Using big data as a vision-creating engine has increased the demand for a skilled data scientist at the enterprise level in all industry verticals. Whether it is refining the product development process, enhancing consumer retention, or mining data to find opportunities, organizations are highly dependent on data scientists. They need them for sustaining, growing, and staying one step ahead in the competition.
As a result of the increase in the need for data scientists, a discipline like python for data science offers an exciting career path for both students and professionals.
Here are the top data science and analytics skills of 2021:
To have a successful career in data science, you have to master several technologies, specifically the open-source ones, such as python for data science, Hadoop, C++, etc.
- Python: Statistics is crucial for data science programming, for which it is most needed for professionals to develop expertise in python. It is essential to learn python for data science on an extensive data system landscape such as Oracle, SAP HANA, and Hadoop. With this, professionals can quickly build industry-based cases in relation to workforce analytics, marketing analytics, and consumer analytics.
- Hadoop: Data science includes extensive scale data analysis for exploring big datasets, mining them, and then speeding up the data-driven innovation. A data scientist should learn Hadoop because it is an open-source tool for easy management and manipulating big datasets from various repositories. In addition, some years of experience in Hive and Pig are available as excellent selling points for all data scientists.
- NoSQL: All data scientists need to function with free data no matter whether it is audio feeds, social media updates, or video feeds. Data science mainly deals with the analysis of free data. This is why high skills in several NoSQL databases like MongoDB are mandatory for writing and implementing complicated queries on free data available.
- Machine learning: A data scientist must have a good understanding of data mining, supervised or unsupervised learning, along with pattern identification. Some of the machine learning perceptions that require mastery are Neural Nets, Clustering, and Decision Trees. This expertise you can easily earn by undergoing training that assists you in juggling with data.
- Data visualization tools: It is required for data scientists to communicate data-driven visions efficiently. Data scientists must describe all findings so that both technical and non-technical audiences can easily interpret them. This is why deep knowledge of several data visualization tools such as D3.js and ggplt assist data scientists in providing apparent insights.
- Estimating and predicting qualities: It is one of the vital parts of data science. When theories of probability are mixed with other statistical methodologies, a data scientist can easily extract meaningful information and insights from data.
Finding data abnormalities
- Recognizing trends or any patterns in data
- Recognizing reliability in between variables
- Forecasting future trends
By knowing various concepts of probability and statistics, such as measurement level of data, central tendency, and asymmetry, it’s easy to work with unstructured data.
Non-technical skills required for data science
The 3 Cs: The role of a data scientist is driven by 3Cs – curiosity, communication, and common sense. In many cases, the company does not know that it has some data-related problem. It’s the curiosity of a data scientist who can bring in various opportunities for bringing invaluable insights from available data.
In the formulation of any issue definition or hypothesis, a data scientist’s common sense plays an important role.
In addition, a data scientist has to do good communication and should have a good understanding of the application needs and business needs. Finally, he/she must be able to figure out the patterns and also the relationship that exists between big data.
Not only that but also the person has to express them to the marketing team and the development team. And to do all these, a data scientist should have storytelling skills so that he/she can utilize the available data to narrate a story efficiently for easy understanding of things.
Innovative: To be a good data scientist, you must be creative and innovative with a great eagerness to know more and figure out more creativeness. This helps a data scientist determine where data can easily add value and bring outcomes for an organization.
With tough competition and challenging skills required for mastering data science, it’s not that easy to be a data scientist.
If you want to be a data scientist and gain some practical experience, then acquire all skills needed. Go outside statistics and math, and try to work on a hands-on project to offer solutions to your organization.