Nine Steps for building business oriented and statistically robust Data Science projects

Data Science and Data Science Projects are the most trending topics and indeed so since it is a fast-evolving field. It is an all-encompassing term inclusive of different components like Data Analytics, Big Data Analytics, Machine Learning algorithms and even Artificial Intelligence Model.

Professionals in the field as well as emerging data scientist especially students learning Data science are struggling to find the right way to create a data science project that not only shows off their specialized data science skills but also have an effective and significant business application.

So, to help you build a strong and useful Data Science project -here is a quick Nine-Step guide to structure your project in a meaningful business-relevant but statistically valid manner.

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Step 1: Define the purpose of your Data Science Project

Remember Data Science Projects do not exist in isolation. They have a purpose. So first decide on the purpose or the business application of your Data Science project.

Decide what business objective you are trying to accomplish with your data science project.
To ensure the success of your project you need to understand the business objective and its related dimensions.
So before designing the project , talk to people to whom the selected business objective matters and how they will use the findings of your data science project(DSP).

Based on that refine the objective of your DSP. Decide what are the Key parameters that you need to consider and

what you are trying to prove/disprove
predict or forecast
any associations/classifications that you are trying to build
Or any other objective

You may have one or more or all of the above purpose for your DSP. Data Science value is only in application in different business domains so examine well the applicability of your project and how it can be enhanced.

Step 2: Develop a logical and structured Data Analysis framework

Based on your Project Objective, develop a step by step logical and scientific data analysis methodology.

The methodology should outline:

What will be your statistical hypothesis.
What will be considered sufficient evidence to support or counter the hypothesis
What type of data will be needed for hypothesis testing
What would be the method/formulae to calculate the Key parametric required for analysis

More often than not, people make mistake in developing the right logical premise and framework to reach their objective so pay attention to this step.

Step 3: Find reliable sources for your data

Once your project purpose is defined,

list all the data, measurement and parameters you will be needing.
Find reliable sources for your data from government publications, news reports and/or secondary survey reports- and of course your own internal organizational data as well as that collected through primary research is most welcome.
You can mix and merge data from various sources using simple techniques in R and Python so look for as many reliable data sources as possible.

However, do keep a note of the following:

Data Sufficiency: Is data from all sources enough to meet your Project needs.
Are you still missing any vital data? if you cannot get it- what can be the substitute or alternative
In case no substitute or alternative, can you make reasonable assumptions?
If not, you may need to reconsider your project objective and find a way around the logical framework. May be a different parametric or variable will work with the available data.
Ensure while merging the data from various sources, all units and measurements are standardized.
Make note of any assumptions or modifications while transforming and merging the data.

Step 4: Explore, Clean and Prepare the Data

Once you have obtained the data, start exploring through different type of visualization starting from scatter plots.
You can also drill down to examine each variable and whether they associate together to provide the required data for the Project.
Check for outliers and examine their impacts. Decide whether to use them or remove them from the data.
Start cleaning the data using outside to inside approach.
Check if recoding and transformation of variables is needed .
Treat the missing values using appropriate feature engineering approach
Keep running various visualization and reports to see the impact of data cleaning and transformation.

Usually data cleaning takes almost 70 to 80% of the project time and is the most important step. Your model’s effectiveness and the success of your DS project will depend largely on this and hence this is the most important step.

Your R and Python programming skills will also be show cased here!

You may decide to merge your different data sources after cleaning them or prior to cleaning depending on the type of data and its sources.

Step 5: Explore Data using Visualizations

Conduct initial data exploration using visualizations like scatter plots, Box plots to start understanding the relationship between different variables, their associations and trends.
Keep notes of anything interesting and significant to test through statistical testing.
Use visualizations to uncover trends and features as it is easy to notice any differences visually.
Save important and interesting visualizations for final presentation and story building.

Step 6: Mine your Data and conduct statistical significance tests

Mine the data to get them ready in the right format and detail for statistical significance Hypothesis testing.
Depending on the number and type of variable , use suitable statistical techniques like paired/unpaired t-test for checking difference in the means of two variable or ANOVA for multiple variable.
Check the independence and correlations of the variables.

Step 7: Create Predictive Models

Based on the type of variables, use regression models either linear or logistic for predicting dependent variables.
By analyzing trends and association in different variables in the past data, find most significant impacting variables to predict the dependent variable.
Using suitable algorithms will help you predict future trends. However, it is very important to understand the logic behind the algorithm
Interpret the model correctly using statistical inference tests.
Make sure the data is clean and correct techniques are used otherwise the model and its prediction will not make any sense.
It is also imperative to evaluate the model by:
- Checking Evaluation and Statistical significance parameters and interpreting them correctly
- By Testing the model using different sets of train and test data
- Enhance model Effectiveness by Adding/removing variables

Based on the data and project objectives , you can create further suitable Machine learning algorithms like Clustering or Recommender system etc. (supervised or unsupervised) to enhance your project, its results and applicability.

Step 8: Prepare Report with Key findings and Model Effectiveness

Prepare a clear and well-structured report highlighting:

The Project Objective
Key Results (Trends, Hypothesis, Predictive Models)
Significance of results and model in terms of their impact on business application
State the limitation and assumptions and sources of the data and modeling,
Embellish your findings with suitable, attractive Explanatory Data Visualization
Use Story Telling Methodology to complement and connect the data findings with the business applications

Step 9: Keep Re-Evaluating and Re-Iterating the model

Nothing is permanent and neither is your model. You need to keep revisiting the data and the model and keep finding ways to enhance it. Some of the important re-iteration will be through :

Adding recent data and keep enriching it to improve the model and the resultant output.
Keep revisiting important parameters, data sources and assumptions that you had to ensure they are updated as necessary to keep your model up-to-date.
Do Decide on frequency for recalibrating and re-checks to ensure it happens lest your model becomes obsolete or non-applicable!

Hope this helps you in your Data Science Journey! Do share your questions or experience in this regard. You are also welcome to contact for any help needed in applying these steps to your project.

You can read more about how data science projects can be applied to different business domains here.

All the Best !