What do you do if you're starting a new data science role?

Before accepting a data role ask within the interview cycle about company mentorship, shadowing, and professional development opportunities. It is important to get a sense of how quickly you will be thrown on a project and what resources they already provide during and after onboarding. I tend to love periods of time to shadow the team and client calls before being thrown on a project if possible so do not be afraid to ask for this.

Understand one thing very clearly when you are starting a data role, its all about skills. You can not fake here. You must analyze the skills required and gap that is there. You can learn on the go as well. Watch some tutorials and ask experts about their way or ask for help when needed, don't hesitate. No one is perfect or knows everything. You have to proactive and work beyond your requirements to learn out of the path. Always understand ask of the job and do get complete understanding of the project and then problem statement and outline your steps to solve it. Make your plan trackable and realistic. Keep some buffer time as well to learn and upgrade.

When starting a new data science role, first, familiarize yourself with the company's data infrastructure and available tools. Next, establish clear communication with stakeholders to understand project goals and expectations. Finally, focus on continuous learning, stay updated with industry trends, and collaborate with colleagues to tackle data-driven challenges effectively.

When you are starting a new role as data scientist - 1. try getting familiarise yourself with existing process and make note of what each process does, data sources it accesses and frequency of execution. 2. if you are expert in R or Python , see if you can automate certain steps of data cleaning or data fetching . 3. check if there's any modelling technique that can be improved , may it be prediction with time series data or implementing NLP in the existing process. 4. Try and propose ideas from your previous work experiences. it could be as simple as file and folder management or implementing good naming conventions practice for files.

🔍 Currently proficient in Python, SQL, and data analysis. 📊 Skilled in data visualization using tools like Matplotlib and Seaborn. 🤖 Basic understanding of machine learning concepts. 👨💻 Familiar with data manipulation libraries like Pandas and NumPy. 📚 Continuous learning through platforms like Kaggle, Coursera, and DataCamp. 🔧 Actively working on improving machine learning expertise. 🔄 Seeking feedback from mentors for skill enhancement. 💡 Recognizing the importance of staying updated on industry trends. 🤝 Collaborating with the team to understand relevant tools and frameworks. 🚀 Goal: Bridge gaps and stay adaptable in the ever-evolving data landscape.

Starting a new data science role is an exciting opportunity to apply skills and techniques learned to a company's success. Here are 5 tips to help navigate the start of a new role effectively: 1. Understand the company's products, services, goals, challenges, and industry trends. 2. Build relationships with team members and identify key stakeholders. 3. Familiarize yourself with the company's data by exploring the dataset and learning about tools, technologies, and platforms. 4. Start projects with smaller tasks to build confidence and understanding of the team's processes. Document findings, decisions, and code from projects for future improvements. 5. Continuously learn and stay updated with the latest data science trends.

Understand the Business: Learn about the company's goals and how data science aligns with them. Meet Stakeholders: Build relationships with team members, managers, and other departments. Master Tools and Technologies: Familiarize yourself with the tools and technologies used in the organization. Data Exploration: Dive into available datasets to understand their structure and potential insights. Learn from Existing Models: If applicable, study any existing models or analytics to understand the current state. Continuous Learning: Stay updated on industry trends and advancements in data science. Effective Communication: Develop clear communication skills to explain complex concepts to non-technical stakeholders.

Starting a new data science role can be exciting!. The few steps I might go with are : -- Understand goals. -- Learn tools (Python, R, etc.). -- Understand data. -- Learn from colleagues. -- Stay curious and keep learning. -- Communicate effectively.

Starting a role in new ecosystem can sometimes be challenging but at the same time exciting. - Firstly you need to understand the culture of the organisation and what are the expectations from your role. - If you think you are lacking in any sense for valuable contribution do the courses, read articles everything is there on the internet. - Don’t fake anything, people will get to know. So, make serious efforts in understanding the scenario and what’s your responsibility. - Lastly no one is perfect keep learning and growing.

