Lakhasly

Big Data is the collection of massive amounts of information, whether unstructured or structured. Today, many
organizations are collecting, storing, and analyzing massive amounts of data. This data is commonly referred to as “big
data “because of its volume, the velocity with which it arrives, and the variety of forms it takes. Big data is creating a
new generation of decision support data management. Businesses are recognizing the potential value of this data and are
putting the technologies, people, and Processes in place to capitalize on the opportunities. A key to deriving value from
big data is the use of analytics

Machine Learning is a branch of Computer Science that, instead of applying high-level algorithms to solve problems in
explicit, imperative logic, applies low-level algorithms to discover patterns implicit in the data. (Think about this like
how the human brain learns from life experiences vs. from explicit instructions.) The more data, the more effective the
learning, which is why machine learning and big data are intricately tied together.
Big data Analytics
Big Data not only changes the tools one can use for predictive analytics, it also changes our entire way of thinking about
knowledge extraction and interpretation. Traditionally, data science has always been dominated by trial-and-error
analysis, an approach that becomes impossible when datasets are large and heterogeneous. Ironically, availability of more
data usually leads to fewer options in constructing predictive models, because very few tools allow for processing large
datasets in a reasonable amount of time. In addition, traditional statistical solutions typically focus on static analytics that
is limited to the analysis of samples that are frozen in time, which often results in surpassed and unreliable conclusions.
Let's begin with a real world example, looking at a farm that is growing strawberries
What would a farmer need to consider if they are growing strawberries? The farmer will be selecting the types of
plants, fertilizers, pesticides. Also looking at machinery, transportation, storage and labor. Weather, water supply and
pestilence are also likely concerns. Ultimately the farmer is also investigating the market price so supply and demand and
timing of the harvest (which will determine the dates to prepare the soil, to plant, to thin out the crop, to nurture and to
harvest) are also concerns.
Let's think about the data available to the farmer, here's a simplified breakdown:

1. Historic weather patterns


2. Plant breeding data and productivity for each Strain


3. Fertilizer specifications


4. Pesticide specifications


5. Soil productivity data


6. Pest cycle data


7. Machinery cost, reliability, fault


8. Water supply data


9. Historic supply and demand data


10. Market spot price and futures data