What is data science? Why it is important? What is the difference between Artificial Intelligence, Data Science, and Machine Learning and Deep Learning? Data Science is an amalgamation of many other fields like mathematics, technology and domain. It has its own concepts, process and tools. It’s really tough to know each and everything related to the subject unless you have really worked on complex data science problems in the industry for a couple of years. You can learn the data science concepts like types of learning and when to use which kind of learning algorithms?
What is data? How to define data from different viewpoints? What are tools in Data Technology & what to use when? How to apply Data Governance & build Data Strategy? And finally, how every aspect mentioned above fits together in business & technology ecosystem? Data at the fingertips of almost every professional can be truly transformational. So building Data-Driven Culture is the most challenging yet the most rewarding aspect. And to create a Data-Driven Culture, first and foremost thing is to make every employee, every professional data literate.
When working as a data scientist, nobody tells us what’s the ML/DS problem that we need to solve or the prediction that we need to make, we need to understand the business process first and identify the problem and qualify the problem suitable for a ML/DS solution. Then we need to collect underlying data being used by the business and assess whether it’s enough & useful to convert this business problem to ML/DS problem. This article covers these aspects to give you a holistic view of Data Science Framework built on CRISP/DM methodology.
Data-driven culture is about setting the foundation for the habits and processes around the use of data. Data-driven companies establish processes and operations to make it easy for employees to acquire the required information, but are also transparent about data access restrictions and governance methods. So, why is it important to build a data-driven culture in your organization? The data can only take an organization so far. The real drivers are the people and hence building the culture around data is important. An organization can work upon to build data-driven culture.
Data analysis helps to make sense of our data otherwise they will remain a pile of unwieldy information; perhaps a pile of figures. This is essential because analytics assist humans in making decisions. Therefore, conducting the analysis to produce the best results for the decisions to be made is an important part of the process, as is appropriately presenting the results. Its an internal organisational function performed by Data Analysts that is more than merely presenting numbers and figures to management. It requires a much more in-depth approach to recording, analysing and dissecting data, and presenting the findings in an easily-digestible format.
Existing data architectures are at the breaking point with a large amount of data, velocity of data ingestion, and variety of data they need to process and store. Industry analysts are predicting that up to 80% of the new data will be semi-structured and unstructured. Modern Data Architecture addresses the business demands for speed and agility by enabling organizations to quickly find and unify their data across hybrid data storage technologies. The Modern Data Architecture stores data as is; it does not require pre-modeling. It handles the volume, velocity, and variety of big data.
Data Modeling refers to the practice of documenting software and business system design. A Data model is used to document, define, organize, and show how the data structures within a given database, architecture, application, or platform are connected, stored, accessed, and processed within the given system and between other systems. Data modeling is required to manage data as a resource, integrate existing Information systems, design databases and repositories, and understanding the business. Using proper modeling and reporting, you can spot business trends, spending patterns, and make predictions that will help your business navigate challenges and opportunities.
Data has become important for everyone like never before, because it makes us to take informed decisions, improve operations. We can only improve things & activities which we can measure, and when we measure anything, it is described in a form of data. If you want to leverage and operationalize data proactively, you need to invest in your underlying data architecture and compile the information map for your organization. Solid information architecture will also set up your foundation for a data governance program. You have to know what the data is and assign business meaning to it, with the proper terminology.
Statistical learning is a framework for understanding data based on statistics, which can be classified as supervised or unsupervised. Supervised statistical learning involves building a statistical model for predicting, or estimating, an output based on one or more inputs, while in unsupervised statistical learning, there are inputs but no supervising output; but we can learn relationships and structure from such data. One of the simple way to understand statistical learning is to determine association between predictors) & response and developing a accurate model that can predict response variable on basis of predictor variables.
While neural networks are responsible for recent breakthroughs in problems like computer vision, machine translation and time series prediction — they can also combine with reinforcement learning algorithms to create something astounding like AlphaGo. Deep reinforcement learning (DRL) is a machine learning method that extends reinforcement learning approach using deep learning techniques. Recent advances in Deep learning area has also fueled in Reinforcement learning as it doesn’t need hand-engineered features any more because of this ability. After appropriate many backpropagations, deep neural network knows which information is important to do the task.