𝟯𝟬+ 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗯𝘆 𝗛𝗣 𝗟𝗜𝗙𝗘 𝘁𝗼 𝗦𝘂𝗽𝗲𝗿𝗰𝗵𝗮𝗿𝗴𝗲 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿😍

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SQL (Structured Query Language) is a standard programming language used to manage and manipulate relational databases. Here are some key concepts to understand the basics of SQL:

1. Database: A database is a structured collection of data organized in tables, which consist of rows and columns.

2. Table: A table is a collection of related data organized in rows and columns. Each row represents a record, and each column represents a specific attribute or field.

3. Query: A SQL query is a request for data or information from a database. Queries are used to retrieve, insert, update, or delete data in a database.

4. CRUD Operations: CRUD stands for Create, Read, Update, and Delete. These are the basic operations performed on data in a database using SQL:
   - Create (INSERT): Adds new records to a table.
   - Read (SELECT): Retrieves data from one or more tables.
   - Update (UPDATE): Modifies existing records in a table.
   - Delete (DELETE): Removes records from a table.

5. Data Types: SQL supports various data types to define the type of data that can be stored in each column of a table, such as integer, text, date, and decimal.

6. Constraints: Constraints are rules enforced on data columns to ensure data integrity and consistency. Common constraints include:
   - Primary Key: Uniquely identifies each record in a table.
   - Foreign Key: Establishes a relationship between two tables.
   - Unique: Ensures that all values in a column are unique.
   - Not Null: Specifies that a column cannot contain NULL values.

7. Joins: Joins are used to combine rows from two or more tables based on a related column between them. Common types of joins include INNER JOIN, LEFT JOIN (or LEFT OUTER JOIN), RIGHT JOIN (or RIGHT OUTER JOIN), and FULL JOIN (or FULL OUTER JOIN).

8. Aggregate Functions: SQL provides aggregate functions to perform calculations on sets of values. Common aggregate functions include SUM, AVG, COUNT, MIN, and MAX.

9. Group By: The GROUP BY clause is used to group rows that have the same values into summary rows. It is often used with aggregate functions to perform calculations on grouped data.

10. Order By: The ORDER BY clause is used to sort the result set of a query based on one or more columns in ascending or descending order.

Understanding these basic concepts of SQL will help you write queries to interact with databases effectively. Practice writing SQL queries and experimenting with different commands to become proficient in using SQL for database management and manipulation.

𝟲 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗙𝘂𝘁𝘂𝗿𝗲-𝗣𝗿𝗼𝗼𝗳 𝗦𝗸𝗶𝗹𝗹𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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1. What are the different subsets of SQL?

Data Definition Language (DDL) – It allows you to perform various operations on the database such as CREATE, ALTER, and DELETE objects.
Data Manipulation Language(DML) – It allows you to access and manipulate data. It helps you to insert, update, delete and retrieve data from the database.
Data Control Language(DCL) – It allows you to control access to the database. Example – Grant, Revoke access permissions.

2. List the different types of relationships in SQL.

There are different types of relations in the database:
One-to-One – This is a connection between two tables in which each record in one table corresponds to the maximum of one record in the other.
One-to-Many and Many-to-One – This is the most frequent connection, in which a record in one table is linked to several records in another.
Many-to-Many – This is used when defining a relationship that requires several instances on each sides.
Self-Referencing Relationships – When a table has to declare a connection with itself, this is the method to employ.

3. What is a Stored Procedure?

A stored procedure is a subroutine available to applications that access a relational database management system (RDBMS). Such procedures are stored in the database data dictionary. The sole disadvantage of stored procedure is that it can be executed nowhere except in the database and occupies more memory in the database server.

4. What is Pattern Matching in SQL?

SQL pattern matching provides for pattern search in data if you have no clue as to what that word should be. This kind of SQL query uses wildcards to match a string pattern, rather than writing the exact word. The LIKE operator is used in conjunction with SQL Wildcards to fetch the required information.

𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗔𝗜 𝗧𝗼𝗼𝗹 𝗘𝘃𝗲𝗿𝘆 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗡𝗲𝗲𝗱𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱!😍

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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.

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Complete Roadmap to learn Power BI 👇👇

Step 1: Power BI Basics
1. Understand the Power BI ecosystem, including Power BI Desktop and Power BI Service.
2. Learn to import data from different sources (Excel, SQL Server, CSV, etc.).
3. Explore Power Query Editor for data transformation and cleaning.
4. Practice creating basic visualizations like bar charts, line charts, and pie charts.

