Want to Become a Data Scientist?
Take these 7 Steps ASAP
Companies like these have realized the importance of Data Science and its professionals. When will you? Don’t you think that this is the right time to do so? So, come join and explore the path of how you can become the data scientist with me.
Data Scientist has been dubbed as the “Sexiest Job” of the 21st Century by Harvard Business Review. It is one of the most highly sought after jobs and many people wish to transform their career into that of a Data Scientist.
Data Scientists require a proficiency over several fields like statistics, mathematics and computer science. To become a Data Scientist, you must be familiar with various topics that form the core of Data Science.
Along with the knowledge of several topics, you must know about the right strategy to reach your goal. We will go through all the steps required to become a Data Scientist and will discuss the validity and scope of the Data Science degree.
How can you become a Data Scientist?
A Data Scientist requires a special set of expertise that sets him apart from the rest of careers. Now, it’s your time to become a data scientist. So, start gaining expertise. Here are some steps which you should follow to become a data scientist:
1. Know if you possess the aptitude for Data Science
Speaking generally, data science deals with number crunching and problem-solving. If you have a knack for solving problems, finding patterns and understanding the logic behind problems then Data Science is the ideal field for you.
Data Science is a field that stems from statistics, math and computer science. Since this is a quantitative field, you must have the perseverance to experiment with data through various statistical techniques.
It is crucially important to know that Data Science is a massive field that spreads across multiple disciplines but shares a common flair of problem-solving and number crunching. You need to have the right analytical mind to churn problems and gain insights from them.
Furthermore, you must be able to translate business problems into Data Science. If you possess all these characteristics as well as a curiosity for finding the unknown then Data Science is the ideal field for you.
2. Earn a Degree
Data Science is a highly quantitative field that requires a higher degree. According to research – 88% of the Data Scientists have a Master’s Degree and 46% have PhDs. With the emergence in Data Science, there are many educational institutes offering degrees that are crafted for Data Science.
Many universities are offering both undergraduate and graduate degrees in data science. Furthermore, if you have a degree in a quantitative field like statistics, math or a scientific field, transitioning to data science will prove much easier.
Having a higher degree like Masters or even PhD in data science or a related field will prove your mettle to the industry professionals who will be much more willing to hire you for data science positions.
Since, data science is a vast field that requires an in-depth understanding, a higher education degree will provide an ideal platform to acquire that form of deep knowledge.
Universities have started to offer data science degrees that cover computer science, mathematics, and statistics. Previously, a computer scientist had to acquire skills separately for statistics through separate boot camps and courses.
However, with the recent degrees in Data Science, it is possible to bridge all the essential concepts in a single avenue.
3. Get a Specialization
Data Science is a vast field with several specializations. These specializations are but not limited to – Big Data, Data Engineer, Data Visualization, Machine Learning Specialist, Financial Analyst, etc.
Since it is not possible to master all these zones of data science, it is quite important to have an in-depth knowledge of at least one field in Data Science.
In Big Data Specialization, you can acquire the knowledge of handling massive amounts of data and unearth insights from this pool of information.
With the Data Engineer Specialization, you are responsible for building data pipelines and models. This specialization mostly involves knowledge of software development and therefore, would appeal most to people having a degree in computer science.
Data Visualization deals with providing visual communication of data to the users. In this specialization, you are required to have knowledge of various data visualization tools. Visualization is an ideal method of conveying and expressing data to the users visually.
Through the Machine Learning Specialization, you can define new ways to predict future events through data. There are several other extensions of machine learning like computer vision, deep learning, natural language processing, and reinforcement learning.
In the Financial Analyst Specialization, you are capable of dealing with financial problems like trading, portfolio management, risk analytics, etc. Banking and financial institutions make the most of data science and therefore, knowledge of data science in finance will provide you with the right skills for managing financial scenarios.
4. Earn Data Science Certifications
In the data science industry, certifications are proof of your skills. There are several certifications that are offered by companies that officially attest to your data science skills.
5. Participate in Data Science Challenges
Data Science is best learned by actually doing it. The best way to implement knowledge of data science is through participation in several challenges.
By performing data cleaning, transformation, analysis, visualization, etc. you get an in-depth idea of how data science is actually implemented in real-life problems.
There are several difficulty levels for data science problems through which you can gradually build up your expertise in the field. Furthermore, there are several live challenges that will enable you to gain experience and build your data science portfolio.
6. Build Connections
Building connections is one of the most important steps for becoming a data scientist. In order to land yourself a data science position, you must have several connections that can be built through active participation in boot camps, data science summits, etc.
Through active engagement in the data science community, you will be able to grow your connections and increase your probability of landing a data science interview.
7. Cracking the Data Science Interview
The final step towards becoming a data scientist is cracking the interview. It is to be kept in mind that while appearing for the data science interview, you must know the domain functioning of the company and therefore, should prepare accordingly.
For example, a data science position at a sales company will have a different set of interview questions in contrast with a position at a biotech company.
You must be well versed in coding and SQL in order to crack the preliminary rounds. Furthermore, you must be well versed in data science concepts in order to crack telephonic round.
You must have experience of working on data science projects in order to clear the technical screen round where you have to undertake a project. After cracking such a rigorous procedure, you will be well qualified to earn the position of a data scientist.
We went through some of the important requirements for becoming a data scientist. Data Science is a vast field that incorporates various underlying subjects. In order to master these subjects, an aspiring data scientist must know the right direction. There are various statistical techniques and tools that a data scientist must be well versed with.
Also, along with the core knowledge and technical expertise, a data scientist should also have strong communication skills to explain the findings in a very easy to understand language.