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What is Data Science?
Table of Contents
Our little planet is now becoming a Digital planet and by 2020 we will have 40 times more bytes than there are stars in the universe. Over 90 percent of the data sitting and floating in all possible devices and systems in the world today was simply generated in the last two years alone. These humongous volumes of data – now called Big Data - can mean a lot to businesses and can help gain insights and trends about their users and user behavior. The massive volume of data in both structured and unstructured formats is difficult to process through traditional database modeling and tools. Hence there is a need to use scientific methods, algorithms, and tools to analyze and to make sense out of Big Data and the need for Data Science and Data Analytics.
What is Data Science?
Data Science is all about creativity. The goal of Data Science is to get insights and trends by analyzing diverse data sets which give a competitive advantage to businesses. Data science is a combination of Mathematics, Statistics, and software with domain expertise in the applied business environment. Another buzz word commonly misinterpreted with Data Science is Business Intelligence (BI). BI is primarily concerned with data analysis and reporting but does not include predictive modeling, so BI can be considered a subset of Data Science. Building predictive models are one of the most important activities in Data Science. Other processes in Data Science are Business Analytics, Data Analytics, Data Mining, and Predictive Analytics. Data Science is also concerned with Data Visualization and presenting results in an understandable format to users.
Why do you need Data Science?
Companies need to use data to run and grow their business. The fundamental goal of data science is to help companies make quick and better business decisions, which can enable them to gain better market share and industry leadership. Besides, it can help them take tactical approaches to be competitive and sustain in difficult situations. Organizations of all sizes are adapting to a data-driven approach with advanced data analytics being the fulcrum of change.
Here are some examples of why organizations use Data Science-
- Netflix analyzes watching patterns to understand what drives user interest and uses the information to make decisions on their next production series.
- Target: On the other hand, identifies what are major customer segments and the unique shopping behavior of customers within those segments. This helps them to guide different market audiences.
- Proctor & Gamble utilizes time series models to more clearly understand future demand, which helps them plan for production levels more optimally.
Life cycle of Data Science
There are five stages in the life cycle of any Data Science project.
Capture: How is the Data captured?
- Data Acquisition: Data acquisition, or data collection, is the very first step in any data science project. The complete set of required data is never found in one place as it is distributed across line-of-business (LOB) applications and systems.
- Data Entry: Data can be created with new data values for the enterprise by human operators or devices. It is a time-consuming process but it is needed in certain cases.
- Signal Reception: Another source to capture data is through data devices, typically important in control systems, but now more important for information systems with the invention of the “Internet of Things.”
- Data Extraction: Data extraction is a process that involves the retrieval of data from various sources. They could be web servers, databases, logs, and online repositories
Maintain: What happens to the captured data?
- Data Warehousing: Data warehousing emphasizes the capture and storing of data from different sources for access and analysis. It is a repository of all the data collected by the organization
- Data Cleansing: Data cleansing or data cleaning is the process of identifying and removing (or correcting) inaccurate records from a dataset, table, or database and refers to recognizing unfinished, unreliable, inaccurate, missing values, duplicate values or non-relevant parts of the data and then restoring, re-modeling, or removing the dirty or crude data
- Data staging: An intermediate storage area is used for data processing during the extract, transform and load (ETL) process. The data staging area sits between the data source(s) and the data target(s), which are often data warehouses, data marts, or other data repositories.
- Data Processing: During this stage, the data is processed for interpretation. Processing is done using machine learning algorithms, though the process itself may vary slightly depending on the source of data being processed (data lakes, social networks, connected devices, etc.) and its intended use (examining advertising patterns, a medical diagnosis from connected devices, determining customer needs, etc.).
- Data Architecture: is a framework built to transfer data from one location to another, efficiently. It is full of models and rules that govern what data is to be collected. It also controls how the collected data should be stored, arranged, integrated and put to use in data systems of an organization. In short, data architecture sets standards for all the data systems as a vision or a model of the functioning of the data systems interactions.
Process: What do you do with the clean data?
Now that the data is collected and stored, we can move on to the next step of data processing.
- Data Mining: Data Mining is about finding the trends in a data set. These trends are used to identify future patterns. It often includes analyzing the vast amount of historical data which was previously disregarded.
- Clustering and Classification: Clustering is the task of dividing or classifying the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. In simple words, the aim is to segregate groups with similar traits and assign them into clusters
- Data Modeling: Data modeling is the process of producing a descriptive diagram of relationships between various types of information that are to be stored in a database.
