Remember: Tough times are opportunities to practice virtue.

Courage, justice, wisdom, self-control. They're forged in fire.

Important Machine Learning Algorithms 👇👇

- Linear Regression
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- k-Nearest Neighbors (kNN)
- Naive Bayes
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Neural Networks (Deep Learning)
- Gradient Boosting algorithms (e.g., XGBoost, LightGBM)

Like this post if you want me to explain each algorithm in detail

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ENJOY LEARNING 👍👍

Hello everyone here are some tableau projects along with the datasets to work on

1. Sales Performance Dashboard:
   - Kaggle: [Sales dataset](https://www.kaggle.com/search?q=sales+dataset)
   - UCI Machine Learning Repository: [Sales Transactions Dataset](https://archive.ics.uci.edu/ml/datasets/sales_transactions_dataset_weekly)

2. Customer Segmentation Analysis:
   - Kaggle: [Customer dataset](https://www.kaggle.com/search?q=customer+dataset)
   - UCI Machine Learning Repository: [Online Retail Dataset](https://archive.ics.uci.edu/ml/datasets/Online+Retail)

3. Inventory Management Dashboard:
   - Kaggle: [Inventory dataset](https://www.kaggle.com/search?q=inventory+dataset)
   - Data.gov: [Inventory datasets](https://www.data.gov/)

4. Financial Analysis Dashboard:
   - Yahoo Finance API: [Yahoo Finance API](https://finance.yahoo.com/quote/GOOG/history?p=GOOG)
   - Quandl: [Financial datasets](https://www.quandl.com/)

5. Social Media Analytics Dashboard:
   - Twitter API: [Twitter API](https://developer.twitter.com/en/docs)
   - Facebook Graph API: [Facebook Graph API](https://developers.facebook.com/docs/graph-api/)

6. Website Analytics Dashboard:
   - Google Analytics API: [Google Analytics API](https://developers.google.com/analytics)
   - SimilarWeb API: [SimilarWeb API](https://www.similarweb.com/corp/developer/)

7. Supply Chain Analysis Dashboard:
   - Kaggle: [Supply chain dataset](https://www.kaggle.com/search?q=supply+chain+dataset)
   - Data.gov: [Supply chain datasets](https://www.data.gov/)

8. Healthcare Analytics Dashboard:
   - CDC Public Health Data: [CDC Public Health Data](https://www.cdc.gov/datastatistics/index.html)
   - HealthData.gov: [Healthcare datasets](https://healthdata.gov/)

9. Employee Performance Dashboard:
   - Kaggle: [Employee dataset](https://www.kaggle.com/search?q=employee+dataset)
   - Glassdoor API: [Glassdoor API](https://www.glassdoor.com/developer/index.htm)

10. Real-time Dashboard:
    - Real-time APIs: Various APIs provide real-time data, such as financial market APIs, weather APIs, etc.
    - Web scraping: Extract real-time data from websites using web scraping tools like BeautifulSoup or Scrapy.

Kaggle is not the only source for dataset.

Get dataset to practice your data science and analytics skills from these 10+ other sources:

UNData:
This is a s statistical database of all United Nations data.
https://data.un.org/

Datasimplifier:
https://datasimplifier.com/data-analytics-portfolio/

Tableau Public Data Sets:
https://lnkd.in/dyM6k5CR

US Census Bureau:
https://data.census.gov/

Amazon AWS DataSet:
This is a repository of large datasets relating to many interralated areas.
https://lnkd.in/dPB33xsk

UC Irvine Machine Learning Repository:
https://lnkd.in/d3czdgJ2

USA Open Data:
https://data.gov/

Wikipedia Data Set:
https://t.co/JxzFu8EvIv

Worldbank dataset:
https://lnkd.in/d6qwV-NW

World Health Organization:
https://lnkd.in/dAFJcqFj

Awesome Public Data Sources:
https://t.co/u12vxk8zU3

Google Dataset:
Contains a wide array of information, including articles, theses, books, abstracts, white papers, and court opinions.
https://lnkd.in/d9Zadmfc

Country Codes List:
https://lnkd.in/dGJX9Z5x

FiveThirtyEight:
https://lnkd.in/d8mU8ZHN

BuzzFeed News:
https://lnkd.in/d9iSbSBB

Kaggle:
https://lnkd.in/dVWutrGN

Socrata:
https://lnkd.in/d5nvMnxt

GitHub:
https://lnkd.in/dfuUw5RS

Google dataset Search:
https://lnkd.in/d8YKUbcP

Data.gov:
https://www.data.gov/

Datahub:
https://lnkd.in/dqWd-QuB


Which of these sources have you used to find datasets for your projects?

