Data science combines multiple fields such as mathematics, data analysis and scientific methods so as to obtain values from a given data. Data is collected from smartphones, the web, consumers, sensors and from other resources. With the modern technology that is growing each data, a large volume of data is exploited. With the increase in data science tools, a variety of projects can be generated.
Various data scientists are doing various researches so as to determine how the climate impacts food produce. With the increase in population and diet needs, food production has to increase in order to meet these needs. With an increase in industrialization comes along climate change. Climate change such as a change in temperature affects food production. Through machine learning, statistics in these changes acts as inputs so as to predict food yield produce.
What is Data Science?
Data Science is an inter-disciplinary field that uses scientific computation methods, statistical methods, mathematics and various data analysis, to generate useful insights.
“The world is one big data problem.” – by Andrew McAfee, co-director of the MIT Initiative
Why Data Science is Important?
It’s very much important to know the importance of data science in the 21 st century. “Data is the new oil”, tons and tons of data are created every second. It’s very much important to analyse the data and provide the right insights from the data. These are some important points that prove the importance of why data science is important :
- Data helps in getting best communication between business and with customers. Data Scientist Analyses the data and provide the best insights to the business to create a data product. The best example is about amazon, you can see recommended items whenever you search on any product.
- Analysing and Checking the efficiency of the present system: A data scientist can provide insights regarding the present working infrastructure from the wide variety of data available with them. If the present architecture is not providing many sales to the enterprises, they can change or modify to get better results.
- Provides best marketing insight: Suppose you want to sell a new pen, So lots of questions appear in your mind, such as What is the size of the pen? How much prize? The look and feel of the pen? How much profit can be generated per sale?.. etc. It’s really hard to get to a conclusion by ourself. In these situations the data scientist plays the major role, he analyses the product and helps the company to get the maximum profit out of the sale. by analysing various factors like the demography, age groups, customers trend of buying, the colour of pen, selling price and lot more..
How Data Science works?
Data Analysis is not at all a fast step, it needs great patience and skills. Without having proper knowledge you cannot bring out anything good whereas a small mistake can create huge loss to the company as well as to you.
Everything comes from a question. A Data Scientist should be a good questionnaire. Let’s see the steps involved in analysing any data:
- Understand the Business and Frame the questions
- Collect raw data
- Transform your data
- Clean your data
- Exploring cleaned data
- Proper Modeling of data
- Visualization of Data
- Communicating the result
1.Understand the Business and Frame the questions
Know your business properly first and frame the questions that have to be answered. Yes, you may be wondering what questions should be framed. The questions that customers wants the answer. If you are able to find solutions and able to solve it in better and easy way, there comes success. This is the key point that a data scientist follows with him. So ask yourself the questions that has to be answers from plate of data you are going to get.
2.Collect raw data
One of the difficult job in data science process. It’s not at all easy to collect data even though tons of data are generated each second. If you want to analyse the internal trend of the business it is very easy to get data, since the company have all the data with them, if moving towards to solve new problem, the real task comes in front of you. So collect your raw data from various sources.
3.Transform your data
The raw data you collected will be of various format. So it is highly necessary to convert these to excel or word documents as of your requirements. It’s same as extracting petrol from petrol mining. You get the first product with lots of impurities. Then it will be filtered and finally transformed as the useful product.
4.Clean your data
This is probably the most time consuming step. You can see lots of missing values, outliers (the values differ from all other data from the dataset), drop unimportant columns from your dataset. So while dealing with this process make sure that you follow these steps to get best data set to work on.
After getting the complete dataset, as a data scientist, he should be a keen observer. Its very important to find various patterns from the data. This is a very important thing while dealing with different data. If you fail to find, the hidden patterns then obivously, you wont get the best reult.
6.Proper Modeling of data
Now its time to fit the data to a suitable statistic model, to better help the machine to learn and deliver the best insight and prediction. Its really hard to get a perfect model, that is 100% perfect. All you have to do is fit the data to various model and choose the best model thats provides you the best output.
7.Visualization of Data
Visualization is one of the best effective means of communication and understand the results. As a data scientist, you have to visualize the data that helps others to understand what your findings is about. There are many visualization tools available such as:
- Google Charts
- HubSpot etc.
8.Communicating the result
Communication of the results/insights to common people, or to other business people are important. Normal people who are not on this field won’t be knowing how to analyse the results by looking on the data model. So proper visualization and communication will help the people to understand what you found from the data.