Ask Me Anything with Sharmistha Chatterjee, Senior Manager Data Sciences, Google Developer Expert and Noonies nominee

Hi! What approaches to data governance when managing vast amounts of data do you use?

What are some examples of dangerous bias and data science and strategies to minimize the impact of those?

Can you advise some tips on how to manage time sucesfully while working on data science project? What are the stages that need the most attention and time, and what stages can be if possible combined with one another etc
Thanks!

Hi Sharmistha,
I have 14 years of experience in ERP (oracle apps and cloud) and looking to move into Analytics. Need your insights on how can I start my journey into this side. Basically which will have more opportunities as in IT side or the Business side where I can utilize some of the technical knowledge I have. I am looking at equal blend of programming and analyzing the data.

Thank you.

Hi! How do you see the integration of women into the AI field? Can the issue of gender inequality in tech be once solved at 100%. and what steps we as a society must make then?

Hi! What can you recommend to someone only starting the work with Google Cloud? Any vital technical skills to develop?

What is your favorite project you’ve worked on? Why?

Hey!
How reliable do you think the data for the Covid cases internationally? And is it enough for this vast datasets to be held as a tool to basically restrict some of the normal life features?

I would recommend following courses and links:
Follow Towardsdatascience on medium, machinelearning mastery, analytics vidhya

Coursera Courses
-----------------------

List of courses: Distributed neural network training


(https://www.coursera.org/learn/machine-learning#syllabus): Standford Univ
Neural Networks for Machine Learning

Applied machine learning in python
Machine learning
Practical Machine Learning
Advance Machine Learning
Machine Learning: Regression
Inferential Statistics

Sequence Models
Practical Reinforcement Learning
Natural Language Processing
Convolution Neural Networks
Applied AI with DeepLearning
Introduction to Deep Learning

Bayesian Statistics
Mathematics for Machine Learning: Linear Algebra
Convolution Neural Networks
Applied AI with DeepLearning – survival model…
Introduction to Deep Learning
Cluster Analysis in Data Mining
Practical Time Series Analysis
Applied Social Network Analysis in Python (https://github.com/rahulpatraiitkgp/Applied-Social-Network-Analysis-in-Python/tree/master/Week%203)
Matrix Factorization and Advanced Techniques
Nerest Neighbor Collaborative Filtering
Regression Models
Classification
Regression
Probabilistic Graphical Models 1: Representation
Probabilistic Graphical Models 2: Inference
Probabilistic Graphical Models 3: Learning
Pricing strategy optimization
Designing, Running, and Analyzing Experiments
Recommender Systems: Evaluation and Metrics
Bayesian Methods for Machine Learning
Introduction to Apache Spark and AWS

How Google Does Machine Learning
Launching into Machine Learning
Intro to TensorFlow
Feature Engineering
Art and Science of Machine Learning


Start with courserra basic courses . Subscribe to hackernoon , medium (towards data science) —read the blogs. Apart from that follow machinelearningmastery.com

List of courses in coursera

Follow Towardsdatascience on medium, machinelearning mastery, analytics vidhya

List of courses:

Applied machine learning in python
Machine learning
Practical Machine Learning
Advance Machine Learning
Machine Learning: Regression
Inferential Statistics

Sequence Models
Practical Reinforcement Learning
Natural Language Processing
Convolution Neural Networks
Applied AI with DeepLearning
Introduction to Deep Learning

Bayesian Statistics
Mathematics for Machine Learning: Linear Algebra
Convolution Neural Networks
Applied AI with DeepLearning – survival model…
Introduction to Deep Learning
Cluster Analysis in Data Mining
Practical Time Series Analysis
Applied Social Network Analysis in Python (https://github.com/rahulpatraiitkgp/Applied-Social-Network-Analysis-in-Python/tree/master/Week%203)
Matrix Factorization and Advanced Techniques
Nerest Neighbor Collaborative Filtering
Regression Models
Classification
Regression
Probabilistic Graphical Models 1: Representation
Probabilistic Graphical Models 2: Inference
Probabilistic Graphical Models 3: Learning
Pricing strategy optimization
Designing, Running, and Analyzing Experiments
Recommender Systems: Evaluation and Metrics
Bayesian Methods for Machine Learning
Introduction to Apache Spark and AWS

How Google Does Machine Learning
Launching into Machine Learning
Intro to TensorFlow
Feature Engineering
Art and Science of Machine Learning

Distributed neural network training
https://www.coursera.org/learn/nlp-sequence-models
(https://www.coursera.org/learn/machine-learning#syllabus): Standford Univ
Neural Networks for Machine Learning

Hi Sharmista

I am Software project manager with 15 yrs of experience, have worked on 1 AI/ML project, want to explore more on the same. Are there any specific skillsets/ hands on experience required for a project manager to work in AI/ML projects which the industry looks for or any certifications or it is specific to the project.
Can you please suggest any material where I can start from.

Thanks

Check out the list of Coursera courses I recommended along with the links. In addition, start with little steps of solving problems in Kaggle. Contribute in Github open source, or build your own project that helps to build your portfolio. That is what employers look in these days.
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My favourite project is Studying election sentiment for India, 2019 General Elections. https://github.com/sharmi1206/elections-2019

You can use AutoML and build your own customized Data Governance framework to check which model performs better under what circumstances.

If data are coming from big cities, where cases are recorded well , we can study that and analyse the impact of lockdown, likely release of vacccines and overall impact on general population , social and economic factors . But of-course there’s a limitation when we think about less developed towns, villages or rural areas.

Most dangerous is in Hiring, recruitment . Also we notice the same in criminal data where mostly black people are accused in higher proportion. We should try to include fair datasets for training and build fair models.

Start by building basic skills on Analytics and Data Science . See what really interests you and try to build your portfolio on that.

Its a responsibility of society as a whole to come forward. Women need to upskill themselves, take up challenging opportunities and senior leader need to provide those to women.

Try to get hands-on experience with GCP, try to do certifications like Data Engineer, or Professional Cloud Architect.

EDA and Preprocessing are the most important steps. You can combine them and check out ur EDA while preprocessing the data, i.e. before and after preprocessing how is your data behaving, correlations, and other factors.