What are the steps to follow in Data Science Projects?
Anyone knows how difficult it is to give a solid explanation of the process of a project on Data Science . There is a lot of information on the internet and various courses that offer you a clear picture. Have you really understood everything about it? Building a Data Science project from scratch is a great job where you need several tools. It faces the problems of the process in order to find deeper information. I continued reading this article by Data Science training institute in Bangalore on the steps to follow in Data Science projects .
The OSEMN model encompasses an acronym where each letter touches a Data Science procedure . This list of tasks should be familiar to you as a data scientist. However, it is understandable that you fail to reach an expert level due to its complexity and depth. OSEMN is a model to treat data problems using machine learning tools. Several authors point out that data hacking works with the letter O and S, while machine learning with E and M. Data Science works with the combination of both worlds.
The O comes from "get data from available sites." Use techniques and tools such as MySQL to process data, as well as receive data in formats such as Microsoft Excel. Other options for collecting information are:
In order to perform very well in obtaining data you must have several skills. It is a requirement to know how to handle MySQL, PostgreSQL or MongoDB (if you are using an unstructured data set). On the other hand, if what you want is to work with projects that demand more data, then I learned from distributed storage with Apache Hadoop, Spark or Flink.
When you have all the data collected you must filter to eliminate what does not work. In this process you will transform the formats into others and generate a general format to consolidate the information.
For your information, when you handle blocked files you will encounter features such as user demographics, the time of entry to their websites, among other things. It should be noted that data cleaning includes the task of extracting and replacing certain values. If you realize that data sets are missing you have to replace them immediately.
Before using the data in AI and Machine Learning you must analyze them . In a corporate or commercial environment it will be your boss who will give you a set of data to which you have to make sense.
So it will be your job to translate the unknowns at the commercial level and translate them into the scope of Data Science . You can start inspecting data and its characteristics. These data could be numerical, ordinal, nominal, among others. Later you will calculate descriptive statistics to extract characteristics and test significant variables. Take into account that some variables are related, but it does not mean that it is a coincidence.
Finally, you will look at the data to identify important patterns. Lean on simple or bar charts and thus you will better understand the important parts in the analysis.
This is the stage where it occurs. Although for the special effect to really happen you had to take care of each of the previous stages. Take into account that to model data you must work on a compact conglomerate.
Not all features of your data collection are necessary to predict your model. So select the ones that are relevant to create the results and you can rely on several procedures. A good idea is to program the models to classify and differentiate the emails you received. You could use tags like "Inbox" and "Spam" through logistic regressions.
Another thing you can do is forecast values using linear regressions. You can also use modeling to group data and understand the logic behind it.
This is the final stage and the one that gives meaning to the rest of the steps in the Data Science process . A model has great predictive power that is able to generalize about various scenarios and their future. In this step you deliver results to commercial questions you asked yourself when you started the project; and some other that has arisen along the way.
Data Science performs predictive and prescriptive analyzes so that you can repeat the positive results in the future; In addition to avoiding negatives. So when you go to present your findings to the company you work for, try to make it understandable for everyone. Present a clear and practical story for an audience that lacks a technical background. Communicate the message so that it triggers actions on the work team .
Data Science is a very powerful tool that in good hands has significant reach in the labor field where you are. Be part of our team at Data Science training institute in Bangalore and be an advanced professional in the field. Contact us now, show us your curriculum and portfolio to be part of the team.
The OSEMN model encompasses an acronym where each letter touches a Data Science procedure . This list of tasks should be familiar to you as a data scientist. However, it is understandable that you fail to reach an expert level due to its complexity and depth. OSEMN is a model to treat data problems using machine learning tools. Several authors point out that data hacking works with the letter O and S, while machine learning with E and M. Data Science works with the combination of both worlds.
Get information
The O comes from "get data from available sites." Use techniques and tools such as MySQL to process data, as well as receive data in formats such as Microsoft Excel. Other options for collecting information are:
- In the case that you use Python or R, do not hesitate to install with the packages that read information directly in your Data Science programs.
- There are databases such as PostgreSQL, Oracle, including non-relational ones such as (NoSQL) such as MongoDB.
- By scrapping you can extract information from websites with programs such as Beautiful Soup.
- Connect to web APIs. Sites like Facebook and Twitter allow users to connect to their web servers and access their data. You just have to access your API and start tracking.
- Use the traditional way of obtaining data. Get the data from files, download them from Kaggle or use CSV, or TSV. Take into account that you must use a programming language like Python.
In order to perform very well in obtaining data you must have several skills. It is a requirement to know how to handle MySQL, PostgreSQL or MongoDB (if you are using an unstructured data set). On the other hand, if what you want is to work with projects that demand more data, then I learned from distributed storage with Apache Hadoop, Spark or Flink.
Clean the data
When you have all the data collected you must filter to eliminate what does not work. In this process you will transform the formats into others and generate a general format to consolidate the information.
For your information, when you handle blocked files you will encounter features such as user demographics, the time of entry to their websites, among other things. It should be noted that data cleaning includes the task of extracting and replacing certain values. If you realize that data sets are missing you have to replace them immediately.
Data exploration
Before using the data in AI and Machine Learning you must analyze them . In a corporate or commercial environment it will be your boss who will give you a set of data to which you have to make sense.
So it will be your job to translate the unknowns at the commercial level and translate them into the scope of Data Science . You can start inspecting data and its characteristics. These data could be numerical, ordinal, nominal, among others. Later you will calculate descriptive statistics to extract characteristics and test significant variables. Take into account that some variables are related, but it does not mean that it is a coincidence.
Finally, you will look at the data to identify important patterns. Lean on simple or bar charts and thus you will better understand the important parts in the analysis.
Data models
This is the stage where it occurs. Although for the special effect to really happen you had to take care of each of the previous stages. Take into account that to model data you must work on a compact conglomerate.
Not all features of your data collection are necessary to predict your model. So select the ones that are relevant to create the results and you can rely on several procedures. A good idea is to program the models to classify and differentiate the emails you received. You could use tags like "Inbox" and "Spam" through logistic regressions.
Another thing you can do is forecast values using linear regressions. You can also use modeling to group data and understand the logic behind it.
Interpret data
This is the final stage and the one that gives meaning to the rest of the steps in the Data Science process . A model has great predictive power that is able to generalize about various scenarios and their future. In this step you deliver results to commercial questions you asked yourself when you started the project; and some other that has arisen along the way.
Data Science performs predictive and prescriptive analyzes so that you can repeat the positive results in the future; In addition to avoiding negatives. So when you go to present your findings to the company you work for, try to make it understandable for everyone. Present a clear and practical story for an audience that lacks a technical background. Communicate the message so that it triggers actions on the work team .
Conclusion
Data Science is a very powerful tool that in good hands has significant reach in the labor field where you are. Be part of our team at Data Science training institute in Bangalore and be an advanced professional in the field. Contact us now, show us your curriculum and portfolio to be part of the team.
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