Taming Big Data Analytics

BIG DATA ANALYTICS

Big Data analytics is the process of gathering, managing, and analyzing large sets of data (Big Data) to uncover patterns and other useful information. These patterns are a minefield of information and analysing them provide several insights that can be used by organizations to make business decisions. This analysis is essential for large organizations like Facebook who manage over a billion users every day, and use the data collected to help provide a better user experience.

According to a recent McKinsey report the demand for ‘Big Data’ professionals could outpace the supply by 50 to 60 percent in the coming years, and U.S.-based companies will be looking to hire over 1.5 million managers and big data analysts with expertise on how big data can be applied. Big Data investments have also sky rocketed, with several top profile companies spending their resources on Big Data related research and hiring big data analysts to change their technology landscape.

According to a recent study done by Forrester, companies only analyze about 12% of the data at their disposal. 88% of the data is ignored, mainly due to the lack of analytics and repressive data silos. Imagine the market share of big data if all companies start analysing 100% of the data available to them. Hence the conclusion is that there is no time like now to start investing in a career in big data. It is paramount that developers upskill themselves with analytical skills and get ready to take a share of the big data career pie.

Advanced analytics won’t produce an ounce of business insight without models, the statistical and machine learning algorithms that tease patterns and relationships from data and express them as mathematical equations. The algorithms tend to be immensely complex, mathematicians and statisticians (think data scientists) are needed to create them and then tweak the models to better fit changing business needs and conditions.

But analytical modeling is not a wholly quantitative, left-brain endeavor. It’s a science, certainly, but it’s an art, too. The art of modeling involves selecting the right data sets, algorithms and variables and the right techniques to format data for a particular business problem. But there’s more to it than model-building mechanics. No model will do any good if the business doesn’t understand its results. Communicating the results to executives so they understand what the model discovered and how it can benefit the business is critical but challenging, it’s the “last mile” in the whole analytical modeling process and often the most treacherous. Without that understanding, though, business managers might be loath to use the analytical findings to make critical business decisions.

Topics covered in this book include:

Big Data Analytics
Architectures for Big Data Analytics
Data Analytics and its type
Predictive Analytics
Descriptive Analytics
Prescriptive Analytics
Diagnostic Analytics
Tools to Mine Big Data Analytics
Data Analytics Programming Languages
R programming language
Python
Scala
Apache Spark
SQL
Apache Hive
Analytical modeling is both science and art
Data Analytics Visualization Tools
Differences between Data Analytics, AI, Machine & Deep Learning
Data Lakes vs. Data Warehouses
Advanced Analytics techniques fuel data-driven organization
Must-have features for Big Data Analytics Tools
Data-driven storytelling opens analytics to all
Use Cases of Big Data Analytics in Real World
Key Skills That Data Scientists Need
Data analytics and career opportunities