Data Science has taken the world by storm. Companies, big and small, are either leveraging the concepts already, or are planning to shift the work process in the near future. However, the widespread adaptation of Data science has also given rise to a few common misconceptions.
As much as I hate to say that Most of them are simple Myths about Data Science, I would also want to break them.
This blog will bust the following myths about Data Science:
- Myth 1# Data Scientists are the Unicorns
- Myth 2# Data Scientists Make Robots
- Artificial Intelligence vs Machine Learning vs Data Science
- Myth 3# Computer Science, Mathematics, & Statistics are Mandatory
- Myth 4# Learning the Tools is Enough!
- Myth 5: Data Scientist, Data Engineer, & Data Analyst are All the Same
- Data Analyst vs Data Scientist vs Data engineer
- The Right Pathway to Become a Data Scientist
Take a quick look at the top 5 myths of Data Science in this video below:
Let’s get to know these myths in detail and break them into reality:
Data Science Myth 1# Data Scientists are the Unicorns
When we watch Hollywood sci-fi movies in which a single person does all the coding, mechanical stuff, makes robots and whatnot, we kind of get an image that there is always one person behind all of this, i.e., Data Scientist.
Reality: Well! The reality is much different. Data Scientists are not unicorns. A Data Scientist is a professional who performs just a portion of an entire project.
Though the job that they do is very significant and many of the critical business decisions depend on them, that are not all.
As real business work begins from there, the main job of a Data Scientist is to provide insights using data, which forms the basis for later stages.
Data Scientists work in teams which have people who are proficient in different areas. So, in a way, Data Science teams are Unicorns, but individual Data Scientists are not.
Data Science Myth 2# Data Scientists Make Robots
Accept it or not, Robots are fascinating. A machine performing tasks that humans can do, has always attracted people towards Artificial Intelligence. However, due to the lack of a clear-cut demarcation, people misapprehend Data Science for AI. It is a common misconception among people that data Scientists make Robots.
Reality: In reality, Data Science is very different from Artificial Intelligence. To separate the Data Science myths and reality, understanding of the concepts like AI and ML becomes imperative.
Artificial Intelligence vs Machine Learning vs Data Science
Let’s understand this difference in a better way:
So, while it is true that robots are part of Artificial Intelligence, it is also true that Data Scientists do not make them.
Data Science Myth 3# Computer Science, Mathematics, & Statistics are Mandatory
Data Science is made up of two scary words ‘Data’ and ‘Science’, the sheer mention of which sends a chill up the spine of those who come from a non-technical background. Commonly, it is understood that Data Science is pursued by people who are computer nerds and gods of Mathematics and Science.
Reality: Not true! Though a little bit of programming and knowledge of Mathematics and Statistics give an upper hand to a Data Science aspirant that does not really mean that people from non-technical backgrounds cannot pursue a career in this domain. Basically, Data Scientists work on tools that have in-built functions and features that do almost half of the work.
An aspirant does not need to be a hard-core programmer to become a Data Scientist. Instead, he/she needs to have technical knowledge along with business expertise to excel in the career.
Data Science Myth 4# Learning the Tools is Enough!
Hitting the mouse button a few times and the result will be in your hands. Data Scientists have to learn a lot of tools, i.e., ETL tools, BI tools, programming languages, etc., and that’s it. There you become a Data Scientist.
Reality: No way! Data Science can’t be learned by simply learning some tools. It is true that Data Scientists work on a lot of tools. But those tools are AI-powered platforms that allow performing activities.
Moreover, in order for the tools to operate, the Data Scientist has to understand the logic behind that and the business motive behind the particular activity.
Tools may definitely do the work, but the Data Scientist is the one who executes the processes.Hence, getting a thorough knowledge of Data Science is a must.
Data Science Myth 5: Data Scientist, Data Engineer, & Data Analyst are All the Same
Just like how Customer Care Executives are also called Customer Support Agents, Data Scientist, Data Engineer, and Data Analyst are nothing but fancy names for the same job roles. They all do the same work.
Reality: The above statement is far away from reality. In practice, all these three terms differ in their operations widely.
Data Analyst vs Data Scientist vs Data Engineer
So, if you want to become a Data Scientist, then you need to know the basic difference between these three terms:
The Right Pathway to Become a Data Scientist
In order to get a clear picture of Data Science, its applications, real-life use-cases, and to practice the operations, one needs to know the right Pathway to the destination. Half knowledge is always dangerous, and hence to know Data Science in and out, one has to follow the given pathway:
Step 1: Choose the Right Data Science Course
Find a suitable Data Science Course that covers all the necessary subjects that you need to master. If you are a student or a professional, you may want to take up an online training course as it comes with convenience and affordability. The lessons can be learned anywhere and anytime, giving you the freedom of continuing with usual daily plans.
Simpliv is one such leading platform that lets you learn with convenience. Detailed course plan combines with case studies to give you hands-on experience on the core topics of Data Science and certification at the end will definitely help you stand out from the crowd.
Step 2: Build the Necessary Data Science Skills
Some of the core topics of Data Science are:
- Programming Languages: Primarily Python and R Programming
- Mathematics and Statistics
- Big Data Technologies like Hadoop and Spark
- Knowledge of Databases: SQL and NoSQL
- Deep Learning Frameworks like TensorFlow
- Data Visualization Tools
You have to keep in mind that a Data Scientist is an all-rounder who has a grasp of analytics. To become one, you need to know the concepts and technologies that come under the purview of Data Science.
Step 3: Get a Certification
Getting certified makes you stand out from the crowd. One such recognized certification is Cloudera’s CCP:DS certification that is a stamp of proficiency. In order to qualify for CCP:DS you need to master the concepts of Data Science with hands-on experience in various practical aspects.
Simpliv’s carefully curated Data Science and Machine Learning Training Course checks all the right boxes that you need to become a successful Data Scientist. Enroll Today!