What Is Data Science?
Data science is related to computer science, but is a separate field. Computer science involves creating programs and algorithms to record and process data, while data science covers any type of data analysis, which may or may not use computers. Data science is more closely related to the mathematics field of Statistics, which includes the collection, organization, analysis, and presentation of data. Data Science is an amalgamation of Statistics, Tools and Business knowledge. So, it becomes imperative for a Data Scientist to have good knowledge and understanding of these.
Why to Learn Data Science?
With the amount of data that is being generated and the evolution in the field of Analytics, Data Science has turned out to be a necessity for companies. To make most out of their data, companies from all domains, be it Finance, Marketing, Retail, IT or Bank. All are looking for Data Scientists. This has led to a huge demand for Data Scientists all over the globe. With the kind of salary that a company has to offer and IBM is declaring it as trending job of 21st century, it is a lucrative job for many. This field is such that anyone from any background can make a career as a Data Scientist.
Mathematical Expertise: Data scientists also work on machine learning algorithms such as regression, clustering, time series etc. which require a very high amount of mathematical knowledge since they themselves are based on mathematical algorithms.
Working with unstructured data: Since most of the data produced every day, in the form of images, comments, tweets, search history etc. is unstructured, it is a very useful skill in today’s market to know how to convert this unstructured into a structured form and then working with them.
Jobs by Salary
Nearly 46% of Data Scientists earn a salary between 6-15 LPA.
Banking & financial are the leaders with over 40% all jobs
Advertised Energy and Utilities contribute 15% of total jobs
Business Analytics Professional
A business analytics professional has the skills to make use of the information from the data to generate insights about the business. To be a data focused business analytics professional, you must know the technical components related to managing and manipulating data.
Recruiters: Walmart, Conduent, Genpact etc.
Business Intelligence Professional
A Business Intelligence Professional analyse the past trends using Data Visualization tools like Tableau, Power BI etc to develop and implement business strategies. They also monitor all the performance metrics of the company and provide insight to the respective department.
Recruiters: Accenture, ZS Associates, Sun Pharma etc.
Data Scientists help build complicated data models and simulations in a Big Data environment. Focusing more on math and statistics, these data scientists have a particular interest in reading statistics and building & deploying machine learning models.
Recruiters: HDFC Bank, Amdocs, Oyo etc.
Big Data Analysts
Job responsibilities of a Big Data Analyst include collaborating with data scientists and data architects to ensure streamlined implementation of services and executing big data processes
Recruiters: Novartis, Allerin Tech, Amazon AWS etc.
HR Analytics Professionals
HR Analytics is the hottest trends in the Industry. HR Analytics professionals are working on how to reduce employee attrition rate, finding out the best recruitment channels and solving appalling problems related to HR Function.
Recruiters: Lenskart, Mearsk, Ericsson etc.
Marketing Analytics Professionals
Due to the abundance of data in all the marketing campaign. Analytics enable the marketing professionals to evaluate the success of their marketing initiatives. This is accomplished by measuring performance.
Artificial Intelligence (AI) is the branch of computer sciences that gives special value to the development of intelligence machines, thinking and working like humans. For example, speech recognition, problem-solving, learning and planning.
In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals.
Artificial intelligence (AI) in business is rapidly becoming a commonly-used competitive tool. ... From better chatbots for customer service to data analytics to making predictive recommendations, deep learning and artificial intelligence in their many forms is seen by business leaders as an essential tool.
Eg.--Amazon Alexa, known simply as Alexa, is a virtual assistant AI technology developed by Amazon, first used in the Amazon Echo smart speakers developed by Amazon.
Benefits of Artificial Intelligence
- 1. for the Economy, Business, and Industries.
- 2. for Humanity and Society
- 3. For Health care and Medicine Health care services
The future of AI involves advanced cognitive systems capable of doing what machine learning systems can't. They will intelligently and fluently interact with human experts, providing them with articulate explanations and answers, even at the edge of the network or in robotic devices.
AI engineers salary
The average Machine Learning engineer salary is about $142,859 per annum, with other AI job titles all make upward of $100,000 per year, including Data scientists earning on average $126,927 per annum and Algorithm engineers making $109,313
Different Job profiles for AI engineers.
- 1. Machine Learning Engineer
- 2. Data Scientist
- 3. Business Intelligence Developer
- 4. Research Scientist
- 5. Big Data Engineer/Architect
Top 10 highest-paying companies for AI engineers:
- 1. Uber
- 2. Walmart Labs
- 3. Netflix
- 4. Facebook
- 5. Salesforce
- 6. Google
- 7. Coupang
- 8. Twitter
- 9. Splunk
- 10. Apple