Do You Need A Certification To Become A Data Scientist? 5 Things You Should Consider before shifting your domain
The world of the 21st century is all about data. Encompassing enormous amounts of data and extracting meaningful information from it and its digitalization is what comes under data science. For extracting meaningful information from these data, analysing the data, and organising the data, a specialist is vital, and that’s where Data Scientists come into the picture. Data Scientists perform different operations on the data to perform advanced data analysis. Data Scientists are needed in all types of businesses or sectors as they review and find patterns from the huge chunk of data that helps businesses to draw informed insights. Thereby, contributing to growth and profitability.
Do you need a certification to become a Data Scientist?
Yes, whether you are a graduate or a working professional or even if you have a technical background, you will require a data science-oriented certification or a course. Data Science can be pursued by anyone, whether they have a technical background or not. However, people with B.tech or B.Sc. in Data Science or any other related field have an edge, as they already met the basic requirements of entering this field. But, to become a data scientist, you must learn data science, as its requirements are very specific.
Advanced Data Science Certificate Program – Rotman School of Management is a 10-month program designed to help you gain professional leads in the field of data science. This program will develop data-driven skills and confidence in you. With lectures by internationally acclaimed instructors, you will also get exposure to different subjects and concepts related to the real business world.
5 things you should consider before shifting your domain
If you are looking for a career in data science or want to switch to data science, here are the five basic things you should consider before entering into this domain.
Learn maths and statistics
Knowing maths and statistics is the preparatory step of becoming a Data Scientist as these two subjects are the fundamentals of data. For analysing and performing operations on data you need to have a good hold on mathematics and statistics. People with technical backgrounds already know maths and stats, but those coming from non-technical backgrounds like business and economics need to develop skills in applied mathematics and statistics.
Data Science is an amalgamation of maths, stats, and computer science. One must have good knowledge of programming languages. Coding is necessary for understanding data sets. Programming languages like python, R, SAS, and data visualization tools like tableau are important to learn, especially python, as it’s one of the most flexible programming languages.
Machine learning is one of the most crucial elements of data science. It is used in almost all data science applications. Machine learning sets the base for learning other advanced-level data science tools. The new age ML is vital for designing algorithms for data modelling.
Build good communication skills
Having good communication is equally important as having technical skills in the data science field. As a Data Scientist, you will be working closely with corporate culture. Data Scientists are part of business meetings as they are the ones explaining data reports and contributing ideas, which require good communication skills. Good communication skills help in a better and accurate understanding of information. With a good hold on speaking skills, you will be able to effectively put forward your points and justify your approach to the company. Poor communication skills often lead to misunderstandings.
Consider having specialisation in data science
Having a specialisation is a must if you want to grow in the field of data science. A person with technical background can only join this field as an entry-level data scientist. Still, to build a strong career in this field, they also require an Advanced Data Science Course or Certification to develop expertise in sub-fields like artificial intelligence, machine learning, database management, etc. Having a specialization not only helps in career growth but also increases your earning potential.
Specialization can upgrade an entry-level data scientist into a senior data scientist or senior data analyst.
Data Science is the hottest and most demanding field of the 21st century. Building a career in data science is not an easy path. It is a rewarding career choice, and it requires dedication and immense hard work. You need constant upskilling and learning.
The Advanced Data Science Certification Program by Rotman school of management (University of Toronto) is an innovative and holistic program specifically designed for individuals looking to build a career in data science. This program provides exposure to the new world data analytical techniques and tools and teaches you how to solve real-world problems.
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