers https://lnkd.in/fus_MiF

#machinelearning hashtag#algorithms hashtag#pythonprogramminglanguage hashtag#analytics hashtag#datascience hashtag#python hashtag#rstats

Freshers who want to be data scientists

Questions asked by young data scientists-Hey Ajay this is XYZ.

I want to learn data science and pursue my career in .currently I am fresher.

Can you tell me how to enter in company with data science profile what should i include in profile to get intern or job .

I want advice from you .

Ajay –

hashtag#machinelearning hashtag#bigdata hashtag#deeplearning hashtag#python

Is Kaggle too tough

Is KAGGLE a website only for super human data scientists? NO NO NO

You can be a kaggler very easily-

1) Understand how kernels function especially input file and output submission- The best is to use Notebook method not script method of using code

2) Have basic knowledge of EDA and Data Viz in either R or Python ( if you dont know that EDA means exploratory data analysis you can start learning – from Kaggle KERNELS itself

3) Have basic knowledge of Machine Learning Algorithms (and how to apply ) and how to compare Area under Curve (AUC)

4) Deep Learning is advanced and for Python preferably

5) Practice one hour a day. Kaggle is like a gym for the brain if you do this for a year, see where your career zooms.

And one more thing- cross post your code on Github hashtag#bigdata hashtag#love hashtag#machinelearning hashtag#analytics hashtag#datascience hashtag#deeplearning hashtag#python hashtag#r hashtag#howto hashtag#github hashtag#datamining hashtag#datavisualization