⚠️ 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

#DataScience #Python #DataAnalysis #DataVisualization #RProgramming #DeepLearning #CommandLine #HandsOnLearning #Statistics #Bayesian #Kafka #MachineLearning #AI #Programming #FreeBooks ✅

Top 5 data science projects for freshers

1. Predictive Analytics on a Dataset:
   - Use a dataset to predict future trends or outcomes using machine learning algorithms. This could involve predicting sales, stock prices, or any other relevant domain.

2. Customer Segmentation:
   - Analyze and segment customers based on their behavior, preferences, or demographics. This project could provide insights for targeted marketing strategies.

3. Sentiment Analysis on Social Media Data:
   - Analyze sentiment in social media data to understand public opinion on a particular topic. This project helps in mastering natural language processing (NLP) techniques.

4. Recommendation System:
   - Build a recommendation system, perhaps for movies, music, or products, using collaborative filtering or content-based filtering methods.

5. Fraud Detection:
   - Develop a fraud detection system using machine learning algorithms to identify anomalous patterns in financial transactions or any domain where fraud detection is crucial.

Free Datsets -> https://t.iss.one/DataPortfolio/2?single

These projects showcase practical application of data science skills and can be highlighted on a resume for entry-level positions.

Join @pythonspecialist for more data science projects

Data Scientist Roadmap
|
|-- 1. Basic Foundations
|   |-- a. Mathematics
|   |   |-- i. Linear Algebra
|   |   |-- ii. Calculus
|   |   |-- iii. Probability
|   |   -- iv. Statistics
|   |
|   |-- b. Programming
|   |   |-- i. Python
|   |   |   |-- 1. Syntax and Basic Concepts
|   |   |   |-- 2. Data Structures
|   |   |   |-- 3. Control Structures
|   |   |   |-- 4. Functions
|   |   |   -- 5. Object-Oriented Programming
|   |   |
|   |   -- ii. R (optional, based on preference)
|   |
|   |-- c. Data Manipulation
|   |   |-- i. Numpy (Python)
|   |   |-- ii. Pandas (Python)
|   |   -- iii. Dplyr (R)
|   |
|   -- d. Data Visualization
|       |-- i. Matplotlib (Python)
|       |-- ii. Seaborn (Python)
|       -- iii. ggplot2 (R)
|
|-- 2. Data Exploration and Preprocessing
|   |-- a. Exploratory Data Analysis (EDA)
|   |-- b. Feature Engineering
|   |-- c. Data Cleaning
|   |-- d. Handling Missing Data
|   -- e. Data Scaling and Normalization
|
|-- 3. Machine Learning
|   |-- a. Supervised Learning
|   |   |-- i. Regression
|   |   |   |-- 1. Linear Regression
|   |   |   -- 2. Polynomial Regression
|   |   |
|   |   -- ii. Classification
|   |       |-- 1. Logistic Regression
|   |       |-- 2. k-Nearest Neighbors
|   |       |-- 3. Support Vector Machines
|   |       |-- 4. Decision Trees
|   |       -- 5. Random Forest
|   |
|   |-- b. Unsupervised Learning
|   |   |-- i. Clustering
|   |   |   |-- 1. K-means
|   |   |   |-- 2. DBSCAN
|   |   |   -- 3. Hierarchical Clustering
|   |   |
|   |   -- ii. Dimensionality Reduction
|   |       |-- 1. Principal Component Analysis (PCA)
|   |       |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
|   |       -- 3. Linear Discriminant Analysis (LDA)
|   |
|   |-- c. Reinforcement Learning
|   |-- d. Model Evaluation and Validation
|   |   |-- i. Cross-validation
|   |   |-- ii. Hyperparameter Tuning
|   |   -- iii. Model Selection
|   |
|   -- e. ML Libraries and Frameworks
|       |-- i. Scikit-learn (Python)
|       |-- ii. TensorFlow (Python)
|       |-- iii. Keras (Python)
|       -- iv. PyTorch (Python)
|
|-- 4. Deep Learning
|   |-- a. Neural Networks
|   |   |-- i. Perceptron
|   |   -- ii. Multi-Layer Perceptron
|   |
|   |-- b. Convolutional Neural Networks (CNNs)
|   |   |-- i. Image Classification
|   |   |-- ii. Object Detection
|   |   -- iii. Image Segmentation
|   |
|   |-- c. Recurrent Neural Networks (RNNs)
|   |   |-- i. Sequence-to-Sequence Models
|   |   |-- ii. Text Classification
|   |   -- iii. Sentiment Analysis
|   |
|   |-- d. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
|   |   |-- i. Time Series Forecasting
|   |   -- ii. Language Modeling
|   |
|   -- e. Generative Adversarial Networks (GANs)
|       |-- i. Image Synthesis
|       |-- ii. Style Transfer
|       -- iii. Data Augmentation
|
|-- 5. Big Data Technologies
|   |-- a. Hadoop
|   |   |-- i. HDFS
|   |   -- ii. MapReduce
|   |
|   |-- b. Spark
|   |   |-- i. RDDs
|   |   |-- ii. DataFrames
|   |   -- iii. MLlib
|   |
|   -- c. NoSQL Databases
|       |-- i. MongoDB
|       |-- ii. Cassandra
|       |-- iii. HBase
|       -- iv. Couchbase
|
|-- 6. Data Visualization and Reporting
|   |-- a. Dashboarding Tools
|   |   |-- i. Tableau
|   |   |-- ii. Power BI
|   |   |-- iii. Dash (Python)
|   |   -- iv. Shiny (R)
|   |
|   |-- b. Storytelling with Data
|   -- c. Effective Communication
|
|-- 7. Domain Knowledge and Soft Skills
|   |-- a. Industry-specific Knowledge
|   |-- b. Problem-solving
|   |-- c. Communication Skills
|   |-- d. Time Management
|   -- e. Teamwork
|
-- 8. Staying Updated and Continuous Learning
    |-- a. Online Courses
    |-- b. Books and Research Papers
    |-- c. Blogs and Podcasts
    |-- d. Conferences and Workshops
    `-- e. Networking and Community Engagement

The Foundation of Data Science

𝟰 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍 

These free, Microsoft-backed courses are a game-changer!

With these resources, you’ll gain the skills and confidence needed to shine in the data analytics world—all without spending a penny.

𝐋𝐢𝐧𝐤 👇:- 

https://pdlink.in/4jpmI0I

Enroll For FREE & Get Certified🎓

𝗟𝗲𝗮𝗿𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 & 𝗘𝗹𝗲𝘃𝗮𝘁𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗚𝗮𝗺𝗲!😍

Want to turn raw data into stunning visual stories?📊

Here are 6 FREE Power BI courses that’ll take you from beginner to pro—without spending a single rupee💰

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4cwsGL2

Enjoy Learning ✅️