Introduction to Data Science
Data science is all about analysis and bringing together a particular set of data, to make predictions and decisions. Data science is a lucrative field that is accompanied by these sets of responsibilities; a Combination of statistical methods, Programming knowledge, and Prediction of hidden insights. Data scientists are a skilled set of people with a sense of thorough calculations and statistics. Data scientists practically find data patterns and organize and analyze the data in a standard form and can make future predictions.
Data scientists can work in any part of business as much as a large database is concerned. Businesses like:
- Consumer goods
- Stock markets
- Industry
- Politics
- Logistic companies
- e-commerce
Training type for Data Scientists
The training type for data scientists could either be Online or Offline (Hybrid).
How long is the average certification in Data Science?
A four-year bachelor’s degree in any related field is the average training duration in data science.
However, the average duration of training in the online courses is six months.
How to become a Data Scientist?
A four-year bachelor’s degree in any related field is the average training duration in data science, as well as attending a three-month boot camp which is not compulsory.
However, the average duration of training in the online courses is six months. If you have decided to pursue your dream of becoming a data scientist by taking online courses rather than going to college for a four-year degree program, must be ready to dedicate more than eight hours a day.
Secondary school department for Data Science
The secondary school department you should be in to pursue a career in data science is the SCIENCE department.
The tertiary courses for Data Science
- Mathematics
- Computer Science
- IT
- Business
Career path for Data Scientists
There’s no well-defined career path in the Data Science field yet, as anyone in a different field can pursue a career in Data Science.
However, Data science is multidisciplinary, as it has four main angles which are: data angle, engineering, business, and product angle. Becoming a Data scientist begins with great interest, followed by the acquisition of the skill whether in college or personal.
Professional grade in Data Science
Profession grade in Data science ranges from the level of :
- Junior
- Senior
- Principal
The three main grades of Data scientists are Statistics, Engineering, and Business. However, as a data scientist, you are not expected to master the three areas. The average level of the profession is Statistics. Experts in statistics are always at the junior level, while experts in Engineering are always at the senior level, then experts in the three areas are at the principal level.
How long does it take at each professional level in Data Science?
The normal Junior Data scientist is a young graduate from any of the related popular fields (Computer Science, Engineering, and mathematics). Nonetheless, a junior Data scientist is someone that has just 0-2 years of experience and who is familiar with data sets and python.
To be a senior Data scientist, you will need 3-5 years of experience and must be vast in software engineering or must have completed a PhD.
However, before moving to the last and highest grade which is the principal level, one must have at least 5+ years of experience and must be an expert that knows best practices when putting models to work, and also must understand the art of business.
What is the average salary for each professional level in Data Science?
The average salary of each professional level depends on the capacity of the company you are working for.
However, the average salary of a Data scientist with experience of 1-4 years in Nigeria according to Payscale is 1,375,767 Naira annually. While a Data scientist with 5-9 years of experience average salary is 1,960,000 Naira annually.
What is the work type for Data Scientists?
Data scientists have the opportunity to either work remotely or in their company’s office. (Hybrid).
Work shift for Data Scientists
As a Data scientist, the work hour always ranges from the hours of 8 am – 6 pm. However, some companies always offer Data scientists to work remotely.
How long do Data Scientists spend at work?
Data scientists spend an average of 10 hours at work daily.
Occupational hazards in Data Science
- Bad data
- Bad assumptions
- Processing errors
These are common hazards in this field. Experiencing any of these hazards can bring about a great loss of collected data and false predictions or decisions.
Gender distribution in Data Science
In Data Science, Data scientists females in the industry hold just 15%- 22, which means males have the highest percentage in the Data science industry.
Talents suitable for Data Scientists
- Technical acumen
- Curiosity
- Statistical thinking
- Creativity
- Well-arranged.
What is the retirement age for Data Scientists?
In Data Science, you can choose to be a freelancer or choose to work under a company. Whichever way, if working for a company, the average retirement age is 60 years – 65 years of age.
However, if you are a freelancer that works based on a contract you can decide to keep on doing your job even at 60 because data science is a field that requires less hard work and more smart work
Popular Professionals In Data Science
- Yann LeCun. (LinkedIn, Twitter)
- Dr. DJ Patil (Linkedin)
- Yoshua Bengio (Linkedin)
- Olumide S (Upwork)
How relevant is Data Science in other countries?
Data science is a very important field and skill that a lot of businesses and institutions will always need.
However, The united states, the United Kingdom, South Africa, and China are some of the countries that actually hold Data scientists in high esteem and they are always demanding their services.
Field related to Data Science
- Statistics
- Data Analysis
- Computer Science
Branches of Data Science
- Data mining and statistical analysis
- Data Engineering
- Database management and architecture
- Machine learning engineering
- Business intelligence and strategy
- Data visualization
- Operations data analysis
- Marketing data analysis
