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MISSION
MISSION
Establish a center of technology knowledge and a "go to organization" to service data creators, providers, managers, curators, and users of the State and the Nation.
Establish a center of technology knowledge and a "go to organization" to service data creators, providers, managers, curators, and users of the State and the Nation.
VISION
VISION
The vision of the Data Science is to create a first-class academic department that trains the next generation of students as data scientists
The vision of the Data Science is to create a first-class academic department that trains the next generation of students as data scientists
WHERE IT ALL STARTED...!?
WHERE IT ALL STARTED...!?
The term “data science” — and the practice itself — has evolved over the years. In recent years, its popularity has grown considerably due to innovations in data collection, technology, and mass production of data worldwide. Gone are the days when those who worked with data had to rely on expensive programs and mainframes. Part of evolution of data science was the inclusion for internet of things
The Future of Data Science
The Future of Data Science
Seeing how much of our world is currently powered by data and data science, we can reasonably ask, Where do we go from here? What does the future of data science hold? While it’s difficult to know exactly what the hallmark breakthroughs of the future will be, all signs seem to indicate the critical importance of machine learning. Data scientists are searching for ways to use machine learning to produce more intelligent and autonomous AI.
DATA SCIENCE FLOW CHART
DATA SCIENCE FLOW CHART
Obtaining the Data:One first needs to identify what kind of data needs to be analyzed, and this data needs to be exported to an excel or a CSV file.
Scrubbing or cleaning the data:It is essential because before you can read the data, you must ensure it is in a perfectly readable state, without any mistakes, with no missing or wrong values.
Exploratory Data Analytics:Analyzing the data is done by visualizing the data in various ways and identifying patterns to spot anything out of the ordinary. To analyze the data, you must have excellent attention to detail to identify if anything is out of place.
Modeling or Machine Learning:The data engineer or scientist writes down instructions for the Machine Learning algorithm to follow based on the Data that has to be analyzed. The algorithm iteratively uses these instructions to come up with the correct output.
Interpreting or “Data storytelling’:In this step, you uncover your findings and present them to the organization. The most critical skill in this would be your ability to explain your results. Hence the term “storytelling.”
Who is a Data Scientist?
Who is a Data Scientist?
A Data Scientist identifies important questions, collects relevant data from various sources, stores and organizes data, decipher helpful information, and finally translates it into business solutions and communicates the findings to affect the business positively.
Apart from building complex quantitative algorithms and synthesizing a large volume of information, data scientists are also experienced in communication and leadership skills, which are necessary to drive measurable and tangible results to various business stakeholders.
Data Science Applications
Data Science Applications
Some of the popular applications of data science are:
Product Recommendation
The product recommendation technique has become one of the most popular techniques to influence customers to buy similar products. For example, a salesperson of Big Bazaar is trying to increase the store’s sales by bundling the products together and giving discounts. So he bundled shampoo and conditioner together and gave a discount on them. Furthermore, customers will buy them together for a discounted price.
Future Forecasting
Predictive analysis is one of the most used domains in data science. We are all aware of Weather forecasting or future forecasting based on various types of data that are collected from various sources.
Fraud and Risk Detection
Since online transactions are booming, losing your personal data is possible. So one of the most intellectual applications of data science is Fraud and Risk Detection. For example, Credit card fraud detection depends on the amount, merchant, location, time, and other variables. If any of them looks unnatural, the transaction will be automatically canceled,, and it will block your card for 24 hours or more.
Self Driving Car
The self-driving car is one of the most successful inventions in today’s world. We train our car to make decisions independently based on the previous data. In this process, we can penalize our model if it does not perform well. The car becomes more intelligent with time when it starts learning through all the real-time experiences.
Image Recognition
When you want to recognize some images, data science can detect the object and classify it. The most famous example of image recognition is face recognition – If you tell your smartphone to unblock it, it will scan your face. So first, the system will detect the face, then classify your face as a human face, and after that, it will decide if the phone belongs to the actual owner or not.
Speech to text Convert
Speech recognition is a process of understanding natural language by the computer. We are quite familiar with virtual assistants like Siri, Alexa, and Google Assistant.
Jobs in Data Science
Jobs in Data Science
As mentioned above, there are a variety of different jobs and roles under the data science umbrella to choose from. Here are different job profiles that can eventually lead you to become a data scientist.
Data Analyst
Data Engineers
Database Administrator
Machine Learning Engineer
Data Architect
Statistician
Business Analyst
Data and Analytics Manager
Data Scientist
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