A complete Beginner's guide in Data Science

By POOJA BISHT |Email | May 14, 2019 | 3642 Views

The rise of big data in recent years has accounted for the increase in the demand of Data Scientists and Data Analysts. Numbers of Data Science job are increasing every day and the demand for skilled professionals are the utmost requirement of the companies today. Data Science is a field which has its usage in every field- from the online shopping giants to the finances. This is also for the reason that Data Science is the hot buzz today. As per a recent report by Glassdoor, Data Science stood out as the best job in America. 

For a beginner who want to start a career in Data Science, a lot of online platforms are available providing a bunch of information about the field and offering several courses but still there is some ambiguity that still lies in the mind of the aspirants about the careers, salaries, growth rate, skills, and the various positions in Data Science. 

A recent report in India stated about the various positions that remained vacant due to the dearth in skills.  So many online platforms and resources available and still positions are left vacant due to the dearth in skills possessed by the candidates. A thing to ponder over!
To the beginner who is starting a career in Data Science, this article will provide a brief overview of the various things concerned with Data Science. 

Let's Start

  • What is Data Science?
Data Science is a vast field which uses various algorithms and processes to gather insights from both structured and unstructured data.
Insights are those particular pieces of information from a bunch of data that are very relevant and important for your business. 

  • Why choose a career in Data Science?
A time of big data when data is getting flooded from various sources like social media, finances, online sites, healthcare, etc. , It has become the utmost priority for any sector and industry to analyze and gather useful insights out of their data. These insights are the only source for the businesses to check their growth, their ROIs, their profits gain over time and the most important- to predict future risks in the business. 
"Data Science is used to forecast future risks involved". 
Without Data Science the important insights that businesses want to improve and grow their business is impossible.

  • What is the career opportunities involved in Data Science?
The career opportunities in Data Science are wide.  You will find a range of positions in Data Science mentioned below. 
  1. Data Scientist
  2. Data Analyst
  3. Business Analyst
  4. Business Intelligence Manager
  5. Data Engineer
  6. Data Architect
  7. Data Administrator

  • Salaries in Data Science
According to indeed.com, as of May 2019, the average Data Scientist salaries for job postings in the US are 80% higher than average salaries for all job postings nationwide. You could predict how the high incomes the professionals in Data Science get.

  • What are the skills required in Data Science?
This is the main point where the individuals always get confused over. I should clear you on this part that there are various positions in Data Science (also mentioned above), so if you are looking for a definite skill-set in Data Science ignoring the positions in it,  you are moving a wrong way. The different roles that are mentioned above require skill-set which is somewhat different from the other. For e.g., a Data Scientist will be requiring a strong hand on in Python, while it may not hold true for a Data Administrator. So the skill-set completely depend upon the position you are applying.

Advise: You should pick one role from the above and then start to explore information about it.

  • What is the educational Qualification required?
Some of the positions in Data Science requires a graduate degree in Statistics, Mathematics, Computer Science or in any relevant field, while others like a Data Scientist requires a Ph.D. or a Postgraduate degree from you.

  • Online Platform you should refer:
Cousera, Edureka, Github, Kaggle, Udacity.

Source: HOB