Zakir P.’s Post

Understanding the Data Project Lifecycle Here’s how key roles contribute to the success of a data project: 💼 Business Stakeholder The project begins with business stakeholders identifying the problem and setting the objectives. They define deliverables, establish KPIs, and ensure alignment between the project's goals and business needs. Their primary role is to provide clarity and direction for the data team. 🔧 Data Engineer Data engineers build the project's foundation by acquiring and preparing data. They gather data from diverse sources, clean and preprocess it to eliminate errors, and structure it for analysis. This includes addressing missing values and ensuring data consistency and quality. 🧠 Data Scientist Data scientists focus on data exploration and model development. During EDA, they use visualization tools to identify patterns, detect trends, and assess correlations. They then perform tasks like feature engineering, selecting algorithms, fine-tuning models, and validating results to derive actionable insights. 📊 Data Analyst Data analysts interpret and communicate results. Working alongside data scientists during EDA, they extract meaningful insights, detect trends, and create reports or dashboards. Their goal is to present findings in a clear, actionable format, enabling stakeholders to make informed decisions. When these roles collaborate effectively, the data project lifecycle progresses seamlessly, transforming business challenges into impactful solutions. 🚀 Follow Rajeev Sangwan for more informative posts. #DataScience #DataAnalytics #DataProject #DataEngineering #BusinessInsights #DataVisualization #EDATechniques #PredictiveAnalytics #MachineLearning #DataDrivenDecisions #BigData

A Day in the Life of a Data Scientist (It's Not Just Coffee & Code!) ☕️💻 Ever wondered what a data scientist actually does all day? It's not always glamorous, but it's definitely impactful. Here's a peek into a typical day's checklist: ☑️ Morning Brew & Data Wrangling: Start the day by catching up on industry news and tackling the messiest part of the job: cleaning and prepping data. ☑️ Exploration & Hypothesis: Dive deep into datasets, looking for patterns, anomalies, and potential insights. Time to brainstorm some hypotheses! ☑️ Model Building & Testing: The fun part! Experiment with different models and algorithms to see which ones best explain the data. ☑️ Collaboration Station: Team up with colleagues, brainstorm new approaches, and get feedback on your work. Two (or more) data heads are better than one! ☑️ Communication is Key: Translate findings into actionable insights. Craft clear visualizations or reports to share with stakeholders. ☑️ Continuous Learning: The data world moves fast! Dedicate time to learning new tools, techniques, or even diving into a research paper. Bonus Task: ☑️ Coffee refills: Essential fuel for debugging and those late-night data marathons! The reality is, data science is a mix of technical skills, creativity, and a healthy dose of problem-solving. Every day brings new challenges and opportunities to uncover the stories hidden within data. What's on your data scientist checklist today? Share in the comments below! #DataScience #DailyLife #BehindTheScenes #DataDriven

Starting a new data science project and feeling overwhelmed? Been there... Here are 5 key questions to answer before starting any project: 🧵 👇🏻 Whenever I was staffed on a new project, I used to dive right in, hoping for the best. I’m not saying it was complete chaos every time… but it definitely could have been smoother. Contrary to popular belief, simply jumping into tasks without a plan isn’t “being proactive.” It’s a recipe for frustration: • missed,  • teammates out of the loop,  • technical issues Not fun. That’s when I created my own Project Essentials Template—a quick checklist I use before I start any project. 1. Deadlines – I ask, “What’s the hard stop here?” 2. Team support – Who’s on board? Who can I turn to if things go wrong? 3. Problem definition – Are we really clear on what we’re solving? 4. Technical requirements – Do we have the right tools? Any blockers? 4. Communication channels – Who are we talking to, and how? 99% of the time, projects go off the rails because these basics aren’t addressed. By starting with this framework, I’ve saved myself countless hours of confusion and last-minute scrambling. ____ Want to learn about 3 Practical Steps for Data Scientists to Handle Stressful People Without Losing Focus (And Your Sanity) Jump to my newsletter (Link in the comments). And follow Penelope Lafeuille for more on Data Scientist Stuffs + Health + Tech

Ever wondered what separates a data science rookie from a seasoned pro? Here's a breakdown of the key differences between junior and senior data scientists across 3 key areas: 📈Mindset: ➡️Junior: Full of enthusiasm and eager to learn, but might get stuck in the weeds of specific tasks. ➡️Senior: Thinks strategically, focusing on the bigger picture and how data can solve real-world problems. 📈Working Nature: ➡️Junior: Takes direction well and excels at executing tasks assigned by seniors. Efficiently completes assigned data analysis or model building. ➡️Senior: Works independently, taking ownership of projects from ideation to execution. Mentors junior colleagues and guides them through challenges. 📈Project Management: ➡️Junior: Focuses on the technical aspects of the project, needing guidance on timelines, resource allocation, and stakeholder communication. ➡️Senior: Juggles technical expertise with strong project management skills. Breaks down complex projects into manageable tasks, manages deadlines, and effectively communicates progress to stakeholders. Remember, growth is a journey! Juniors bring fresh perspectives and seniors provide invaluable experience. Together, they make a data dream team! #datascience #datascientist #machinelearning #artificialintelligence #careergoals #careerdevelopment #juniorvssenior #datasciencecareers #datasciencetutorial #datascienceprojects #winorbitlearning

