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An overview of the technical, analytical, and programming skills for data scientists in 2021

An overview of the technical, analytical, and programming skills for data scientists in 2021

Introduction

There is no doubt in the fact that data science is evolving and evolving at a rapid pace. This requires the scientists and the entire community to keep pace with the skills popping up in the field. Be it the analytical skills associated with mathematical operations or the programming capabilities, data science incorporates it all. The tools of SQL, Spark, and Hadoop which are all analytical in nature are not new to data science. While machine learning has broadened the spectrum of data science, on one hand, artificial intelligence has roped in novel applications on the other hand.

Operations with mathematical acumen

Data scientists need detailed knowledge about statistics. For this, a data science online course holds the key. The key concepts taught include mean, median, and mode that are extremely important to get an idea of the data set before its full-fledged operation. Other concepts like standard deviation and likelihood indicators are necessary as they enable us to extract necessary samples from large data sets. With the help of different types of bar charts and histograms, we are able to visualize the picture of the entire data set. Hence, the knowledge of descriptive statistics and inferential statistics is very important while dealing with raw and unstructured data sets.

Next in line is the knowledge of probability. The concepts like Bayes theorem and central limit theorem are extremely important for a data scientist. Other important topics like the probability distribution function help in finding the expected values without actual calculations.

In addition to this, the knowledge of different types of vectors and matrices is essential while dealing with machine learning computation.

Insight of the programming skills

There is no doubt in the fact that Python remains the prime programming language when we talk about data science. A report by Statista in 2020 noted that more than two-thirds of the data scientists use Python for their day-to-day projects. The reason for such a popularity of python is its ease of deployment when it comes to programming applications. The most notable types of programming libraries used in Python include Pandas, TensorFlow, and Seaborn. Another important language that data scientists use for programming is R. R as a programming language is noted for its use in statistical analysis due to its open-source nature. It has numerous tools for visualizing different types of data-driven results. Another type of programming skill available to data scientists is called SAS. SAS is known for its graphical user interface which enables even amateurs to use this software suite. The disadvantage associated with SAS is that it is very expensive when compared to Python and R.

The number game

It is important to see the appropriate use of the above-mentioned language in various sectors. In the telecom sector, 50% of the users use R as a programming language, 30% use Python, and 20% use SAS. For corporate, consulting, and retail, R is used by 30% of the users. Python is used by 40% of the users and SAS is used by 30% of the users. In Marketing Services, R is used by 45% of the users, Python is used by 30% of the users, and SAS used by 25% of the users. When it comes to healthcare and financial services, the users of R and SAS stand at 40% each whereas the uses of python stand at 20%.

Machine learning as the prerequisite of data engineering

The skill of machine learning is very important when it comes to data engineering. Machine learning enables us to derive insights from various types of data sets using classification and regression algorithms. It also enables us to predict future outcomes of an event based on a specific sample set. Machine learning is also connected with other domains like natural language processing and Big Data Analytics. With the help of ensemble models and random forest models, we are able to classify different types of data sets and derive appropriate insights. Machine learning also aids in decision sciences with the help of decision trees. In one word, machine learning is one of the most important skills for data scientists and it is intimately connected to various types of applications.

Concluding remarks

The above-mentioned skills are not only helpful in data visualization but are also helpful in data analysis and decision making. There are other types of skills that are needed by data scientists in the 21st century and these include data wrangling and business acumen. The list of skills needed for data scientists is only slated to increase in the near future.

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