The main goal of data science is to extract useful information from data sets. The business community recognize the value of the data set as a long-term business asset and use the huge amounts of data in management approaches. A large number of engineers and scientists are developing systems to apply data science in different industries.
Data science begins with data collection and refinement. It is becoming popular with each passing day. The most well-known sites, such as Google, Amazon, Facebook, and LinkedIn have their own scientific data processing teams.
Google’s development of the PageRank algorithm is one of the first examples of data science. This algorithm serves as a means of ranking Web content based on search conditions. Large online retailers, such as Amazon and Walmart, use data science to form individual recommendations for users based on their previous records and increase sales. But data science is not everybody’s cup of tea. Let’s know why?
Not All People Strike a Perfect Balance Between Theory and Practical Classes
Many beginners try to study multiple theories at one time, such as volume, velocity, variety, machine learning, supervised and unsupervised learning, feature selection, ensemble learning, predictions and forecasts, innovation and experimentation, algorithms, derivations, etc. The process to learn these tricks is slow and daunting. Many people fail to strike a perfect balance between theory and practical classes and they don’t develop a clear concept at all which harm them in the long term.
Sometimes Things Go out of Control
In data science, control over what is happening gives the illusion of security and the harder it is to experience its collapse. It’s not a child’s play to deal with different accepts of data science.
Insufficient understanding and acceptance of big data, confusing variety of big data technologies, the complexity of managing data quality, dangerous big data security holes, complex process of converting big data into valuable insights, troubles of upscaling, etc, are some of the main problems faced by novice people trying their hands-on Data Science.
Even a single mistake in data science implementation can cause big financial losses and force companies to face the music. They can take admission in Data Science Course Toronto to clear their concept & increase their command on the various aspects of data science.
Not All Are Comfortable with Coding Several Algorithms from the Scratch
In the beginning, individuals don’t need to code every algorithm from scratch. However, most novice professionals try to code different algorithms from the scratch for learning purpose. Due to mature machine learning libraries and cloud-based solutions, many practitioners don’t code algorithms from scratch and remain ignorant about the functions of different algorithms. So, their ability to deal with the data science problems remains negligible. If you feel troubles on this aspect, enrolment in Data Science Course in Toronto will help you learn the necessary art of coding different algorithms comfortably.
My Job is To Make Machines Replace Me Eventually- Automation
The sole aim of a data scientist is to create machines that automatically manage a large amount of data, segment in different groups, and helps companies to make personalized recommendations to customers for more sales and leads. So, data science aims to automate different business activities, which results in the expulsion of several professionals, including the data scientist himself. That is why many folks don’t think Data Science Certification Toronto is beneficial for them in the long run.
You Need to Learn Languages That become Outdated in Every 5-10 years
To become a professional and experienced data scientist in demand, professionals need to learn different programming languages continuously. Also, new languages keep coming in market every year, reducing the need for old programming languages. So, you always need to stay in learning mode, which becomes difficult for a full-time data scientist. Not all people are comfortable with it.
Not All Are Professional keynoters
In simple words, Communication skills are the ability of a person to interact with other people, adequately interpreting the information and transmit it correctly. These skills are very important when you work in IT companies where you have to meet with different clients, explain your thought process fluently to them in English and other languages. Not all people are professional speakers. That is why many people fail in data science field despite having a decent theoretical and practical knowledge.
Not Having Proper Domain Knowledge
Technical skills and machine learning knowledge are the two basic conditions for becoming a data scientist. But your task doesn’t end here. To stand out from the crowd, beat the huge competition in the business, and make accurate predictions, first of all, you need to study the current trends of the industry you want to work in.
It’s not an easy task and this is reason why many people abandon their journey to the data science. However, you can take admission in Data Science Programs in Toronto to learn data science skills comfortably as per the needs of specific industries and be a high-demanding professional in the business world.
Inability to Cope with Problems
The field of Data Science is full of challenges on each and every step. Just a single mistake is sufficient to trail your business organization in the competition and give unfair advantages to your rivals. For every nonsense, data scientists are hold responsible and asked to fix the problem as soon as possible. Sometimes, they have to work even on holidays to fix the problem and keep the business going on as usual. The continued high-pressure makes professionals feel frustrated and they start to leave the field as soon as possible.
Final Words
The demand for Data Science Experts is increasing with each passing day because of rapid industrialization in different countries, the use modern technologies in the business world, and an increased necessity to collect customer data and make accurate predictions. A lot of people rush to become data scientist, but leave their journey in the middle because they find themselves unfit for this field. So, analyze your potential very well and take a solid decision accordingly.
