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Starting your journey as a data analyst is an amazing start for your career. As you progress, you might find new areas that pique your interest: • Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge. • Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you. • Business Analysis: If you're passionate about translating data into strategic business insights, consider transitioning to a business analyst role. But remember, even if you stick with data analysis, there's always room for growth, especially with the evolving landscape of AI. No matter where your path leads, the key is to start now.

This is a very COMMON issue that I observe in the projects of aspiring candidates They download a DATASET from Kaggle or any other website Export it to a Data Analysis TOOL And START the project with data cleaning After cleaning the data, they PLUG it into a dashboard In the dashboard, they put EVERY column into the visuals Also they APPLY the filters of top/bottom 10 Once done, they crack their KNUCKLES And put this project in a list of SUCCESSFULLY completed projects Over time, I have REVIEWED so many portfolio projects And I see this ISSUES almost every time When I go to their portfolio, for every project there is a DASHBOARD But WHAT should I do after seeing a dashboard? What is it trying to SAY? What should I do after SEEING top or bottom 10 cities, states or products? Every dashboard lacks CONTEXT And why NOT? Because they DON'T even know the business problem or problem statement So the dashboard you created is of NO use Your job is not just to create DASHBOARDS Your job would be to create DASHBOARDS to take out important INSIGHTS And from those insights, you will build RECOMMENDATIONS And these recommendations will be given to stakeholders as a SOLUTION to their business problem If they implemented your IDEAS and the problem gets solved Now you can say your work is DONE If you are SHOWING bottom 10 states, then what? You should write the INSIGHTS too For example, the sales of North India zone are FALLING The insights can be used like this Delhi that used to be in TOP 5 states is now in the BOTTOM 10 states And this might be the REASON why our North India sales are DROPPING so hard This is just a RANDOM example showing how your charts become UNDERSTANDABLE Well, everyone can EXTRACT insights from charts Even a KID can do this after looking at the tallest and smallest bar The real task is to give RECOMMENDATIONS to solve the BUSINESS problem And I have NEVER seen this in anyone's portfolio If you are doing this, then you are easily STANDING out in the crowd In my PORTFOLIO, I used to keep business problem, insights, dashboard and recommendations Even in the bullet point of projects in my resume, I included RECOMMENDATIONS Now this is what you can call a STRONG portfolio Because your analysis skills are the SAME as those used in the real life by a Data Analyst I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://t.me/DataSimplifier Like if it helps 😄

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Applied to 100+ jobs but still struggling? 9 out of 10 professionals take 4-6 months to switch to their targeted company! To solve this, Newton School has launched a 2-Month Placement Training & Referral Program for Software Development and Data Science roles. What you get: ✅ 1:1 Mentorship from top industry experts ✅ Skill gap analysis and targeted grooming ✅ Company-specific prep + mock interviews with expert feedback ✅ Resume & LinkedIn optimization to beat ATS ✅ Guaranteed 5+ first-round interviews at top companies We select only 10 candidates per month for each domain (Software Development & Data Science). 🚀 Interested? Apply here: 👇 https://tinyurl.com/DPKXCLRTE This program is only for those are graduated on or before 2025

