- Overview of Data Science
- What Does a Data Scientist Do?
- Course Details
- Career Options after pursuing B.Tech (Data Science)
- Dive Deep- Further Resource Materials
- Few Online Courses to Start you with Data Science
Technology-savvy organizations, as well as “digital non-natives,” can benefit from analytics and augmented intelligence across all disciplines by using an infusion strategy.
Infusion means that by embedding analytics and artificial intelligence (AI) into the very core of your business processes, we can help you drive capital allocation strategies and investment decisions, create an end-to-end digital audit, generate new revenue opportunities, manage risk, conduct investigations, measure financial and non financial performance, capture tax big data to inform decisions, increase customer satisfaction, and improve the customer experience.
Overview of Data Science
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, deep learning and big data.
Data science is a “concept to unify statistics, data analysis, machine learning, domain knowledgeand their related methods” in order to “understand and analyze actual phenomena” with data.
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. 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.
“The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.”-Hal Varian, chief economist at Google and UC Berkeley professor of information sciences, business, and economics”
What Does a Data Scientist Do?
In the past decade, data scientists have become necessary assets and are present in almost all organizations. These professionals are well-rounded, data-driven individuals with high-level technical skills who are capable of building complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organization. This is coupled with the experience in communication and leadership needed to deliver tangible results to various stakeholders across an organization or business.
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:
R Apache Spark Apache pig
Python NoSQL Databases Tableau
Apache Hadoop Cloud Computing iPython notebooks
MapReduce D3 GitHub
Data science is a multidisciplinary field. It encompasses a wide range of topics.
- Understanding of the data science field and the type of analysis carried out
- Applying advanced statistical techniques in Python
- Data Visualization
- Machine Learning
- Deep Learning
Career Options after pursuing B.Tech (Data Science)
Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.
However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist. Presently only few Indian Universities are offering this course and they have been slow at creating specialized data science programs. Most have started the course without sufficient resources to manage it (not to mention that the ones that exist are extremely expensive)
Dive Deep- Further Resource Materials & Research Articles
- Python Basics for Data Science
- Data Science Matrix
- How data can help you innovate when change is constant
- How data can become a source of power
- What’s next for the data science and analytics job market?
- Executive perspectives on data and analytics
- Data and analytics
- Data and analytics: Why does it matter and where is the impact?
- Democratizing data science to bridge the talent gap