Data science has created so much hype in the world of IT sectors that from big to small companies all are now hiring employees who have knowledge regarding this subject. Data science is helpful for the employees to get understand about data and then make it in a proper way so that it can be communicated in a better way which is valuable for the companies.
Need for Data Science:
You must all already heard about self-driving cars or autonomous cars so m sure you all are interested a car driving by itself which will take you from home to office or office to home. Here in this whole process a car needs a lot of decisions whether to speed up, stop up, apply the break, take a left turn or right or slow down the car. So all these decisions are basically a part of data science. The study says that the self-driving cars will minimize the accidents and in fact it will root out more than 2 million deaths caused by car accidents annually. Self-driving cars right now is a lot of research and there is lot of testing going on and every automotive company worth its name is investing in self-driving cars. So, in the future most of the cars will be autonomous or self-driving cars.
If we take another example of airlines this is another area where data science contributes in a big major way. Flights get delayed due to weather conditions because the weather is not predicted and the demand of passengers is not probably seen at ahead of time. For all these unique data science than this could be improper planning and some customers might miss some flights than that gain data science and similarly it could be incorrect decisions in selecting the right equipment which leads to unplanned delays and cancellations. So these are some of the challenges in some of the representative industries. So if we use data science properly then most of the problems can be avoided and that will help in reducing the pain for both airlines and also for the passengers. So in same situation we can do better route planning so that there is less cancelations and less frustrated people we can predict by using predictive analysis that there is any delays in fight so that some flights can be rescheduled. Data science is also used to make promotional offers and the last but not the least in deciding which class of planes to purchase for better performance. So these are some examples that how data science can be used in airlines.
And data science is also used and can be benefited in other industry like logistic. So companies like FedEx use data science models to increase their operational efficiency drastically to optimize the roots and cut costs and so on. Before their delivery truck actually sets out for a day, they determine which is the best possible route to ship their items to the customers and based on the various inputs they also predict or come up with the best suited time to delivery and last but not least they also determine what is the best mode for transport their delivery.
So the data science is mainly used for the better decision making, predictive analysis means what will happen next and the pattern discovery or pattern recognition.
What is data Science?
What exactly is data science we understand it by the real life example on a day to day basis we try to make some decisions for example if we want to buy furniture online for our new office here we need a bunch of decisions like from which website or portal we want to purchase. So this is the first decisions we need to take that which website I should use so once we have multiple websites and out of them we have chosen the best website for purchasing. So within this we also check hat is the rating of the website. If ratings are high than it means those websites are liable, quality is probably good then only we prefer that particular website for purchasing. So anything that doesn't satisfy these criteria's we just simply close the website and search the suitable portal for the same.
So, we can answer a lot of questions using data science for example if we book a cab to go from location A to location B. What is the best route that a cab can take to reach in the fastest way or the least amount of time? There could be several factors like there could be traffic, bad road or the bad weather now all these comes in inputs and requires decisions to be taken in the particular situation.
Hence, the data science or data-driven science is about asking the right questions and exploring the data after this we do modeling of the data using various algorithms and finally communicating and visualizing the results thereof.
Business Intelligence versus Data Science:
Business intelligence was one of the initial phases where people started making or wanted to make some sense out of the data. Business intelligence can be used by the enterprise to support a wide range of business decisions ranging from operational to strategic.
Here are the following difference between business intelligence and data science:
Prerequisites for learning Data Science:
There are three essential traits required for data scientists. Following are the three C's:
Curiosity: Which means only when you ask questions, you will have a better understanding of the business problem. The data scientists must ask the right questions for the problem. Only after that he will get the right answer of the problem. This is the very crucial step where a lot of data science projects fail because you may be asking the wrong question and then obviously you will get the wrong answer.
Common Sense: Data scientists need to be creative and also need to come up the ways to use the data that he have and try to solve the business problem on hand. In many cases he didn't have all the data that is required or sometimes the data is incorrect as well. Thus, the data scientists need to come up with the new ways to solve a business problem and to detect priority problems.
Communication Skills: After doing all these analysis if data scientists are unable to communicate the results in the right way the whole exercise will fail so communication is the key trait thus data scientists need to communicate their findings to business teams to act upon the insights.
Hence there are many other prerequisites like
1. Machine Learning as this is the backbone of the data science. It is one of the many ways that data sciences use to find solution to a problem.
2. Mathematical Modeling which is extremely helpful to make fast calculation and predictions from what you know of your data.
3. Statistics is the foundation of data science, to extract knowledge and obtain better results from the data.
4. Computer Programming up to some extent require a programming language preferably Python or R language for data modeling.
5. Databases, the discipline of querying databases teaches you to ask better questions as data scientists.
Tools or Skills Used in Data Science:
Skills: R, Python and Statistics
Tools: SAS, Jupyter, R studio, MATLAB, Excel, RapidMiner.
Skills: ETL, SQL, Hadoop, Apache Spark.
Tools: Informatica or Talend, AWS Redshift
Skills: R, Python Libraries.
Tools: Jupyter, Tableau, Cognos, RAW.
Skills: Algebra, ML Algorithms, Statistics.
Tools: Spark MLib, Mahout, Azure ML Studio.
Data Science Lifecycle with Examples:
Concept Study: Understanding the problem statement, thorough study of the business model is required. Various use cases comes under this study are as follows:
What is the use case?
What are the various specifications?
What is the budget?
What is the end goal?
Data Preparation: It is also known as Data Mining, it is the most important aspect of data science lifecycle for any valuable insights to pop up. In short data should be clean and valuable.
Model Planning: This step involves exploratory data analysis to understand the relation between variables and to see what the data can tell us. Deeper the analysis of dataset leads to the better understanding of the data. Various tools are used in model planning like R, Python, SAS and MATLAB.
Model Building: Model building means, using various analytical tools and techniques, data is transformed with the goal of discovering useful information to build the right model.
Communicate Results: Here key findings are identified and conveyed to the stakeholders.
Operationalize: The last step in the data science life cycle to operationalize. The final reports, codes and technical documents are delivered by the team.
Demand for Data Scientists:
Hence, a data scientist is someone who is better at statistics. Yes, Data Science is on pace and the hottest demanding role right now both form companies and employee's perspective hence it is the highest paid field to get into.