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The Real Truth About Junior Data Analytics Interviews DataAnalytics (From someone who's interviewed 50+ analysts) Let me save you hours of interview prep... SQL Round WHAT THEY SAY: "Complex SQL knowledge" WHAT THEY ACTUALLY TEST: Can you clean messy data Do you check for NULL values How do you handle duplicates Can you explain your logic Do you verify results REAL QUESTIONS: "Find duplicate transactions" "Calculate monthly sales" "Show top customers" That's it. Really. ⤵️ Excel Interview WHAT THEY SAY: "Advanced Excel skills" WHAT THEY ACTUALLY TEST: VLOOKUP/XLOOKUP usage Pivot Table comfort Basic formulas Data cleaning approach Problem-solving process Business Case WHAT THEY SAY: "Data analysis presentation" WHAT THEY REALLY WANT: Can you explain simply Do you ask good questions Can you structure analysis Do you focus on impact Are you confident with data ⤵️ Common Scenarios The "Messy Data" Test They give you: Inconsistent formats Missing values Duplicate records They watch: How you spot issues What questions you ask Your cleaning approach The "Explain It" Challenge They ask: "Walk me through your analysis" They assess: Communication clarity Technical understanding Business thinking Confidence level ⤵️ How to Actually Prepare Practice Basics: Simple SQL queries Excel fundamentals Clear explanation Business Understanding: Read company metrics Understand industry Know basic KPIs Prepare good questions Real Scenarios to Practice: Monthly sales analysis Customer segmentation Product performance Marketing campaign results Reality Check: They care more about: How you think How you communicate How you solve problems Than: Perfect technical knowledge Complex code Advanced statistics I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://t.me/DataSimplifier Like this post for more content like this 👍♥️

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Don't Limit Yourself to Just One Title, "𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭" in Your Job Search! Don't get caught up in the confines of a single job title! There are countless roles out there that might align perfectly with your skills and interests. Here are a few alternative titles for data analyst roles to broaden your search horizons: 1. QI Analyst 2. Risk Analyst 3. Data Modeler 4. Research Analyst 5. Business Analyst 6. Reporting Analyst 7. Operations Analyst 8. Social Media Analyst 9. Statistical Analyst 10. Statistical Analyst 11. Product Data Analyst 12. Analytics Engineer 13. Supply Chain Analyst 14. Data Mining Engineer 15. Data Science Associate 16. Financial Data Analyst 17. Cybersecurity Analyst 18. Marketing Data Analyst 19. Quantitative Analyst 20. HR Analytics Specialist 21. Decision Support Analyst 22. Machine Learning Analyst 23. Fraud Detection Analyst 24. Healthcare Data Analyst 25. Data Insights Specialist 26. Data Visualization Specialist 27. Customer Insights Analyst 28. Business Intelligence Analyst 29. Predictive Analytics Analyst Remember, the right opportunity might be hiding behind a different title than you expect. Keep an open mind and explore all avenues in your job search journey! Also, there might be fewer applicants for these roles as many don't search for titles other than data Analyst or Business Analyst. Maybe you can get more calls or interviews this way. You don't have to try all the titles, filter out based on your interests and skills! After all, 𝐉𝐨𝐛 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐭𝐡𝐞 𝐭𝐢𝐭𝐥𝐞!! 😉 I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://t.me/DataSimplifier Hope this helps you 😊

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Data analysis is a gateway to becoming a: - Data Scientist - Business Analyst - Data Engineer - BI Engineer - Analytics Engineer And many other roles. Learning the skills doesn't close doors, if anything, it opens many more.

𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝟭. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀: Master Python, SQL, and R for data manipulation and analysis. 𝟮. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Use Excel, Pandas, and ETL tools like Alteryx and Talend for data processing. 𝟯. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Learn Tableau, Power BI, and Matplotlib/Seaborn for creating insightful visualizations. 𝟰. 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀: Understand Descriptive and Inferential Statistics, Probability, Regression, and Time Series Analysis. 𝟱. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Get proficient in Supervised and Unsupervised Learning, along with Time Series Forecasting. 𝟲. 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗧𝗼𝗼𝗹𝘀: Utilize Google BigQuery, AWS Redshift, and NoSQL databases like MongoDB for large-scale data management. 𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴: Implement Data Quality Monitoring (Great Expectations) and Performance Tracking (Prometheus, Grafana). 𝟴. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗧𝗼𝗼𝗹𝘀: Work with Data Orchestration tools (Airflow, Prefect) and visualization tools like D3.js and Plotly. 𝟵. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗿: Manage resources using Jupyter Notebooks and Power BI. 𝟭𝟬. 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗘𝘁𝗵𝗶𝗰𝘀: Ensure compliance with GDPR, Data Privacy, and Data Quality standards. 𝟭𝟭. 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: Leverage AWS, Google Cloud, and Azure for scalable data solutions. 𝟭𝟮. 𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴: Master data cleaning (OpenRefine, Trifacta) and transformation techniques. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you 😊

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8 must-know Data Analytics Terms.

