DATA ANALYTICS

 

The Data Analytics specialization is a four-week bunch that helps the students to develop and accelerate student ability in building data products, develop a web application and data visualization.

          Data science and analysis require efforts and practice in mastering, therefore, a careful analysis of skills you acquire is an important one.

Since data science and analysis requires efforts in mastering it, therefore, a careful analysis of which skill you should acquire to become a Data scientist is an important one.

This detailed analysis will help you to make the best choice for Data Analytics to blueprint you without any bias.

MUST HAVE IN BASICS

·        SQL

·        EXCEL

MUST HAVE IN ADVANCE

·        PYTHON

·        R

·        STATISTICAL ANALYSIS SYSTEM(SAS)

MASTER OF COMPUTER SCIENCE WITH A SPECIALIZATION IN DATA ANALYTICS

              Intelligent analysis of large amounts of data is a crucial component in supporting numerous business applications and making significant business decisions. With a Specialization in Data Analytics, gain a firm grasp on discovering patterns in large amounts of data from information systems and drawing conclusions based on these patterns.

To qualify for the specialization in Data Analytics, you must satisfy general master of computer science requirements and pass four of the following specialization courses.

·        » CS 422 Data Mining

·        » CS 520 Data Integration, Warehousing, and Provenance

·        » CS 522 Advanced Data Mining

·        » CS 554 Data-Intensive Computing

·        » CS 579 Online Social Network Analysis

·        » CS 583 Probabilistic Graphical Models

·        » CS 584 Machine Learning

·        » CS 585 Natural Language Processing

Standardization in Academia

                      Today’s analytics programs integrate proficiencies from computer science/engineering, applied mathematics/statistics, and business communications into a core curriculum that typically also includes data mining and database fundamentals. This has two potential benefits: it can offer students a clearer path to a career in analytics; and it can provide businesses with a stronger pool of qualified professionals.

Specialization in the Field

                       Growth and standardization is often accompanied by specialization, and analytics appears to fit that pattern. The division of labor in analytics seems to break down into three broad areas that do overlap: jobs that focus on IT systems, database architecture, and BI platforms; jobs that focus on data mining for business/marketing purposes and other specialized applications in government, healthcare, and other fields; and jobs that focus on using advanced programming, algorithms, and artificial intelligence to make sense of big data.

Impacts on Academia

                            

                              Our research indicates that online master’s in analytics programs are on a parallel track with the aforementioned developments. General data analytics programs are more prevalent in data science and BI. These data analytics programs, which we group with business analytics programs because the curricula are so similar, target core proficiencies in applied mathematics, statistics, computer programming, data mining, and descriptive and predictive modeling. Some offer students the option of specialization through courses in subjects like marketing analytics, government analytics, and clinical research analytics. Data Science programs build off of the core analytics curriculum, incorporating advanced computer science coursework in highly technical areas like machine learning and artificial intelligence. BI programs emphasize database design, data warehousing, and dashboarding.

HOW TO CHOOSE DATA ANALYTICS SPECIALIZATION?

              Data science is an emerging and growing field where tools and technologies keep on evolving.so, when you look around, research about the data analytics specialization, tools and programs available, you will be confused as to what to choose. And why not? There are a plethora of choices created by education providers.

Undoubtedly, the thought of becoming a data scientist will trigger a lot of questions and confusion.

One of the most commonly expected questions when it comes to data analytics specialization is “If I am going to become a data scientist, which programming language/tool should I choose out of R, Python, SAS, SQL, and Excel?

R

• If you are an experienced and trained data scientist, you will be asked how well, you know R.
• The development and usage of R is on the rise since 2007, challenging almost 40 years of monopoly by SAS language. Being an open-source and free to use tool, most of the data scientist is now leveraging R.
• It has a huge number of statistical, graphical, and analytical packages. This makes it important for you to learn the basics of R to be a successful data scientist.
• However, learning R is not enough to be a data scientist. This is because many Big Data production systems use Python and with R alone, you can’t survive with the data!

Python

• Python is a general-purpose programming language with many key advantages over R. For example, readability of code, suitability in production environments, and more.
• This is another free and open-source alternative available for you.  
• When compared with R, Python lacks many statistical packages. Although there are some limitations, Python data science still provides you with panda’s data frame package, sickie-learn machine learning, and stats models package. This ensures a suitable array of options for data scientists for conducting a descriptive and predictive analysis.
• Its usage in analytics is rising tremendously, therefore, knowing how to code in Python & using Python tools will fireproof your career and cover any risks from knowing R alone.

SAS

• The most important language in data science is SAS. This is a statistical language dominating the data world today.
• It is easy to learn when compared to R and Python.
• Being the godfather of analytics, it has been in businesses since 1976 contributing and financing statistical computing to a great extent.  It also has great documentation and customer support meaning enterprises using SAS rarely switch, while it is newer companies that begin with R and Python.
• SAS is one of the hottest job interviews qualifying skills right now. This means if you have SAS language certification in your resume, the probability of you getting hired is high. 
• Learning SAS could be expensive and it is used mostly by big enterprises, so having at least one open-source language (Python or R) other than SAS is crucial. Using SAS University Edition, you as a student get SAS language for learning for free just like Python and R.

SQL

• Acronym for Structured Query Language, it is one of the easiest data analysis languages to learn.
• SQL is also used from within SAS, R, and Python.
• With SQL, you can select data from tables meeting conditions.
• Since it is easy to use operators on it and do group by analysis, this programming skill is very popular among data scientists.

Excel

• Knowing how to use Excel to analyze data is on the rise.
• You can analyze data  interactively with functions, graphs, and limited statistics.
• So, if you are going to apply in a company where presenting data or something with the financial domain, Excel is a much-needed skill.

You will be catered to the basics of SQL and Excel besides complete comprehension of SAS, R, and Python in our Data Analytics Course.

Conclusion

It is important to remember that it takes a learning curve and time to memorize the basic syntax of any programming language for data science and you can only learn a couple of things at a time during your course of taking Data Analytics specialization.