Introduction to Data Science - Unit : 1 - Topic 7 : DEFINING GOALS AND CREATING PROJECT CHARTER

 

DEFINING GOALS AND CREATING PROJECT CHARTER

A project starts by understanding the what, the why, and the how of your project. What does the company expect you to do? And why does management place such a value on your research? Is it part of a bigger strategic picture or a “lone wolf” project originating from an opportunity someone detected? Answering these three questions (what, why, how) is the goal of the first phase, so that everybody knows what to do and can agree on the best course of action. The outcome should be a clear research goal, a good understanding of the context, well-defined deliverables, and a plan of action with a timetable. This information is then best placed in a project charter.

1.     Spend time understanding the goals and context of your research

An essential outcome is the research goal that states the purpose of your assignment in a clear and focused manner. Understanding the business goals and context is critical for project success. 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.

2.     Create a project charter

Clients like to know upfront what they’re paying for, so after you have a good understanding of the business problem, try to get a formal agreement on the deliverables. All this information is best collected in a project charter. For any significant project this would be mandatory.

A project charter requires teamwork, and your input covers at least the following:

Ø  A clear research goal

Ø  The project mission and context

Ø  How you’re going to perform your analysis

Ø  What resources you expect to use

Ø  Proof that it’s an achievable project, or proof of concepts

Ø  Deliverables and a measure of success

Ø  A timeline

RETRIEVING DATA

The next step in data science is to retrieve the required data . Sometimes you need to go into the field and design a data collection process yourself, but most of the time you won’t be involved in this step. Many companies will have already collected and stored the data for you, and what they don’t have can often be bought from third parties. Don’t be afraid to look outside your organization for data, because more and more organizations are making even high-quality data freely available for public and commercial use.

Data can be stored in many forms, ranging from simple text files to tables in a database. The objective now is acquiring all the data you need. This may be difficult, and even if you succeed, data is often like a diamond in the rough: it needs polishing to be of any use to you.

CLEANSING, INTEGRATING AND TRANSFORMING DATA

The data received from the data retrieval phase is likely to be “a diamond in the rough.” Your task now is to sanitize and prepare it for use in the modeling and reporting phase. Doing so is tremendously important because your models will perform better and you’ll lose less time trying to fix strange output. It can’t be mentioned nearly enough times: garbage in equals garbage out. Your model needs the data in a specific format, so data transformation will always come into play. It’s a good habit to correct data errors as early on in the process as possible.

  

EXPLORATORY 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. This phase is about exploring data, so keeping your mind open and your eyes peeled is essential during the exploratory data analysis phase.

MODEL BUILDING

With clean data in place and a good understanding of the content, you’re ready to build models with the goal of making better predictions, classifying objects, or gaining an understanding of the system that you’re modeling. This phase is much more focused than the exploratory analysis step, because you know what you’re looking for and what you want the outcome to be,

Building a model is an iterative process. The way you build your model depends on whether you go with classic statistics or the somewhat more recent machine learning school, and the type of technique you want to use. Either way, most models consist of the following main steps:

3.     Selection of a modeling technique and variables to enter in the model

4.     Execution of the model

5.     Diagnosis and model comparison

1. Model and variable selection

You’ll need to select the variables you want to include in your model and a modelling technique. Your findings from the exploratory analysis should already give a fair idea of what variables will help you construct a good model. Many modeling techniques are available, and choosing the right model for a problem requires judgment on your part. You’ll need to consider model performance and whether your project meets all the requirements to use your model, as well as other factors:

Ø  Must the model be moved to a production environment and, if so, would it be easy to implement?

Ø  How difficult is the maintenance on the model: how long will it remain relevant if left untouched?

Ø  Does the model need to be easy to explain?

When the thinking is done, it’s time for action.

2. Model execution:

Once you’ve chosen a model you’ll need to implement it in code. most programming languages, such as Python, already have libraries such as StatsModels or Scikit-learn. These packages use several of the most popular techniques. Coding a model is a nontrivial task in most cases, so having these libraries available can speed up the process.

3. Model diagnostics and model comparison

You’ll be building multiple models from which you then choose the best one based on multiple criteria. Working with a holdout sample helps you pick the best-performing model. A holdout sample is a part of the data you leave out of the model building so it can be used to evaluate the model afterward.

PRESENTING FINDINGS AND BUILDING APPLICATIONS ON TOP OF THEM

After you’ve successfully analyzed the data and built a well-performing model, you’re ready to present your findings to the world. This is an exciting part; all your hours of hard work have paid off and you can explain what you found to the stakeholders.

Sometimes people get so excited about your work that you’ll need to repeat it over and over again because they value the predictions of your models or the insights that you produced. For this reason, you need to automate your models