Overview of Data Science?

As the world entered the era of big data, the need for its storage also grew. It was the main challenge and concern for the enterprise industries until 2010. The main focus was on building a framework and solutions to store data.
Now when Hadoop and other frameworks have successfully solved the problem of storage, the focus has shifted to the processing of this data.
Data Science is the secret sauce here.

All the ideas which you see in Hollywood sci-fi movies can actually turn into reality by Data Science.
Data Science is the future of Artificial Intelligence. Therefore, it is very important to understand what is Data Science.
It continues to evolve as one of the most promising and in-demand career paths.
It is a field that uses scientific methods, processes, algorithms, and systems to extract knowledge insights from structured and unstructured data.
It is a unified concept of statistics, data analysis, Machine Learning, and their related methods.
It draws its techniques from the context of mathematics,statistics, computer science, and information science.

It is also a complex combination of skills such as programming, data visualization,command-line tools, database, statistics, machine learning.
Now let us see about data science life cycle

As we can see this is how the data science cycle goes on…
Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills.

In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.

The term “data scientist” was coined as recently as 2008 when companies realized the need for data professionals who are skilled in organizing and analyzing massive amounts of data. (https://datascience.berkeley.edu/about/what-is-data-science/#fn1b).
In a 2009 McKinsey&Company article, Hal Varian, Google’s chief economist and UC Berkeley professor of information sciences, business, and economics, predicted the importance of adapting to technology’s influence and reconfiguration of different industries.

Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions.
These skills are required in almost all industries, causing skilled data scientists to be increasingly valuable to companies.

Data scientists need to be curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.

They must also be able to utilize key technical tools and skills, including:



Apache Hadoop


Apache Spark

NoSQL databases

Cloud computing


Apache Pig


iPython notebooks


Where Do You Fit in Data Science?

Data is everywhere and expansive. A variety of terms related to mining, cleaning, analyzing, and interpreting data are often used interchangeably, but they can actually involve different skill sets and complexity of data.

Data Scientist

Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization.

Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning

Data Analyst

Data analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.

Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization

Data Engineer

Data engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.

Skills needed: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)

Data Science Career Outlook and Salary Opportunities

Data science professionals are rewarded for their highly technical skill set with competitive salaries and great job opportunities at big and small companies in most industries. With over 4,500 open positions listed on Glassdoor, data science professionals with the appropriate experience and education have the opportunity to make their mark in some of the most forward-thinking companies in the world.(https://datascience.berkeley.edu/about/what-is-data-science/#fn6b)

Below are the average base salaries for the following positions: (https://datascience.berkeley.edu/about/what-is-data-science/#fn7b)

Data analyst: $65,470

Data scientist: $120,931

Senior data scientist: $141,257

Data engineer: $137,776

Gaining specialized skills within the data science field can distinguish data scientists even further. For example, machine learning experts utilize high-level programming skills to create algorithms that continuously gather data and automatically adjust their function to be more effective.