What Qualifications Are Required To Become Data Scientist?

The Data Scientist is one of the hottest career options in the USA for 2018, According to Glassdoor. It is not surprising that the annual salary paid to a data scientist professional is $123,000 on an average. Still, the data science market is not saturated and there is an estimated shortage of 190,000 data scientists in the USA alone.

If you are planning to start a career in data science then 2019 offers a plethora of job opportunities for aspirants. The aim of this blog is to highlight the top skills that are actually needed to become a successful Data Engineer.

As per the research, profile of a Data Scientist looks like:

• 70 percent of Data Scientists in the research findings were male.
• Every Data scientist speaks at least one foreign language on an average.
• The minimum needed experience for transitioning to the data scientist role is 2 years and they are named as entry-level junior data scientists.
• People who have already worked as a Data scientist have an average work experience of 4-5 years and named as mid-level data scientists professionals.
• More than 50 percent of data scientists have work experience in R or Python programming languages.

Here, you can quickly notice that data scientist space is heavily men dominated who account for 70 percent out of 1000 profiles. Still, it does not signify that there is no space for skilled women. With an ongoing demand in the field, there is a place for everyone who is knowledgeable either male or female. The only condition is that you need to be qualified and skilled enough for getting hired by recruiters quickly. In the next section, we are listing a set of top skills needed by every Data Scientist to become successful in the domain.

What Qualifications Are Required To Become Data Scientist?

To become a data scientist, there is a need for hard skills and soft skills both. It is tough to summarize skills into a few categories. Still, we tried to include maximum details here for your reference. 

1). Good Programming Skills

This is one of the most fundamental skills set of a Data Scientist. Today, the job role of a data scientist is much more applied as compared to the traditional statistician. Programming is certainly important in multiple ways and three popular styles are given below.

Read: What Exactly Does a Data Scientist Do?

• A Data Scientists is evaluated for statistical skills and his ability to do statistics. If he has enough statistical knowledge but does not know how to implement it, then it becomes an issue.
• The ability to analyze large datasets is also evaluated. While you are working within an organization, the size of datasets is extremely larger and it needs to be analyzed carefully to derive meaningful insights from available data.
• A few programmers are able to design better tools for better data science. It includes everything from building tools to visualize data, create frameworks, manage data pipelines in such a way that necessary data can be in the right place and right times.

The software engineering training will help you in gaining necessary skills here. It would be great if you have worked as a software programmer with some IT Company before to apply for the Data Scientist role.

2). Quantitative Analysis

Quantitative analysis is put at the heart of Data Scientist skillset. Data Science is all about understanding the behavior of a complex system by analyzing data produced by the system. Data can be analyzed either naturally or via experiments. The quantitative analysis is needed in multiple ways and few of them are given below.

• Experimental design or analysis: Data scientists working on internet apps analyze the way how data is logged and the way experiments run to test the various hypothesis. There are plenty of ways when experiment analysis can go wrong, so data scientist can help you here.
• Modeling of Complex economic and growth systems: Typical models like churn models or customer lifetime value models are common here. More complicated systems like supply and demand modeling and methods to model the growth systems are most valuable.
• Machine Learning: Data scientists don’t have to implement machine learning models themselves, still they can help in creating prototypes to test assumptions, select or create features, identify areas of strength and opportunities in existing machine learning systems.

Because of this tough analysis, data science field is pretty attractive to statisticians, economists, operation researchers, physicists, and more.

3). Product Knowledge

Product knowledge is one of the important skills set for data scientist that defines its ability to perform quantitative analysis on the system. Product knowledge means understanding data generated by complex systems and all of this collected data is analyzed by data scientists. It is important for quite a few reasons given below.

• Hypothesis Generation: A person who knows the product well can generate a hypothesis about ways how the system behaves. Hypothesis gives a clear idea of how a system can behave if its behavior changes and its exact working process too.
• Defining Metrics: This skillset includes defining primary or secondary metrics that Company can use to keep track of success at particular objectives. Also, Data scientist should learn the product in order to create metrics and know why it was made and measure features that are worth moving.
• Debugging analyses: Results that are not predictable are usually caused by bugs. With deep debugging analyses, it is easy to find problems and fix them quickly that might have gone wrong.

The product knowledge usually includes information about the system that is created by your Company. If it is not possible then try to get to know people who can use the product.

Read: An Ultimate Guide To Python For Data Science (2024)

4). Communication Skills

This skill is probably more important to leverage all the skills listed above. It is particularly important and helps to distinguish a good data scientist from a great one. The communication skills can manifest in multiple ways as given below.

• Communication Insights: It is similar to storytelling where it is important to communicate insights in a clear and concise manner so that others could act effectively on those insights.
• Data visualization: There is nothing more satisfying than presenting data in the form of a graph to convey your point.
• General communication: While working as a data scientist, you should be a good team player. You should know how to work with engineers. Managers, operation engineers, researchers etc. Good communication always helps to facilitate trust and understanding that is highly important for someone who plays with data and responsible for deriving meaningful insights from available data.