I was named one of the Amazing People at LexisNexis less than 2 years after starting my career as a data scientist. This recognition came from building a simple classifier model that dramatically increased our data processing capabilities and accuracy scores. Classification model for the win! My advice to anyone starting out as a data scientist is - be bold and fearless! You don't need to know everything to make a significant impact at your company.

Understanding the business problem is crucial for a successful data science project. 📊 Start by defining the project's goal, identifying users or customers, and determining success metrics and key performance indicators (KPIs). 🎯 Research the domain, industry, and market to gain insights into existing solutions and benchmarks. Ask questions to clarify objectives and usage of results. Consider the practical implementation of findings and how they align with business goals. 🔄 Ensure alignment with stakeholders to foster collaboration and enhance project outcomes. Investigate potential challenges and opportunities within the problem space. 🚀 This initial step sets the foundation for a targeted and impactful data science approach.

Data Science is a science as well as an art. Understand the problem and the stakeholders. Making software is easy, there are several tools available for that, explaining it to the stakeholders and then convincing them eventually, is the real challenge, no tool teaches you that.

One lesson about this point: "The problem may not be the problem". Believe it or not, this happens more often than not. Stakeholders usually try to fit solutions to their problems, and because of that, they try to explain them so that you build what they initially think they need. That's a big mistake. For example, this may end up in a model that really needed a completely different design approach. Put some effort into really understanding what your internal clients need. Work on your empathy to read between the lines. Collect facts and synthesize them. If you conclude that the problem is not the problem, talk to the stakeholders to expose the real problem and your proposed approach to solve it.

Business acumen gives one an added advantage because we can't apply the tech until we understand and solve the business problems first. To ensure that your data science efforts are in line with strategic objectives and that you can effectively leverage data to drive actionable insights and data-driven business solutions. It is imperative that you have a thorough understanding of the business problem you will be addressing before taking on a new data science role. This entails gaining insight into the organization's goals, challenges, and particular problem domains.

Understanding the business problem also involves understanding the stakeholders involved. Who will be using your models or analyses? What are their needs and how can your work address them? Also, consider the ethical implications of your work. Data science isn’t just about finding patterns in data, it’s about making decisions that can have real-world impacts.

To solve the business problem using data science, first, understand the project's goal, users, success metrics, and implementation plan. Research the domain, industry, and seek existing solutions or benchmarks.

As I always emphasize, a data scientist is someone who solves business problems using data. Without business problems, there is no data science. You should begin your role in the company by thoroughly understanding the business. Try to understand their challenges, initiatives they've previously undertaken, and areas where you think you can contribute, especially by leveraging knowledge from your past experiences. Also, it's very important to engage with stakeholders, meet with key people to gain insights into their expectations. And identify what metrics are important to the business, this will guide your data analysis and model development.

In the exploratory data analysis (EDA) phase, 📊 I'll examine the dataset to understand its structure, patterns, and quality. I'll identify missing values, outliers, and inconsistencies, and apply cleaning techniques like imputation, removal, or transformation as needed. 🧹 Cleaning the data involves handling missing or invalid values, standardizing formats, and ensuring consistency. Documentation of data sources, assumptions, and decisions will be crucial for transparency and reproducibility. 🔍 Exploring data distribution, correlations, and relationships helps in gaining insights for subsequent analysis. This process lays the foundation for reliable and meaningful analysis in the project.

Take care of the class imbalance Most of the datasets you will work on in real life will have a class imbalance, where there will be more samples of the more important classes (from the business's perspective). Training your model directly on an imbalanced dataset would lead to a biased model. The following techniques could be leveraged to handle class imbalance: - Resampling (under-sampling or over-sampling) - Synthetic data generation - Data augmentation

Strong data science solutions start with proper data exploration and preparation. Be ready to explain your approach to initial data profiling through summary statistics, visualizations, correlation analysis, and more to characterize trends, distributions and relationships. Discuss techniques you would use to handle issues like missing values, duplicates, outliers and inconsistencies through methods such as imputation, dropping rows, normalization and transformations. Document your cleaning approach to ensure reproducibility. Ask clarifying questions on data sources, formats, assumptions and business rules to properly interpret the meaning. Showcase SQL, Python or R skills for manipulation.

