With the introduction of big data, the need for its storage increased gradually. Companies were focussing on building solutions and frameworks to store as much data as possible. When this problem is addressed by big names such as Hadoop, companies shifted their focus on data processing. Here, the popular term that everyone might have heard once is “data science.” Undoubtedly, data science is considered as the future of AI (Artificial Intelligence).
Data science is a blend of algorithms development, data inference, and technology to solve analytical problems.
Now, you have a bit clear idea of what data science is and why it is needed, let’s deep dive into what it takes to be a data scientist.
Here are some general skills that are required to become a data scientist.
1) Understanding of Stats
The basic blocks of data science are probability, hypothesis testing, descriptive and inferential statistics. In order to be a successful data scientist, you should have an intuitive understanding of statistics. It entails interpretation of statistical output in the business context. You should be able to use the basics of statistics as a strong foundation to the business analytics.
2) Statistical Programming
In this technology-driven era, you can’t be satisfied with the knowledge of only one programming language. Data scientists recruiter are looking for candidates who are familiar with multiple languages like SAS and R. Along with this, knowing Python would be a bonus for you and the recruiter.
As a beginner, it is necessary to have an understanding of certain tools. Companies don’t expect employees to set back because of unfamiliarity. All they want is efficient employees who can help in building analytical solutions. It’s one thing to be a programmer but being comfortable working in multiple programming environments is another. In order to increase your probability of becoming a data scientist, make sure you have the potential to adapt to new statistical languages.
3) Statistical Algorithms and Techniques
To become a data scientist, you should be good at some statistical algorithm as well as techniques, like logistic regression, linear regression, clustering, time series forecasting, decision tree, machine learning, neural networks, and their business applications. Along with that, having a good understanding of the latest happening in the analytics arena such as NLP or deep learning is an additional advantage.
4) Business Knowledge
Having domain expertise is not crucial in the beginning stage, but as you get more experienced in the analytics, having strong business knowledge will keep adding value. Hence, it is necessary to spare some time and learn about various trends, norms, and terminologies in your area of interests.
Having strong communication skills is a crucial part of your personality irrespective of the field you are working in. When it comes to data science, knowing how to communicate your views properly lets you concisely present analytical solutions, manage team perception, and interpret statistical output into the actionable approaches.
Here are some specific languages, tech tools, and libraries that add value to your profile as a data scientist:
Python is one of the most demanded programming languages of today. It’s a general-purpose language, currently doing data science. Companies across the world are using Python to get insights from their collected data and compete in the market.
Like Python, R is also an important language for data science. Though, it’s a bit older language but still in demand. The core of the R language is statistics and it’s popular among statisticians.
Note: Hands-on experience on R on Python is must for every data scientist.
SQL is an acronym for the Structured Programming Language. With the help of this, a database can be accessed. There is not a prominent place for SQL in the data science market, but having a strong database skill is worth when you are competing in the market.
Hadoop and Spark:
Hadoop and Spark are open-source tools from Apache framework for big data. Hadoop is a software platform for distributed processing and storage of large data sets on the computer clusters that are built from commodity hardware.
Whereas Apache Spark is an in-memory engine that processes data with elegant and expressive APIs to let data scientists execute streaming, SQL workloads, or machine learning efficiently to access datasets.
Tableau is also one of the most demanded visualization tools and analytics platforms in the market. It is easy-to-use and growing rapidly in terms of popularity. It has both free as well as paid version. You can opt for any version depend on your data privacy requirements.
To put everything together, data science is a continuously evolving and fast-growing job where demand has been exceeding supply for years and expected to do it for many more years. Hence, it is the right time for you to explore this completely new world of data science and Artificial Intelligence with your knowledge and zeal for learning.