When we talk about Data Science, some of you must get an impression of complicated clusters, complex tables, tricky codes, or big excel sheets. But is Data Science really that complicated?
Data Science has been divided into various sub-topics like statistics, warehouse management, data visualization, machine learning, business intelligence, etc. to make it less complicated to use in the real world. Industries like healthcare, education, and finance require knowledge of different tools & technologies to serve the needs of the industry.
Here are some tools which you might get to learn when you learn data scientist:
But it becomes more complicated when you think about it as a Science. Before joining any job in the Data domain, one must have made up their mind and get the idea about the kind of work they’re interested in as a data engineer. Jobs in the data domain are industry-oriented and there is no foundation or prerequisites to become a data scientist.
One who has worked in the healthcare industry, technical or non-technical, knowledge of your industry will be beneficial for you in becoming a successful data scientist for that particular industry. You must think of data science as a business, more like an industry in which you’re going to pursue your career.
In the end, you can work as a Data Warehouse Manager, Data Engineer, Statistician, Machine Learning Engineer, BI Analyst, or Data Scientist, rather than thinking of it as a technology or science, you must focus on the industry knowledge or how your knowledge can be useful to your/client’s/employer’s business.
Data Science Courses
If you’re interested in pursuing your career as a Data Scientist and eager to work with thousands, and millions of rows of data, we’ve listed few useful courses here which can help you explore your passion and give you a chance to work in real-time live projects.