Complete Roadmap to learn Machine Learning and Artificial Intelligence
👇👇

Week 1-2: Introduction to Machine Learning
- Learn the basics of Python programming language (if you are not already familiar with it)
- Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning
- Study linear algebra and calculus basics
- Complete online courses like Andrew Ng's Machine Learning course on Coursera

Week 3-4: Deep Learning Fundamentals
- Dive into neural networks and deep learning
- Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
- Implement deep learning models using frameworks like TensorFlow or PyTorch
- Complete online courses like Deep Learning Specialization on Coursera

Week 5-6: Natural Language Processing (NLP) and Computer Vision
- Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis
- Dive into computer vision concepts like image classification, object detection, and image segmentation
- Work on projects involving NLP and Computer Vision applications

Week 7-8: Reinforcement Learning and AI Applications
- Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks
- Explore AI applications in fields like healthcare, finance, and autonomous vehicles
- Work on a final project that combines different aspects of Machine Learning and AI

Additional Tips:
- Practice coding regularly to strengthen your programming skills
- Join online communities like Kaggle or GitHub to collaborate with other learners
- Read research papers and articles to stay updated on the latest advancements in the field

Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.

2 months are good as a starting point to get grasp the basics of ML & AI but mastering it is very difficult as AI keeps evolving every day.

Best Resources to learn ML & AI 👇

Learn Python for Free

Prompt Engineering Course

Prompt Engineering Guide

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

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ENJOY LEARNING👍👍

Here are some SQL project ideas tailored for data analysis:

🔟 SQL Project Ideas for Data Analysts

1. Sales Database Analysis: Create a database to track sales transactions. Write SQL queries to analyze sales performance by product, region, and time period.

2. Customer Churn Analysis: Build a database with customer data and track churn rates. Use SQL to identify factors contributing to churn and segment customers.

3. E-commerce Order Tracking: Design a database for an e-commerce platform. Write queries to analyze order trends, average order value, and customer purchase history.

4. Employee Performance Metrics: Create a database for employee records and performance reviews. Analyze employee performance trends and identify high performers using SQL.

5. Inventory Management System: Set up a database to track inventory levels. Write SQL queries to monitor stock levels, identify slow-moving items, and generate restock reports.

6. Healthcare Patient Analysis: Build a database to manage patient records and treatments. Use SQL to analyze treatment outcomes, readmission rates, and patient demographics.

7. Social Media Engagement Analysis: Create a database to track user interactions on a social media platform. Write queries to analyze engagement metrics like likes, shares, and comments.

8. Financial Transaction Analysis: Set up a database for financial transactions. Use SQL to identify spending patterns, categorize expenses, and generate monthly financial reports.

9. Website Traffic Analysis: Build a database to track website visitors. Write queries to analyze traffic sources, user behavior, and page performance.

10. Survey Results Analysis: Create a database to store survey responses. Use SQL to analyze responses, identify trends, and visualize findings based on demographic data.

Tackle Real World Data Challenges with These SQL Key Queries...



Scenario 1: Calculating Average

Question:
You have a table Employees with columns EmployeeID, Department, and Salary. Write an SQL query to find the average salary for each department.

Answer:
Assuming the table Employees with columns EmployeeID, Department, and Salary
SELECT Department,
AVG(Salary) AS AverageSalary
FROM Employees
GROUP BY Department;


Scenario 2: Finding Top Performers

Question:
You have a table Sales with columns SalesPersonID, SaleAmount, and SaleDate. Write an SQL query to find the top 3 salespeople with the highest total sales.

Answer:
Assuming the table Sales with columns SalesPersonID, SaleAmount, and SaleDate
SELECT SalesPersonID,
SUM(SaleAmount) AS TotalSales
FROM Sales
GROUP BY SalesPersonID
ORDER BY TotalSales DESC
LIMIT 3;

Scenario 3: Date Range Filtering

Question:
You have a table Orders with columns OrderID, OrderDate, and Amount. Write an SQL query to find the total amount of orders placed in the last 30 days.

Answer:
Assuming the table Orders with columns OrderID, OrderDate, and Amount
SELECT SUM(Amount) AS TotalAmount
FROM Orders
WHERE OrderDate >= CURDATE() - INTERVAL 30 DAY;

Many people ask this common question “Can I get a job with just SQL and Excel?” or “Can I get a job with just Power BI and Python?”.

The answer to all of those questions is yes.

There are jobs that use only SQL, Tableau, Power BI, Excel, Python, or R or some combination of those.

However, the combination of tools you learn impacts the total number of jobs you are qualified for.

For example, let’s say with just SQL and Excel you are qualified for 10 jobs, but if you add Tableau to that, you are qualified for 50 jobs.

If you have a success rate of landing a job you’re qualified for of 4%, having 5 times as many jobs to go for greatly improves your odds of landing a job.

Does this mean you should go out there and learn every single skill any data analyst job requires?

NO!

It’s about finding the core tools that many jobs want.

And, in my opinion, those tools are SQL, Excel, and a visualization tool.

With these three tools, you are qualified for the majority of entry level data jobs and many higher level jobs.

So, you can land a job with whatever tools you’re comfortable with.

But if you have the three tools above in your toolbelt, you will have many more jobs to apply for and greatly improve your chances of snagging one.

