Taming the big data science wave generates the need or prominence of hiring data scientists in various niches.
Data science is a vast field where data scientists usually make a significant impact on the overall growth of an organisation.
If you are an aspiring data scientist heading toward the path of success, you need to know about the steps involved in becoming a data scientist.
Moreover, you need to develop specific skills to separate yourself from the crowd.
We have compiled specific vital skills required for a data scientist to get a powerful career push.
Let’s get started!
Who is a Data Scientist?
A data scientist is regarded as a person mainly responsible for handling extensive data – both structured and unstructured – with the objective of extracting actionable items from the analysis done.
The roles combine mathematics, computer science, statistics, and technical skills to use information for creating actionable insights. Data scientists play an integral role in gathering, processing, and modelling data with the help of technology and industry trends to develop outcome-oriented plans.
Basics of Becoming a Data Scientist
The area of data science involves extracting the useful insight from gathered data. There is a surging need for professionals who can turn data analytics into a competitive advantage for the business.
Step 1: Earn a Degree
You can earn a bachelor’s degree in the field of data science, computer, or statistics to start your career. This is not strictly necessary as the core skills can be learnt elsewhere as well.
Step 2: Learn Programming Languages
It is essential to get a stronghold on programming languages like Python, SQL, R, SAS. These are touted to be vital languages to work with large databases.
Step 3: Learn Related Skills
In addition to learning languages, you should also have in-depth knowledge of tools for data visualisation, extraction, cleaning, etc.
Step 4: Earn Certification
Certifications are a great way to showcase your knowledge and get enriched skills. Tableau/PowerBI certification, and other data science courses will help you learn the data science basics that you need to master this field.
Technical Skills Required to Become a Data Scientist
To kickstart your career as a data scientist, an aspirant must be comfortable with several programming languages, namely Python, SQL, and R language to name a few.
Here, is a list of some of the most essential technical skills that every budding data scientist should possess:
SQL
To no one’s surprise, SQL is widely used as a database language, and hence its operational knowledge is required to become a data scientist.
This programming language is mainly used for managing data and queries in the relational database system. It is also used to retrieve or insert new data and delete existing ones, etc
Hadoop Platform
Knowledge and experience with the Hadoop platform can boost your CV considerably. As a data scientist, you may encounter situations where the data may exceed your system’s limit. Therefore, you may need to send data to different servers. In this case, Hadoop can help. It is helpful to convey data to various system points.
Python
Python is the most popular programming language among coders and programmers. It is used in almost all data science processes. From data mining to embedded systems, Python is utilised everywhere.
It also has a vast array of libraries available for you to utilise complex functions easily.
R Programming
R language is specially designed for data scientists. You can solve any data science problem with it. About 43% of the data scientists use this language to solve statistical problems.
It uses various statistical techniques while employing ML algorithms.
Machine learning
Machine learning analyses large sets of data with the help of algorithms. With it, the majority of data scientist functions can easily be automated such as data cleaning. Techniques like neural networks, adversarial learning, time series, and outlier detection are included in ML.
Mathematics and Statistics
Mathematical techniques are a prerequisite to becoming a data scientist. Moreover, probability and statistics are also used in the case of data manipulation, data visualisation, feature engineering, and model evaluation.
The techniques like mean, median, correlation, matrix, hypothesis testing, dot products, cross products, and many more techniques are an essential part of data science learning.
Excel and Tableau
The bread-and-butter of digital data, Excel needs no introduction. The data from Excel can be daunting, and hence Tableau fulfils the need for high quality data visualisation. Excel is useful for the manipulation of data and the computation of results.
Interpersonal and analytical skills
Besides technical skills, a budding data scientist should also have analytical and logical skills. Some of these skills are mentioned below:
- Communication skills: Data scientist should know the art of communication so that they can easily translate their technical findings to team members
- Teamwork: The skill of working with a team is essential for all data scientists. For every problem, they need to work with their team to get desired outcomes
- Business Strategies: Budding data scientists should also know about the businesses. They need to have a strong understanding of their industry or how a problem might affect their business
- Critical Thinking: This is a valuable skill that a data scientist will need. To find insights, you can frame adequate questions and understand how the results will be generated for a particular problem
Wrapping Up
Data scientists are gaining the utmost importance in today’s generation. With the rising number of data science jobs, the demand for data scientists is also rising.
If you want to join the bandwagon, you need to have certain skill sets so that you can emerge as a professional. Take cues from this blog. Also, you can enrol in data science courses for better learning.
