Data science projects provide actionable value, such as creating reports, designing and executing machine learning models, and more. These projects are essential for budding and reputable organizations to help boost business growth.
No matter your experience in the field, to improve your chances of success, we recommend taking out some time to understand how data science works in practice and how you can broach the topic with a logical approach.
Hence, this blog will explore steps you can apply to your data science projects to tackle them. Furthermore, we have provided several tips and tricks and ensure a successful outcome.
Problem statement
Whether working on a personal project or a business, figuring out the right problem statement can save you time and trouble. A problem statement is a crisp description of an issue or a premise that requires improvement. If you understand the problem clearly, you can sum up the problem in just a few sentences.
To ease the process, you can follow the SMART guideline provided below:
Specific: Problem statements must be detailed and specific to the problem.
Measurable: Do you have any metrics to track at the end of the project to determine if it was successful?
Action: What actions must you take to solve the problem at hand methodically?
Relevant: It is essential to choose the most appropriate method for solving a problem rather than trying to solve it in multiple ways.
Timebound: Is there a deadline for resolving your problem?
After you have figured out the problem statement, you’ll better understand your project and its finer details.
Data Collection
Data collection information on targeted variables is gathered and measured, allowing questions to be answered and outcomes to be assessed. Some examples of data collection processes include:
- Government institutions
- Employee database
- Self-collected data
Before using the data sets, ensure they are relevant and validated. If the data collected does not apply to the problem you are attempting to solve, then the outcomes will not be of much value despite the good model you’ve built.
Data Cleaning
Data cleaning is a process analysts use to ensure that the data collected is in the proper format. Through this process, you can identify if the data is consistent and if any existing errors are identified promptly and dealt with.
For a clean dataset, you can follow the steps mentioned below:
- Remove irrelevant observations
- Address any missing values
- Get rid of duplicate values
- Reformat data types
- Filter out any unwanted outliers
- Reformat the strings
- Validate the data
Following the steps mentioned above is crucial as it will enable you to produce high-quality outcomes and result in a decisive and accurate decision.
Exploratory Data Analysis (EDA)
An exploratory data analysis (EDA) summarizes the characteristics of data sets, often using statistical graphics and other visualization techniques. During this stage, you will better understand the data and its statistical features, enabling you to create visualizations and test your hypothesis. There are four types of EDA that you can opt to take up:
- Univariate non-graphical: Using this approach, you can make observations about a large group of people and understand the sample distributions of a single variable.
- Univariate graphical: This is a graphical analysis of a single variable.
- Multivariate non-graphical: This technique will provide data on the relationship between two or more variables.
- Multivariate graphical: Through this approach, you can graphically show the relationship between two or more variables.
Feature Engineering
In databases, features are attributes that are helpful to solve your problem. You cannot include a feature if it has no impact on the problem you are trying to solve. So, now, let’s try to understand what feature engineering is.
In feature engineering, raw data is transformed into features that better describe the underlying problem, improving model accuracy for unseen data.
Predictive models will perform better if you create and choose better features. There are many approaches to feature engineering in which raw data is decomposed or aggregated to help solve your problem. These approaches include:
- Feature extraction: Through this process, you can select and/or combine the variables in a dataset to reduce the dimensionality.
- Feature selection: This approach allows people to select the features that contribute to the problem you are trying to solve.
- Feature construction: Raw data can be utilized to build more efficient features through feature extraction manually.
- Feature learning: This is the process of identifying and utilizing the features.
Modeling
Machine learning models are either Supervised or Unsupervised learning problems.
When a function maps an input to an output based on the input-output pairs, the problem is called a Supervised problem. Learning from input-output training data allows the machine learning model to predict data that has not been seen before (test data). Unsupervised problems involve looking for patterns in unlabeled data.
To optimize the performance of the machine learning models, the hyperparameters must be tuned after they are built. When these hyperparameters are utilized, the learning process can be regulated, and a predefined loss function can be reduced. You can then compare the predefined parameters for each model to find the optimal model.
Communication
The last step is communicating the results, which you can do through presentations, formal reports, or even blog posts. But you must keep the following points in mind:
- Avoid overcrowding slides
- Use apt visualizations
- Understand the target audience
The field of data science requires proper communication of the outcomes, which involves ensuring that it flows and intimates the audience about the essential findings and why they are interesting.
Conclusion
To conclude, in this article, we have provided seven steps that are not too exhaustive and may help you face the challenges you may encounter on your journey.
