⚠️ O'Reilly Media, one of the most reputable publishers in the fields of programming, data mining, and AI, has made 10 data science books available to those interested in this field for free .

✔️ To use the online and PDF versions of these books, you can use the following links:👇

0⃣ Python Data Science Handbook
┌ Online
└ PDF

1⃣ Python for Data Analysis book
┌ Online
└ PDF

🔢 Fundamentals of Data Visualization book
┌ Online
└ PDF

🔢 R for Data Science book
┌ Online
└ PDF

🔢 Deep Learning for Coders book
┌ Online
└ PDF

🔢 DS at the Command Line book
┌ Online
└ PDF

🔢 Hands-On Data Visualization Book
┌ Online
└ PDF

🔢 Think Stats book
┌ Online
└ PDF

🔢 Think Bayes book
┌ Online
└ PDF

🔢 Kafka, The Definitive Guide
┌ Online
└ PDF

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SE :- https://pdlink.in/42feu5D

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Dxc :- https://pdlink.in/4ctIeis

EY:- https://pdlink.in/4lwMQZo

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Difference between linear regression and logistic regression 👇👇

Linear regression and logistic regression are both types of statistical models used for prediction and modeling, but they have different purposes and applications.

Linear regression is used to model the relationship between a dependent variable and one or more independent variables. It is used when the dependent variable is continuous and can take any value within a range. The goal of linear regression is to find the best-fitting line that describes the relationship between the independent and dependent variables.

Logistic regression, on the other hand, is used when the dependent variable is binary or categorical. It is used to model the probability of a certain event occurring based on one or more independent variables. The output of logistic regression is a probability value between 0 and 1, which can be interpreted as the likelihood of the event happening.

Data Science Interview Resources
👇👇
https://topmate.io/coding/914624

Like for more 😄

Complete Machine Learning Roadmap
👇👇

1. Introduction to Machine Learning
- Definition
- Purpose
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement)

2. Mathematics for Machine Learning
- Linear Algebra
- Calculus
- Statistics and Probability

3. Programming Languages for ML
- Python and Libraries (NumPy, Pandas, Matplotlib)
- R

4. Data Preprocessing
- Handling Missing Data
- Feature Scaling
- Data Transformation

5. Exploratory Data Analysis (EDA)
- Data Visualization
- Descriptive Statistics

6. Supervised Learning
- Regression
- Classification
- Model Evaluation

7. Unsupervised Learning
- Clustering (K-Means, Hierarchical)
- Dimensionality Reduction (PCA)

8. Model Selection and Evaluation
- Cross-Validation
- Hyperparameter Tuning
- Evaluation Metrics (Precision, Recall, F1 Score)

9. Ensemble Learning
- Random Forest
- Gradient Boosting

10. Neural Networks and Deep Learning
- Introduction to Neural Networks
- Building and Training Neural Networks
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)

11. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Named Entity Recognition (NER)

12. Reinforcement Learning
- Basics
- Markov Decision Processes
- Q-Learning

13. Machine Learning Frameworks
- TensorFlow
- PyTorch
- Scikit-Learn

14. Deployment of ML Models
- Flask for Web Deployment
- Docker and Kubernetes

15. Ethical and Responsible AI
- Bias and Fairness
- Ethical Considerations

16. Machine Learning in Production
- Model Monitoring
- Continuous Integration/Continuous Deployment (CI/CD)

17. Real-world Projects and Case Studies

18. Machine Learning Resources
- Online Courses
- Books
- Blogs and Journals

📚 Learning Resources for Machine Learning:
- [Python for Machine Learning](https://t.iss.one/udacityfreecourse/167)
- [Fast.ai: Practical Deep Learning for Coders](https://course.fast.ai/)
- [Intro to Machine Learning](https://learn.microsoft.com/en-us/training/paths/intro-to-ml-with-python/)

📚 Books:
- Machine Learning Interviews
- Machine Learning for Absolute Beginners

📚 Join @free4unow_backup for more free resources.

ENJOY LEARNING! 👍👍

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Join a live hands-on session by GeeksforGeeks & Salesforce for working professionals

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🚀👉Data Analytics skills and projects to add in a resume to get shortlisted

1. Technical Skills:
Proficiency in data analysis tools (e.g., Python, R, SQL).
Data visualization skills using tools like Tableau or Power BI.
Experience with statistical analysis and modeling techniques.

2. Data Cleaning and Preprocessing:
Showcase skills in cleaning and preprocessing raw data for analysis.
Highlight expertise in handling missing data and outliers effectively.

3. Database Management:
Mention experience with databases (e.g., MySQL, PostgreSQL) for data retrieval and manipulation.

4. Machine Learning:
If applicable, include knowledge of machine learning algorithms and their application in data analytics projects.

5. Data Storytelling:
Emphasize your ability to communicate insights effectively through data storytelling.

6. Big Data Technologies:
If relevant, mention experience with big data technologies such as Hadoop or Spark.

7. Business Acumen:
Showcase an understanding of the business context and how your analytics work contributes to organizational goals.

