Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources

Roadmap to Become a Data Analyst: 📊 Learn Excel & Google Sheets (Formulas, Pivot Tables) ∟📊 Master SQL (SELECT, JOINs, CTEs, Window Functions) ∟📊 Learn Data Visualization (Power BI / Tableau) ∟📊 Understand Statistics & Probability ∟📊 Learn Python (Pandas, NumPy, Matplotlib, Seaborn) ∟📊 Work with Real Datasets (Kaggle / Public APIs) ∟📊 Learn Data Cleaning & Preprocessing Techniques ∟📊 Build Case Studies & Projects ∟📊 Create Portfolio & Resume ∟✅ Apply for Internships / Jobs React ❤️ for More 💼

Python Functions 👆

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Dataset Name: Malaria Bounding Boxes Basic Description: P. vivax (malaria) infected human blood smears 📖 FULL DATASET DESCRIPTION: ================================== Malaria is a disease caused by Plasmodium parasites that remains a major threat in global health, affecting 200 million people and causing 400,000 deaths a year. The main species of malaria that affect humans are Plasmodium falciparum and Plasmodium vivax. For malaria as well as other microbial infections, manual inspection of thick and thin blood smears by trained microscopists remains the gold standard for parasite detection and stage determination because of its low reagent and instrument cost and high flexibility. Despite manual inspection being extremely low throughput and susceptible to human bias, automatic counting software remains largely unused because of the wide range of variations in brightfield microscopy images. However, a robust automatic counting and cell classification solution would provide enormous benefits due to faster and more accurate quantitative results without human variability; researchers and medical professionals could better characterize stage-specific drug targets and better quantify patient reactions to drugs. Previous attempts to automate the process of identifying and quantifying malaria have not gained major traction partly due to difficulty of replication, comparison, and extension. Authors also rarely make their image sets available, which precludes replication of results and assessment of potential improvements. The lack of a standard set of images nor standard set of metrics used to report results has impeded the field. Images are in .png or .jpg format. There are 3 sets of images consisting of 1364 images (~80,000 cells) with different researchers having prepared each one: from Brazil (Stefanie Lopes), from Southeast Asia (Benoit Malleret), and time course (Gabriel Rangel). Blood smears were stained with Giemsa reagent. 📥 DATASET DOWNLOAD INFORMATION ================================== 🔴 Dataset Size: Download dataset as zip (5 GB) 🔰 Direct dataset download link: https://www.kaggle.com/api/v1/datasets/download/kmader/malaria-bounding-boxes 📊 Additional information: ================================== File count not found Views: 54,400 Downloads: 4,657 📚 RELATED NOTEBOOKS: ================================== 1. Malaria | YoloV5 | FasterRCNN | Upvotes: 114    URL: https://www.kaggle.com/code/polomarco/malaria-yolov5-fasterrcnn 2. Malaria Bounding Box | Upvotes: 93    URL: https://www.kaggle.com/code/vishnu123/malaria-bounding-box 3. Malaria Preview | Upvotes: 32    URL: https://www.kaggle.com/code/kmader/malaria-preview 4. Malaria Cell Images(Shuffled and Split) | Upvotes: 6    URL: https://www.kaggle.com/datasets/sagnikmazumder37/malaria-cell-imagesshuffled-and-split 5. P.Vivax malaria image dataset | Upvotes: 4    URL: https://www.kaggle.com/datasets/jxxn03x/p-vivax-malaria-image-dataset ==================================

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🔅SQL Revision Notes for Interview💡

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Essential Topics to Master Data Analytics Interviews: 🚀 SQL: 1. Foundations - SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING - Basic JOINS (INNER, LEFT, RIGHT, FULL) - Navigate through simple databases and tables 2. Intermediate SQL - Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN) - Embrace Subqueries and nested queries - Master Common Table Expressions (WITH clause) - Implement CASE statements for logical queries 3. Advanced SQL - Explore Advanced JOIN techniques (self-join, non-equi join) - Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag) - Optimize queries with indexing - Execute Data manipulation (INSERT, UPDATE, DELETE) Python: 1. Python Basics - Grasp Syntax, variables, and data types - Command Control structures (if-else, for and while loops) - Understand Basic data structures (lists, dictionaries, sets, tuples) - Master Functions, lambda functions, and error handling (try-except) - Explore Modules and packages 2. Pandas & Numpy - Create and manipulate DataFrames and Series - Perfect Indexing, selecting, and filtering data - Handle missing data (fillna, dropna) - Aggregate data with groupby, summarizing data - Merge, join, and concatenate datasets 3. Data Visualization with Python - Plot with Matplotlib (line plots, bar plots, histograms) - Visualize with Seaborn (scatter plots, box plots, pair plots) - Customize plots (sizes, labels, legends, color palettes) - Introduction to interactive visualizations (e.g., Plotly) Excel: 1. Excel Essentials - Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.) - Dive into charts and basic data visualization - Sort and filter data, use Conditional formatting 2. Intermediate Excel - Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF) - Leverage PivotTables and PivotCharts for summarizing data - Utilize data validation tools - Employ What-if analysis tools (Data Tables, Goal Seek) 3. Advanced Excel - Harness Array formulas and advanced functions - Dive into Data Model & Power Pivot - Explore Advanced Filter, Slicers, and Timelines in Pivot Tables - Create dynamic charts and interactive dashboards Power BI: 1. Data Modeling in Power BI - Import data from various sources - Establish and manage relationships between datasets - Grasp Data modeling basics (star schema, snowflake schema) 2. Data Transformation in Power BI - Use Power Query for data cleaning and transformation - Apply advanced data shaping techniques - Create Calculated columns and measures using DAX 3. Data Visualization and Reporting in Power BI - Craft interactive reports and dashboards - Utilize Visualizations (bar, line, pie charts, maps) - Publish and share reports, schedule data refreshes Statistics Fundamentals: - Mean, Median, Mode - Standard Deviation, Variance - Probability Distributions, Hypothesis Testing - P-values, Confidence Intervals - Correlation, Simple Linear Regression - Normal Distribution, Binomial Distribution, Poisson Distribution. Show some ❤️ if you're ready to elevate your data analytics journey! 📊 ENJOY LEARNING 👍👍