When you explore and clean data, remember the goal is not just to clean the dataset or structure it, but to make it useful for your specific problem. This can include creating new variables to catch patterns. Also, look for biases that are certain groups represented more or less. Consider how this might affect your analyses. In the exploratory data analysis (EDA) phase, I examine the dataset to understand its structure, patterns, and quality. Identify missing values, outliers, and then handle missing or invalid values, by standardizing formats, and ensuring consistency. Documenting sources and decisions is important for transparency. Exploring data distribution, correlations, and relationships provides insights for meaningful analysis.

Cleaning data is about structuring datasets to facilitate analysis, visualization, report generation, or modelling. I recommend everyone to internalize Hadley Wickham's notion of tidy data. Tidy data organizes information into a clear and consistent format, where each type of observational unit forms a table, each variable is in a column, and each observation is in a row. If these things are well understood and documented, then data analysis becomes more intuitive and fun.

Exploring and cleaning data in a new Data Science role: 1. Data Profiling:Begin by profiling the data to understand its structure, types,& distributions📉. 2. Handle Missing Values:Identify and handle missing 🗒️values appropriately. Depending on the situation, you might choose to impute missing values or exclude incomplete records. 3. Outlier Detection:Explore and address outliers that might skew your analysis 📈or modeling. 4. Data Quality Checks:Implement data quality checks to ensure ✅consistency and accuracy. 5. Feature Engineering:Create new features or modify existing ones to enhance the dataset's predictive power🦾. 6. Data Visualization📊:Utilize visualization techniques to gain insights into data patterns and distributions.

If you are working in a new domain, it might be quite daunting to figure out the nuances in a dataset. Noting down your observations during EDA and discussing these with the SMEs in the domain within your organisation is very key. Having the correct understanding of the data will save you a lot of stress down the line.

Data Exploration: Conduct exploratory data analysis to understand data characteristics, distributions, &relationships. Address Missing Data: Identify&address missing, invalid,or inconsistent data using methods like imputation, removal,or transformation. Document Decisions: Document data sources, assumptions,&decisions made during the exploration and cleaning process for transparency and reproducibility. Maintain Data Integrity: Ensure data integrity by checking for outliers or anomalies and applying appropriate techniques to handle them. Verify Data Quality: Validate data quality by cross-referencing with external sources or performing data validation checks. Iterative Process: Approach data exploration and cleaning as an iterative process.

Some important considerations in building and evaluating models are: Consider the nature of your problem (classification, regression, etc.) and data type (structured, unstructured) to select suitable algorithms (decision trees, neural networks, etc.). Use techniques like k-fold cross-validation or stratified k-fold cross-validation to obtain an estimate of your model's performance. Also utilize automated grid search or randomized search to efficiently find the optimal hyperparameters for your model. Use techniques like feature importance plots, confusion matrices, and ROC curves to understand your model's behavior. If interpreting model predictions is crucial, invest in explainable AI (XAI) techniques like LIME or SHAP.

In the model building and evaluation stage, it's crucial to select suitable algorithms, techniques, and parameters tailored to the data, problem, and objectives. Apply cross-validation, testing, and tuning to gauge model performance and accuracy. Compare various models and choose the optimal one based on predefined criteria and metrics. Consider metrics like accuracy, precision, recall, F1-score, and area under the curve (AUC). Utilize techniques such as grid search, random search, or Bayesian optimization for hyperparameter tuning. Ensure robust evaluation through techniques like k-fold cross-validation. Finally, consider interpretability, scalability, and computational efficiency when selecting the final model.