Creating a one-month data analytics roadmap requires a focused approach to cover essential concepts and skills. Here's a structured plan along with free resources: 🗓️Week 1: Foundation of Data Analytics ◾Day 1-2: Basics of Data Analytics Resource: Khan Academy's Introduction to Statistics Focus Areas: Understand descriptive statistics, types of data, and data distributions. ◾Day 3-4: Excel for Data Analysis Resource: Microsoft Excel tutorials on YouTube or Excel Easy Focus Areas: Learn essential Excel functions for data manipulation and analysis. ◾Day 5-7: Introduction to Python for Data Analysis Resource: Codecademy's Python course or Google's Python Class Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas. 🗓️Week 2: Intermediate Data Analytics Skills ◾Day 8-10: Data Visualization Resource: Data Visualization with Matplotlib and Seaborn tutorials Focus Areas: Creating effective charts and graphs to communicate insights. ◾Day 11-12: Exploratory Data Analysis (EDA) Resource: Towards Data Science articles on EDA techniques Focus Areas: Techniques to summarize and explore datasets. ◾Day 13-14: SQL Fundamentals Resource: Mode Analytics SQL Tutorial or SQLZoo Focus Areas: Writing SQL queries for data manipulation. 🗓️Week 3: Advanced Techniques and Tools ◾Day 15-17: Machine Learning Basics Resource: Andrew Ng's Machine Learning course on Coursera Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics. ◾Day 18-20: Data Cleaning and Preprocessing Resource: Data Cleaning with Python by Packt Focus Areas: Techniques to handle missing data, outliers, and normalization. ◾Day 21-22: Introduction to Big Data Resource: Big Data University's courses on Hadoop and Spark Focus Areas: Basics of distributed computing and big data technologies. 🗓️Week 4: Projects and Practice ◾Day 23-25: Real-World Data Analytics Projects Resource: Kaggle datasets and competitions Focus Areas: Apply learned skills to solve practical problems. ◾Day 26-28: Online Webinars and Community Engagement Resource: Data Science meetups and webinars (Meetup.com, Eventbrite) Focus Areas: Networking and learning from industry experts. ◾Day 29-30: Portfolio Building and Review Activity: Create a GitHub repository showcasing projects and code Focus Areas: Present projects and skills effectively for job applications. 👉Additional Resources: Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus. Online Platforms: DataSimplifier, Kaggle, Towards Data Science Data Science Course Google Cloud Generative AI Path Unlock the power of Generative AI Models Machine Learning with Python Free Course Machine Learning Free Book Deep Learning Nanodegree Program with Real-world Projects AI, Machine Learning and Deep Learning Join @free4unow_backup for more free courses ENJOY LEARNING👍👍

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Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself. 1. Basic python and statistics Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness Automobile :- https://www.kaggle.com/toramky/automobile-dataset 2. Advanced Statistics Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset 3. Supervised Learning a) Regression Problems How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview b) Classification problems Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview Titanic :- https://www.kaggle.com/c/titanic San Francisco crime:- https://www.kaggle.com/c/sf-crime Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification Categorize cusine:- https://www.kaggle.com/c/whats-cooking 4. Some helpful Data science projects for beginners https://www.kaggle.com/c/house-prices-advanced-regression-techniques https://www.kaggle.com/c/digit-recognizer https://www.kaggle.com/c/titanic 5. Intermediate Level Data science Projects Black Friday Data : https://www.kaggle.com/sdolezel/black-friday Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset Million Song Data : https://www.kaggle.com/c/msdchallenge Census Income Data : https://www.kaggle.com/c/census-income/data Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2 Share with credits: https://t.me/sqlproject ENJOY LEARNING 👍👍

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