Get Hired as a Data Scientist in 2021: Six Checkpoints
Data is a goldmine for businesses in this day and age. If you seek to become a data scientist in 2021, you must know what you need.
In this in-depth guide, we will walk you through the six most important checks you must pass to become prepared for a data science career — from the scratch.
We will explore the how’s of becoming a data scientist in this decade — when the worth of data has increased, and the data skills required have surpassed the basic know-how. Up for the challenge? Read on.
You will learn about six checkpoints of paramount importance to lead any career in data science.
Six-Point Checklist for Data Career
1. Should you or should you not become a data scientist?
2. Do you know the role you desire to play in data science?
3. Do you have the right qualifications?
4. Do you have the skills, or do you know the skills to gain to level up?
5. Do you have a strong resume — your projects — to show for?
6. Do you have the right network to get hired?
Let’s have a look at the them one-by-one.
1. Should I or Should I not? Check.
It’s not just personal, but also related to market demand.
Knowing about the demand for data science career will help you gauge whether you should opt for data science as your desired field.
Here are a few facts for your perusal:
- 91% companies, according to a report by Forrester, cited a lack of skilled professionals as the biggest hurdle in making recommender systems for their business.
- Glassdoor dubbed data science as the hottest career. Also, it revealed the median base salary for them to be $107,801.
- HBR called the job of a data scientist as the hottest job of this century.
- WEF predicted that data science related jobs and roles will increase by 58% by 2022.
For those interested in data science career, it’s worth it.
Companies are struggling to find data mavens who can extract insights from gazillions of data.
The best time to embark on a data science career is, NOW.
2. Desire Check
Three are three major roles in data science that you can choose from. You need to figure out which of these careers is for you.
One being, Data Engineer. They are proficient in several programming languages and frameworks. They work as developers, engineers and technical leads.
Second being, Senior Big Data Analyst. Within data science, it is one of the most demanded roles. Their core skills lie in statistics and visualization. They work as BI Analysts, Product Analysts, CxO, among others.
Third, Data Scientist. They have strong know-how in mathematics, programming languages, as well as modeling. They work as consultants, senior data scientists, ML experts, etc.
3. Skill Check
Focusing on these skills is important to leading a data science career: In programming R and Python; In visualization Tableau; the Knowledge of multivariate Calculus; Algebra; Know-how of tools such as SQL; JAVA; Hadoop.
Initially, you may feel overwhelmed, but with the right approach these will come easy and you will be able to prepare yourself for a data science career.
4. Qualification Check
Once you know the skills and knowledge you need to gain, you need to figure which qualification and knowledge routes would suit you the best — from learning point of view, as well as for scoring jobs at big companies.
Bootcamps, degree courses, MOOCs can give you a good start for learning. You could opt for a combination of these.
Additionally, getting certified as a data scientist will give you a good standing in the industry. Professional certifications are a badge of one’s good technical proficiency and help in staying ahead of scores of resumes and candidates.
Validate your knowledge with world’s reputed professional certifications offered by the Data Science Council of America (DASCA). Its qualification standards encompass one of the best data science certifications on which many Big Tech data scientists have been assessed.
5. Profile Check
Develop your profile by completing projects and internships to build your resume, experience and network.
Kaggle, GitHub, Medium, and other such community platforms can be used to build profiles.
Kaggle is one of the best data science platforms. While it is known for competition, working on it is an enriching experience of completing the projects. Companies often provide their real data to tap into the strength of data science professionals’ community to solve their hard-pressed problems.
Medium is a publishing platform. During the pandemic, many data scientists published their studies and data online on the platform to share with the larger populace — ranging from fellow scientists to media and others.
GitHub is a development platform, famous for its open-source codes uploaded by the developers in form of a repository. They say you can find any and all the project codes on it.
6. Network Check
Senior and fellow professionals can best help in traversing an unknown territory for they have traveled on it.
Build your network. Share ideas and connect with fellow professionals on social media, conferences, through online platforms discussed above. Building your network with like-minded people will help you professionally as well as personally to grow in the field.
Get Hired! Congratulations.
No matter when you begin, where you begin from, and which data science career role you set your heart on; armed with the right skills and qualifications and other checks mentioned above, you will be able to build a rewarding career.