For example, you might use linear regression, decision trees, or neural networks. You should also use cross-validation, testing, and tuning to measure the performance and accuracy of your models. Cross-validation helps you avoid overfitting or underfitting by splitting your data into training and validation sets. Testing helps you measure how well your models generalize to new data. Tuning helps you optimize your models by adjusting the hyperparameters. You should also contrast different models and pick the best one, based on your criteria and metrics. You might use metrics such as accuracy, precision, recall, or F1-score. You might also use visualizations to compare and select your models.

When you're making models, it's important to match how complicated the model is with how much data you have. If you don't, the model might learn too much from the data and not work well with new information. So, if you have just a little bit of data, a simpler model might work better. But if you have a lot of data, you might need a more complex model like deep learning. Also, some industries, like finance or healthcare, need models that people can understand easily. For example, if a model decides who gets a loan or what treatment someone gets, people need to know why. So, it's important to pick a model that can explain its decisions clearly, especially for these kinds of problems.

One should choose the appropriate algorithms, techniques, and parameters for your models, based on your data, problem, and goal. You should also apply cross-validation, testing, and tuning to assess the performance and accuracy of your models.

- One time, I iterated through several machine learning models, fine-tuning each based on cross-validation scores to identify the one that best predicted customer churn. - In my experience, experimenting with different algorithms and parameters, and rigorously evaluating their performance through testing, is vital for finding the most effective model. - One thing I've found helpful is to use a combination of metrics, like accuracy, precision, recall, and AUC, to assess model performance comprehensively. This approach ensures the selected model aligns closely with the business objectives and provides actionable insights.

Now it's time to create and check your models. Pick the right methods and settings for your data and goal. Test and adjust your models to see how well they work. Compare different models and choose the one that fits your needs the best. It's like trying out different recipes to find the tastiest one!

Employ state-of-the-art machine learning algorithms relevant to your problem. Implement a robust model evaluation strategy to gauge performance accurately. Iterate on models based on feedback and continuously refine your approach.

Agreed, the fourth step is pivotal. Selecting the right algorithms and techniques tailored to the specific data and problem is key. Rigorous cross-validation, testing, and tuning are essential to gauge model performance. Comparing models to find the optimal one based on set criteria and metrics has been a critical part of my data science workflow.

In my experience, one of the most crucial aspects that you and the firm must agree upon is the performance metric used for optimization. Although it may not seem very important at first, there have been numerous instances where a significant amount of time was invested in optimizing a model successfully, only to find its real-world performance to be subpar. Usually, the reason behind this discrepancy is related to the choice of performance metric used during development for model optimization. 📊🎯

To make your findings accessible and impactful, employ a mix of visual aids like charts, graphs and tables that best represent your data's story. Tailoring the presentation to your audience's expertise and interests ensures that your message resonates, highlighting key insights and recommendations with clarity. Python's Matplotlib for visualization and adopting storytelling techniques can significantly amplify the value of your analyses.

Convey findings, insights, and recommendations while transparently addressing model limitations, assumptions, and uncertainties. Adapt communication style and format based on audience, purpose, and medium for impactful information dissemination.

The three key aspects to communicate and visualize the results are to know your: 1. Data 2. Business 3. Audience It is important to correlate the data with the business in order to convey the meaning and importance of the visualizations. The three questions that can be asked are: - why is this data and pattern important to my business - what do I understand better with this data about my business - how can I improve the business to reach the desired graphical pattern or numbers This framework allows you to understand the value and communicate it in a simple and concise manner to your audience.

This is the step where your storytelling skills come into play. Before preparing a presentation, investigate the audience, and based on their skills and knowledge, choose the most appropriate tone, theme color, etc., to make your presentation more effective. There will also be times when you need to prepare a report. The difference between a presentation and a report is fairly obvious—in a report, you are not present. So, investing some time to improve your writing skills can also be beneficial. 📊📝💡

As a data scientist, you need good communication skills to round you off and make your work accessible to the rest or the organisation. Communicating well with those who work in other departments can help open up opportunities that will ensure your career longevity within the organisation. Data visualization helps to tell stories by curating data into a form easier to understand, highlighting the trends and outliers. A good visualization tells a story, removing the noise from data and highlighting useful information.

