Here's how you can establish practical timelines for your data science projects.

In my work I frequently use the ML Canvas or another type of canvas to create use cases to detail the project objectives, scope, data sources, in addition to establishing success metrics to measure results. The canvas makes it easy to visualize project stages, from data collection and preparation to model implementation and monitoring, ensuring that all critical aspects are considered and aligned with business objectives.

Define Clear Objectives: Understand the goals and deliverables of the project. Determine Key Milestones: Break down the project into smaller, manageable tasks. Estimate Effort: Assess the time required for each task based on previous experience.

Consider project scope as your project's DNA. The clearer the better you are at predicting the growth. A well-defined project scope outlines the crux of the accurate timeline. Start with breaking the project into smaller manageable tasks. Bring them to life using project management tools like Trello or Asana for visualization and tracking progress. Projects with well-defined scopes are 50% more likely to be completed on time (Source: PMI, 2020)

In the landscape of data science, establishing realistic timelines hinges profoundly on the intricacies of data availability. Through my consultancy, I've observed that projects often falter not due to technical challenges, but from delays in accessing or procuring the right datasets. Addressing data availability upfront—securing permissions, understanding data formats, and ensuring quality—can dramatically streamline project timelines. It's essential to integrate this planning phase directly into your initial timeline estimations to mitigate unforeseen delays and align expectations with reality, ensuring a smoother execution phase.

Now, that's like taking a road trip without knowing whether there's fuel along the way. So that's like just diving into a data project without taking stock of data availability. One has to make sure that the needed data sets are available and accessible in a usable manner. Most of the time in a data project, data wrangling, and cleaning takes much more time than expected. Generally, data preparation consumes 80% of any data scientist's time (Source: Forbes, 2022)

Check Data Sources: Ensure the necessary data is accessible and reliable. Data Collection Time: Include time for data collection, cleaning, and preprocessing. Address Gaps: Plan for potential delays if data is incomplete or needs additional sourcing.

Evaluate Techniques: Determine which machine learning models or algorithms will be used. Training Time: Account for the time needed to train models, especially if dealing with large datasets or complex algorithms. Iteration Needs: Factor in the need for multiple iterations to optimize model performance.

Model complexity directly influences your timeline. It could take days for simple linear regression models and weeks or even months for complex deep learning models. It's highly recommended to assess computational requirements and possible challenges early in the project. This is greatly eased by TensorFlow or PyTorch frameworks. Complex models extend project timelines by up to 60% (Source KDnuggets, 2021)

Creating effective timelines for data science projects is essential due to their iterative nature. For instance, in a customer churn prediction project, initial data collection and model building might reveal the need for more data or features. This requires revisiting and refining your model. Allocate time for these iterations and build flexibility into your timeline to accommodate new insights and adjustments without causing delays. This ensures your project remains on schedule and delivers valuable results.

Initial Analysis: Plan an initial analysis phase to gain insights and identify issues. Feedback Loop: Include time for iterative analysis based on feedback from stakeholders or findings from the initial analysis. Refinement: Allocate time for refining models and analyses based on iterative testing and validation.

The process of data science is iterative. Expect to have to refine your models and analysis several times before you have the final deliverables. Leave yourself headroom in your timeline for iterations. Make sure you have clear checkpoints and review stages for change analysis and the incorporation of alterations. Agile methodologies can be of great help in controlling these iterations. According to the Standish Group (2019), agile projects are 28% more successful than traditional projects.

Regular Updates: Schedule regular check-ins and updates with stakeholders. Incorporate Feedback: Plan for time to incorporate feedback and make necessary adjustments. Final Review: Include a final review phase before project completion to ensure all requirements are met.

Effective risk management is vital for establishing practical timelines in data science projects. Early identification of potential risks, like data privacy issues or technical challenges, enables more effective contingency planning. For example, in a project developing a predictive model for healthcare, the team recognizes data privacy as a major concern. They allocate extra time to secure privacy approvals and implement encryption. Additionally, they anticipate potential technical limitations with their machine learning framework and prepare a backup plan to switch to a more scalable option if necessary. This proactive approach results in a flexible, realistic timeline that can adapt to unforeseen events, ensuring the project's success.

Projects are the best way to practice and applying the skills that you have learnt. And setting project timeline is most important thing. So here are 6steps data science framework: 1. Problem -> Define the problem on which you want to make project. 2. Data -> Collect the data for the same, better data means better project. 3. Modelling -> Create the machine learning model for the data to learn the patterns. 4. Evaluation -> Evaluate the model performance by using some evaluation metrics according to the problem 5. Features -> Try to figure out what features are important, want to remove or add. 6. Experimentation -> Try the model on the real-world data. I recommend you give 1 week to each of the steps, so it will 42 days project.