“The world is one big data problem”
Data Science is an indispensable element for growth of the digital world which requires professionals with strong skills and adaptability for staying on the forefront of technology. Data can be used simultaneously by many people, it is estimated that less than 5%of the 2.5 quintillion bytes of data produced every day is used effectively.
This article sheds light on the sense of Data Science and its lifecycle, from Data Discovery to the Communication stage. It introduces Data Science, Scope of Data science in projects, Data Science components and lifecycle, Tools used for Data science, Understanding the relationship between Data, Data science and Machine learning, Industrial applications of Data Science.
Elucidating general acronyms- AI,DS,ML,PG
AI – Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to mimic the traits associated with a human mind such as learning and problem-solving.It has the ability to rationalize and take actions that have the best chance of achieving a specific goal.
DS – Data science(DS) is the study of data. It formulates methods of recording, storing, and analysing data to effectively extract useful information from them. The goal is to gain insights and knowledge from any type of data. It is repeatable, replicable and logical.
ML – Machine learning(ML) is the study of computer algorithms that improve automatically with time. The algorithms build a mathematical model based on sample data, in order to make predictions without being explicitly programmed to do so. The main goal is to allow machines learn automatically without human assistance and adjust accordingly.
PG – Programming(PG) is applying logic to the data that can be used. It performs specified computing operations and functionality by implementing logic to it. It occurs in various languages (C++,java,python,R etc.), which differ by application, domain and programming model.
Correlation among the acronyms:
These technologies are correlated to each other,ML is a subset of AI. ML is a part of data science that draws features from algorithms and statistics to work on the data extracted from multiple resources. The algorithms obtained from machine learning to develop a solution are merged together by data science, and during the process, lots of ideas from traditional domain expertise, statistics and mathematics are utilized.
Why do we need data science?
Data is used by organizations to run and grow their everyday business. The fundamental goal is to assist companies to make quicker and better decisions, which can take them to the top of their market. The crucial aspects in which data science assists are:
Better decision making: Organizations are now able to make better-informed choices than in the past by shaping and filtering the data they have collected; example, airlines are able to make better decisions regarding route planning and equipment usage due to available data which minimizes unwanted delays.
Predictive analyses: Organizations use predictive analyzes to sift through current and historical data to detect trends and formulate predictions of the future based on supplied parameters, for example, weather prediction is done based on sample data collected from past which helps in saving lives.
Pattern discovery: It is the ability to detect characteristics of data that yield information about a given system or data set. It make use of pattern recognition algorithms to isolate statistically probable movements of time series data into the future. For example, booking companies use customer booking patterns and formulate promotional offers.
Data science Life-cycle
The life cycle is the sequence of stages that a particular unit of data goes through from its initial data discovery to scrubbing the data at the end of its useful life.About 60–70% of our time is spent just on gathering and cleaning the data. Although specifics vary, data management experts often identify the following as the major stages in the data life cycle:
Obtain Data: The very first step of a data science project is straightforward. The data is obtained from available data sources. In this step, we query databases, using technical skills like MySQL to process the data. We may receive data in any formats, python or R has specific packages that can read data from the data sources directly into the data science programs.
Data Clean up: Real data is messy thus it has to be filtered and made usable. In this process the files are converted into one standardized format across all data in order to process and analyze it. It also includes the task of extracting and replacing values. This is the time to replace the missing data accordingly, transform the variables(data formats, String to integer). This process is for organizing and tidying up the data, removing what is no longer needed, replacing what is missing and standardizing the format across all the data collected.
Data exploration: Once data is ready to be used, we will need to explore the data. First of all, you will need to inspect the data and its properties like numerical data, categorical data, ordinal and nominal data etc. require different treatments. Then, we need to compute descriptive statistics to extract features and test significant variables. Testing significant variables often is done with correlation. We utilize data visualization to help us to identify significant patterns and trends in our data.
Model Data: One of the first things that has to be done in modelling data is to reduce the dimensionality of data. PCA (Principal component Analysis) is one of the methods for this. A transparent machine learning model is made, some features are generated so that the data visualization prepared for people can be more digestible. The input data is split randomly for modelling into a training data set and a test data set. We build the models by using the training data set and evaluates the data sets. A series of machine learning algorithms along with the various associated parameters that are geared toward answering the problem is used. The best solution is determined by comparing the success metrics between alternative methods.
Interpreting Data: Interpretation involves constructing a logical scientific argument that explains the data. They are inferences about what the data mean, based on a foundation of scientific knowledge and individual expertise.The predictive power of a model lies in its ability to generalize. Actionable insight is a key outcome that shows how data science can bring about predictive analytics and later on prescriptive analytics which is used to repeat a positive result, or prevent a negative outcome. In this process, the data should trigger actions in audience, it means that communication must be efficient.
Business intelligence Vs Data Science:
The following table differentiates between business intelligence & data science based on features;
|Features||Business Intelligence||Data Science|
|Data sources||Structured||Both structured and unstructured|
|Approach||Statistics and visualization||Statistics, ML, NLP|
|Focus||Past and Present||Present and Future|
|Tools||Pentaho, Microsoft, Qlikview, R||Rapidminer, Bigml, R, Weka|
Data Science use-cases
The data science finds its application in a wide range of fields. It is used by the travel industries to predict weather and to generate promotional offers for booking. It is used by insurance companies to improve fraud and risk detection. It is an important aspect of marketing and sales where it predicts the lifetime value of the customer and also predicts the changes in market. Additionally, it used in healthcare to predict medicine effectiveness in automation sector self-driven cars,planes. Thus, it forms the core of every sector.
To conclude, the importance of data science is undeniable, it is the core of any scientific investigation. Data, programming, statistics and probability are its major component. The scientists collect and record data, find patterns in data, explain those patterns, and share their research with the larger scientific community.It is the time when renowned organizations are embracing it to boost efficiency and productivity. Thus, it becomes important for professionals to keep up with the latest trend.