Radhika
Is data analytics a hard field? If so, can you explain what it is as well as how it will evolve in the future?
I am hoping to major in Computer Science and some people have been telling me to look into data analytics. However I can't find that much information about it and don't quite understand the implications of it. I would also like to know my job outlook ( I will graduate from college in about 5 years). #computer-science #data-analysis
6 answers
Davor Gornik
Davor’s Answer
Data analytics is a field that requires you to understand the data meaning of data. What it means is that you have to be able to turn the data into something else of value for the business user, scientist, CEO, etc. It basically produces two ways of results: simplification (meaning that you have to know how to abstract the data in a way that will not loose the essential meaning) and correlation (meaning that you have to understand how different data sets relate to each other and be really good in analyzing trends and dependencies between those data sets).
The necessary skill set for those are basically two: mathematics (mostly statistics) and the domain knowledge of the data. Most great data analysts are domain experts or people with strong knowledge of statistics that acquired specific domain knowledge.
Is it hard? This really depends on you. If you are good with abstraction and correlation it might be easy for you, if you are bad on this you will have a hard time in this field.
Hareesh G
Hareesh’s Answer
Is it hard?
a. Being successful in data analytics requires knowledge of tools, techniques, business and good communication skills. People passionate in data crunching typically tend to focus less on soft skills which will hinder their progress. We need to be a jack of all trades here & play smart.
b. Meaningful Insights?
What is meaningful insights- This is defined as per research problem. (For example, if a company is interested in identifying customers who are likely to respond to a promotion, the meaningful information will be the set of customers who have certain characteristics which maximize their promotion response).
c. Insights do not automatically become insightful!!
Data analytics is mix of science and art. Data analysts need to be artistic in weaving out stories that the analysis brings in and fit to context of users.
d. How analytics derives meaningful Insights?
Traditionally business has relied a lot on their hunch or gut feeling to take decisions. Data was viewed for operational purpose like reporting etc. Now with evolution of analytical techniques based on statistics, data is turned to a strategic asset. Companies are investing a lot on extracting insights from data stored in their data warehouse and other sources to even predict the future scenarios.
e. Evolution of data analytics
Data analytics has evolved rapidly with advent of Big data and IOT. The terms Big data analytics, IOT analytics are now common. Also the massive data availability, availability of high processing platforms like GPUs, complex machine learning algorithms like deep learning , whole of analytics industry is reshaping itself into Artificial Intelligence industry.
f. What tools & skills are currently used in industry?
Tools :Recently lot of companies are using Python & R for their analytical projects. Typically banking firms use SAS.
Skills : Be good in communication & passionate of data/math. An attitude to continuously learn & unlearn
g. What recruiters typically are interested in an analysts' resume?
For entry levels, candidates should mention any academic projects they did and any data analysis were used there, tools which were used for that etc. I personally look at communication skills & confidence/logic with which candidates speak during their interviews
Caryn Conklin, MBA
Caryn’s Answer
Like any field of study, your passion and innate abilities will determine how challenging or straightforward you find it. Data analytics revolves around processing vast amounts of data, identifying trends and points of interest, and then visually presenting the information in a way that simplifies understanding for others.
I had the opportunity to oversee a project recently, where a communications company tasked the team with analyzing extensive data about subscribers and communication performance. The goal was to pinpoint when and where communication issues were occurring and how these problems influenced subscriber behavior, such as customer service calls and cancellations. The findings were enlightening, showing the company how repeated outages over a specific period could lead to certain behaviors, like cancellations. The company responded by enhancing certain segments of their communication lines to minimize failures and bolstering teams in specific areas to address issues. The team utilized R for data processing and PowerBI for data presentation.
Last week, while exploring future job prospects with my teenage nephew, we found that data science was ranked as the 4th or 5th highest paying job, even outpacing lawyers, in the coming years. With the ever-increasing amount of data and companies' ongoing efforts to enhance customer service and cut delivery costs, I'm confident this field will continue to flourish and offer lucrative opportunities. As AI becomes more prevalent, anticipate shifts in the landscape.
Caryn recommends the following next steps:
Joanne Mammarelli
Joanne’s Answer
Take a look at the PBS video "The Human FAce of Big Data"
http://thehumanfaceofbigdata.com/
Professionals will be needed just for the ETL (E = extract the data, T = Transform the data, and L = lay down the data) for others to use.
Billie Palmer
Billie’s Answer
Data analytics, like any other computer science field, can be complex but is learnable. The process of analyzing the seemingly endless amount of data can seem overwhelming, but when broken down into smaller goals, it becomes a process to resolving the question that has been posed. Computing languages like Python and SQL are necessary to truly mine the data, and I recommend classwork for both of those topics.
As we begin to harvest all of this data that IoT and the internet is bringing in, we have to figure out what it all means. Sometimes a question is posed and we dig through the data to determine the answer...sometimes we have to organize the data and just try to determine what its telling us. Scripts can be created to help in this endeavor.
Data Mining and Data Analytics, in my opinion, is the future of Computer Science. The marketing benefits are where the real need will be.
Krishna Chaitanya Tumuluru
Krishna Chaitanya’s Answer
As the computer science world is moving towards open source tools and technologies, we might see increase in open source data visualization tools in future.