When stepping into a new data science role, it's vital to share your findings in a way that resonates with your audience. Think of it as telling a story rather than just presenting data. Use language that's clear and relatable, and leverage visuals like graphs and charts to illustrate your points. Be transparent about the limitations of your models, and take into account that not everyone may be familiar with the technical jargon. Tailor your communication style to suit the different levels of understanding among stakeholders, ensuring everyone feels included and informed. This leads to comprehension but also builds trust in the insights you're delivering.

- One time, I presented a model's findings to non-technical stakeholders using simplified charts and relatable analogies, ensuring the insights were accessible and actionable. - In my experience, being transparent about a model's limitations and assumptions builds trust with stakeholders. It's crucial to discuss how these factors might influence the results. - One thing I've found helpful is to tailor the visualization and presentation of results to the audience's expertise. For instance, interactive dashboards can be very effective for users who wish to explore the data further, while executive summaries are best for high-level overviews.

Develop a compelling narrative around your findings, translating complex insights into actionable recommendations. Utilize data visualization tools like Tableau or Matplotlib to present results in an accessible format. Foster clear communication with stakeholders, ensuring they grasp the significance of your data-driven insights.

Absolutely, effective communication of results is crucial. Clear visuals and straightforward language are key to conveying insights. It's also important to discuss model limitations and tailor the presentation to the audience's understanding and needs.

The fifth step involves communicating and visualizing your model results to stakeholders. Use clear language, graphs, charts, and tables to convey findings, insights, and recommendations. Explain the limitations, assumptions, and uncertainties of your models and how they impact results. Tailor your communication style and format to match your audience, purpose, and medium, ensuring effective and meaningful interaction with stakeholders.

Learning and improving is a continuous process in a data science role. For example, after completing a project, reflecting on what worked well and what didn’t can provide valuable insights for future projects. Seeking feedback from peers or managers can help identify areas for improvement. Keeping up-to-date with the latest trends and best practices in data science ensures your skills remain relevant and effective.

- One time, after project completion, I held a review session with my team to discuss what techniques were effective and which areas needed improvement, fostering a culture of continuous learning. - In my experience, constructive feedback from peers and mentors has been invaluable. It provided me with different perspectives and helped me refine my approach to data science projects. - One thing I've found helpful is to stay current with data science trends and advancements. Regularly participating in online forums, attending webinars, and taking online courses have been effective ways to enhance my skills and adapt to new methodologies.

Embrace a growth mindset by actively seeking feedback and staying abreast of industry advancements. Participate in online communities, attend conferences, and engage in continuous learning to stay at the forefront of data science innovations. Encourage a culture of knowledge sharing within your team.

Si de una cosa me he dado cuenta en el mundo tecnológico, es que hay que estar en constante formación o te quedas atrás. Es una ventaja y un inconveniente, En poco tiempo eres de los que más experiencia tienes en X tecnología, por que es algo nuevo que nadie tiene/sabe/conoce. Pero en poco tiempo, te quedas colgado si no sigues atento al día a día. Hay infinidad de formación online, Microsoft Learn, es la bomba Read, stay curious.

Feedback is the "food for thought" of any data professional (at least, the ones "worth their salt"). If you're averse to feedback, you will not improve. If the feedback you get is not direct, relevant, and constructive, you will not improve.

Visualizing the impact in real-time painted a vivid picture of positive change, turning data into a narrative of improved lives and a community united by the flow of clean water. By employing data visualization techniques, we illuminate the transformative impact of our efforts. This approach resonates with both my team and manager, as it not only demonstrates our commitment to social responsibility but also provides clear, visual evidence of the positive changes our projects bring. The compelling data stories not only foster team morale but also showcase our company's dedication to making a meaningful difference in communities.🔆

The sixth step involves learning and improving from your experience and feedback. Reflect on successes and challenges, seeking feedback from managers, mentors, peers, or customers. Use feedback to enhance your skills, processes, and outcomes. Stay informed about the latest trends and best practices in data science, continuously updating your knowledge and skills to adapt to evolving requirements.

