Introduction to Data Science
Data Science is a new powerful approach and discipline which combines aspects of statistics, mathematics, programming and visual techniques to turn data into information in Semi and Fully-Automated fashion. Data Scientists are highly in demand and valued and as a data scientist, it will be your job to analyze the data and offer solutions and ideas for improvement in organizations.
Have you ever been asked by your stakeholders, “Who your existing customers are and how do you retain them in this competitive market?” or “How do you find new business opportunities” or “How do you analyze the existing and historical data to offer some new line of data driven products and services?” or “How do you build a system which is highly available and scalable by analyzing the server logs and understanding the issue patterns” well Data Science is the answer for all the questions.
Python is one of the liveliest used technologies in this domain. It has an extensive library with built-in libraries providing easy access to system functionalities which not only improves accessibility but also Provides standardized solutions for everyday programming challenges. As python is an open-source ecosystem and its incredible libraries are platform independent. Python is the part of the winning formula for productivity, Software Quality and Maintainability for many companies and because of its flat learning curve, it’s one of the leading technologies used globally.
How does the future look good for python as a tool for Data Science? Based on surveys, 46% of the Data Science Jobs will list python as a required skill. Python has surpassed JAVA, as the top language used to introduce the US students to programming and computer science.
The Defense Advanced Research Projects Agency, awarded $3 Million to develop data analytics and data processing libraries for python.
Mckinsey & Company Projects That, by 2018, The demand for Data Scientists who know python may surpass supply by perhaps 60%. Making python the must-know skill for Data Scientists.
Forbes.com mentions that, the demand for python programmers in Big Data related positions increased by 96.9% in the last twelve months.
What is Data Science
- A powerful new approach to make discoveries from data.
- An automated way to analyze enormous amounts of data and extract information.
- A new discipline that combines aspects of statistics, mathematics, programming, and visualization to turn data into information.
Components of Data Science
When we combine expertise and scientific methods with technology, we get Data Science.
Domain Expertise and Scientific Methods
Data Scientists should also be domain experts as they need to have a passion for data and discover the right patterns in them. Traditionally domain experts like scientists collect and analyze the data in a laboratory set up or in a controlled environment then the data is subject to relevant law or mathematics and statistical models to analyze the data and derive the information from it.
For instance, they use models to calculate the mean, median, mode, standard deviation and so on another dataset. It helps them test their hypothesis or create a new one.
Data analysis can be:
- Descriptive: Study a dataset to decipher the details.
- Predictive: Create a model based on existing information to predict outcome and behavior.
- Prescriptive: Suggests action for a given situation using the collected information.
There are modern tools and technologies that have made data processing and analytics faster and efficient. For instance, there are Data Processing Tools for Data wrangling. There are new and flexible programming languages and they are more efficient and easier to use. With the creation of operating systems, they support multiple OS platforms, it’s now easier to integrate systems and process big data. Application design and its extensive software libraries help develop more robust, scalable and data driver applications.
Data Scientists use these technologies to: –
- Build data models and run them in an automated fashion to predict the output efficiently. This is called machine learning which helps provide insights into underlying data.
- They can also use data science technology to manipulate data and extract information from it and use it to build data tools, applications, and services.
Note: – Data Analysis that uses only technology and domain knowledge without mathematical and statistical knowledge often leads to incorrect patterns and wrong interpretations. These can cause serious damage to the business.
What a Data Scientist does in a day
Data scientists start with a question or a business problem then they use data acquisition to collect data sets from the real world. The process of Data wrangling is implemented with the help of data tools and modern technologies that includes data cleansing, data manipulation, data discovery and data pattern identification.
The next step is to create and train models for Machine Learning. Then they design mathematical and statistical models after designing the data model it’s represented as a data visualization technique. The next task is to prepare the Data report, after the report is prepared, they finally create the data products and services.
Basic Skills of a Data Scientist
- Data scientists should ask the right questions in which they need domain expertise, the curiosity to learn and create concepts and the ability to communicate the questions effectively to domain experts.
- Data scientists should think analytically to understand the hidden patterns in the data structure.
- Data scientists should wrangle the data by removing redundant and irrelevant data collected from various sources.
- Data scientists need statistical thinking and the ability to apply mathematical methods are important traits for a data scientist.
- Data should be visualized by graphics and proper storytelling to summarize and communicate the analytical results to the audience.
To get these skills, they should follow a straight road map. It’s important they adapt the required tool techniques like python and its libraries, they should build projects using real world data sets, that include data.gov, NYC open data, gap minder and so on. They should also build data driven applications for their services and data products.
Sources of Big Data
- Data Scientists work with different types of datasets for various purposes. Now that big data is generated every second through different media, the role of data science has become more important.
Every time you record your heartbeat through your phones or watches biometric sensors, post a tweet on a social network, create any blog and website, switch on your phones GPS network, upload or view an image, video or audio or anytime your log into the internet you are constantly generating data about yourself, your preference or your lifestyle. Big data is these and a lot more data that the world is constantly creating. In this age of Internet of Things (IOT), Big Data is a reality and a need.
Big Data is usually referenced by Three V’s.
- Volume: – It refers to the enormous amount of data generated from various sources.
- Velocity: – Huge amounts of data flow at a tremendous speed from different devices, sensors and applications. To deal with it, efficient and timely data processing is required.
- Variety: – Different formats of data: structured (RDBMS (SQL)), Semi-structured (JSON, XML, NoSQL), and unstructured (text, images, videos).
Note: – Big data is a huge collection of data stored on distributed systems/machines popularly referred to as Hadoop clusters. But to be able to use this database, we have to find a way to extract the right information and data pattern from it. That’s where data science comes in. Data science helps extract information from the Data and build information-driven enterprises.
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