Before starting a career in data science, you first need to understand what data science is. The professionals in data science are called data scientists.
Data scientists are computer scientists and mathematicians who collect large volumes of data and further analyze it to know the present trends and the trends in the future. This helps the company to make better decisions and the business grows.
Data scientists are employed in various fields and each one has a separate job title. Some of them are:
Data Architect– A data architect creates blueprints for data management systems to analyze and maintain the data sources. A data architect is required to be at the top of every new innovation in the business.
Machine Learning Engineer– Machine learning engineers focus more on producing data-driven products. These companies are mainly consumer-facing companies dealing with a voluminous amount of data.
Database Administrator– A data administrator is responsible for all the tasks relating to databases like monitoring and maintenance, installation, training users, documentation, etc.
Statistician– A statistician is considered as a leader of data science. It represents what data science actually stands for. A statistician applies statistical methods to the data to derive meaningful information. A statistician can handle all types of data.
Business Analyst– Business analysts possess specialized knowledge in their businesses. They apply this knowledge in business operations and recommend improvements in the methods to gain a market.
Data Analyst- Data analysts’ responsibilities are a big list from creating systems to performing data analysis. But they are considered to be junior-level professionals rather than advanced analysts who have the same job but at a higher level.
Each job title has a separate task to perform, but all of these fall under the category of data scientist.
The amount of data is increasing due to artificial intelligence and a data scientist is required in every business and every field. The career as a data scientist is one of the best choices since its demand has always been high and will grow in the future also.
To become a data scientist, you do not need a professional degree but a bachelor’s degree and a professional data science course is the must. A background in the following can increase your chances of becoming a data scientist:
- Computer Science
- Computer Science
- Physics, Chemistry, Biology
- Psychology, History, Finance
Students from an entirely different field can also apply to this course if they hold some basic knowledge and certification related to data science. Newcomers with absolutely no experience can also learn and gain knowledge to start a career in data science.
Learning data science can be quite technical especially when you are new to computers. In this article, we will discuss some do’s and don’ts if you want to begin and also excel in the career of data science.
First, let’s discuss some do’s:
1) Have knowledge about data science
Data science is a vast subject. It requires special knowledge and understanding to get into this field. The main decision-making depends upon the work and recommendation of a data scientist. You first need to understand the basics of data science and the role of a data scientist.
2) Choose a Job Title
As discussed above, a data scientist has various job titles like data analyst, data engineer, data architect, database administrator, etc. but a person can be only one professional at one point in time. So first, you have to decide which job title fits your interest and what suits your personality. The job title is always your choice. The choice completely depends on your instincts and your area of interest. Until and unless you are not clear about what post you want to handle, you will be confused about choosing the right path further. For this, you can research it on the internet. Talk to the people in this industry who can guide you about the roles and responsibilities and the kind of work pressure you will undergo.
3) Complete a Course
When it is decided which title to go for, the next step is to learn about it. This will enhance professionalism and expertise. There are many courses you can learn from. When you take a course, be active. Actively participate in the discussions, assignments, homework and practical knowledge. Learning a professional course might be a little difficult, but it is worthwhile.
4) Learn One Language at a Time
There are a number of computer languages and to be a computer expert, you need to know all of them. But learning all the languages together will not make you good at any of them. Learn step by step. Choose one language and gain expertise in it. Learn its functions and applications. When you get confident enough in one language, move on to another. In the same way, you will be able to learn more and better. Also, the companies are interested in the candidates who have expertise in their skills rather than a list of skills with no experience.
5) Practical Applications
Merely learning a course or a skill is not enough if you want to work on a professional level. While training for the courses, make sure that you learn to apply them in different situations. Carefully understand the assumptions and formulas and learn how to apply the right methods to the right problems. Only take the course from the place that promises you practical applications as it will help you to gain experience, and experience is the only key to be a company’s first preference and help your career excel.
6) Communication Skills
Communication skills are the most important when you are working in a professional field. Communication is not a skill that you can learn. You have to develop it with continuous practice. To share your views and prove your point, correct communication is important.
7) Work on Various Skills
Various technical, practical and soft skills are to be developed and worked on in order to increase your chance in the career of data science. Skills such as confidence, ability to take initiatives, accountability, leadership skills, written and verbal communication, analytical skills, and experience in databases, programming skills, etc. help to improve your work performance.
Now, let’s move on to Don’ts:
These are some points you need to keep in mind to avoid mistakes and work more productively. Also, there are some ways that you can avoid these problems.
1) Theory-Based Learning
Data Science is all about working on raw data. Learning theory and jotting down notes will only increase your learning capabilities. To think analytically and professionally, you need to earn experience. A 100% theory-learning approach can never teach you to apply your knowledge and gain expertise. Only a practical mind can help you move forward smoothly into data science. Balance your studies with projects and learn to work with available data to gain more and achieve better outcomes.
2) Learning Every Code from Scratch
There are multiple computer languages and in order to become an expert in all of them, you need to practice them all, but their basics are almost the same. If you have learned 2-3 languages in detail, then there’s no need to learn further languages from the start. Learning every new language from scratch will waste your time and you may lose available opportunities. So, once you know how to code, then you can learn other languages from the intermediate level instead of the beginner level.
3) Directly Moving to the Last Step
Not learning from scratch does not mean that you get to the final results directly. You need to work step by step. Work experience and practice are a must to achieve success. Also, some people enter the field of data science just because they have a craze to develop new technology. For this, they just learn the main tasks and ignore the basics. This can take your career downwards. Fundamentals need to be practiced and all of the knowledge needs to be obtained in order to develop new technologies. A systematic approach needs to be followed to reach the end result.
4) Being Unprepared
Being unprepared to answer questions or solve problems can take you many steps back in your data science career. In this field, the main ability is considered as ‘self-sufficient’. This means that you should acquire all required basic knowledge in your specific field in order to work on projects effectively and efficiently. For this, undergo internships and gain experience. Experience and practice make you more confident and self-sufficient.
5) Complex Algorithms
Sometimes, junior-level professionals try to make their data and code fancy by trying to apply all of their knowledge in one code. It makes data very complex to understand. It needs to be understood that a simple algorithm can beat a fancy one. To avoid this, it is necessary to work and learn from the seniors and to be a good listener. This will help you understand what is to be applied, when, and in what way.
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