Students and working professionals find the field more attractive as the demand for Data Scientists increases. Data Scientists are highly sought after at all levels of the organization due to their role as an additional perspective engine. Data Scientist skills are in high demand by organizations to stay ahead of the competition. This is true whether it’s to improve customer retention, refine product design, or mine data to find new business opportunities.
In this article, we will discuss some of the most important skills for Data Scientists.
Skills required to become a data scientist
Let’s take a look at two types of skills required to become a data scientist:
Skills that are not technical
Let’s now examine the technical skills needed for the role as Data Scientist.
Data Scientists need technical skills
Here are some essential skills for Data Scientists:
1. Programming Languages (Python and R) To be a Data Scientist you will need to be familiar with programming languages like Python, R, Java. Perl. C/C++. SQL. R and Python are the most commonly used coding languages for data science roles. These programming languages can be used by data scientists to organize unstructured data.
Python: As you gain more knowledge about Python basics, you will want to explore Python libraries. These are replaceable pieces that you can use in place of writing basic instructions.
R: R is an open source statistical programming language that allows you to present and interact with data-driven outcomes.
SAS: SAS is a package of software that includes installed statistical functions as well as a Graphical User Interface (GUI), to aid non-technical users.
2. Machine Learning
Machine learning is the process of writing code to allow a computer learn from initial stored data. Machine learning is beneficial for data scientists as it allows them to make accurate estimates and make wise decisions without human intervention.
3. Data visualization is the process of interfacing and converting data and information using visual aids such as graphs, charts, bars, and other visual aids. Images can also be used to communicate the relationships between different data sets.
Power BI: PowerBI is available in desktop and mobile versions. It generates a variety visualization techniques using Azure and SQL. It is easy to learn for beginners.
Tableau: Tableau offers more functionality and speed. You can create stunning dashboards and reports using drag-and drop functions.
4. MathematicsMathematics is essential for data science because mathematical concepts aid in identifying patterns and the development of algorithms. To put these algorithms into practice in data sciences, you need to be familiar with multiple probability theory concepts and statistics.
Linear Algebra – Linear algebra is the foundation of many popular algorithms. Knowing how to identify matrices or vectors will prove very useful, especially if you are a machine learning expert.
Multivariate Calculus – Refresh your knowledge about mean value theorems and gradients, derivatives and limits, product and chain rules and Taylor series, as well as beta and gamma functional area.
5. Data WranglingAfter collecting data from different sources, you will almost certainly find some sloppy data that must be corrected. Data wrangling uses coding languages to correct data errors such as incomplete data, chain formatting, date formatting, and other data issues. It is also necessary for data fields to be mapped from the source to destination.
6. Statistics Statistics is a collection mathematical methods and tools that allows us to answer important data questions