Essential Topics to Master Data Science Interviews: 🚀

SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace 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 science game! 📊

ENJOY LEARNING 👍👍

Essential Skills to Master for a Data Analytics Career

1️⃣ SQL 🗂️ Learn how to query databases, use joins, aggregate data, and write optimized SQL queries.

2️⃣ Data Visualization 📊 Communicate insights effectively using tools like Power BI, Tableau, and Excel charts.

3️⃣ Python for Data Analysis 🐍 Use libraries like Pandas, NumPy, and Matplotlib to manipulate and analyze data efficiently.

4️⃣ Statistical Thinking 📈 Understand key concepts like probability, hypothesis testing, and regression analysis for data-driven decisions.

5️⃣ Business Acumen 💼 Know how to translate raw data into actionable insights that drive business growth.

6️⃣ Data Cleaning & Wrangling 🧹 Real-world data is messy—learn techniques to handle missing values, duplicates, and outliers.

7️⃣ Excel Proficiency 📑 Master formulas, PivotTables, and Power Query for quick and effective data analysis.

8️⃣ Communication & Storytelling 🎤 Turn complex data findings into compelling narratives that stakeholders can understand.

9️⃣ Critical Thinking & Problem-Solving 🔍 Go beyond numbers—ask the right questions and identify meaningful patterns in data.

🔟 Continuous Learning & AI Integration 🤖 Stay updated with new analytics trends and leverage AI for automation and insights.

Master these skills, and you’ll be well on your way to becoming a top-tier data analyst! 🚀

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Mathematics for Machine Learning

Published by Cambridge University Press (published April 2020)

https://mml-book.com

PDF: https://mml-book.github.io/book/mml-book.pdf

🔎 Gender & Age Detection using Python Machine Learning! 🤖

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Complete Data Science Roadmap
👇👇

1. Introduction to Data Science
- Overview and Importance
- Data Science Lifecycle
- Key Roles (Data Scientist, Analyst, Engineer)

2. Mathematics and Statistics
- Probability and Distributions
- Descriptive/Inferential Statistics
- Hypothesis Testing
- Linear Algebra and Calculus Basics

3. Programming Languages
- Python: NumPy, Pandas, Matplotlib
- R: dplyr, ggplot2
- SQL: Joins, Aggregations, CRUD

4. Data Collection & Preprocessing
- Data Cleaning and Wrangling
- Handling Missing Data
- Feature Engineering

5. Exploratory Data Analysis (EDA)
- Summary Statistics
- Data Visualization (Histograms, Box Plots, Correlation)

6. Machine Learning
- Supervised (Linear/Logistic Regression, Decision Trees)
- Unsupervised (K-Means, PCA)
- Model Selection and Cross-Validation

7. Advanced Machine Learning
- SVM, Random Forests, Boosting
- Neural Networks Basics

8. Deep Learning
- Neural Networks Architecture
- CNNs for Image Data
- RNNs for Sequential Data

9. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Word Embeddings (Word2Vec)

10. Data Visualization & Storytelling
- Dashboards (Tableau, Power BI)
- Telling Stories with Data

11. Model Deployment
- Deploy with Flask or Django
- Monitoring and Retraining Models

12. Big Data & Cloud
- Introduction to Hadoop, Spark
- Cloud Tools (AWS, Google Cloud)

13. Data Engineering Basics
- ETL Pipelines
- Data Warehousing (Redshift, BigQuery)

14. Ethics in Data Science
- Ethical Data Usage
- Bias in AI Models

15. Tools for Data Science
- Jupyter, Git, Docker

16. Career Path & Certifications
- Building a Data Science Portfolio

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Python Project Ideas: https://t.iss.one/dsabooks/85

Best Resources to learn Data Science 👇👇

Python Tutorial

Data Science Course by Kaggle

Machine Learning Course by Google

Best Data Science & Machine Learning Resources

Interview Process for Data Science Role at Amazon

Python Interview Resources

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Few ways to optimise SQL Queries 👇👇

Use Indexing: Properly indexing your database tables can significantly speed up query performance by allowing the database to quickly locate the rows needed for a query.

