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Optimization Techniques for Data Scientist

Optimization Techniques for Data Scientist

Are you looking for optimization techniques for data scientist? If yes then this article is for you. As a data scientist, we spent a lot of our time in making better decisions. We are always looking to fabricate the predictive models to provide enhanced insights. Here in this article, you will not get the information about making better predictions, but instead how to make the best decision. Here you’ll get to know about some of the unique methods of optimizing your python code.

Optimization Techniques for Data Scientist: Purpose

Data science and AI-based streamlining have likewise to a great extent been utilized to take care of issues identified with logical programming. Composing advanced Python code is extremely, significant as a data scientist. Writing a messy or wasteful scratchpad will cost you time and your venture a great deal of money.

As experienced information researchers and experts know, this is inadmissible when we’re working with a customer. Different models are accounted for by the writing on the task in knowledge discovery, distributed/parallel systems, high-performance computing, big data, large-scale data mining, text analysis, optimization for manufacturing, distributed/parallel search, scheduling, and finance and civil engineering, among others.

In this sense, this region gives a wide arrangement of exploration lines and applications that must be explored. By using code optimization methods we can reduce the number of operations to carry out any task while producing the right results.

Optimization Techniques for Data Scientist: Convergent Parallel Algorithms

When managing big data issues it is essential to structure strategies ready to decay the first issue into smaller and more controllable pieces. While dealing with larger datasets there are some issues that might occur i.e. the training of Support Vector Machines might get difficult to manage, time management, and memory management issues.

To conquer these drawbacks a lot of parallel algorithms have been introduced and successfully implemented by most of the data scientists. The convergent parallel algorithms lead to an answer by simultaneously dealing with various pieces that are dispersed among accessible specialists so that abusing the computational intensity of multi-center processors and accordingly proficiently tackling the issue.

Similarly, the gradient-type methods can also be easily parallelized but sometimes it suffers from practical drawbacks. To deal with this problem, a convergent decomposition framework for the parallel optimization of (possibly non-convex) big data problems were projected. The framework is very supple and includes both fully sequential and fully parallel schemes. The framework also helps in dealing with different big data problems like logistic regression, support vector machines training, and LASSO.

Optimization Techniques for Data Scientist: Limited Memory Bundle Algorithm

Most of the big data problems entail non-smooth functions of hundreds and thousands of variables with different constraints. This will sometimes bring a lot of issues. Non-smooth optimization is usually based on convex examination and most solution methods rely robustly on the convexity of the setback. Different efficient adaptive limited memory bundle methods are developed for providing outsized, possible non-convex, and inequality constrained optimization.  

Methods to Optimize your Python Code

Being a data scientist, there are some methods by using to which you can optimize your python code for your project. Optimizing your python code will not only save your time but it also helps in saving a lot of computational power. To get the best possible outcome, we must use some optimized techniques to get the results quickly. Some of the best techniques that most of the data scientist used to improve and optimize their Python code are as follows:

1.) Pandas.apply() – A Feature Engineering Gem

Pandas is a profoundly streamlined library however the majority of us do not know how to make use of it. Consider the normal spots in your data science project where you use it.

One of the amazing functionality of this library is “Feature Engineering”. This feature helps in creating new features using existing features. This feature is a standout amongst other additional items to the Pandas library as this capacity assists with isolating information as per the conditions required.

We can then proficiently utilize it for data manipulation tasks.

2.) Pandas.DataFrame.loc – A Brilliant Hack for Data Manipulation in Python

This method is highly preferred for those who deal with data manipulation tasks. While working on some project we are required to update some values of a particular column or row in a dataset based upon some conditions. 

This method can help us in performing that operation more effectively. “Pandas.DataFrame.loc” provides the most optimized solution for these kinds of problems.

3.) Multiprocessing in Python

It provides an ability to get support from more than one processor at a time. For using this method we smash our progression into multiple tasks and all of them run in parallel. It will help in speeding things up.

4.) Vectorize your Functions in Python

This method can help in getting rid of slow loops. Using a vectorizing method in your python code will speed up your calculation by at least two iterations. The vectorization method not only speeds up your code but also makes it look cleaner.


Handling big data is not an easy job to do. Often, newcomers in the field of Data Science (DS) and Machine Learning (ML) are encouraged to become familiar with everything related to linear algebra and statistics. The utility of a solid establishment in these two subjects is important for an effective career in DS/ML. However, optimization is equally important if you are looking for the best possible outcomes.

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