Calling on Young Professionals to join the Digital Transform
Hi everyone,About a week ago, I used my LinkedIn page to call on young professionals in general and in the petroleum exploration and production (E&P) field in particular to braze up and prepare to contribute to the new ways of conducting business in the 21st century. I noted that the conventional ways of doing things are changing. They need to key in to the digital transformation agenda and be part of the new history that has started to be written in all fields of endeavors.I feel that it is good to share same here for wider dissemination.https://www.linkedin.com/posts/fataianifowose_upstream-digitalization-is-proving-itself-activity-6651770007371988993-LNSx
They need to key in to the digital transformation agenda and be part of the new history that has started to be written in all fields of endeavors. - I agree
Python for Data Analysis
A practical guide to getting started with Python for Data Analysis with examples of code and easy to access libraries
Very helpful and quite nicely written . Code driven approach is always the best way to explain a concept
Name various types of Deep Learning Frameworks
PytorchMicrosoft Cognitive ToolkitTensorFlowCaffeChainerKeras
How will you assess the statistical significance of an insight whether it is a real insight or just by chance?
Statistical importance of an insight can be accessed using Hypothesis Testing.
What is the importance of having a selection bias?
Selection Bias occurs when there is no appropriate randomization acheived while selecting individuals, groups or data to be analysed.Selection bias implies that the obtained sample does not exactly represent the population that was actually intended to be analyzed.Selection bias consists of Sampling Bias, Data, Attribute and Time Interval.
Can you explain the difference between a Test Set and a Validation Set?
Validation set can be considered as a part of the training set as it is used for parameter selection and to avoid Overfitting of the model being built. On the other hand, test set is used for testing or evaluating the performance of a trained machine leaning model.In simple terms ,the differences can be summarized as-Training Set is to fit the parameters i.e. weights.Test Set is to assess the performance of the model i.e. evaluating the predictive power and generalization.Validation set is to tune the parameters.
How can you deal with different types of seasonality in time series modelling?
Seasonality in time series occurs when time series shows a repeated pattern over time. E.g., stationary sales decreases during holiday season, air conditioner sales increases during the summers etc. are few examples of seasonality in a time series.Seasonality makes your time series non-stationary because average value of the variables at different time periods. Differentiating a time series is generally known as the best method of removing seasonality from a time series. Seasonal differencing can be defined as a numerical difference between a particular value and a value with a periodic lag (i.e. 12, if monthly seasonality is present)
Is it possible to perform logistic regression with Microsoft Excel?
It is possible to perform logistic regression with Microsoft Excel. There are two ways to do it using Excel.a) One is to use Add-ins provided by many websites which we can use.b) Second is to use fundamentals of logistic regression and use Excel’s computational power to build a logistic regression
What is the goal of A/B Testing?
It is a statistical hypothesis testing for randomized experiment with two variables A and B. The goal of A/B Testing is to identify any changes to the web page to maximize or increase the outcome of an interest. An example for this could be identifying the click through rate for a banner ad.
How can you assess a good logistic model?
There are various methods to assess the results of a logistic regression analysis-• Using Classification Matrix to look at the true negatives and false positives.• Concordance that helps identify the ability of the logistic model to differentiate between the event happening and not happening.• Lift helps assess the logistic model by comparing it with random selection.