Phases of a Data Science project

Now that we know from the previous blog what Machine Learning is and how Mathematics is related to Machine Learning, let me give you a brief introduction to the phases of a Data Science project.

To start with any project and before we even think of creating a model, we have to gather the necessary data. One must understand what data needs to be there to start working and how to extract the data. One may need to gather data from various sources which includes Databases, Rest APIs, capturing user activity and behavior, mouse clicks, web scraping etc.

Skills needed: Programming, SQL, No-SQL, Web Scraping, Cloud, Big Data

Now with great challenges that you have gathered all the required data, and you start working to understand the data; Suddenly, you will realize that it is all messed up with missing values, unstructured files and have different formats which makes no sense.

Here is the time the project needs help from the industry or domain experts who can tell you what data means what and how to model it, what you should do to rectify missing values, which data to discard due to quality issues and how to reshape your data, which values can hinder your analysis. In short, the data has to pass a series of Data Wrangling steps so that we have clean data to do some analysis.

Skills needed: Domain knowledge, Programming, Exploratory Data Analysis, Data imputation, Data correction, Innovative thinking, Spark, Data Visualization

One must know what are different algorithms and when to use each one of them. Which model can work better depending on the complexity of data and required value you want to get as an output.

Skills needed: Data Structure, Algorithms, Complexity computation, Maths for Machine Learning, Programming, Cloud

The last phase is the presentation phase. Suppose that you have implemented a model which you think fits perfect for your project, you must figure out how you can show your solution to the customers, business partners or stakeholders. Presentation may include building a dashboard, making an application, blogging etc.

After the whole team works hard in a project to find a solution, you must know how to present it.

Skills needed: Tableau, QuickSight, Notebooks, API, Content Writing, Presentation, REST API, Programming

Phewwww, just before you think you know enough about the phases of a Data Science project, one last word :

You may need to go over and over to start acquiring additional data from the very beginning to do all the intermediate steps to presentation in case the desired output is still not achieved. Yes, these four steps are in an iteration.

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