The Data Scientist’s Playbook – Strategies for Successful Analysis

Data science projects can be complicated. They require engineering and product leaders to prioritize features, determine which data sets are available, understand the analytic tradecraft, and implement models to improve the business.

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1. Know Your Data

The first step in the data analysis process is identifying the questions you want to answer. This helps you avoid collecting unnecessary data or performing irrelevant analysis, which can be expensive and time-consuming.

Then you can collect and analyze the appropriate data sets. This can include both quantitative and qualitative data (non-numerical information). Qualitative data can be found in many different tools, such as customer feedback, competitor analysis, market research, etc.

Once you have collected and analyzed your data, it’s important to understand the results. You can do this by comparing your findings to previous trends or analyzing anomalies. Honest communication is also essential, so that everyone can understand the implications of the results. This may include creating data visualizations and providing clear documentation for future reference.

2. Know Your Business

As a business leader, it’s important to take time out of the day-to-day running of your company and think strategically. Taking the time to perform a SWOT analysis will help you assess your strengths, weaknesses, opportunities and threats.

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A critical component of a successful data science project is creating practical documentation to enable sustainability and use. This includes ‘how to’ guides with screenshots, video tutorials and clear project roadmaps. It’s also essential to consider your organisational culture and how your project will integrate with wider systems.

3. Know Your Models

Strategists, architects, process experts and software developers often put a lot of effort into making all kinds of useful models. However, these models are often not fully utilized for analyses.

It’s important to create practical documentation that enables the sustainability and use of data science projects. This includes clear “how to” guides, screenshots and video tutorials as well as a roadmap outlining project maintenance requirements.

As with any product planning process, the goal should be to pursue meaningful features that deliver value to customers. Working together with engineering leaders, the product leader can prioritize features and be prepared to reduce scope when necessary in order to ensure that a new feature is capable of being delivered quickly. This is the best way to reduce risk and minimize the chance of a feature being abandoned.

4. Know Your Tools

A top information designer creates a sleek data visualization for her company’s decision makers. The scientists love it, but they are nervous that the charts might inadvertently reinforce wrong ideas.

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Data science is a complex discipline, but it doesn’t require a PhD to work with the tools. A basic understanding of Excel, Google Sheets, and Airtable, plus a no-code text analysis tool like MonkeyLearn, can help you get started. You can also purchase comprehensive software suites that include a variety of data analytics tools in one package. This includes everything from a data warehouse to machine learning models.

5. Know Your Limitations

In the case of data science, knowing your limits is the first step in overcoming them. In the research world, limitations can be a huge obstacle to finding valuable insights and developing effective solutions for your business objectives.

A study’s limitations are usually outlined in the discussion section of the resulting paper. This section explains how the results of your research are affected by its shortcomings, and it’s important to acknowledge these limitations in a clear and straightforward way.

For example, you might have to use proxy data for analysis in cases where you don’t have the proper information. This may require sacrificing accuracy, but it’s still better than glossing over the limitations or representing them in an misleading manner. This will help readers avoid misinterpreting your findings.

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