5). Teamwork

Teamwork connects all the listed skills together. A data scientist cannot exist in isolation so it is necessary for him to learn the teamwork for effective findings. Let us learn how teamwork is important in multiple ways:

• Here, you should be selfless and put Company objectives ahead of your personal ambitions.
• A data scientist majorly works on feedback. So, the major task of a data scientist revolves around back-and-forth iteration and analyze feedback to reach to an impactful solution.
• Since data scientist profession is quite new and there is no one accommodating all skills together. For example, looking for a possible with statistical knowledge, frameworks, libraries, and tools is nearly impossible. The best solution is sharing knowledge, ideas, methods, and findings with each other.

6). Education and Training

Most data scientists are highly educated and research suggests that 75 percent of data scientists have either a master degree or Ph.D.

At the same time, a fancy degree is your choice but it is not mandatory. Almost 25 percent of data scientists are graduated from average universities, still, they are able to start a good career in the data science space.

Most data scientists have a degree in Computer Science, IT (Information Technology), Statistics and Maths, Social Science etc. you can be more established in the data science domain if you have a quantitative background. Therefore, you don’t have to necessarily jump to an extra academic program to acquire the skills needed to become a Data Scientist.

Read: Unlock the Advanced Power of Augmented Analytics in Data Science: A Transformative Fusion

To upgrade your skills, online Data Science courses are more suitable. Nearly 40 percent of data scientists reported that they enrolled in the online course and it is clearly mentioned in their LinkedIn resumes too.

The Overall Verdict

The first two skills quantitative analysis and programming should be taken seriously when planning to start your career as a Data Scientist. These skills are necessary to create the technical foundation of a data scientist skillset. Last four are not technical skills but they are soft skills and are equally important. The third skill is optional and taken into consideration when you are working with a service-focused Company.

The fourth and fifth are critical to every data scientist while you start working within an organization. The education background sets the eligibility for the profile and makes you a suitable choice for the data scientist position. Good luck.

FaceBook

Twitter

LinkedIn

Pinterest

Email

Trending Courses

Cyber Security

• Introduction to cybersecurity
• Cryptography and Secure Communication 
• Cloud Computing Architectural Framework
• Security Architectures and Models

Upcoming Class

1 day 22 Nov 2024

View Details

QA

• Introduction and Software Testing
• Software Test Life Cycle
• Automation Testing and API Testing
• Selenium framework development using Testing

Upcoming Class

2 days 23 Nov 2024

View Details

Salesforce

• Salesforce Configuration Introduction
• Security & Automation Process
• Sales & Service Cloud
• Apex Programming, SOQL & SOSL

Upcoming Class

1 day 22 Nov 2024

View Details

Business Analyst

• BA & Stakeholders Overview
• BPMN, Requirement Elicitation
• BA Tools & Design Documents
• Enterprise Analysis, Agile & Scrum

Upcoming Class

1 day 22 Nov 2024

View Details

MS SQL Server

• Introduction & Database Query
• Programming, Indexes & System Functions
• SSIS Package Development Procedures
• SSRS Report Design

Upcoming Class

2 days 23 Nov 2024

View Details

Data Science

• Data Science Introduction
• Hadoop and Spark Overview
• Python & Intro to R Programming
• Machine Learning

Upcoming Class

1 day 22 Nov 2024

View Details

DevOps

• Intro to DevOps
• GIT and Maven
• Jenkins & Ansible
• Docker and Cloud Computing

Upcoming Class

6 days 27 Nov 2024

View Details

Hadoop

• Architecture, HDFS & MapReduce
• Unix Shell & Apache Pig Installation
• HIVE Installation & User-Defined Functions
• SQOOP & Hbase Installation

Upcoming Class

1 day 22 Nov 2024

View Details

Python

• Features of Python
• Python Editors and IDEs
• Data types and Variables
• Python File Operation

Upcoming Class

9 days 30 Nov 2024

View Details

Artificial Intelligence

• Components of AI
• Categories of Machine Learning
• Recurrent Neural Networks
• Recurrent Neural Networks

Upcoming Class

2 days 23 Nov 2024

View Details

Machine Learning

• Introduction to Machine Learning & Python
• Machine Learning: Supervised Learning
• Machine Learning: Unsupervised Learning

Upcoming Class

36 days 27 Dec 2024

View Details

Tableau

• Introduction to Tableau Desktop
• Data Transformation Methods
• Configuring tableau server
• Integration with R & Hadoop

Upcoming Class

1 day 22 Nov 2024

View Details

Related Posts

How Satistical Inference Like Terms Helps In Analysis?

 Apr 20, 2020  4.9k

Read More

R Programming for Data Science: Tutorial Guide for beginners

 Jul 17, 2024  959.6k

Read More

How to work with Deep Learning on TensorFlow?

 Apr 22, 2020  3.5k

Read More