Step 2: Intermediate Power BI
1. Dive deeper into data modeling concepts such as relationships, calculated columns, and
measures.
2. Learn about DAX (Data Analysis Expressions) for advanced calculations and
aggregations.
3. Explore Power BI visuals and custom visuals for enhanced data visualization.
4. Practice creating interactive dashboards and reports with slicers, filters, and drill-down
features.

Step 3: Advanced Power BI
1. Master advanced data modeling techniques like role-playing dimensions and
many-to-many relationships.
2. Learn about Power BI Service features such as workspaces, datasets, and dataflows.
3. Explore Power BI administration and security settings.
4. Practice optimizing performance and refreshing data in Power BI reports.

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Step-by-Step Approach to Learn Data Analytics

➊ Learn Programming Language → SQL & Python
↓
➋ Master Excel & Spreadsheets → Pivot Tables, VLOOKUP, Data Cleaning
↓
➌ SQL for Data Analysis → SELECT, JOINS, GROUP BY, Window Functions
↓
➍ Data Manipulation & Processing → Pandas, NumPy
↓
➎ Data Visualization → Power BI, Tableau, Matplotlib, Seaborn
↓
➏ Exploratory Data Analysis (EDA) → Missing Values, Outliers, Feature Engineering
↓
➐ Business Intelligence & Reporting → Dashboards, Storytelling with Data
↓
➑ Advanced Concepts → A/B Testing, Statistical Analysis, Machine Learning Basics

React with ❤️ for detailed explanation

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

𝟲 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗦𝗤𝗟 & 𝗠𝗟 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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Advanced Data Science Concepts 🚀

1️⃣ Feature Engineering & Selection

Handling Missing Values – Imputation techniques (mean, median, KNN).

Encoding Categorical Variables – One-Hot Encoding, Label Encoding, Target Encoding.

Scaling & Normalization – StandardScaler, MinMaxScaler, RobustScaler.

Dimensionality Reduction – PCA, t-SNE, UMAP, LDA.


2️⃣ Machine Learning Optimization

Hyperparameter Tuning – Grid Search, Random Search, Bayesian Optimization.

Model Validation – Cross-validation, Bootstrapping.

Class Imbalance Handling – SMOTE, Oversampling, Undersampling.

Ensemble Learning – Bagging, Boosting (XGBoost, LightGBM, CatBoost), Stacking.


3️⃣ Deep Learning & Neural Networks

Neural Network Architectures – CNNs, RNNs, Transformers.

Activation Functions – ReLU, Sigmoid, Tanh, Softmax.

Optimization Algorithms – SGD, Adam, RMSprop.

Transfer Learning – Pre-trained models like BERT, GPT, ResNet.


4️⃣ Time Series Analysis

Forecasting Models – ARIMA, SARIMA, Prophet.

Feature Engineering for Time Series – Lag features, Rolling statistics.

Anomaly Detection – Isolation Forest, Autoencoders.


5️⃣ NLP (Natural Language Processing)

Text Preprocessing – Tokenization, Stemming, Lemmatization.

Word Embeddings – Word2Vec, GloVe, FastText.

Sequence Models – LSTMs, Transformers, BERT.

Text Classification & Sentiment Analysis – TF-IDF, Attention Mechanism.


6️⃣ Computer Vision

Image Processing – OpenCV, PIL.

Object Detection – YOLO, Faster R-CNN, SSD.

Image Segmentation – U-Net, Mask R-CNN.


7️⃣ Reinforcement Learning

Markov Decision Process (MDP) – Reward-based learning.

Q-Learning & Deep Q-Networks (DQN) – Policy improvement techniques.

Multi-Agent RL – Competitive and cooperative learning.


8️⃣ MLOps & Model Deployment

Model Monitoring & Versioning – MLflow, DVC.

Cloud ML Services – AWS SageMaker, GCP AI Platform.

API Deployment – Flask, FastAPI, TensorFlow Serving.


Like if you want detailed explanation on each topic ❤️

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Hope this helps you 😊

7 Must-Have Tools for Data Analysts in 2025:

✅ SQL – Still the #1 skill for querying and managing structured data
✅ Excel / Google Sheets – Quick analysis, pivot tables, and essential calculations
✅ Python (Pandas, NumPy) – For deep data manipulation and automation
✅ Power BI – Transform data into interactive dashboards
✅ Tableau – Visualize data patterns and trends with ease
✅ Jupyter Notebook – Document, code, and visualize all in one place
✅ Looker Studio – A free and sleek way to create shareable reports with live data.

Perfect blend of code, visuals, and storytelling.