- Data Summarization: Summarization is a key data mining concept which involves techniques for finding a compact description of a dataset. Data Summarization is a simple term for a short conclusion after an analysis of a big dataset. Data summarization has great importance in data mining.
Analyze: Now that you have classified your data and modeled it, it is time to analyze your data. How do you analyze your data?
- Exploratory/Confirmatory: Examining data often falls into two phases: exploratory and confirmatory analysis. The two operate most effectively side-by-side. Exploratory data analysis is sometimes compared to detective work: it is the process of gathering evidence. Confirmatory data analysis is comparable to a court trial, it is the process of evaluating evidence.
- Predictive Analysis: Predictive analytics is the process of using data analytics to make predictions based on data. This process uses data along with analysis, statistics, and machine learning techniques to create a predictive model for forecasting future events. Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers. Many companies use predictive models to forecast inventory and manage resources
- Regression: Regression analysis is a form of predictive modeling technique which investigates the relationship between a dependent (target) and independent variable (s) (predictor). This technique is used for forecasting, time series modeling and finding the causal effect relationship between the variables.
- Text mining: This refers to using data mining techniques for discovering useful patterns from texts. The text mining the data is unstructured. Information and relations are hidden into the language structure and not explicit as in data mining.
- Qualitative analysis: When data is not in the form of numbers it is even tougher to understand it. Qualitative data is defined as the data that approximates and characterizes. Qualitative data can be observed and recorded. This data type is non-numerical in nature. This type of data is collected through methods of observations, one-to-one interview, conducting focus groups and similar methods.
Qualitative data analysis is simply the process of examining qualitative data to derive an explanation for a specific phenomenon. Qualitative data analysis gives you an understanding of your research objective by revealing patterns and themes in your data. Data scientists and their models can benefit greatly from qualitative methods.
Communicate: How do you display your results?
- Data Reporting: Reports communicate information which has been compiled as a result of research and analysis of data and of issues. Reports can cover a wide range of topics but usually focus on transmitting information with a clear purpose, to a specific audience. Good reports are documents that are accurate, objective and complete.
- Data visualization: Data visualization is a graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.
- Business Intelligence: BI is an integral part of Data Science. To do a predictive analysis first we need to know what went wrong. Hence BI is a simpler version of data science
- Decision making: The importance of data in decision lies in consistency and continual growth. It enables companies to create new business opportunities, generate more revenue, predict future trends, optimize current operational efforts, and produce actionable insights.
All of the five stages require different techniques, programs and, in some cases, skill sets.
Applications of Data Science
Data science has proved useful in about every industry.
Data Science is heavily used in the banking sector. Following are the key application areas where data science is used in the banking sector
- Risk modeling
- Fraud detection
- Customer Lifetime value
- Customer segmentation
- Real-time Predictive analysis
Data Science is used in the healthcare industry in many ways.
- Patient Prediction: A Forbes article details how four hospitals which are part of the Assistance Publique-Hôpitaux de Paris have been using data from a variety of sources to come up with daily and hourly predictions of how many patients are expected to be at each hospital. This helps them is staffing resources.
- Electronic Health Records: Every patient has his digital record which includes demographics, medical history, allergies, laboratory test results, etc. Records are shared via secure information systems and are available for providers from both the public and private sector. Every record is comprised of one modifiable file, which means that doctors can implement changes over time with no paperwork and no danger of data replication.
- EHRs can also trigger warnings and reminders when a patient should get a new lab test or track prescriptions to see if a patient has been following doctors’ orders.
- Patient tracking: Patients are directly involved in the monitoring of their own health, and incentives from health insurances can push them to lead a healthy lifestyle (e.g.: giving money back to people using smartwatches). They have smart devices that track every step they take.
- Predictive analytics: The goal of healthcare business intelligence is to help doctors make data-driven decisions within seconds and improve patients’ treatment. This is particularly useful in case of patients with complex medical histories, suffering from multiple conditions.
- Big data medical imaging: Big data analytics for healthcare could change the way images are read algorithms developed analyzing hundreds of thousands of images could identify specific patterns in the pixels and convert it into a number to help the physician with the diagnosis. They even go further, saying that it could be possible that radiologists will no longer need to look at the images, but instead analyze the outcomes of the algorithms that will inevitably study and remember more images than they could in a lifetime.
