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Data Science – 21st Century Skills – Microsoft Professional Program

Data Science:

  • Career in Data Science:

Earlier,  mostly structured, which could be analysed by using the simple BI tools. Today, most of the data is unstructured or semi-structured, rather than being structured, as it used to be in the traditional systems. This data is generated from different sources. These sources are multimedia forms, financial logs, text files. The traditional BI tools cannot of processing this huge volume and variety of data. Here comes the need for more complex and advanced analytical tools and algorithms for processing, analysing and inferring useful insights out of it.

Data scientist has become the most sought-after job of the decade. Companies use the insights provided by data scientists to stay one step ahead of their competition. This enables them to keep low overhead costs. A lot of companies like Apple, Oracle, Microsoft, State Farm, Walmart, and more all regularly recruiting data scientists.

Data scientists are people with good command as mathematicians, computer scientists, analysts. The data scientist’s role is to work on and categorize large volumes of data and carry out further analysis to find trends in the data. With the help of this analysis, they can gain a deeper insight into what it all means. These insights are further used by companies and is used to decide further actions. We can say that Data scientists are a medium between the business and IT worlds and influence industries by analysing complex datasets to aggregate observations that companies can leverage into actions.

  • Career Growth for an Individual:

As of now, there is a huge demand for data scientists in the finance and insurance industries. Around 50 percent of data science and data analytics job demand is from the industries like finance, insurance, professional and IT services.

Companies where there is large turnover of data, specially banks are always on a lookout for data scientists as they realize that these employees can manage predictive analytics and machine learning. They offer competitive salaries to these employees, as the potential of big data in banks is enormous, and the large firms want to stay competitive in the market.

While mostly finance and insurance industries are known for recruiting top talent, other industries are also looking for data science professionals to match with the competitive industry. Many professionals are now keen to learn more about data science to stay relevant and enjoy job security.

  • Job profiles and Job Titles are listed below:

 

  1. Data Scientist:

Data Scientist are professionals who are specialized in Retrieving, mining and statistically analysing large chunk of data. Data scientists understand the challenges of a business and offer the best solutions using data analysis and data processing. They perform predictive analysis and run a fine-toothed comb through an “unstructured/disorganized” data to extract actionable insights. They also identify trends and patterns that can help the companies in making decisions, that would otherwise be difficult to make.

  1. Data Analyst:

Data Analysts are professionals who perform analysis on data using various mathematical calculations to find how best a particular data set can be used in business decisions?

They perform variety of tasks like visualization, munging, and processing of massive amounts of data. They also analyse databases by performing queries on the databases from time to time. The most important skills of a data analyst is optimization. They create and modify algorithms that can be used to pull information from very large databases without affecting and corrupting the data.

  • Data Architect:

Data Architects are professionals who determine how data will be stored, used, integrated and managed by different data entities and data systems.

A data architect is responsible for data management. He has to create the blueprints for data management so that the databases can be easily integrated, centralized, and protected with the best security measures. They also explore the best tools and systems to work with, for the data engineers.

  1. Data Engineers:

Data Engineers are specialists who transform data into a format that can easily analysed. They do this by using various tools and developing, testing and maintaining infrastructures of data conversion.

Data engineers help the Data Scientists by building and testing scalable Big Data ecosystems for the businesses.  They are responsible to provide stable and highly optimized data systems so that data scientists can run their algorithms on it. Data engineers are also responsible for constantly updating the existing systems with upgraded versions of the current technologies in order to improve the efficiency of the database systems.

  1. Database Administrator:

Database Administrator is a specialist who is responsible for the performance, integrity and security of a database

The name itself explains the job profile of a database administrator. They are responsible for the proper functioning of all the databases of an organization. They also have to understand the requirements of employees and grant or revoke its services to them. They are also responsible for database backups and recoveries.

  1. Business Analyst:

Business Analysts are professionals who plan, design and develop efficient business, and operation system that supports core organizational functions and business processes.

The role of business analysts is slightly different than the other data science jobs. They have a good understanding of how data-oriented technologies work. They also know how to handle large volumes of data and are responsible to separate the high-value data from the low-value data. They actually decide how Big Data and its insights can be used for business growth.

  • Data and Analytics Manager:

data and analytics manager is responsible to understand the data science operations and assigns the duties to their team according to skills and expertise. Their strengths should include knowledge of technologies like SAS, R, SQL, etc. and of course management.

  • Who Can Learn Data Science?

  • Any professional from Business Analytics/ Business Intelligence background

  • Engineering or Non-Engineering aspirants wanting to become a Data Scientist

  • Managers from Analytics background and those who are leading a team of Analysts

  • Any Data Analyst or Software Developer aspiring to be a Data Scientist

  • Professionals wanting to build machine learning models, using distributed storage and distributed processing

  • Prerequisites to Study Data Science      

To pursue Data Science, there isn’t a specific strict educational background requirement. Data Scientists come from various educational and employment backgrounds. The majors that accord themselves with Data Science are:

  • Statistics
  • Mathematics
  • Economics
  • Operations Research, and
  • Computer Science.

 

  • Courses in Data science:
  • Microsoft Professional Program in Data Science.
  • PG Program in Data Science
  • Advanced Program in Data Sciences
  • Post Graduate Diploma in Business Analytics