5 Free Python Courses for Data Science Beginners

1️⃣ Python for Beginners – freeCodeCamp

2️⃣ Python – Kaggle

3️⃣ Python Mini-Projects – freeCodeCamp

4️⃣ Python Tutorial – W3Schools

5️⃣ oops with Python- freeCodeCamp

Complete roadmap to learn Python for data analysis

Step 1: Fundamentals of Python

1. Basics of Python Programming
- Introduction to Python
- Data types (integers, floats, strings, booleans)
- Variables and constants
- Basic operators (arithmetic, comparison, logical)

2. Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
- List comprehensions

3. Functions and Modules
- Defining functions
- Function arguments and return values
- Importing modules
- Built-in functions vs. user-defined functions

4. Data Structures
- Lists, tuples, sets, dictionaries
- Manipulating data structures (add, remove, update elements)

Step 2: Advanced Python
1. File Handling
- Reading from and writing to files
- Working with different file formats (txt, csv, json)

2. Error Handling
- Try, except blocks
- Handling exceptions and errors gracefully

3. Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance and polymorphism
- Encapsulation

Step 3: Libraries for Data Analysis
1. NumPy
- Understanding arrays and array operations
- Indexing, slicing, and iterating
- Mathematical functions and statistical operations

2. Pandas
- Series and DataFrames
- Reading and writing data (csv, excel, sql, json)
- Data cleaning and preparation
- Merging, joining, and concatenating data
- Grouping and aggregating data

3. Matplotlib and Seaborn
- Data visualization with Matplotlib
- Plotting different types of graphs (line, bar, scatter, histogram)
- Customizing plots
- Advanced visualizations with Seaborn

Step 4: Data Manipulation and Analysis
1. Data Wrangling
- Handling missing values
- Data transformation
- Feature engineering

2. Exploratory Data Analysis (EDA)
- Denoscriptive statistics
- Data visualization techniques
- Identifying patterns and outliers

3. Statistical Analysis
- Hypothesis testing
- Correlation and regression analysis
- Probability distributions

Step 5: Advanced Topics
1. Time Series Analysis
- Working with datetime objects
- Time series decomposition
- Forecasting models

2. Machine Learning Basics
- Introduction to machine learning
- Supervised vs. unsupervised learning
- Using Scikit-Learn for machine learning
- Building and evaluating models

3. Big Data and Cloud Computing
- Introduction to big data frameworks (e.g., Hadoop, Spark)
- Using cloud services for data analysis (e.g., AWS, Google Cloud)

Step 6: Practical Projects
1. Hands-on Projects
- Analyzing datasets from Kaggle
- Building interactive dashboards with Plotly or Dash
- Developing end-to-end data analysis projects

2. Collaborative Projects
- Participating in data science competitions
- Contributing to open-source projects

👨‍💻 FREE Resources to Learn & Practice Python 

1. https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course
2. https://www.hackerrank.com/domains/python
3. https://www.hackerearth.com/practice/python/getting-started/numbers/practice-problems/
4. https://news.1rj.ru/str/PythonInterviews
5. https://www.w3schools.com/python/python_exercises.asp
6. https://news.1rj.ru/str/pythonfreebootcamp/134
7. https://news.1rj.ru/str/pythonanalyst
8. https://pythonbasics.org/exercises/
9. https://news.1rj.ru/str/pythondevelopersindia/300
10. https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
11. https://news.1rj.ru/str/pythonspecialist/33

Join @free4unow_backup for more free resources

ENJOY LEARNING 👍👍

Roadmap for Learning Machine Learning (ML)

Here’s a concise and point-wise roadmap for learning ML:

1. Prerequisites
- Learn programming basics (e.g., Python).
- Understand mathematics:
1 - Linear Algebra (vectors, matrices).
2 - Probability and Statistics (distributions, Bayes’ theorem).
3 - Calculus (derivatives, gradients).
4 - Familiarize yourself with data structures and algorithms.

2. Basics of Machine Learning
-Understand ML concepts:
Supervised, unsupervised, and reinforcement learning.
Training, validation, and testing datasets.
- Learn how to preprocess and clean data.
- Get familiar with Python libraries:
NumPy, Pandas, Matplotlib, and Seaborn.

3. Supervised Learning
- Study regression techniques:
Linear and Logistic Regression.
- Explore classification algorithms:
Decision Trees, Support Vector Machines (SVM), k-NN.
- Learn model evaluation metrics:
Accuracy, Precision, Recall, F1 Score, ROC-AUC.