Optimize Joins: Minimize the number of joins and use appropriate join types (e.g., INNER JOIN, LEFT JOIN) to ensure efficient data retrieval.

Avoid SELECT * : Instead of selecting all columns using SELECT *, explicitly specify only the columns needed for the query to reduce unnecessary data transfer and processing overhead.

Use WHERE Clause Wisely: Filter rows early in the query using WHERE clause to reduce the dataset size before joining or aggregating data.

Avoid Subqueries: Whenever possible, rewrite subqueries as JOINs or use Common Table Expressions (CTEs) for better performance.

Limit the Use of DISTINCT: Minimize the use of DISTINCT as it requires sorting and duplicate removal, which can be resource-intensive for large datasets.

Optimize GROUP BY and ORDER BY: Use GROUP BY and ORDER BY clauses judiciously, and ensure that they are using indexed columns whenever possible to avoid unnecessary sorting.

Consider Partitioning: Partition large tables to distribute data across multiple nodes, which can improve query performance by reducing I/O operations.

Monitor Query Performance: Regularly monitor query performance using tools like query execution plans, database profiler, and performance monitoring tools to identify and address bottlenecks.

Hope it helps :)

Data Analyst Interview Questions 👇

1.How to create filters in Power BI?

Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways.

Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries.
Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters)


2.How to sort data in Power BI?

Sorting is available in multiple formats. In the data view, a common sorting option of alphabetical order is there. Apart from that, we have the option of Sort by column, where one can sort a column based on another column. The sorting option is available in visuals as well. Sort by ascending and descending option by the fields and measure present in the visual is also available.


3.How to convert pdf to excel?

Open the PDF document you want to convert in XLSX format in Acrobat DC.
Go to the right pane and click on the “Export PDF” option.
Choose spreadsheet as the Export format.
Select “Microsoft Excel Workbook.”
Now click “Export.”
Download the converted file or share it.


4. How to enable macros in excel?

Click the file tab and then click “Options.”
A dialog box will appear. In the “Excel Options” dialog box, click on the “Trust Center” and then “Trust Center Settings.”
Go to the “Macro Settings” and select “enable all macros.”
Click OK to apply the macro settings.

Three different learning styles in machine learning algorithms:

1. Supervised Learning

Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.

A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.

Example problems are classification and regression.

Example algorithms include: Logistic Regression and the Back Propagation Neural Network.

2. Unsupervised Learning

Input data is not labeled and does not have a known result.

A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.

Example problems are clustering, dimensionality reduction and association rule learning.

Example algorithms include: the Apriori algorithm and K-Means.

3. Semi-Supervised Learning

Input data is a mixture of labeled and unlabelled examples.

There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.

Example problems are classification and regression.

Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.

100 Days Data Analysis Roadmap for 2025

Daily hours: 1-2 hours. the practical application of what you learn is crucial, so allocate some time for hands-on projects and real- world applications.

Days 1-10: Foundations of Data Analysis

Days 1-2:Install Python, Jupyter Notebooks, and necessary libraries (NumPy, Pandas).

Days 3-5: Learn the basics of Python programming.

Days 6-10: Dive into data manipulation with Pandas.

Days 11-20: SQL for Data Analysis

Days 11-15: Learn SQL for querying and analyzing databases.

Days 16-20: Practice SQL on real-world datasets.

Days 21-30: Excel for Data Analysis

Days 21-25: Master essential Excel functions for data analysis.

Days 26-30: Explore advanced Excel features for data manipulation and visualization.

Days 31-40: Data Cleaning and Preprocessing

Days 31-35: Explore data cleaning techniques and handle missing data.

Days 36-40: Learn about data preprocessing techniques (scaling, encoding, etc.).

