Data science is the fastest growing job in the United States and was named as a top job by LinkedIn in 2017 and Glassdoor in 2018. Also, a recent study by PricewaterhouseCoopers states: “Top jobs in the U.S. today include titles such as data scientist, business analyst, and data engineer.”
If you want to start a career in the rewarding and in-demand field of data science, a data scientist degree from a top American university can help make that dream come true.
Data Science is a comprehensive bachelor’s or master’s degree program that teaches you to think like a data scientist while you master the essential software skills and data analysis tools applied in top data science jobs throughout industries.
The current incredible job growth in the field of data science is very compelling. As a data science and statistics major you will take an important first step on a career path you can be proud of
The USA The Bureau of Labor Statistics estimates 11.5 million new jobs by 2026. The average annual salary with the data scientist job title is $120,931, and the average annual salary is $84,000 among the 14 specific data analytics and data science careers ranked among our universities.
According to a recent study by LinkedIn, the highest growth jobs related to data science and data analytics include:
Machine Learning Engineer: 8X Job Growth. Average salary according to Payscale: $100,956.
- Data Scientist: 5X job growth. Average salary according to Glassdoor: $120,931.
- Sales Development Representative: 7X job growth. Average salary according to Glassdoor: $47,973.
- Customer Success Manager: 6X job growth Average salary according to Glassdoor: $78,818.
- Big Data Developer: 5X job growth, median salary according to PayScale: $85,000.
- Full stack engineer: 5X job growth typical salary as per Glassdoor: $104,463.
- Unity Developer: 1X job growth typical salary as per PayScale: $83,318 per year.
- Director Data Science: 9X job growth, average salary as per PayScale: $158,911 per year.
- Full stack developer: 5X job growth Average salary as per PayScale: $76,063 per year.
Data Science Syllabus/ content of the course
Data Science is one professional sphere that is bursting with rewarding job opportunities. All thanks to the data bang in the 21st century that has created the need for data scientists who can make sense of this gigantic data.
Not only does the role of a data scientist attract profitable return, but also pledges a high degree of professional satisfaction. Data Science is one area that is skill-based and entails one to possess an inclusive set of skills. In this blog you will get the complete knowledge related to the Data Science syllabus and course. The main field of data science are statistics, programming language R/Python, ETL (Extraction, Transformation, and Loading), Data Wrangling and Data Exploration, Machine Learning and Deep Learning, Big Data Processing Frameworks, Data Visualization
Statistics is an area of study that involves collecting data, analysis, interpretation, and organizing data. Hence, it seems relatively usual that data scientists should have a good clench of statistics.
- Programming Language R/ Python:
In order to control data and derive useful insights from it, a programming language by applying certain algorithms is required. Python and R are the languages most preferred by Data scientists for scientific computing and data manipulation.
- ETL (Extraction, Transformation, and Loading)
With the help of ETL skills, a data scientist have the ability to collect data from multiple sources such as MySQL and MongoDB, transmute these into a proper format for storage in order to query and examine them, and then load the data into a data warehouse for exploration.
- Data Wrangling and Data Exploration
For easy access and Data analysis, Data wrangling is use in the process of cleaning up and merging the messy and complex data in the data warehouse
After that Exploratory Data Analysis (EDA) is required and then, data scientists effort to make sense of the data at their disposal and then get an idea as to what queries to ask and how to operate the data sources at hand to seek the answers they need.
- Machine Learning and Deep Learning
Making machines intelligent and training them to think, analyse and make decisions, Machine Learning is the process of which is required. Therefore Data scientists are likely to have concrete knowledge and understanding of several Supervised and Unsupervised algorithms.
The succeeding level of machine learning is Deep Learning. The idea of Deep learning is exactly the same to the human brain cells well-known as neurons. The main intention of deep learning is to imitate the functioning of the neurons. Scientists use a huge and composite network of artificial neurons to create what is known as a deep neural network. Basically, Deep learning knowledge is preferred by most companies considering to onboard data scientists. Hence, this is one skill that is very much needed by the Data Science program.
- Big Data Processing Frameworks:
The creation of detailed machine learning models was not possible due to a lack of large data and computing power, earlier but the case is unlike today. Substantial amounts of data are required to effectually train Machine Learning models.
Today, in every second, vast amounts of data are being generated. This data can be structured or unstructured and cannot be processed by conventional data processing systems. Therefore advanced big data processing frameworks such as Hadoop, MapReduce, and Spark are required. Hence, every aspiring data scientist essentially be conversant in big data processing frameworks.
Data visualization is sometimes also referred to as information graphics, information visualization, and statistical graphics. Data visualization is the conversion of data into a visual form, such as a graph or map. It is one of the most vital and indispensable skills of data analysis for Data Scientists aspects. It actually helps them connect more effectively with end-users. Tableau and Power BI are some of the most common Data visualization tools.
In conclusion, the data science syllabus is relatively multifarious and detailed, therefore it’s imperative that you should acquire skilled specialized supervision to have good knowledge of all the data science syllabus or courses.
Employment opportunity with earning potential For Data Analysis
RMC Elite affiliated US universities offer more open job opportunities than qualified applicants through a data science and statistics degree program because of a shortage of data scientists and a severe bottleneck in some fields. A degree in statistics and data science from RMC elite tie-up US universities will put you in a prime position to fill employment gaps in this field. IBM predicts that you can earn an average of $8,736 more per year than most other bachelor’s degree jobs.
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