Get a Lucrative Data Science Career with these Popular Courses

• The availability of data has increased, usage of data tools have increased and software is making it much easier to understand data. Some examples of data analysis tools are Tableau, KNIME, Qlikview etc.
• This helps in improving our decision making skills, leading to better predictions. Examples include fraud detection, risk analysis, curating content for specific audiences, energy exploration and more.
• In higher education, data has been used to track usage of learning systems by students. It is reflected in an Australian university which tracked a student's progression over time to gain proper insights of their learning abilities and challenges.
• Another important area is healthcare, as demonstrated by the University of Florida, which has used Google maps and data about public health to analyze and track spread of chronic diseases. With the recent pandemic, the data analysis came in handy at various intervals.
• It showed correlation with disease control and helped in predicting the potential number of patients and the required number of caregivers that will be required.

As explained earlier, data science is a wide umbrella and there are multiple job opportunities. One can be a Data Scientist, Data Manager, Data Engineer, Data Architect, Data Analyst, Database Administrator, Data Journalist, Financial Analyst, Product Analyst, Marketing Analyst, Quantitative Analyst, Business Analyst, Functional Analyst, Data Visualization specialist and so on. 

Each profile has a different role and demands a set of skills; there are mainly two roles in data science:

• Data Scientist
• Data Engineers

Coined in 2008 by D.J. Patil, former LinkedIn executive, and Jeff Hammerbacher cofounder of Cloudera, data science is defined as “a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.”

Today, it is one of the hottest buzzwords and one of the most sought-after professions in the world. Glassdoor ranked jobs based on median base income, job satisfaction, and number of job openings in the US and, for the fourth year in a row, has declared that the top job in the US is that of a data scientist. The median salary of a data scientist depends on the job title and requirement and also the company which he/she is employed at.

A mid-level data scientist with 5 to 9 years of experience earns ₹1,004,082 per annum in India. As your experience and skills grow, your earnings rise dramatically as senior-level data scientists around more than ₹1,700,000 a year in India.

Skill Sets for Data Scientist

A data scientist should have the expertise to convert data into useful information.  There are different types of essential skills for a data scientist: statistical, mathematical and programming. A data scientist should be able to deal with projects from end to end, understand the problem, get insights from data, construct models and derive conclusions from the data set. The basic skill sets can be defined as follows:

• Statistics to understand and identify patterns in data as well as check for anomalies.
• Machine learning to implement algorithms and models
• Computer science concepts to understand database systems and programming in Java, Python etc.
• Soft skills like analytical and critical thinking, business intuition and interpersonal skills will be an advantage.

Qualifications of Data Scientists

• Data scientists should have industry specific knowledge and business intelligence tools, like Tableau, Microsoft Power BI, Sisense and so on. This is required to convert a business problem to a data based one, create hypotheses, extract, explore and model the available data and then apply the model to the problem on hand.
• They must have a Masters or Doctorate degree in mathematics/ statistics/ computer science or engineering. In addition, they would benefit by taking courses on R programming, Python, Hadoop, Apache Spark and others.

Joining groups and participating in challenges would hone your skills and give you confidence to handle data. In addition, good communication skills, domain knowledge, team work, curiosity in the latest developments and interest in updating oneself will go a long way in landing a good job and becoming a better data scientist.

Online Courses for Data Scientists

Careervira has some amazing courses which can help you to become a data scientist:

• Beginners can explore the Datacamp's Data Science for Everyone course to get a good idea about the basic concepts.
• The next level would be to understand regression, predictive modeling etc., which can be accessed at the Data Science Fundamentals Specialization course by Coursera.

When there are massive data sets, we generally look for a data engineer. They are capable of handling large amounts of data, cleaning it and helping data scientists whose models are converted into ready codes. They need to have knowledge about developing and testing solutions, along with visualization and programming. 

The work of a data engineer depends on the size of the company: the bigger the company, the more data that will have to be handled. Data can be structured, that is, it can be organized into databases or it can be unstructured, like audio/video files, text, images etc., and Data engineers have to deal with both, as well as understand various approaches to data architecture. 

Understanding data warehouses, data lakes and operating systems are also desired from data engineers. We can safely say that, all that data is subject to and changed appropriately, before getting analyzed, will be taken care of by data engineers. LinkedIn lists more than 35,000+ job listings for data engineers alone. Any field where data has to be extracted, analyzed and interpreted requires data engineers. Examples are retail, information technology, government and academic research, finance, health, e-commerce etc. 

Analytics Inside mentions Amazon, Airbnb, AT &T, Microsoft, Capital One, Google, Salesforce, IBM and Cisco as the top companies hiring data engineers.

Skill Sets for Data Engineer

It is essential to have at least a bachelor’s degree in computer science, computer engineering or a related field like physics, applied math or statistics. A good internship or learning project, courses on coding, database management, algorithms etc. are required even for entry level. Essential skills would include technologies based on Hadoop, SQL, NoSql as well as data warehousing technologies, Python, R, Kafka and others. 

Online Courses for Data Engineers

Some of the courses which help you upskill on data engineering are:

• Introduction to Data Engineering by Datacamp
• Data Engineering for Data Scientists by O'Reilly
• IBM Data Engineering Professional Certificate by Coursera 
• Data Engineering Foundations Specialization by Coursera