Days 41-50: Exploratory Data Analysis (EDA)

Days 41-45: Understand statistical concepts and techniques for EDA.

Days 46-50: Apply data visualization tools (Matplotlib, Seaborn) for EDA.

Days 51-60: Statistical Analysis

Days 51-55: Deepen your understanding of statistical concepts.

Days 56-60: Learn hypothesis testing and regression analysis.

Days 61-70: Advanced Data Visualization

Days 61-65: Explore advanced data visualization with tools like Plotly and Tableau.

Days 66-70: Create interactive dashboards for data storytelling.

Days 71-80: Time Series Analysis and Forecasting

Days 71-75: Understand time series data and basic analysis.

Days 76-80: Implement time series forecasting models.

Days 81-90: Capstone Project and Specialization

Work on a practical data analysis project incorporating all learned concepts.

Choose a specialization (e.g., domain-specific analysis) and explore advanced techniques.

Days 91-100: Additional Tools

Days 91-95: Introduction to big data concepts (Hadoop, Spark).

• Days 96-100: Hands-on experience with distributed computing using Spark.

Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

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Here are some advanced SQL techniques that are game-changers

Window Functions: Learn how to use OVER() for advanced analytics tasks. They are crucial for calculating running totals, rankings, and lead-lag analysis in datasets.

CTEs and Temp Tables: Common Table Expressions (CTEs) and temporary tables can simplify complex queries, especially when dealing with large datasets.

Dynamic SQL: Understand how to construct SQL queries dynamically to increase the flexibility of your database interactions.

Optimizing Queries for Performance: Explore how indexing, query restructuring, and understanding execution plans can drastically improve your query performance.

Using PIVOT and UNPIVOT: These operations are key for converting rows to columns and vice versa, making data more readable and analysis-friendly. If you're looking to deepen your SQL knowledge, these areas are a great start.

5 Algorithms you must know as a data scientist 👩‍💻 🧑‍💻

1. Dimensionality Reduction
- PCA, t-SNE, LDA

2. Regression models
- Linesr regression, Kernel-based regression models, Lasso Regression, Ridge regression, Elastic-net regression

3. Classification models
- Binary classification- Logistic regression, SVM
- Multiclass classification- One versus one, one versus many
- Multilabel classification

4. Clustering models
- K Means clustering, Hierarchical clustering, DBSCAN, BIRCH models

5. Decision tree based models
- CART model, ensemble models(XGBoost, LightGBM, CatBoost)

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Top 10 Data Science Concepts You Should Know 🧠

1. Data Cleaning: Garbage In, Garbage Out. You can't build great models on messy data. Learn to spot and fix errors before you start. Seriously, this is the most important step.

2. EDA: Your Data's Secret Diary. Before you build anything, EXPLORE! Understand your data's quirks, distributions, and relationships. Visualizations are your best friend here.

3. Feature Engineering: Turning Data into Gold. Raw data is often useless. Feature engineering is how you transform it into something your models can actually learn from. Think about what the data represents.

4. Machine Learning: The Right Tool for the Job. Don't just throw algorithms at problems. Understand why you're using linear regression vs. a random forest.

5. Model Validation: Are You Lying to Yourself? Too many people build models that look great on paper but fail in the real world. Rigorous validation is essential.

6. Feature Selection: Less Can Be More. Get rid of the noise! Focusing on the most important features improves performance and interpretability.

7. Dimensionality Reduction: Simplify, Simplify, Simplify. High-dimensional data can be a nightmare. Learn techniques to reduce complexity without losing valuable information.

8. Model Optimization: Squeeze Every Last Drop. Fine-tuning your model parameters can make a huge difference. But be careful not to overfit!

9. Data Visualization: Tell a Story People Understand. Don't just dump charts on a page. Craft a narrative that highlights key insights.

10. Big Data: When Things Get Serious. If you're dealing with massive datasets, you'll need specialized tools like Hadoop and Spark. But don't start here! Master the fundamentals first.

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