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Machine Learning Project Ideas ✅

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📖 7 must-know strategies to scale your database. 1 - Indexing: Check the query patterns of your application and create the right indexes. 2 - Materialized Views: Pre-compute complex query results and store them for faster access. 3 - Denormalization: Reduce complex joins to improve query performance. 4 - Vertical Scaling Boost your database server by adding more CPU, RAM, or storage. 5 - Caching Store frequently accessed data in a faster storage layer to reduce database load. 6 - Replication Create replicas of your primary database on different servers for scaling the reads. 7 - Sharding Split your database tables into smaller pieces and spread them across servers. Used for scaling the writes as well as the reads.

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If you're serious about getting into Data Science with Python, follow this 5-step roadmap. Each phase builds on the previous one, so don’t rush. Take your time, build projects, and keep moving forward. Step 1: Python Fundamentals Before anything else, get your hands dirty with core Python. This is the language that powers everything else. ✅ What to learn: type(), int(), float(), str(), list(), dict() if, elif, else, for, while, range() def, return, function arguments List comprehensions: [x for x in list if condition] – Mini Checkpoint: Build a mini console-based data calculator (inputs, basic operations, conditionals, loops). Step 2: Data Cleaning with Pandas Pandas is the tool you'll use to clean, reshape, and explore data in real-world scenarios. ✅ What to learn: Cleaning: df.dropna(), df.fillna(), df.replace(), df.drop_duplicates() Merging & reshaping: pd.merge(), df.pivot(), df.melt() Grouping & aggregation: df.groupby(), df.agg() – Mini Checkpoint: Build a data cleaning script for a messy CSV file. Add comments to explain every step. Step 3: Data Visualization with Matplotlib Nobody wants raw tables. Learn to tell stories through charts. ✅ What to learn: Basic charts: plt.plot(), plt.scatter() Advanced plots: plt.hist(), plt.kde(), plt.boxplot() Subplots & customizations: plt.subplots(), fig.add_subplot(), plt.title(), plt.legend(), plt.xlabel() – Mini Checkpoint: Create a dashboard-style notebook visualizing a dataset, include at least 4 types of plots. Step 4: Exploratory Data Analysis (EDA) This is where your analytical skills kick in. You’ll draw insights, detect trends, and prepare for modeling. ✅ What to learn: Descriptive stats: df.mean(), df.median(), df.mode(), df.std(), df.var(), df.min(), df.max(), df.quantile() Correlation analysis: df.corr(), plt.imshow(), scipy.stats.pearsonr() — Mini Checkpoint: Write an EDA report (Markdown or PDF) based on your findings from a public dataset. Step 5: Intro to Machine Learning with Scikit-Learn Now that your data skills are sharp, it's time to model and predict. ✅ What to learn: Training & evaluation: train_test_split(), .fit(), .predict(), cross_val_score() Regression: LinearRegression(), mean_squared_error(), r2_score() Classification: LogisticRegression(), accuracy_score(), confusion_matrix() Clustering: KMeans(), silhouette_score() – Final Checkpoint: Build your first ML project end-to-end ✅ Load data ✅ Clean it ✅ Visualize it ✅ Run EDA ✅ Train & test a model ✅ Share the project with visuals and explanations on GitHub Don’t just complete tutorialsm create things. Explain your work. Build your GitHub. Write a blog. That’s how you go from “learning” to “landing a job Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 All the best 👍👍

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SQL Interview Questions 1. How would you find duplicate records in SQL? 2.What are various types of SQL joins? 3.What is a trigger in SQL? 4.What are different DDL,DML commands in SQL? 5.What is difference between Delete, Drop and Truncate? 6.What is difference between Union and Union all? 7.Which command give Unique values? 8. What is the difference between Where and Having Clause? 9.Give the execution of keywords in SQL? 10. What is difference between IN and BETWEEN Operator? 11. What is primary and Foreign key? 12. What is an aggregate Functions? 13. What is the difference between Rank and Dense Rank? 14. List the ACID Properties and explain what they are? 15. What is the difference between % and _ in like operator? 16. What does CTE stands for? 17. What is database?what is DBMS?What is RDMS? 18.What is Alias in SQL? 19. What is Normalisation?Describe various form? 20. How do you sort the results of a query? 21. Explain the types of Window functions? 22. What is limit and offset? 23. What is candidate key? 24. Describe various types of Alter command? 25. What is Cartesian product? Like this post if you need more content like this ❤️

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