4 Python practical projects to do for freshers in data analytics

🧵⬇️

1️⃣ Exploratory Data Analysis (EDA) on a Public Dataset

Use a dataset from Kaggle or data.gov

Clean and preprocess the data

Perform statistical analysis and visualization

Draw insights and present findings

2️⃣ Stock Market Analysis Tool

Fetch real-time stock data using an API (e.g., yfinance)

Implement technical indicators (e.g., moving averages, RSI)

Create visualizations of stock performance

Build a simple prediction model

3️⃣ Social Media Sentiment Analysis

Collect tweets or Reddit posts using APIs

Preprocess text data

Perform sentiment analysis

Visualize sentiment trends over time

4️⃣ Customer Churn Prediction

Use a telecom or e-commerce dataset

Perform feature engineering

Build and compare multiple machine learning models

Evaluate model performance and interpret results

Hope it helps :)

SQL Projects with Datasets 👇

📌E-commerce Sales Analysis:
Dataset: Online retail dataset from the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Online+Retail)

📌Social Media Analytics:
Twitter API or Twitter datasets available on platforms like Kaggle (https://www.kaggle.com/datasets?search=twitter)

📌Healthcare Data Management:
MIMIC-III (Medical Information Mart for Intensive Care III) dataset (https://mimic.mit.edu/docs/iii/)

📌Retail Inventory Management:
Sample retail sales dataset available on platforms like Kaggle (https://www.kaggle.com/datasets?search=retail)

📌Financial Portfolio Analysis:
Yahoo Finance API or finance datasets available on platforms like Kaggle (https://www.kaggle.com/datasets?search=finance)

📌Real Estate Market Analysis:
Zillow dataset (https://www.zillow.com/research/data/) or real estate datasets available on platforms like Kaggle (https://www.kaggle.com/datasets?search=real+estate)

📚 9 must-have Python developer tools.

1. PyCharm IDE

2. Jupyter notebook

3. Keras

4. Pip Package

5. Python Anywhere

6. Scikit-Learn

7. Sphinx

8. Selenium

9. Sublime Text

5 Handy Tips to Master Data Science ⬇️

1️⃣ Begin with introductory projects that cover the fundamental concepts of data science, such as data exploration, cleaning, and visualization. These projects will help you get familiar with common data science tools and libraries like Python (Pandas, NumPy, Matplotlib), R, SQL, and Excel

2️⃣ Look for publicly available datasets from sources like Kaggle, UCI Machine Learning Repository. Working with real-world data will expose you to the challenges of messy, incomplete, and heterogeneous data, which is common in practical scenarios.

3️⃣ Explore various data science techniques like regression, classification, clustering, and time series analysis. Apply these techniques to different datasets and domains to gain a broader understanding of their strengths, weaknesses, and appropriate use cases.

4️⃣ Work on projects that involve the entire data science lifecycle, from data collection and cleaning to model building, evaluation, and deployment. This will help you understand how different components of the data science process fit together.

5️⃣ Consistent practice is key to mastering any skill. Set aside dedicated time to work on data science projects, and gradually increase the complexity and scope of your projects as you gain more experience.

✅ 5 of the best Kaggle datasets
💸 For data science projects (in finance)

👨🏻‍💻 If you are looking for datasets to do financial projects, the datasets presented on the Kaggle site can be a great option.

⏪ These datasets are usually clean and ready to use and are very suitable for machine learning models. Some of these datasets are even updated daily and you can use them for deeper analysis.👇


1️⃣ S&P 500 stock dataset (daily update)

📎 Link: S&P 500 Stocks


2️⃣ Database of loans and debts

📎 Link: Loans & Liability


3️⃣ Dataset of frequent use of credit card

📎 Link: Credit Card Spending Habits


4️⃣ Company bankruptcy prediction dataset

📎 Link: Company Bankruptcy Prediction


5️⃣ Credit score classification dataset

📎 Link: Credit score classification

Hope this helps you

FREE FREE FREE

10 Books on Data Science & Data Analysis will be posted on this channel daily basis

Book 1. Python for Data Analysis

Publisher: O'Reilly

wesmckinney.com/book/

Give it a like if you want me to continue ❤️

Top 10 Python libraries commonly used by data scientists

1. NumPy: A fundamental package for scientific computing with support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions.

2. pandas: A powerful data manipulation and analysis library that provides data structures and functions for working with structured data.

3. matplotlib: A widely-used plotting library for creating a variety of visualizations, including line plots, bar charts, histograms, scatter plots, and more.

4. scikit-learn: A comprehensive machine learning library that provides tools for data mining and data analysis, including algorithms for classification, regression, clustering, and more.

5. TensorFlow: An open-source machine learning framework developed by Google for building and training machine learning models, particularly for deep learning tasks.

6. Keras: A high-level neural networks API that is built on top of TensorFlow and provides an easy-to-use interface for building and training deep learning models.

7. Seaborn: A data visualization library based on matplotlib that provides a high-level interface for creating informative and attractive statistical graphics.

8. SciPy: A library that builds on NumPy and provides a wide range of scientific and technical computing functions, including optimization, integration, interpolation, and more.

9. Statsmodels: A library that provides classes and functions for the estimation of many different statistical models, as well as conducting statistical tests and exploring data.

10. XGBoost: An optimized gradient boosting library that is widely used for supervised learning tasks, such as regression and classification.

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