If you're a technical data practitioner whether an engineer, scientist or analyst, it's essential to work closely with other team members (the data engineers, scientists or analysts) in order to understand the data ecosystem within your company. ⁤ ⁤Additionally, don't ignore the importance of being surrounded by the business team (e.g., project managers) to deeply understand the business needs that drive your practices. ⁤ ⁤Conversely, if you're part of the business team, it's important to collaborate closely with technical data practitioners within your company to understand how your business challenges can be addressed using data-driven solutions. #dataengineering #dataanalyst #datascience

Imagine, data engineer, data scientist and business analyst are working on a one big project. But they are facing the challenge of collaboration. Today we are going to discuss about a most common challenge. That is the collaboration between the different teams and different roles. So, suppose our team, Vivek, Prince and Rashmi are working on one big project. Vivek is working as a data engineer, Prince is working as a data scientist, and Rashmi is a business analyst person. So, if Vivek wants to work, so he'll goanna work from the data engineering perspective. Will gonna to push the code into the repositories. When Prince, the data scientist needs to work on his work, so what he'll gonna do, he'll just pull the code and start coding it on the top of it. Once Rashmi needs to see that hey, if the code is as per the business requirement, if there is an issue in the code or not, she'll just simply look into the notebooks and can analyze it. If there is any change required, she's going to make the change and can commit back to the database. This way the collaboration becomes very easy and team can focus on building the amazing data solution. This is how we solve the challenge of collaboration using the database bricks environment. I hope this video is informative to you. If yes, please comment in the below section and see you in next video. Thank you. #TeamCollaboration #DataEngineering #DataScience #BusinessAnalytics #BigDataProjects #CollaborativeWork

The 4 Hats Every Exceptional Data Engineer Should Wear If you aspire to be a truly exceptional data engineer, try to embrace the following four roles in your project's lifecycle: · Be a Data Creator: Understand the source and genesis of data. · Be a Data Curator: Organize and maintain data quality. · Be a Data Consumer: Analyze and utilize data insights. · Be a Data Critique: Evaluate and improve data processes. Wearing all these hats will not only enhance your skillset but will also ensure the success of your projects. For me, the challenge of data curation is balanced by the immense satisfaction of seeing clean data drive powerful results. What roles do you find most rewarding? #dataengineering #dataanalysis #datascience #exceptional #data

"Should stay as a Data Analyst or I should move to Data Engineering?" So, my friends's weighing the pros and cons of diving into data engineering or sticking with analysis. Here's what I told her: 👇 1. Tech vs. Business: While data analysis offers insights into business decisions, engineering empowers you to build the infrastructure that drives those insights. Choose based on where you find fulfillment – crunching numbers or building systems. 2. Find Your Niche: Figure out what aspect of data work ignites your passion. Whether it's diving deep into algorithms or crafting compelling visualizations, focus on what excites you most. 3. Soft Skills Matter: Don't underestimate the power of communication and teamwork. Being able to articulate findings and collaborate effectively is just as important as technical prowess. 4. Stay Curious: Embrace the learning curve. Technology evolves rapidly, so never stop exploring new tools and methodologies. Ultimately, it was the allure of creating scalable solutions and diving into the intricacies of data architecture that drew me towards engineering. But hey! there's no one-size-fits-all path – follow your interests and the rest will fall into place. . . . . #DataEngineering #Data #DataAnalytics #DataProduct #CareerAdvice

7 Things You Should Know Before Becoming a Data analyst 1. You'll Make Mistakes, a Lot of It. What matters more is how you recover and grow from them. Everyone messes up. It's okay to make mistakes. What really counts is learning from them and improving. 2. Data quality is often more important than fancy algorithms. Having clean and accurate data is usually more helpful than using super complicated math tricks. 3. You will spend so much more time on communication than you expect. Talking to people about your work is a big part of the job. You'll be explaining things a lot. 4. A lot of Data Science work is tedious and boring and repetitive. Many parts of the job are repetitive and not very exciting. 5. You won’t get along with every business partner. But you have to learn how to work with them. You won't like everyone you work with. But you need to learn to work well with different people. 6. The best Data Scientists do much more than Data Science. They lead product teams, they talk to customers, they build pipelines etc. Being a good data scientist means doing more than just math. You also need to be a leader, talk to customers, and understand how things work together. 7. Higher complexity solutions =/= higher impact solutions. Using very complicated methods doesn't always give you the best results. Sometimes, simple solutions are better. #Data #Dataanalyst #Tips #Businessanalysis

Who thrives in data engineering? 🔍🛠️ 1️⃣ 𝗗𝗲𝘁𝗮𝗶𝗹-𝗼𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗣𝗲𝗼𝗽𝗹𝗲 Creating data pipelines involves meticulous testing and tracking every nuance. Attention to detail is crucial. 2️⃣ 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝘃𝗲 𝗣𝗲𝗼𝗽𝗹𝗲 Understanding business needs before building a pipeline saves time. Clear communication prevents overengineering. 3️⃣ 𝗣𝗮𝘁𝗶𝗲𝗻𝘁 𝗣𝗲𝗼𝗽𝗹𝗲 Writing pipelines requires patience. Troubleshooting bugs and complex data issues needs a calm approach. Success in data engineering means balancing these traits. 🌟 Hit the 🔔 on my profile John K. Moran and share your thoughts in the comments.👇 #DataEngineering #TechCareers #DetailOriented #CommunicationSkills #Patience #TechSuccess #EngineeringLife