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You’re working on a data science project alone. How do you know if you’re making the right decisions?

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Data science is a complex and dynamic field that requires constant learning, experimentation, and evaluation. Working on a data science project alone can be challenging, especially when you have to make important decisions that affect the quality and outcome of your work. How do you know if you are on the right track, if you are using the best methods, and if you are avoiding common pitfalls? In this article, we will share some tips and best practices that can help you make better decisions when working on a data science project alone.

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Akshat A.

Director, Sales Operations & Strategy | APAC Data Science & AI Leader
Oluwatobi Afolabi PhD

Data Science | Machine Learning | Deep Learning | Computer Vision | NLP
Naman Solanki

Building TheraWin | Data Scientist at IBY, Japan | IIT Delhi

1 Define your problem and goal

The first step in any data science project is to clearly define your problem and goal. What are you trying to achieve, and what are the criteria for success? What are the main questions you want to answer, and what are the assumptions and limitations you have to consider? Having a clear problem statement and a SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) goal can help you focus your efforts, avoid scope creep, and measure your progress.

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Oluwatobi Afolabi PhD

Data Science | Machine Learning | Deep Learning | Computer Vision | NLP
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In defining your problem and goal, it is always a good idea to begin by understanding the perspective of stakeholders. Engage in conversations with decision-makers, end-users, and those impacted by the project. What challenges are they facing? What insights could drive meaningful outcomes? This dialogue lays the groundwork for problem definition.

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Naman Solanki

Building TheraWin | Data Scientist at IBY, Japan | IIT Delhi
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Working on a data science project solo can feel like navigating uncharted territory. Here's how: Define Your Destination: Define your problem and goal with laser focus before setting sail. What you're trying to achieve? What's the end impact? This clarity acts as your compass, keeping you focused and avoiding potholes. Question Everything (Even Yourself): Don't be afraid to wear multiple hats – user, developer, and even CEO! Identify your assumptions, limitations, and key questions. Talk to stakeholders – the "who" and "why" behind your analysis. Understanding their needs helps ensure your project delivers real value. Once you have this, iterate and re-iterate to get a final and stronger problem statement.

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Vivek S Menon

Senior Data Scientist at Telstra |Microsoft Certified Data Scientist | PGP - AI/ML | MBA |Ex -Accenture, TCS | AI | ML | NLP|LLM
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When you are working solo , eventhough it has lot of positive things like, you will manage work, time , deliverables and commitments, it also has its own share of downsides.One should be as specific that he /she could when it somes to setting the goal and defining the problem.Should communicate all in scope , out of scope, exemptions ,assumptions to everyone involved in the project.We also should take time to check the progress on weekly basis and its ideal to send atleast send a status report summary on a weekly basis .Need to also call out potential risks and challenges that may come along.

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Ankit Banerjee

Data Scientist@Wells Fargo | Machine Learning | Time Series | NLP | RAG | LLM | Python | SQL
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A data scientist should engage in weekly discussions with stakeholders to understand business requirements & goals. Furthermore, collaborating with subject matter experts (SMEs) in the domain is crucial for delving into the business domain. SMEs not only aid in understanding the business but also contribute to data validation.

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2 Choose your data sources and tools

The next step is to choose your data sources and tools. Depending on your problem and goal, you may need to collect, clean, transform, and integrate data from different sources, such as databases, APIs, web scraping, surveys, or experiments. You also need to select the appropriate tools and platforms for your data analysis, such as Python, R, SQL, Excel, or cloud services. You should choose your data sources and tools based on their availability, reliability, compatibility, scalability, and cost-effectiveness.

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Akshat A.

Director, Sales Operations & Strategy | APAC Data Science & AI Leader
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Going solo on a project has its upsides, but sometimes that nagging doubt creeps in – am I heading in the right direction? Choosing the right data sources and tools is one of those make-or-break decisions. It's overwhelming because there are so many options out there! I've learned a few tricks for cutting through the noise. First, be ruthlessly specific about what I'm trying to solve. That seriously narrows down my options. Then, it's about balancing the familiar with the new. Tried-and-true tools get me up and running quickly, but I always try to keep an eye out for something that could give me an edge for this specific project.

