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The way Data Science is being heralded as this big phenomenon within the IT industry, it has become common that IT professionals and students alike are thronging towards learning and mastering this field. So today, we are here to make you understand the Top 10 Data Science skills that are to be mastered.
If you are new to Data Science and want to learn more about what it is and what is to be done to excel in the field, check out our Data Science blog catalog for more on this amazing field. Some of them are linked below for your reference.
4 Advanced-Data Science Skills to stay relevant in the Post-Pandemic World.
Why should I do a certification in Data Science.
For your convenience I have divided this article into 2 sections: Non-technical skill & technical skills.
So first let us have a look at the non-technical skills which every data scientist should posess
Non-technical skills required to mater Data Science
- Ability to solve problems related to business
- Teamwork
As an expert in programming languages, business transitioning skills are vital in Data Science too. The ability to translate business requirements into data-driven problems that d has to be solved as a part of your project is essential to the success of the company or group that has taken you up.
It is your responsibility to identify real and present problems that may be unidentified or missed out by the stakeholders of the company.
Data Scientists remain cognizant of interpersonal sensitivities and interdepartmental rivalries. While mastering these aspects of the corporate world can go a long way towards the successful completion of data science projects instead of endless bureaucratic delays.
As a Data Scientist, you need to channelize your data science learnings to accelerate the pace of the output to ensure the sustainability of the organization. You cannot do it alone so you need to collaborate with your teammates and overcome organizational challenges.
According to Glassdoor, Data science is ranked 1st among the top 50 jobs in the U.S. Almost 60% of global companies cannot analyze or classify their data. This is why they are in desperate need of Data Scientists. This is where someone like you with the brain and the management capabilities will prove to be indispensable.
Technical Skills required for Data Science
- Mathematics and Stats
- SQL
- R programming
- AutoML
- Data wrangling
- Data Visualization
- Deep learning
- GitHub
A fundamental understanding of the mathematical foundation for Data Science is critical to avoid guessing at hyperparameter values when tuning algorithms. Calculus, linear algebra, and statistics are considered among the most in-demand Data Science skills.
Probability with the help of statistical methods helps in making estimates for future analysis. Statistics is mostly dependent on the theory of probability, in simple words, both are entwined.
Any quantifiable source of data is used for undergoing the process termed as “Data Science”. Hence, databases are no exception to this fact. SQL(Structured Query Language) is arguably the most prominently used Database language used in programming and designed for managing data held in a relational database management system.
It is useful in handling data, i.e. data incorporating relation among entities and variables. SQL is your conduit for acquiring data, you should be well versed in SQL to gain maximum benefit for data acquisition. Besides, using R packages like SQLDF is a great way to query data in a data frame using SQL.
R is among the modern set of Data Science skills that are deemed an essential data science skill to add to your set. It is primarily used for data mining and can also support scientific operations using data.
AutoML tools are required to expand the capabilities of a resource if the data scientist is in short supply. By automating many routine tasks carried out by the data scientist and evaluating the machine learning models, more work can be done with efficiency.
The technology is being taken seriously by many companies, so widen your knowledge with all available tools.
Data wrangling is considered as a key part of Data Analytics. It is considered among the primary Data Science skills to possess and is among the first processes. Data Wrangling involves the conversion of the data from an unkempt, raw format to a more comprehensible format through various steps and measures.
It is using this formal and arranged data that the rest of the process is run through. Data is structured and cataloged using Data Wrangling.
Data visualization is the best way to showcase the data results coming from any algorithm. It is another important aspect to add to your growing repertoire of Data Science Skills.
It is understood that the key aspect of a Data Scientist is to have a well-crafted visualization. Experts always look for new data visualizations to make new progress in the business.
Some of the prominent Data Visualization tools include Tableau, PowerBI etc.
Deep learning is a class of knowledge suited for specific problem domains like image recognition and NLP. Routine learning algorithms like XGBoost, are recommended to use structured or tabular data. Most Data Scientists do consider that image recognition and NLP as a mere specialization of Data Science that everyone cannot master.
The use cases for image classification and NLP are getting more and more frequent even in typical applications. If you don’t have direct applications of deep learning in your current job, you can always experiment with an appropriate data set that will allow you to understand if the need arises in the future.
GitHub is a cloud-based hosting service that helps you to manage Git repositories. These tools are of great help while managing different versions and sources of the data that is supposed to be used at different intervals. It can also commit and hold various stages of the project as well which can be retrieved as and when required.
They check all the changes that are made to a code base and also, they ease in and collaborate with multiple developers to make changes to the same project at the same time.
Git is becoming considered an essential on-job requirement and it takes time to get used to using Git. It is easy to start working on Git when you’re working alone, but it becomes difficult when you join a team or collaborating with someone.
Conclusion
I hope I have helped you in providing a genuine list of all the skills required to become a successful data scientist. These are some of the most common skills which every data scientist should possess and there are a ton of other skill sets to master apart from the above-mentioned ones.
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