Sign in

Engineer | Data Science | Aviation Enthusiast
Photo by ThisisEngineering RAEng on Unsplash

What’s Control theory?

This story focuses largely on emphasizing the importance of control theory and its application on complex systems through dimensionality reduction, machine learning, and dynamical systems modeling[1]. First, I will cover a primer about control theory and its connectedness to the so-called ‘Data science.’ The very purpose of an effective control system is its ability to manipulate its behavior for a given engineering objective actively. The study and practice of manipulating dynamical systems (here, dynamical systems could mean anything from biological to financial models)are broadly known as control theory. It is one of the most successful fields combining the area of…

Photo by on Unsplash

Data has never been more available, everyone’s talking about data. Data is the key to unlock insights and time and again it is proved that organizations that are data/information-driven end up being more profitable in their business. The open data movement has also evolved, and more businesses and organizations are releasing data into the wild. The result is an overwhelming desire in the business community to use data to get smarter about their operations and deliver results in every industry, across every department. In fact, for the year 2018, 98.6% of firms aspired to a data-driven culture. …

Photo by Ricardo Frantz on Unsplash

The ever-increasing use of IoT, social media, and the rise of Industry 4.0 has led to discussions in organizations to value the need for real-time data storage infrastructure. The basic concept of data lake implies that we can store data in any form, without having to structure the data as in the case of the data warehouse. We could then perform different types of analytics including machine learning, big data processing, real-time analytics, etc. It helps an organization to perform new types of analytics for example perform machine learning over new sources like log files, social media, data from clickstreams.

Photo by Myriam Jessier on Unsplash

Do you have too many variables for your machine learning problem, and confused about how to get the best from those variables?

The principal Component analysis is probably the one you must consider. The basic intuition behind PCA is that you will use mathematics to extract important variables from a large pool. Basically, it combines highly correlated variables together to form a smaller data set. In other words, it is called “Principal Components” which consists of variables that account for maximum variance in the data.

The first step is to subtract out the mean from each sample data(Variable).

Photo by Joshua Sortino on Unsplash

Passive-Aggressive algorithms are a family of Machine learning algorithms that are popularly used in big data applications.

Passive-Aggressive algorithms are generally used for large-scale learning. It is one of the online-learning algorithms. In online machine learning algorithms, the input data comes in sequential order and the machine learning model is updated sequentially, as opposed to conventional batch learning, where the entire training dataset is used at once.

This is very useful in situations where there is a huge amount of data, and it is computationally infeasible to train the entire dataset because of the sheer size of the data.


Photo by Michael Longmire on Unsplash

Big data in finance is often thought of as a large, diverse (structured and unstructured), and complex collection of data that can be used to provide solutions to challenging business problems for financial services and banking companies around the world. The hype is no longer just confined to the realm of technology, but is now considered a business imperative.

Conventionally, financial computations were done by humans to an extent, and decisions were made based on inferences drawn from calculated risks, trends, and past history of returns. However, in recent times, such functionality is replaced by computers. …

Considering the mutual excitement of engineers, analysts, and policymakers, it starts to feel inevitable that the 2020s will be the decade when electric vehicles (EVs) will achieve their potential.

So, how's the EV market scaling? (Feel free to skip to the next section if you’re not into sales reports)

Finally, 2020 became a great year for plug-in vehicles. With only a few details still to be reported, the expected global BEV+PHEV sales of 3.24 million, compared to 2.26 million against the previous FY. What started with an unexpected economic downfall during the 1st quarter of 2020 due to COVID-19 became…


Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store