Disclosure: Hackr.io is supported by its audience. When you purchase through links on our site, we may earn an affiliate commission.
Table of Contents
Who is a Data Scientist?
The term “Data Scientist” has been coined as Data Scientist draws information from the scientific applications and fields, whether it is mathematics or statistics. A Data Scientist is responsible for analyzing and interpreting complex digital data. E.g., statistics of a website.
A Data Scientist is one who deals with an enormous mass of structured/unstructured data and uses their skill in math, statistics, programming, machine learning, and more.
He has the upper hand on data-related activities when it comes to business-related decision-making; data scientists have higher proficiency.
Skill-set required to be data scientist:
- Statistical and analytical skills
- Data mining activities
- Machine learning and Deep learning principles.
- In-depth programming knowledge (SAS|R|Python coding)
Apart from the technical abilities, Data Scientist needs to be effective communicators, leaders, and team members as well as high-level analytical thinkers.
Data scientists should be creative in visualizing the data in various graphical forms and present the highly complex data in a straightforward and friendly way! If a data scientist can convert terrifying Petabytes of structured as well as unstructured data (images, videos, log files) into a straightforward and simple format, he is an – ‘Artist’!
Data Scientist Salary
Data scientists mine complex data and provide valuable insights to their organization. They work with other IT professionals and other departments of an organization to analyze and manage statistical data to create different models based on the needs of their company. The process researches information for more natural consumption and transforms it into actionable plans. IT data scientists follow specific, strict company and industry guidelines in their work. They observe data privacy rights to ensure client satisfaction and avoid legal issues. They create networks of professionals to consult, including internal partners and external colleagues. They have the best tools available at their disposal to deal with cutting edge technologies on a regular basis. IT data scientists usually must possess previous work experience in a similar position. They are expected to have advanced knowledge of different data mining techniques such as clustering, regression analysis, decision trees, and support vector machines. A higher or professional degree (such as a Ph.D.) in computer science is recommended for this position, in addition to previous years of work experience in a related field
Glassdoor mentions the following responsibilities of a data scientist:
Factors Determining The Data Scientist Salary
According to Forbes, the median base salaries range from $95,000 at Level 1, to $165, 000 at level 3. This is depicted in the graph below:
There are various factors that determine the Data Scientist salary let us see some of them below:
A qualified data scientist with experience is paid handsomely. An undergraduate degree in computer science, data science, mathematics, statistics, are the requirements. Degrees play a vital role in adding structure, internships, networking, and recognized academic qualifications for your resume. Certified courses and boot camp certifications also boost up the salary of data scientists.
Holding a Master's degree or a Ph.D. in any Data Science field is a plus. An aspiring candidate can go for a higher degree after graduation or gain some experience and study further as per the requirement of the project.
A data scientist is expected to be well versed in both technical and non-technical skills. A candidate is expected to be smart and dedicated and must have excellent analytical and logical skills, should also be good at storytelling, and also have teamwork qualities and knowledge about different departments of an organization to work with them. The candidate should also possess excellent communication skills as it really helps while communicating with the client and understanding his business needs.
Whereas technical skills are concerned, a candidate must excel in one or all of the following skills:
- Machine Learning techniques
- Data Visualization and Reporting
- Risk Analysis
- Statistical analysis and Math
- Effective Communication
- Software Engineering Skills
- Data Mining, Cleaning, and Munging
- Big Data Platforms
- Cloud Tools
- Data warehousing and structures
Source: Prompt Cloud
3. Experience and Role
Salaries of different levels of data scientists in a hierarchy are given as under based on their experience.
|Salary||Rs 297,414 - Rs 1,195,066||$61,598 - $122,827|
|Bonus||Rs 2,004 - 161,146||$1,010 - $15,019|
|Profit-Sharing||Rs 0.00 - 322,976||$503 - $ 16,638|
|Total Pay||Rs 306,054 - Rs 1,215, 966||$60,894 - $127,894|
|Salary||Rs 590,734 - Rs 2,070,477||$74,623 - $ 140,210|
|Bonus||Rs 1,030 - Rs 792,758||$1,973 - $19,998|
|Profit-Sharing||Rs 95,000||$ 2,007 - $ 20,608|
|Total Pay||Rs 595,982 - 2,506,994||$77,215 - $ 158,409|
|Salary||Rs 972,106 - Rs 2,927,745||$78,424 - $ 157,653|
|Bonus||Rs 35,000 - Rs 400,000||$2,449 - $22,400|
|Total Pay||Rs 972,106 - Rs 2,928,194||$79,321 - $167,947|
Geographical location plays a deciding factor and greatly influences the salary of the candidate.
Different salaries in different countries are shown in the graph below:
Data Scientist Salary in India
Data Scientist Salary in USA [United States]
Data Science Project Life-Cycle
The life cycle of a Data Science project involves the following steps:
1. Data Acquisition
The step includes identifying various data sources, which could be –logs from web servers, data from online repositories like US Census datasets, social media data, data streamed from online sources via APIs, web scraping or data can come from any other source or could be present in an excel. Data acquisition involves acquiring data from all the identified internal and external sources that can help answer the business question.
2. Data Preparation
After acquiring the data, data has been to clean and reformat by manually editing it in the spreadsheet or by writing code. No meaningful insights are produced in this step of the project lifecycle. However, through iterative data cleaning, data scientists can quickly identify what foibles exist in the data collection process, what assumptions they should make, and what models they can apply to produce analysis results. Data after reformatting can be converted to JSON, CSV, or any other format that makes it easy to load into one of the data science tools.
3. Hypothesis and Modelling
This is the core step of a data science project that derives meaningful business insights from data and requires writing, running, and refining the programs to analyze. These programs are generally written in languages like R, Python, Perl, or MATLAB.
4. Evaluation and Interpretation
Different performance metrics require different evaluation metrics. Classification of spam emails, then performance metrics like AUC, average accuracy, and log loss are to be considered. Which dataset should use to measure the performance of the machine learning model is a standard question that professionals come across when evaluating the performance. Since the model is already adapted to the training dataset, considering performance metrics on the trained dataset is not always right because the numbers obtained might be overly optimistic.
Iteration of all the above steps from 1 to 4 is done as data is acquired continuously, and business understanding becomes much clearer.
There could arise a case when the production environment supports Java, but data scientists favor Python programming language, so machine learning models are required to be recorded before deployment. The machine learning models are the first pre-production environment and then later deployed into the production environment.
The step covers developing a plan for monitoring and maintaining the data science project in the long run. The model performance is monitored, and performance downgrade is monitored as well. Data scientists can archive their learnings from specific data science projects for shared learning and to speed up similar data science projects shortly.
It is the final phase of any data science project that involves retraining the machine learning model in production whenever new data sources come in. A well-defined workflow for a data science project becomes easy for any data professionals to work. The lifecycle of a data science project is not definitive and can be altered accordingly to improve the efficiency of a specific data science project as per the business requirements.
Data scientists jobs are in demand as it is becoming a pivot technology and concept for the analysis of the massive amount of data to generate and predict useful insights for a business organization. With an increase in demand, the salary for this job role is quite competitive as well and is various bound factors like experience, skills, location, and more. These job roles continue to dominate others and would be much in demand as the data would always be increasing, and there would always be a requirement for professionals to manage this data.
People are also reading: