“Data Science is named as the sexiest job of 21st Century by Harward Business Review”. It’s true that data science has been in demand since the last few years with billions of data been exchanging worldwide and the demand for data scientists has rapidly increased with the increase in data. According to a study, it was found that there is a requirement of 28% more data scientists worldwide. If you are looking to make a career in data science never mind to regret your choice startups today are welcoming young data scientists to join their community.
In 2019, 2.9 Million data science job openings were required it is said that the current year 2020 will see more increase in job openings than in the previous year.
What is Data Science?
Data Science is a field that uses scientific methods, algorithms, processes, and systems to extract insights from structured and unstructured data. In simple terms, it is the science of dealing with data in order to convert the data into information and knowledge.
What is Structured Data?
The name itself defines the data which is well structured, highly organized, well-formatted and searchable. The machine language can easily understand the structured data. Examples: name, address, date, etc. RDBMS, CRM, ERP are suitable for structured data.
What is the Unstructured Data?
The name itself defines the data which is not well structured, unformatted, unorganized, cannot process and analyze by utilizing conventional methods and gadgets. Examples: text, audio, video, social media activity, etc. Non-relational and NoSQL databases are the best examples of unstructured data.
Why Data Science?
- Practical Knowledge
- Analytical Skills
- Higher Growth
- Better Opportunities
- Skills Demand
- Higher Salary
- More Jobs
What are Data Science Components?
In a big organization (ex: bank) a large amount of data is present in multiple systems of different departments. Data engineering is used to extract data from multiple sources and clean it. Visualization is used to represent the data in graphical format in order to understand the visible patterns and characteristics of the process which generated the data. Statistical algorithms and domain expertise are used to find hidden patterns and relations that can be used to benefit the organization. Advanced computing infrastructure helps in processing a large amount of data.
What are the phases in Data Science?
- Discovery: Identify the use case where data science can be applied in order to benefit the organization and define clearly the objective of a data science project
- Data Preparation: Identify the sources of data, extract the data from different sources, clean the data, explore the data and transform the data
- Model Planning: Identify which statistical algorithms to be used and plan the model building
- Model Building: Use different statistical algorithms to build the different models, valid the model accuracy, rebuild the model to achieve the required accuracy levels, compare the different models built and finalize the model to be used
- Communicate Results: Communicate the observations made during data exploration and interpret the model and its performance to business
- Operationalize: Make the finalized model available for business
As shown in the above diagram, data science is an iterative process. Based on the findings in a particular phase, it may be required to go back to previous phases in order to achieve the required model accuracy.
Data Science Job Roles in 2020:
Below are some sectors where the need for a Data Scientist is huge with the demand of data increasing the jobs certainly will have a growth in 2020.
Cyber Security, Healthcare, Agriculture, Aviation, eCommerce Industries, and Information Technology. These are the industries where the data flow like water and the demand for data scientists can skyrocket in these sectors.
Below mentioned job roles will come into a place where ever the data is available for processing and dealing with it.
- Project Sponsor: The person or department which funds the project
- Business User: The person or department which uses a machine learning model
- Project Manager: Plans project schedule and monitors project execution. Coordinates with other departments and teams in order to get required resources for the data science team
- Database Administrator: Helps the data science team in identifying the sources of the data, metadata about the data. It helps the data engineers in getting the data from various sources.
- Data Engineer: With the help of database administrator, extracts the data from different sources, cleans the data and transforms the data into the required format
- Data Scientist: Explores the data, preprocess the data, build the machine learning model and evaluate it
- Business Intelligence Analyst: Has domain knowledge and helps data scientist in exploring the data
Salaries of a Data Scientist in 2020:
As the name suggests the career of a data scientist is challenging, but the credit generated out of it adds value to it making it the highest paid jobs in the world. Yes, as of 2020, According to Glassdoor, the average salary of data scientists in India is Rs.1050K/Year. And, when it comes to the United States the average base pay is $113,309/Year.
The above image describes how good a salary is for a data scientist and how companies are striving to hire a data scientist because data is dominating the world, without data their no industry on this planet. Data makes decisions, strategy, planning, implementations, and executions.
More Resources:
15 Astonishing Facts About Data Science Analytics That Everyone Should Know
Why is Data Science so Important?
How and When Can Data Science be Used in SEO?
