Data Collection

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Do curso por IBM

Data Science Methodology

33 ratings

IBM

Data Science Methodology

33 ratings

Curso 3 de 4 em Specialization Introduction to Data Science

Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand. This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand. Accordingly, in this course, you will learn: - The major steps involved in tackling a data science problem. - The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment. - How data scientists think!

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From Problem to Approach and From Requirements to Collection

In this module, you will learn about why we are interested in data science, what a methodology is, and why data scientists need a methodology. You will also learn about the data science methodology and its flowchart.You will learn about the first two stages of the data science methodology, namely Data Requirements and Data Understanding. Finally, through a lab session, you will learn how to complete the Business Understanding and the Analytic Approach stages as well Data Requirements and Data Collection stages pertaining to any data science problem.

Data Requirements3:28

Data Collection2:54

Conheça os instrutores

Alex Aklson
Ph.D., Data Scientist
IBM Cognitive Class

Welcome to Data Science Methodology 101 From Requirements to Collection Data Collection!

After the initial data collection is performed, an assessment by the data scientist takes

place to determine whether or not they have what they need.

As is the case when shopping for ingredients to make a meal, some ingredients might be

out of season and more difficult to obtain or cost more than initially thought.

In this phase the data requirements are revised and decisions are made as to whether or not

the collection requires more or less data.

Once the data ingredients are collected, then in the data collection stage, the data scientist

will have a good understanding of what they will be working with.

Techniques such as descriptive statistics and visualization can be applied to the data

set, to assess the content, quality, and initial insights about the data.

Gaps in data will be identified and plans to either fill or make substitutions will

have to be made.

In essence, the ingredients are now sitting on the cutting board.

Now let's look at some examples of the data collection stage within the data science methodology.

This stage is undertaken as a follow-up to the data requirements stage.

So now, let's look at the case study related to applying "Data Collection".

Collecting data requires that you know the source or, know where to find the data elements

that are needed.

In the context of our case study, these can include:

demographic, clinical and coverage information of patients,

provider information, claims records, as well as

pharmaceutical and other information related to all the diagnoses of the congestive heart

failure patients.

For this case study, certain drug information was also needed, but that data source was

not yet integrated with the rest of the data sources.

This leads to an important point: It is alright to defer decisions about unavailable data,

and attempt to acquire it at a later stage.

For example, this can even be done after getting some intermediate results from the predictive

modeling.

If those results suggest that the drug information might be important in obtaining a good model,

then the time to try to get it would be invested.

As it turned out though, they were able to build a reasonably good model without this

drug information.

DBAs and programmers often work together to extract data from various sources, and then

merge it.

This allows for removing redundant data, making it available for the next stage of the methodology,

which is data understanding.

At this stage, if necessary, data scientists and analytics team members can discuss various ways

to better manage their data, including automating certain processes in the database, so that

data collection is easier and faster.

Thanks for watching!

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