Top 10 technology skills for data scientist

Data science is the discipline of obtaining relevant insights from huge data contained in enterprise datasets. In today's competitive business world, it has become the trendiest technology trend for organizations. Large organizations receive massive volumes of data every day, and they require strategies to organize and analyze this massive data in order to gain relevant insights. As a result, emerging technologies such as Hadoop, Cassandra, and Apache Spark have altered the entire landscape of big data storage, analysis, and management. At the same time, businesses are looking for top data science expertise to work on their big data projects, which is why becoming a data scientist is regarded as the hottest career of the twenty-first century.

Finding a suitable data scientist has gotten increasingly difficult as competition has increased. A data scientist assists organizations in developing policies and practices based on scientific evidence rather than wishful thinking. In other words, a data scientist assists in the development of policies and processes based on facts discovered in recorded data.

A data scientist must have a diverse set of abilities, including mathematics, statistics, computer science, and business communication. The top ten skill sets a data scientist must have before being hired on your enterprise data project are listed below.

1. Basic Tools

A data scientist is in high demand, and he must be skilled with his tools. This simply means that a data scientist should be familiar with statistical programming languages such as R or Python, as well as SQL. You must ensure that your data science team contains a skilled scientist capable of making your datasets useful while also productive.

2. Statistics

 As a data scientist, you must have a fundamental understanding of statistics. He should be familiar with statistical tests, distribution and likelihood estimators, and so on. Statistical abilities are essential in all types of businesses, notably in data-driven businesses.

3. Machine Learning

If you work in a large data-driven organization that deals with terabytes or petabytes of data on a regular basis, you should be conversant with machine learning methodologies and techniques. In such instances, you should be familiar with random forests, k-nearest neighbors, and ensemble approaches. These data science methodologies can be expanded upon using Python or R packages. It is critical to understand how and when to employ a specific strategy.

4. Mathematics 

To hone your data science skills as a data scientist, you need to have a fundamental understanding of multivariable calculus and linear algebra. A data scientist must be well-versed in mathematical computations in order to design your own in-house implementations. This is critical for organizations when the product is defined by data. Small gains in predicting performance can make a big impact when it counts. When such data-driven decisions must be made, businesses can gain an advantage over their competitors.

5. Data Mining

A data scientist works with huge data, which can be untidy. It is critical for a data scientist to deal with data defects such as missing data, uneven string formatting, and date formatting. A skilled data scientist turns complex or poor data into a manageable set of data in order to get insights using various science or data methodologies.

6. Data Visualization

Visualizing data in an appealing yet simple manner is a combination of science and art. This is a critical ability in areas where data-driven decisions have the greatest impact on the organization. Visualization tools such as ggplot and D3.js should be familiar to a data scientist. He should understand the basics of visually encoding data in order to communicate it to an audience in a simple yet compelling manner.

7. Software Engineering

 A data scientist will be in charge of all data logging, which is essential in developing a product or other data-driven strategies for any organization.

8. Analytics

 A data scientist's analytical skills must range from data analysis encompasses things like mix modeling, attribution modeling, retail optimization, sales projections, user profiling and segmentation, customer attrition, and estimating long-term value, among other things. A data scientist should analyze data from a business standpoint, which includes finance, marketing, ROI, and so on.

9. Business Acumen

A data scientist should be well-versed in the market. He should be aware of the challenges that a firm faces. This will make it easier for a data scientist to locate an appropriate business solution. He can also use data to offer a new strategy to deal with an existing problem. A data scientist can only fully utilize data if he is well-versed in the business with which he is working. A data scientist may facilitate informed decision-making for firms by empowering them with quantifiable information.

10. Communication skills

The ability to communicate effectively is an essential skill for quantitative experts. Enterprises are searching for someone who is familiar with technical jargon and can convey it to a non-technical individual in an understandable manner. A data scientist should be able to communicate technical concepts to the marketing or sales team.

If a data scientist possesses all of these characteristics, you should recruit him for your corporate data project; otherwise, you should look for another one. A tech company that works on data science projects is the best place to get resources. You can use such firms on your data initiatives to obtain a competitive advantage and profit.