Why should you do a Data Science Tutorial Course

Blog: GestiSoft

A recent survey showed that there is an acute shortage of managerial and analytical talent in the USA. Recent studies have shown that there is a growing demand for professionals who are data savvy in organizations such as businesses and public agencies. The supply of professionals who are experts at working with data is highly limited in the market today. The massive salary offered to data scientists by businesses today reflects this shortage in supply highly. Further, reports say that there will be at least 4 million jobs in the USA by 2018, which require the skills of data scientists.

Who is a data scientist?

A data scientist is a person who is an expert at coding and analyzing data. There are mainly two different types of data scientists. Type A data scientist and type b Data scientist. The A in type A stands for analysis. A type A data scientist is very much like a statistician and is involved in working with the practical tenets of data. The type A data scientist is an expert at coding and possesses knowledge on subjects such as data cleaning methods, statistical inference, Modeling and forecasting. A type A data scientist is also known as a statistician and a quantitative analyst.

The need for data scientists

Earlier before they were called data scientists, commonly they were known as statisticians, computer scientists. Somewhere on the side, we realized how much power data could bring to us. Hence, we needed the resources to create professionals who were skilled at handling this data. The only problem that arose was that statisticians were not able to handle the enormous amounts of data and computer scientists were unable to complete a thorough analysis on the data. This led to the term data scientist to be created.

Skillets required becoming a data scientist

Linear algebra is required to be understood to gain an understanding on various algorithms.
Multivariable calculus and probability and statistics
Coding in the languages of python or R
Knowledge on SQL and excel

Process of learning data science

A data science-training tutorial helps prepare students for the challenges that come up in the future in the field of data science. Usually a data science-training tutorial consists of boot camp models, and projects for honing and developing your technical and soft skills. The tutorial necessarily prepares student’s analytical skills, and programming skills through a series of portfolio building assignments. Students are required to conduct specific assignments in scientific training in the field of software engineering, statistics, number computation, and data visualization. The data science tutorial program for beginners will provide extra career furthering opportunities and help in employing graduates into direct employment.

Various tenets of a data science tutorial

In person online training format
Languages taught are python, SQL, Hadoop , Hive , Spark , and map reduce
The usual class length is like 6 weeks
The classes are full time and require a minimum investment of 20 plus hours online.

The design of the program

The program of data science tutorial for beginners usually go on for five consecutive days after which a student is required to write a mock test on the sixth day . The mock test consists of real world data problems and data science problems. After the tutorial process is over the students are divided into groups. Each group is assigned a mentor. Each group is assigned separate projects to handle. The program is created for those who are seeking to gain expertise on data science and managing big data. The courses are taught using R and python. The tutorial usually lasts for five days in which, subjects such as clustering, predictive modeling, recommender stems, event queues, analytics, metrics and distributed databases are taught.

Basic Aim of the Data Science Tutorial

During the term of the tutorial, students are required to work with local nonprofit organizations and different federal agencies. The student is meant to collaborate with these agencies to be able to provide solutions to high impact problems. The basic aim of this tutorial is to ensure that the students can achieve a high degree of practical understanding of data science. The course requires the student to solve daily data analyzing tasks alongside the experts who are in charge of creating it. The advantages of the tutorial are that the students get to interact with one another and form groups to solve real problems.

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