Manufacturing: Here are 8 of the most popular types of data science used in manufacturing and how they affect productivity, minimize risk, and increase profit. Data-driven manufacturers will be leveraging data science for:
- Performance, quality assurance and defect tracking
- Predictive and conditional maintenance
- Demand and throughput forecasting
- Supply chain and supplier relations
- Global market pricing
- Automation and the design of new facilities
- New processes and materials for product development and production techniques
- Sustainability and greater energy efficiency
Transport: Another important application of data science is transport. In the transportation sector, Data Science is actively making its mark in making safer driving environments for the drivers. It also plays a key role in optimizing vehicle performance and adding greater autonomy to the drivers. Furthermore, in the transport sector, Data Science has actively increased its manifold with the introduction of self-driving cars.
Also, various transportation companies like Uber is using data science for price optimization and providing better experiences to their customers. Using powerful predictive tools, they accurately predict the price based on parameters like a weather pattern, availability of transport, customers, etc.
eCommerce: You can explore four ways where online retailers can leverage data science to achieve business value.
- Identify Your Most Valuable Customers
- Discover Which Customers Are Likely to Churn
- Drive Sales with Intelligent Product Recommendations
- Automatically Extract Useful Information from Reviews
Data Science tools
Here are some popular Data Science tools used today.
- SAS: It is one of those data science tools which are specifically designed for statistical operations. SAS is a closed source proprietary software that is used by large organizations to analyze data. SAS uses base SAS programming language which for performing statistical modeling.
- Apache Spark: Apache Spark or simply Spark is an all-powerful analytics engine and it is the most used Data Science tool. Spark is specifically designed to handle batch processing and Stream Processing. It comes with many APIs that facilitate Data Scientists to make repeated access to data for Machine Learning, Storage in SQL, etc. It is an improvement over Hadoop and can perform 100 times faster than MapReduce. Spark has many Machine Learning APIs that can help Data Scientists to make powerful predictions with the given data.
- MATLAB: In Data Science, MATLAB is used for simulating neural networks and fuzzy logic. Using the MATLAB graphics library, you can create powerful visualizations. MATLAB is also used in image and signal processing. This makes it a very versatile tool for Data Scientists as they can tackle all the problems, from data cleaning and analysis to more advanced Deep Learning algorithms.
- TensorFlow: TensorFlow has become a standard tool for Machine Learning. It is widely used for advanced machine learning algorithms like Deep Learning. Developers named TensorFlow after Tensors which are multidimensional arrays. It is an open-source and ever-evolving toolkit which is known for its performance and high computational abilities.
- NLTK: Natural Language Processing has emerged as the most popular field in Data Science. It deals with the development of statistical models that help computers understand human language. These statistical models are part of Machine Learning and through several of its algorithms, are able to assist computers in understanding natural language. Python language comes with a collection of libraries called Natural Language Toolkit (NLTK) developed for this particular purpose.
Skills needed to become a Data Scientist
To become a data scientist, you could earn a Bachelor’s degree in Computer Science, Social Science, Physical Science, and Statistics. The most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%). A degree in any of these courses will give you the skills you need to process and analyze big data.
Most data scientists have a Master's degree or Ph.D. and they also undertake online training to learn a special skill like how to use Hadoop or Big Data querying. Therefore, you can enroll for a master's degree program in the field of Data Science, Mathematics, Astrophysics or any other related field. The skills you have learned during your degree program will enable you to easily transition to data science.
Some technical skills that could be acquired in the process of becoming a Data Scientist are R, Python, Apache Spark, Hadoop platform, SQL/Database, Machine Learning, and AI and Data Visualization.
You could also go through the online tutorials in https://hackr.io/data-science to know all about Data Science.
Demand for Data Science: According to LinkedIn’s 2017 U.S. Emerging Jobs Report, the numbers of data scientists have grown over 650% since 2012. Yet there are very few people exploiting the opportunities in this field.
IBM predicts demand for data scientists will soar 28% by 2020. Machine learning and data science are generating more jobs than there are experts to fill them, which is why these two fields are the fastest-growing tech employment areas today.
In the coming years, there will be a need for 140,000 to 190,000 practicing data scientists. Data scientists in the US make an average of $144,000 a year. There is a need for both specialists and resources who generally understand data, making it a compelling career choice.
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