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Anurag Singh Kushwah

Co-founder & Data Scientist | Mentoring the Next Generation | Expert in AI and ML and Data Engineering
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Identify relevant data sources that align with your problem statement. Select appropriate tools and frameworks based on the nature of your data and the requirements of your project.

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3 Explore and visualize your data

Once you have your data and tools ready, you should explore and visualize your data to gain insights, identify patterns, and discover anomalies. Exploratory data analysis (EDA) is a crucial step in any data science project, as it helps you understand your data, validate your assumptions, and generate hypotheses. You should use descriptive statistics, such as mean, median, mode, standard deviation, and correlation, to summarize your data. You should also use graphical techniques, such as histograms, box plots, scatter plots, and heat maps, to visualize your data.

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Anurag Singh Kushwah

Co-founder & Data Scientist | Mentoring the Next Generation | Expert in AI and ML and Data Engineering
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Thoroughly explore and understand your data through descriptive statistics and visualization techniques. Use visualizations to uncover patterns, trends, and anomalies in your data.

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4 Apply your models and techniques

After exploring and visualizing your data, you should apply your models and techniques to answer your questions and achieve your goal. Depending on your problem and goal, you may need to use different models and techniques, such as regression, classification, clustering, dimensionality reduction, or natural language processing. You should choose your models and techniques based on their suitability, accuracy, simplicity, and interpretability. You should also test and validate your models and techniques using appropriate metrics, such as mean squared error, accuracy, precision, recall, or F1-score.

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Anurag Singh Kushwah

Co-founder & Data Scientist | Mentoring the Next Generation | Expert in AI and ML and Data Engineering
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Implement appropriate machine learning models or analytical techniques to address your problem. Validate and evaluate the performance of your models using relevant metrics and validation techniques.

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5 Communicate your results and recommendations

The final step in any data science project is to communicate your results and recommendations to your stakeholders, such as clients, managers, or peers. Communication is a key skill for any data scientist, as it helps you convey your findings, explain your methods, and persuade your audience. You should use effective communication strategies, such as storytelling, visualization, and presentation, to deliver your message. You should also use clear and concise language, avoid jargon and technical details, and highlight the main takeaways and action points.

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Anurag Singh Kushwah

Co-founder & Data Scientist | Mentoring the Next Generation | Expert in AI and ML and Data Engineering
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Clearly communicate your findings, insights, and recommendations to stakeholders using visualizations, reports, or presentations. Ensure your results are understandable and actionable for non-technical audiences.

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6 Seek feedback and improvement

Even if you are working on a data science project alone, you should not work in isolation. You should seek feedback and improvement from other sources, such as online forums, blogs, podcasts, courses, books, or mentors. Feedback and improvement can help you learn from others, avoid mistakes, and enhance your skills. You should be open to constructive criticism, ask for help when needed, and apply the lessons learned to your future projects.

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Anurag Singh Kushwah

Co-founder & Data Scientist | Mentoring the Next Generation | Expert in AI and ML and Data Engineering
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Solicit feedback from peers, mentors, or domain experts to validate your approach and identify areas for improvement. Continuously iterate and refine your methods based on feedback and new insights.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Anurag Singh Kushwah

Co-founder & Data Scientist | Mentoring the Next Generation | Expert in AI and ML and Data Engineering
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Regularly review and reassess your project objectives and progress to ensure alignment with your goals. Stay updated with the latest developments in data science techniques and tools to enhance the effectiveness of your project.

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You’re working on a data science project alone. How do you know if you’re making the right decisions?

1

2

3

4

5

6

7

1 Define your problem and goal

2 Choose your data sources and tools

3 Explore and visualize your data

4 Apply your models and techniques

5 Communicate your results and recommendations

6 Seek feedback and improvement

7 Here’s what else to consider

Data Science

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You’re working on a data science project alone. How do you know if you’re making the right decisions?

1

2

3

4

5

6

7

1 Define your problem and goal

2 Choose your data sources and tools

3 Explore and visualize your data

4 Apply your models and techniques

5 Communicate your results and recommendations

6 Seek feedback and improvement

7 Here’s what else to consider

Data Science

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