Don't just crunch numbers and disappear into the data vortex. Communicate your findings clearly and creatively. Visualize your insights with stunning charts and graphs that captivate your audience (think storytelling with data, not snooze-inducing spreadsheets). Remember, even the most brilliant insights are useless if no one understands them. So, channel your inner data rockstar and make those spreadsheets sing!

Data science is an iterative process. Use feedback from peers and stakeholders to refine your models and approaches. Stay abreast of new techniques, tools, and best practices in the field to continuously improve your work.

Don't forget to consider if YOU are enjoying the work! Data science gets a lot of hype, but it's okay if you're not loving your new role. The nice thing about data work is that it can be done in so many different industries, companies, and projects! After you've been in the new role awhile and feel solid, take a moment to assess how you're feeling. Do you like the subject matter, or would another industry or company energize you more? Do you like your projects, or are there other projects on the team you're drawn to more or new ideas you have? If you're not loving all of the work, bring some ideas about how to slowly evolve your role to your manager. Good companies will work with you because they know you're hard to replace!

Just a couple of things from my side 1. Understand the business- This is the key 2. Remember tools are here to help you to get the work done Tools != Data Science

Always make sure you familiarise yourself with the data, methods, types of analyses that the company / your team, is doing. When you have done that, close the gaps in your skillset if there are any. Finally, challenge procedures that may not make the most sense, based on your expertise. Starting out is a good time to question, as the worst outcome is that you learn something you didn’t know, and the best outcome is that you help define a new and improved procedure. Best of luck to everyone starting in new data science roles!

The most important task is to understand the business context and structured/unstructured data that are collected from various sources. Thorough knowledge and applications of data visualization, data collection techniques and use of best analytical tools would be helpful in order to both present and represent data. In addition to this, you should be aware of data management techniques to apply data filtering, validation and transformation rules. The other key skill needed for this role is the effective communication between various stakeholders and understanding of data governance protocols for sensitive customer data.

Emphasize the importance of ethical considerations in data science, addressing potential biases and ensuring responsible AI practices. Build a professional network within and outside your organization for diverse perspectives and collaborative opportunities. Stay adaptable to evolving technologies, as the data science landscape is dynamic and ever-changing.

A critical step in ensuring success is being able to prioritize one's work (or that of a team) in terms of effort required and the expected impact of that work to the business. The impact may be financial -- i.e. it drives revenue or reduces cost -- or it may be more tied to product goals -- increase user engagement, reduce churn, etc -- but either way it should ultimately tie in to your company's goals. High impact, low effort projects are always the most attractive types of projects, but when starting out even medium impact / low effort projects can be great for quickly demonstrating success. Keep in mind that prioritization should be an iterative process -- frequently reassessing priorities helps stay well aligned with your objectives.

If starting a new data science role: Understand objectives: Clarify goals and expectations for the role. Familiarize with data: Review available datasets and data sources. Learn tools and technologies: Master relevant programming languages, libraries, and analytics tools. Build domain knowledge: Understand the industry or domain you'll be working in. Establish workflows: Create processes for data collection, analysis, and reporting. Collaborate with team: Engage with colleagues to understand team dynamics and projects. Continuous learning: Stay updated on the latest trends, techniques, and advancements in data science. Set goals: Define short-term and long-term objectives to guide your progress in the role.

Data science is part of the data value chain. So, when when starting a new data science role, one way to proceed is to understand how that role aligns with other roles in the data value chain. For example, following the architecture abstraction approach, one needs to know the role responsilities and accountabilities at each level: Contextual level, Conceptual level, Logical level, Physical level. Regards