A database administrator uses various software to store and organize the company’s data.
He is also responsible for planning, developing, integrity and security of a database so that it is easily available for the employees to use. This ensures that the data is clearly defined and remains consistent across the database.
Skills needed to become a database administrator:
Problem-solving and good analytical skills
Communication, teamwork and negotiation skills
Familiarity with the principles of database design and data manipulation languages
Understanding of business requirements of IT
Willingness to keep up to date with developments in new technology
Commitment to continuing professional development (CPD)
Understanding of tools such as MySQL Workbench, Toad, Adminer, and DatabaseSpy.
As the name suggests, a machine learning engineer should be an expert at machine learning algorithms. He simply puts machine learning models into production.
He does not have to understand the predictive models and mathematics, but just the tools that make the models usable (data analysis, database management, etc).
Skills needed to become a machine learning engineer:
Programming languages: R, Python, C/C++, Java
Probability and statistics
Data modeling and evaluation
Advanced machine learning algorithms
Distributed computing: Apache Hadoop and cloud services
Advanced signal processing techniques/algorithms: wavelets, shearlets, curvelets.
A business analyst has specialized knowledge of the business domain. He helps the company to answer questions and solve business-related problems.
He is responsible to bring and manage changes in the business and improve the company’s products and services. Every business requires change, so for this purpose, the guidance of a business analyst is required.
Skills needed to become a business analyst:
Advanced knowledge of MS Excel to clean the data
Excellent communication and presentation skills (written and oral)
A statistician is required to have an understanding of the changing market trends, collecting them, analyzing them, and converting that information into a useful format. As the name suggests, they must have a very strong background in statistics.
A statistician is a term that was earlier used for data scientists. So basically their work is very similar. A data scientist is just a level higher.
Skills needed to become a statistician:
Mathematical ability and computer literacy
A clear understanding of statistical concepts: descriptive and inferential
Analytical skills
Programming skills: R
Written and oral communication skills
Problem-solving skills
Ability to communicate results and findings to non-statisticians
A high level of accuracy and attention to each and every detail and mistake
A data architect builds and maintains the company’s database systems. He works with database administrators and data analysts and makes plans to integrate, secure, and maintain the company’s information.
He is responsible for creating database solutions, preparing design reports, and monitoring the systems.
Skills needed to become a data architect:
Applied math and statistics
Data visualization and data mining
RDMSs (relational database management systems) or foundational database skills
Database management system software, especially Microsoft SQL Server
Databases such as NoSQL and cloud computing
Hadoop technologies, like MapReduce, Hive, and Pig
Information management and data processing on multiple platforms
Machine learning
Programming languages:
Python and Java, as well as C/C++
Operating systems, including UNIX, Linux, Solaris, and MS Windows
A data engineer is not the one who analyzes data but manages a company’s data infrastructure. Hence, their job requires a lot of software development and programming skills and a lot less statistical analysis.
He is responsible for building certain data infrastructure to get the sales and marketing data to data analysts and data scientists in a usable format for further analysis.
For example, if a company starts to generate a huge amount of data from different sources, a data engineer will be responsible to organize the collection of information, it’s processing and storage.
Data scientists do many of the same things as data analysts, but they also typically build models to make accurate predictions about the business problems based on past data. They are responsible for finding interesting patterns and trends hidden in the data.
For example, as a data scientist, you might be asked to assess how a change in marketing strategy could affect the company’s reputation and working.
This would involve a lot of analysis work, but it would also require building and training a few machine learning models that can make reliable future predictions based on past data.
Skills needed to become a data scientist:
Programming languages:
Python, R, SAS
Machine learning tools
Data visualization and reporting
Statistics and maths
Software engineering skills
Big data platform
Cloud tools
Risk analysis
Effective communication skills and business expertise
The first step in any job search is to identify the types of jobs you should be looking for. In this field, there are different types of data science jobs available to you.
Below I am sharing all the different types of data science jobs with their skills and salary so that you can easily select the best job in data science for you.
Let’s start with the Big Three: Data analyst, Data scientist and Data engineer.
1. Data analyst
Average salary: $68,752
This is usually known as an entry-level position in the field of data science, although not all data analysts are junior.
A data analyst’s primary job is to analyze the company’s data and use it to answer various business-related questions, and then communicate those answers to other members of the company. The answers play a huge role in making key decisions for the benefit of the company.
For example, a data analyst might be asked to look at the sales data from a company’s recent marketing campaign to assess its effectiveness.
This process would involve accessing the data, probably cleaning it, analyzing it to answer the relevant business questions, and then visualizing and communicating the results with the help of tools and techniques.
Skills needed to become a data analyst:
Programming languages (R/SAS)
Creative and analytical thinking to answer questions
Data visualization
SQL databases
Data cleaning, munging and mining
Advanced MS Excel
Basic machine learning
Strong and effective verbal and written communication skills
We are generating a crazy amount of data every day and the world is turning to data for making decisions. This has led to huge demands for Data scientists role in startups and well-established companies.
The International Data Corporation(IDC) predicted the future of the world’s data.
“The global data sphere will grow from 33 zettabytes in 2018 to 175 by 2025. Nearly 30% of the world’s data will need real-time processing. Is your business ready?” – Seagate
The McKinsey Global Institute study report on Big Data said that “The United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of Big Data.”
The vast amount of big data is of no use unless we tag and analyze. Global companies are rushing to hire Data scientists to make use of their Big Data.
To name a few, top companies that are competing with each other to hire Data Scientists include Facebook, Google, Spotify, Netflix, Apple, LinkedIn, IBM, Microsoft, etc.
It’s hard to clearly define the set of capabilities of a “Data Scientist” as different problems require a different set of skills. Some of the Job titles related to data science are –
Data Scientist
Data Architect
Data Administrator
Business Analyst
Data Analyst
Business Intelligence Manager
Data/Analytics Manager
A Data Scientist has many skills under his belt. Not only are Data scientists responsible for business analytics process, but they are also involved in building machine learning models, data products and software platforms, along with and developing visualizations.
We are moving to an “analytics everywhere” world where all the business will deeply depend on data for making any decisions. This upward hill in Data Science career opportunities is only going to go up and expected to continue for a long time.
Big and small businesses are all going to seek support from Data Scientists in making business-driven decisions.
Indeed.com reports from the employers of the past two years calculates the average salary of a Data Scientist in India is 8,37,235 Rs and the average salary in the US is $121,212 per year.
This is the right time to up your skills in Data Science and Big Data Analytics to take advantage of the Data Science career opportunities coming your way.