React with ❤️ for free tutorials on each tool

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Hope it helps :)

𝟱 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗔𝗱𝗱 𝘁𝗼 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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Data Visualization Tools Comparison

Power BI:

Best for: Interactive dashboards and reports.

Strengths: Seamless integration with Microsoft products, strong DAX functions.

Weaknesses: Can be resource-heavy with large datasets.


Tableau:

Best for: Advanced data visualizations and storytelling.

Strengths: User-friendly drag-and-drop interface, powerful visual capabilities.

Weaknesses: Higher cost, steeper learning curve for complex analyses.


Excel:

Best for: Quick data analysis and small-scale visualizations.

Strengths: Widely used, simple to learn, great for quick charts.

Weaknesses: Limited in handling large datasets, fewer customization options.


Google Data Studio:

Best for: Free, cloud-based visualizations.

Strengths: Easy collaboration, integrates well with Google products.

Weaknesses: Fewer advanced features compared to Tableau and Power BI.

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Like this post if you want me to continue this Power BI series 👍♥️

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

𝟱 𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱 (𝗪𝗶𝘁𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀!)😍

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1. Explain data cleansing.

Data cleaning, also known as data cleansing or data scrubbing or wrangling, is basically a process of identifying and then modifying, replacing, or deleting the incorrect, incomplete, inaccurate, irrelevant, or missing portions of the data as the need arises. This fundamental element of data science ensures data is correct, consistent, and usable. 

2. What is an Affinity Diagram?

Ans. An Affinity Diagram is an analytical tool used to cluster or organize data into subgroups based on their relationships. These data or ideas are mostly generated from discussions or brainstorming sessions and are used in analyzing complex issues.

3. Which questions should you ask the user/client before you create a dashboard?

Though this depends on the user’s requirements, still some of the common questions that I would ask the client before creating a dashboard are :

What is the purpose of the dashboard?Should the dashboard be retrospective or real-time?How detailed the dashboard should be?How tech and data-savvy is the end-user?Does the data need to be segmented?Should I explain the dashboard design to you?

4. What is an Alias in SQL?

An alias is a feature of SQL that is supported by most, if not all, RDBMSs. It is a temporary name assigned to the table or table column for the purpose of a particular SQL query. In addition, aliasing can be employed as an confusion technique to secure the real names of database fields. A table alias is also called a correlation name.
An alias is represented explicitly by the AS keyword but in some cases, the same can be performed without it as well.

𝟯 𝗙𝗿𝗲𝗲 𝗢𝗿𝗮𝗰𝗹𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗙𝘂𝘁𝘂𝗿𝗲-𝗣𝗿𝗼𝗼𝗳 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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The Data Science skill no one talks about...

Every aspiring data scientist I talk to thinks their job starts when someone else gives them:
    1. a dataset, and
    2. a clearly defined metric to optimize for, e.g. accuracy

But it doesn’t.

It starts with a business problem you need to understand, frame, and solve. This is the key data science skill that separates senior from junior professionals.

Let’s go through an example.

Example

Imagine you are a data scientist at Uber. And your product lead tells you:

    👩‍💼: “We want to decrease user churn by 5% this quarter”

We say that a user churns when she decides to stop using Uber.

But why?

There are different reasons why a user would stop using Uber. For example:

   1.  “Lyft is offering better prices for that geo” (pricing problem)
   2. “Car waiting times are too long” (supply problem)
   3. “The Android version of the app is very slow” (client-app performance problem)

You build this list ↑ by asking the right questions to the rest of the team. You need to understand the user’s experience using the app, from HER point of view.

Typically there is no single reason behind churn, but a combination of a few of these. The question is: which one should you focus on?

This is when you pull out your great data science skills and EXPLORE THE DATA 🔎.

You explore the data to understand how plausible each of the above explanations is. The output from this analysis is a single hypothesis you should consider further. Depending on the hypothesis, you will solve the data science problem differently.

For example…

Scenario 1: “Lyft Is Offering Better Prices” (Pricing Problem)

One solution would be to detect/predict the segment of users who are likely to churn (possibly using an ML Model) and send personalized discounts via push notifications. To test your solution works, you will need to run an A/B test, so you will split a percentage of Uber users into 2 groups:

    The A group. No user in this group will receive any discount.

    The B group. Users from this group that the model thinks are likely to churn, will receive a price discount in their next trip.

You could add more groups (e.g. C, D, E…) to test different pricing points.

In a nutshell

    1. Translating business problems into data science problems is the key data science skill that separates a senior from a junior data scientist.
2. Ask the right questions, list possible solutions, and explore the data to narrow down the list to one.
3. Solve this one data science problem