Top 10 Latest Data Science Techniques to Land a FAANG Job in 2023

FAANG, an acronym made up of the initials of five of the most famous tech companies with high stock prices namely Facebook, Amazon, Apple, Netflix and Google, has become a buzzword these days. FAANG is extremely popular in the data science job market. Not only do the data scientists in FAANG get best-in-the-industry salary packages, but also excellent professional growth opportunities. If you are aspiring to land up a FAANG job, you have landed at the right place. Here, we will talk about top 10 latest data science techniques to land a FAANG job in 2023. Keep reading to learn more!

Strong command on fundamental technical concepts

Having a strong command on technical topics such as Data Structure concepts including linked list, queue, stack, trees, etc., Core CS subjects like Operating Systems, DBMS, and Computer Networks, always help. Additionally, analysis of algorithms, sorting/searching, etc., turn out to be no less than a cherry on the cake.

Starting early

If you start early in the professional space, there can be nothing better than that. As a matter of fact, FAANG companies open up their internship positions for the next year in the summer itself. What better opportunity than this to grab? If you are highly dedicated and perform well, your internship can lead to full-time position too. Now, you have a reason as to why you should start early.

Programming

Programming is one of the key requirements to land up a FAANG job. A fair knowledge of various programming languages, such as Python, Perl, C/C++, SQL, and Java, will always add up to your benefit. The reason why you need to be good at programming is because with programming languages, data scientists organize unstructured data sets.

Strong Communication Skills

Yet another critical technique to land a FAANG job in 2023 is strong communication. As a data scientist, you need to have a clear understanding of how to extract, understand, and analyze data. That's not all. You should also be able to communicate your findings with team members clearly. This is why strong communication is the key.

Networking

The extent to which networking has helped people flourish in their careers cannot be put into words. Thus, having contacts with professionals who have a similar background and have made it to getting a FAANG job, will help you get a similar job comparatively easily. An additional benefit of the same is that it helps in comprehending the job's challenges and the skills that must be prioritised.

Problem Solving

With this technique, you can aim to transform data into solutions. As far as a FAANG job is concerned, the experts use logic, statistical analysis, and predictive analytics. Now, you know why it is important to have this skill.

Data management

Needless to say, data scientists must be able to manage complex data and comprehend how their recommendations will impact a company's bottom line. Data is enormous and is increasing at a rapid pace. Taking that into account, having access to a lot of information is absolutely meaningless if you don't know how to use it to examine and enhance methods, procedures, and plans which is why data management technique is considered to be important.

Excelling at Excel

Data management in organizations has become so much easy and convenient. Thanks to MS Excel, with a FAANG job in hand, you will be required to undertake several computations, data analysis, and budget analysis. All of this is a cakewalk if you have a solid grip on Excel. No wonder why this technique is considered to be one of the most important one.

Eager to learn

If you are desperate to grab a FAANG job, it is expected of you that you should be naturally curious and motivated to learn more and understand how things work as a whole. In this domain, it is highly important that you stay up to date with the industry and its changes even as analysts transition to management positions.

Analytical thinking

This is one of those techniques that almost all jobs require. As a data scientist, you should consider critically both the ramifications of the data you acquire and the appropriateness of their initial data collection. Only the data that can aid in decision-making should be the focus of their analysis.