Data Science is an emerging technical field. It is the use of modern tools and techniques to pile up a vast amount of data to find unapparent patterns, attain significant and meaningful information out of data to help in making business decisions. So, to put it in Layman’s, Data Science is the formation of predicting models for future help using pre-existing data.
Heaps of big data are being collected, processed, analyzed, and formulated by means of Data Science via Artificial Intelligence. This field finds scope in many domains. Some major areas are:
- Media & Communications
- Artificial Intelligence
- Information Technology
- Health Care
- Bio-Technology
- Corporate Services
- Sports
- Law Enforcement
- Marketing & Advertisements
Steps of a Data Science Job
Data Science jobs consist of five stages:
- Capturing: The first stage is collecting the data which might be unstructured or partially structured. It involves Data Entry, Data Acquisition, and Data extraction.
- Maintaining: This is the stage of Data Cleansing, Data Staging. Some data which is non-useful might be discarded.
- Processing: Data Scientists use the maintained data and examine patterns, motifs, and models. It requires data Mining, Data Modeling, and Data Summarization.
- Analyzing: This is the most important step. Processed Data is subjected to various analyses to come out with a final model. It is based on Predictive Analyses, Regression, and Qualitative & Quantitative Analyses.
- Communicating: This stage is shaping the Analyzed Data into easily readable forms like graphs, charts, and reports. Data Reporting and Data Visualization are key steps.
Job Roles in Data Science
Top job roles in Data Science are:
- Data Analyst
- Operations Analyst
- Data Engineers
- Database Administrator
- Machine Learning Engineer
- Data Scientist
- Data Architect
- Statistician
- Business Analyst
- Data and Analytics Manager
1. Data Analyst
Data Analyst is the most important role in data science. This job needs analyzing and organizing data after obtaining data from different sources and producing a database.
Skills needed for Data Analysts:
- Statistical skills
- Mathematical skills
- Programming skills(SAS, R, Python, HTML, C/C++)
2. Operations Analysts
They implement new strategies or modify previously existing ones to solve problems or increase efficiency within a company. Mainly they are involved in Data management.
The skillset needed for Operations Analyst are:
- Verbal and Communication skills
- Analytical skills
- High efficiency in Microsoft Office
3. Data Engineers
Data Engineers design, build, optimize and manage the company’s data infrastructure or information pool. They work in accord with Data Architectures by transforming data for queries.
The skillset required for Data Engineers are:
- SQL Database (Oracle, PostgreSQL)
- Other programming languages (SAS, MATLAB, Python, JAVA)
- Frameworks (Apache Hadoop)
4. Database Administrator
They optimize and maintain the entire database. They protect the data, monitor it and make it easily accessible at the time of need.
Basic skills needed by Database Administrators are:
- Analytical skills
- Communications skills
- Problem-solving skills
5. Machine Learning Engineer
Machine Learning Engineer is an experienced person who uses mathematical data to program computers to utilize real-world data.
The skillset needed by Machine Learning Engineers are:
- Software Engineering
- Basic Programming Languages
- Statistics
6. Data Science
Data Scientists are the ones who determine the problem and come up with solutions. They determine where to find data and what questions should be answered. They also clean the captured data.
Skills used by a Data Scientist are:
- Programming Skills (SAS, R, Python)
- Hadoop
- SQL
- Machine Learning
7. Data Architect
A Data Architect maintains a company’s database by installing solutions that centralize and protect the data with the best security measures. They are also providing the best tools and systems to Data Engineers.
The skillset needed for a Data Architect is:
- Programming Skills (SQL, Python, Java)
- Designing
- Applied Math
- Database Architecture
8. Statistician
They are using mathematical and statistical techniques to help analyze data. They create new methodologies for the Data Engineers. They devise new tools for making the collection, analysis, and interpretation of data easier.
A Statistician uses these skills
- Mathematical ability
- Computer Literacy
- Statistical terms and concepts
9. Business Analyst
Business Analyst collects data through various sources and compares it with competitors. They are good at Data visualization and Data Modeling tools. They generally have higher domain knowledge than the others Data Science professionals.
Skills used by business analysts are:
- Understanding of Business/Organization
- Forecasting/Budgeting
- Pricing Analysis
- Designing
10. Data & Analytical Manager
Data and Analytical Manager is more of a supervisor with various data Science teams working under him. It is achieved with experience. It is not very common amongst the smaller industries, whereas in larger industries Data and Analytical Manager holds key to success.
The main skills for Data and Analytical Manager are:
- Experience
- Leadership abilities
- Grip overall Data Science Jobs
- Communication skills
Why Data Science is a fascinating field to enter?
Data Science is a field that continues to grow and requires more people. It has scope in almost every kind of industry.
Data science also works well in the freelancing world. Many hiring agencies are currently in search of Data Scientists. Entering into data science does not require you to be a master. You can easily start as a consultant and later on become a Data Scientist yourself.
Here is the average salary overview of different fields of data science:
- Data Analyst : $75,000
- Data Scientist : $90,000
- Data Engineer : $90,000
- Business Analyst: $95,000
- Operation Analyst : $67,000
- Data Architect : $140,000
- Machine Learning Engineer: $130,000
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