Steps for Data Science Processes
Step 1: Defining research goals and creating a project charter
- Spend time understanding the goals and context of your research.Continue asking questions and devising examples until you grasp the exact business expectations, identify how your project fits in the bigger picture, appreciate how your research is going to change the business, and understand how they’ll use your results.
Create a project charter
A project charter requires teamwork, and your input covers at least the following:
- A clear research goal
- The project mission and context
- How you’re going to perform your analysis
- What resources you expect to use
- Proof that it’s an achievable project, or proof of concepts
- Deliverables and a measure of success
- A timeline
Step 2: Retrieving Data
Start with data stored within the company
- Finding data even within your own company can sometimes be a challenge.
- This data can be stored in official data repositories such as databases, data marts, data warehouses, and data lakes maintained by a team of IT professionals.
- Getting access to the data may take time and involve company policies.
Step 3: Cleansing, integrating, and transforming data-
Cleaning:
- Data cleansing is a subprocess of the data science process that focuses on removing errors in your data so your data becomes a true and consistent representation of the processes it originates from.
- The first type is the interpretation error, such as incorrect use of terminologies, like saying that a person’s age is greater than 300 years.
- The second type of error points to inconsistencies between data sources or against your company’s standardized values. An example of this class of errors is putting “Female” in one table and “F” in another when they represent the same thing: that the person is female.
Integrating:
- Combining Data from different Data Sources.
- Your data comes from several different places, and in this sub step we focus on integrating these different sources.
- You can perform two operations to combine information from different data sets. The first operation is joining and the second operation is appending or stacking.
Joining Tables:
- Joining tables allows you to combine the information of one observation found in one table with the information that you find in another table.
Appending Tables:
- Appending or stacking tables is effectively adding observations from one table to another table.
Transforming Data
- Certain models require their data to be in a certain shape.
Reducing the Number of Variables
- Sometimes you have too many variables and need to reduce the number because they don’t add new information to the model.
- Having too many variables in your model makes the model difficult to handle, and certain techniques don’t perform well when you overload them with too many input variables.
- Dummy variables can only take two values: true(1) or false(0). They’re used to indicate the absence of a categorical effect that may explain the observation.
Step 4: Exploratory Data Analysis
- During exploratory data analysis you take a deep dive into the data.
- Information becomes much easier to grasp when shown in a picture, therefore you mainly use graphical techniques to gain an understanding of your data and the interactions between variables.
- Bar Plot, Line Plot, Scatter Plot ,Multiple Plots , Pareto Diagram , Link and Brush Diagram ,Histogram , Box and Whisker Plot .
Step 5: Build the Models
- Build the models are the next step, with the goal of making better predictions, classifying objects, or gaining an understanding of the system that are required for modeling.
Step 6: Presenting findings and building applications on top of them –
- The last stage of the data science process is where your soft skills will be most useful, and yes, they’re extremely important.
- Presenting your results to the stakeholders and industrializing your analysis process for repetitive reuse and integration with other tools.
Data Science Process
If you are in a technical domain or a student with a technical background then you must have heard about Data Science from some source certainly. This is one of the booming fields in today’s tech market. And this will keep going on as the upcoming world is becoming more and more digital day by day. And the data certainly hold the capacity to create a new future. In this article, we will learn about Data Science and the process which is included in this.