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.

𝗙𝗿𝗲𝗲 𝗢𝗿𝗮𝗰𝗹𝗲 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿😍

Here’s your chance to build a solid foundation in artificial intelligence with the Oracle AI Foundations Associate course — absolutely FREE!💻📌

𝐋𝐢𝐧𝐤👇:-

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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.

I have curated the best interview resources to crack Power BI Interviews 👇👇
https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c

Hope you'll like it

Like this post if you need more resources like this 👍❤️

𝟳+ 𝗙𝗿𝗲𝗲 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿😍

Here’s your golden chance to upskill with free, industry-recognized certifications from Google—all without spending a rupee!💰📌

These beginner-friendly courses cover everything from digital marketing to data tools like Google Ads, Analytics, and more⬇️

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Tag them or share this post!✅️

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://news.1rj.ru/str/sqlspecialist

Hope it helps :)

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

Looking to break into data analytics, data science, or machine learning this year?💻

These 6 free online courses from world-class universities and tech giants like Harvard, Stanford, MIT, Google, and IBM will help you build a job-ready skillset👨‍💻📌

𝐋𝐢𝐧𝐤👇:-

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Enjoy Learning ✅️

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 ❤️

Data Science & Machine Learning Resources: https://news.1rj.ru/str/datasciencefun

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

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

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

Looking to land an internship, secure a tech job, or start freelancing in 2025?👨‍💻

Python projects are one of the best ways to showcase your skills and stand out in today’s competitive job market🗣📌

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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.

Free Resources: https://news.1rj.ru/str/PowerBI_analyst

You can refer these Power BI Interview Resources to learn more: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

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

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

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

Start Here — With Zero Cost and Maximum Value!💰📌

If you’re aiming for a career in data analytics, now is the perfect time to get started🚀

𝐋𝐢𝐧𝐤👇:-

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A great starting point if you’re brand new to the field✅️

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😍

Oracle, one of the world’s most trusted tech giants, offers free training and globally recognized certifications to help you build expertise in cloud computing, Java, and enterprise applications.👨‍🎓📌

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𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 𝗶𝗻 𝟮𝟬𝟮𝟱😍

Ready to upskill in data science for free?🚀

Here are 3 amazing courses to build a strong foundation in Exploratory Data Analysis, SQL, and Python👨‍💻📌

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Take the first step towards your dream career!✅️

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

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

Oracle, one of the world’s most trusted tech giants, offers free training and globally recognized certifications to help you build expertise in cloud computing, Java, and enterprise applications.👨‍🎓📌

𝐋𝐢𝐧𝐤👇:-

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All at zero cost!🎊✅️

Top 10 machine Learning algorithms for beginners 👇👇

1. Linear Regression: A simple algorithm used for predicting a continuous value based on one or more input features.

2. Logistic Regression: Used for binary classification problems, where the output is a binary value (0 or 1).

3. Decision Trees: A versatile algorithm that can be used for both classification and regression tasks, based on a tree-like structure of decisions.

4. Random Forest: An ensemble learning method that combines multiple decision trees to improve the accuracy and robustness of the model.

5. Support Vector Machines (SVM): Used for both classification and regression tasks, with the goal of finding the hyperplane that best separates the classes.

6. K-Nearest Neighbors (KNN): A simple algorithm that classifies a new data point based on the majority class of its k nearest neighbors in the feature space.

7. Naive Bayes: A probabilistic algorithm based on Bayes' theorem that is commonly used for text classification and spam filtering.

8. K-Means Clustering: An unsupervised learning algorithm used for clustering data points into k distinct groups based on similarity.

9. Principal Component Analysis (PCA): A dimensionality reduction technique used to reduce the number of features in a dataset while preserving the most important information.

10. Gradient Boosting Machines (GBM): An ensemble learning method that builds a series of weak learners to create a strong predictive model through iterative optimization.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://news.1rj.ru/str/datasciencefun

Like if you need similar content 😄👍

𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝗳𝗼𝗿 𝗧𝗲𝗰𝗵 & 𝗗𝗮𝘁𝗮 𝗥𝗼𝗹𝗲𝘀 – 𝗙𝗿𝗲𝗲 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝗚𝘂𝗶𝗱𝗲😍

If you’re aiming for a role in tech, data analytics, or software development, one of the most valuable skills you can master is Python🎯

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4jg88I8

All The Best 🎊

SQL Tricks to Level Up Your Database Skills 🚀

SQL is a powerful language, but mastering a few clever tricks can make your queries faster, cleaner, and more efficient. Here are some cool SQL hacks to boost your skills:

1️⃣ Use COALESCE Instead of CASE
Instead of writing a long CASE statement to handle NULL values, use COALESCE():

SELECT COALESCE(name, 'Unknown') FROM users;

This returns the first non-null value in the list.

2️⃣ Generate Sequential Numbers Without a Table
Need a sequence of numbers but don’t have a numbers table? Use GENERATE_SERIES (PostgreSQL) or WITH RECURSIVE (MySQL 8+):

SELECT generate_series(1, 10);

3️⃣ Find Duplicates Quickly
Easily identify duplicate values with GROUP BY and HAVING:

SELECT email, COUNT(*) 
FROM users 
GROUP BY email 
HAVING COUNT(*) > 1;

4️⃣ Randomly Select Rows
Want a random sample of data? Use:
- PostgreSQL: ORDER BY RANDOM()
- MySQL: ORDER BY RAND()
- SQL Server: ORDER BY NEWID()

5️⃣ Pivot Data Without PIVOT (For Databases Without It)
Use CASE with SUM() to pivot data manually:

SELECT 
    user_id,
    SUM(CASE WHEN status = 'active' THEN 1 ELSE 0 END) AS active_count,
    SUM(CASE WHEN status = 'inactive' THEN 1 ELSE 0 END) AS inactive_count
FROM users
GROUP BY user_id;

6️⃣ Efficiently Get the Last Inserted ID
Instead of running a separate SELECT, use:
- MySQL: SELECT LAST_INSERT_ID();
- PostgreSQL: RETURNING id;
- SQL Server: SELECT SCOPE_IDENTITY();

Like for more ❤️

𝟯 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿-𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗶𝗻 𝟮𝟬𝟮𝟱😍

👩‍💻 Want to Break into Data Science but Don’t Know Where to Start?🚀

The best way to begin your data science journey is with hands-on projects using real-world datasets.👨‍💻📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/44LoViW

Enjoy Learning ✅️

Data Analyst Roadmap:

- Tier 1: Learn Excel & SQL
- Tier 2: Data Cleaning & Exploratory Data Analysis (EDA)
- Tier 3: Data Visualization & Business Intelligence (BI) Tools
- Tier 4: Statistical Analysis & Machine Learning Basics

Then build projects that include:

- Data Collection
- Data Cleaning
- Data Analysis
- Data Visualization

And if you want to make your portfolio stand out more:

- Solve real business problems
- Provide clear, impactful insights
- Create a presentation
- Record a video presentation
- Target specific industries
- Reach out to companies

Hope this helps you 😊