8. Problem-Solving:
Highlight instances where you solved business problems through data-driven insights.

9. Collaboration and Communication:
Demonstrate your ability to work in a team and communicate complex findings to non-technical stakeholders.

10. Projects:
List specific data analytics projects you've worked on, detailing the problem, methodology, tools used, and the impact on decision-making.

11. Certifications:
Include relevant certifications such as those from platforms like Coursera, edX, or industry-recognized certifications in data analytics.

12. Continuous Learning:
Showcase any ongoing education, workshops, or courses to display your commitment to staying updated in the field.

💼Tailor your resume to the specific job description, emphasizing the skills and experiences that align with the requirements of the position you're applying for.

Important Topics to become a data scientist
[Advanced Level]
👇👇

1. Mathematics

Linear Algebra
Analytic Geometry
Matrix
Vector Calculus
Optimization
Regression
Dimensionality Reduction
Density Estimation
Classification

2. Probability

Introduction to Probability
1D Random Variable
The function of One Random Variable
Joint Probability Distribution
Discrete Distribution
Normal Distribution

3. Statistics

Introduction to Statistics
Data Description
Random Samples
Sampling Distribution
Parameter Estimation
Hypotheses Testing
Regression

4. Programming

Python:

Python Basics
List
Set
Tuples
Dictionary
Function
NumPy
Pandas
Matplotlib/Seaborn

R Programming:

R Basics
Vector
List
Data Frame
Matrix
Array
Function
dplyr
ggplot2
Tidyr
Shiny

DataBase:
SQL
MongoDB

Data Structures

Web scraping

Linux

Git

5. Machine Learning

How Model Works
Basic Data Exploration
First ML Model
Model Validation
Underfitting & Overfitting
Random Forest
Handling Missing Values
Handling Categorical Variables
Pipelines
Cross-Validation(R)
XGBoost(Python|R)
Data Leakage

6. Deep Learning

Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
TensorFlow
Keras
PyTorch
A Single Neuron
Deep Neural Network
Stochastic Gradient Descent
Overfitting and Underfitting
Dropout Batch Normalization
Binary Classification

7. Feature Engineering

Baseline Model
Categorical Encodings
Feature Generation
Feature Selection

8. Natural Language Processing

Text Classification
Word Vectors

9. Data Visualization Tools

BI (Business Intelligence):
Tableau
Power BI
Qlik View
Qlik Sense

10. Deployment

Microsoft Azure
Heroku
Google Cloud Platform
Flask
Django

Join @datasciencefun to learning important data science and machine learning concepts

ENJOY LEARNING 👍👍

Some useful telegram channels to learn data analytics & data science

Python interview books
👇👇
https://t.iss.one/dsabooks

Data Analyst Interviews
👇👇
https://t.iss.one/DataAnalystInterview

SQL for data analysis
👇👇
https://t.iss.one/sqlanalyst

Data Science &  Machine Learning
👇👇
https://t.iss.one/datasciencefun

Data Science Projects
👇👇
https://t.iss.one/pythonspecialist

Python for data analysis
👇👇
https://t.iss.one/pythonanalyst

Excel for data analysis
👇👇
https://t.iss.one/excel_analyst

Power BI/ Tableau
👇👇
https://t.iss.one/PowerBI_analyst

Data Analysis Books
👇👇
https://t.iss.one/learndataanalysis

🕯 Sites to practice programming and solve challenges to improve programming skills 🕯

1️⃣ https://edabit.com
2️⃣ https://codeforces.com
3️⃣ https://www.codechef.com
4️⃣ https://leetcode.com
5️⃣ https://www.codewars.com
6️⃣ https://www.pythonchallenge.com
7️⃣ https://coderbyte.com
8️⃣ https://www.codingame.com/start
9️⃣ https://www.freecodecamp.org/learn

ENJOY LEARNING 👍👍

Please go through this top 10 SQL projects with Datasets that you can practice and can add in your resume

📌1. Social Media Analytics:
(https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset)

🚀2. Web Analytics:
(https://www.kaggle.com/zynicide/wine-reviews)

📌3. HR Analytics:
(https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-
attrition-dataset)

🚀4. Healthcare Data Analysis:
(https://www.kaggle.com/cdc/mortality)

📌5. E-commerce Analysis:
(https://www.kaggle.com/olistbr/brazilian-ecommerce)

🚀6. Inventory Management:
(https://www.kaggle.com/datasets?
search=inventory+management)

📌 7.Customer Relationship Management:
(https://www.kaggle.com/pankajjsh06/ibm-watson-
marketing-customer-value-data)

🚀8. Financial Data Analysis:
(https://www.kaggle.com/awaiskalia/banking-database)

📌9. Supply Chain Management:
(https://www.kaggle.com/shashwatwork/procurement-analytics)

🚀10. Analysis of Sales Data:
(https://www.kaggle.com/kyanyoga/sample-sales-data)

Small suggestion from my side for non tech students: kindly pick those datasets which you like the subject in general, that way you will be more excited to practice it, instead of just doing it for the sake of resume, you will learn SQL more passionately, since it’s a programming language try to make it more exciting for yourself.

Join for more: https://t.iss.one/DataPortfolio

Hope this piece of information helps you