Tech

6 Data Science Skills That Are Inevitable For Future Data Scientists

Most of the data science courses rely on skills like statistics, machine learning, deep learning, and artificial intelligence that are pivotal for the industrial ecosystem and act as a virtual passport for entry into the business sector. Data science courses have gained momentum in the recent past owing to the scale of employability available in this domain. We have also seen a lot of proliferation in the data science courses in Delhi in the last few years and such courses have attracted a large number of students due to their interdisciplinarity.

Let us take a look at some of the most important data science skills. 

SQL Queries

It is important to master the art of writing SQL queries and scheduling them according to a particular assignment. This is important because companies are looking for data scientists who can model data as well as build corresponding pipelines of data. The management of data pipelines aids in two important functions. The first important function is related to workflow management and the second function is related to garnering effective insights. It also aids in the fabrication of lucid reports with the help of data visualization tools.

Feature engineering 

Feature engineering is related to data wrangling and data manipulation. The construction of new models is necessary for transforming data from one form to another so that it can be subject to numerous operations. Feature engineering is also important as it enables us to derive insights from raw and unstructured data sets. Usually, for the extraction of features, we rely on the information gain ratio. For instance, a sample space is available that consists of thousands of data entries. We set the threshold limit to a particular value so that entries that have higher information gain can be selected. Dimensionality reduction techniques like principal component analysis are also used for extracting various types of features.

Communication 

The skill of communication is as important as any technical skill for a data scientist. After all the work related to data mining, preprocessing, feature extraction and insights have been carried out, it needs to be communicated to the audience in a lucid manner. Various visualization tools like Google data studio are available but the art of communication of these results determines the level of impact that the research work is going to make. It is here that the communication skills of a data scientist convince the audience that a particular analytical report is inevitable for business growth and expansion.

Predictive analytics 

Predictive analytics involves two significant machine learning techniques of regression and classification. Handling of a discrete data set is usually treated as a classification problem in machine learning. Similarly, the handling of continuous data sets is usually treated as a regression problem in machine learning. These two techniques help in predicting the course of an event on the basis of historical data sets. These techniques are also pivotal for the fabrication of high-performing machine learning models dedicated to understanding business dynamics.

Explanation and experimentation 

Apart from predictive analytics, explanation and experimentation are extremely important skills for a data scientist. They help in comprehending the relationship between multiple variables that affect the performance of a model. Experimentation is necessary to compare various types of metrics and test data sets in numerous ways to establish validity and reliability. Similarly, explanation enables the evaluation of the results, and synchronizing them gives inputs to the team involved in decision sciences.

Recommendation systems 

One of the most important skills of a data scientist is his know-how about various types of recommendation systems. At an advanced level, data scientists acquire the capability to conceive their own recommendation systems. Such systems act as a catalyst for business organizations and increase revenues and profits in the long run. For instance, the e-commerce marketing platform of Amazon reported a more than 25 percent increase in their profits after they brought sweeping changes in their recommendation systems in 2020. Recommendation systems can not only cater to the personalized needs of customers but can also act as an effective mechanism for retaining customers for longer durations.

The way ahead 

The aggregate of all the above skills can open a window of opportunities for data scientists. Even if a data scientist or any other professional specializes in any of the above skills, his inputs can serve as gold dust for an emerging industry. The inputs from a data scientist can serve as building blocks for upcoming sunrise sectors that are desirous of making a mark in the age of big data analytics.

Related Articles

Leave a Reply

Your email address will not be published.

Back to top button