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Data Science Myths You Should Avoid at All Costs
While we all will agree that Data Science is a catchy and trending term, people search for it. It has some underlying concept that has solid roots. Data Science includes techniques and methodologies used to analyze, process, and extract valuable knowledge from information. By Statistics, Machine Learning and AI. While we all emphasize Machine Learning and AI, its basic usage as a statistical analysis tool makes it interesting for the average user.
Data Science has emerged as one of the most trending fields in recent times. It is growing at a fantastic pace by creating huge demand for Data Scientists. Talking about the role of a data scientist, it is extremely dynamic, unique and exciting. As no two days are the same for them, so as their role. Since it is a new field, it is exciting as well as confusing at the initial stage.
Myths about data science creates a lot of confusion in functioning.
Let’s take you through top data science myths that runs in mind and try to clear those:
Myth #1: You have to know how to code if you are a data scientist:
I don’t code (other than a smattering of R and HTML), nor do countless other data scientists. It doesn’t imply that you shouldn’t worry about learning to code. Basic knowledge of coding will definitely help you in your career path. As part of the development process and many popular algorithms, coding is no longer a necessity. If you’re aspiring to do a master’s degree course in Data Science, you don’t necessarily have to have a programming background. There are options available for data scientists who don’t know how to code.
Myth #2: You need to be an egghead to become a data scientist:
Knowing is different but claiming to be a super studious person to become a data scientist is again a myth. You need not to be a great statistician or mathematician to become a Data scientist—but if you are, then it’s a cherry on the cake. Data Science is certainly about teamwork, and team members have strengths and weaknesses in their respective areas. But there are certain data science myths that revolve around all of us. If some Data Scientists are cross-disciplinarians, they possess some knowledge of stats and coding, then some have a dash of business ethics/acumen/interpersonal skills thrown in.
It is amalgamation of teamwork and skillsets fitted into certain areas to get the job done nicely. Working with data requires analyst skills you’ve got to show it through the results and make people understand the algorithm. Data Science allows for predictive data analysis and is important in making informed business decisions and identifying trends in the market. Sometimes, even if you excel at coding and statistics, you might lack interpersonal skills or creativity to prove if the data science worth it. So, it is teamwork and doable with everybody’s efforts. All it requires is basic mathematical and coding skills.
Not just the aspiring students who want to pursue their career in data science but there are a few business owners as well who have some notion in their mind about their industry and if data science is required in their business or not. Some of them think-
Myth #3: Data Science is a cutting-edge technology that is not required in my industry:
Not just tech-oriented companies require data scientists, but several other sectors require these professionals. Industries like supply chain optimization are indulged in drug making. Today’s high-tech cures and production methods tend to manufacture more and store the production in cold storage and special storage rooms. To maintain the data of daily storage and keep a check on the techniques, they require a mechanism to keep an eye on the stock and manage the whole supply. Here are Data science methods that helps to manage it properly.
So, What Python and Jupyter Notebooks brings into the table is highly simplified data analysis. Jupyter Notebooks have a custom analysis to levels where anyone with basic development skills can use a much better environment to analyze data. It is much complex and expensive to use any other approach, including BI tools.
To conclude, it is much easier to enable your project and team with Data Science capabilities, basis their expertise. All you need is some infrastructure and be steady and not frightened by data science’s complexity and theoretical nature. The role of Data Scientist is to analyze data and give outstanding, achievable results and give doable targets with team members by working collaboratively on plain statistics.