4. Unsupervised Learning
- Learn clustering techniques:
k-Means, DBSCAN, Hierarchical Clustering.
- Understand Dimensionality Reduction:
PCA, t-SNE.

5. Advanced Concepts
- Explore ensemble methods:
Random Forest, Gradient Boosting, XGBoost, LightGBM.
- Learn hyperparameter tuning techniques:
Grid Search, Random Search.

6. Deep Learning (Optional for Advanced ML)
- Learn neural networks basics:
Forward and Backpropagation.
- Study Deep Learning libraries:
TensorFlow, PyTorch, Keras.
Explore CNNs, RNNs, and Transformers.

7. Hands-on Practice
- Work on small projects like:
1 - Predicting house prices.
2 - Sentiment analysis on tweets.
3 - Image classification.
4 - Explore Kaggle competitions and datasets.

8. Deployment
- Learn how to deploy ML models:
Use Flask, FastAPI, or Django.
- Explore cloud platforms: AWS, Azure, Google Cloud.

9. Keep Learning
- Stay updated with new techniques:
Follow blogs, papers, and conferences (e.g., NeurIPS, ICML).
- Dive into specialized fields:
NLP, Computer Vision, Reinforcement Learning.

Join for more: https://news.1rj.ru/str/datalemur

import requests

def asteroidOrbits(year, orbitclass):
    base_url = "https://jsonmock.hackerrank.com/api/asteroids/search"
    page = 1
    res = []

    while True:
        response = requests.get(f"{base_url}?orbit_class={orbitclass}&discovery_date={year}&page={page}").json()
        res.extend(response['data'])

        if page >= response['total_pages']:
            break

        page += 1
    res.sort(key=lambda x: (float(x.get('period_yr', 1.00)), x['designation']))
    return [x['designation'] for x in res]

Rest API: Asteroid Orbits ✅

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

Hope this piece of information helps you

Top Platforms for Building Data Science Portfolio

Build an irresistible portfolio that hooks recruiters with these free platforms.

Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.

1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace

Build Data Analyst Portfolio in 1 month

Path 1 (More focus on SQL & then on Python)
👇👇

Week 1: Learn Fundamentals
Days 1-3: Start with online courses or tutorials on basic data analysis concepts.
Days 4-7: Dive into SQL basics for data retrieval and manipulation.
Free Resources: https://news.1rj.ru/str/sqlanalyst/74

Week 2: Data Analysis Projects
Days 8-14: Begin working on simple data analysis projects using SQL. Analyze the data and document your findings.

Week 3: Intermediate Skills
Days 15-21: Start learning Python for data analysis. Focus on libraries like Pandas for data manipulation.
Days 22-23: Explore more advanced SQL topics.

Week 4: Portfolio Completion
Days 24-28: Continue working on your SQL-based projects, applying what you've learned.
Day 29: Transition to Python for your personal project, applying Python's data analysis capabilities.
Day 30: Create a portfolio website showcasing your projects in SQL and Python, along with explanations and code.

Hope it helps :)

🚀Here are 5 fresh Project ideas for Data Analysts 👇

🎯 𝗔𝗶𝗿𝗯𝗻𝗯 𝗢𝗽𝗲𝗻 𝗗𝗮𝘁𝗮 🏠
https://www.kaggle.com/datasets/arianazmoudeh/airbnbopendata

💡This dataset describes the listing activity of homestays in New York City

🎯 𝗧𝗼𝗽 𝗦𝗽𝗼𝘁𝗶𝗳𝘆 𝘀𝗼𝗻𝗴𝘀 𝗳𝗿𝗼𝗺 𝟮𝟬𝟭𝟬-𝟮𝟬𝟭𝟵 🎵

https://www.kaggle.com/datasets/leonardopena/top-spotify-songs-from-20102019-by-year

🎯𝗪𝗮𝗹𝗺𝗮𝗿𝘁 𝗦𝘁𝗼𝗿𝗲 𝗦𝗮𝗹𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 📈

https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/data
💡Use historical markdown data to predict store sales

🎯 𝗡𝗲𝘁𝗳𝗹𝗶𝘅 𝗠𝗼𝘃𝗶𝗲𝘀 𝗮𝗻𝗱 𝗧𝗩 𝗦𝗵𝗼𝘄𝘀 📺

https://www.kaggle.com/datasets/shivamb/netflix-shows
💡Listings of movies and tv shows on Netflix - Regularly Updated

🎯𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗷𝗼𝗯𝘀 𝗹𝗶𝘀𝘁𝗶𝗻𝗴𝘀 💼

https://www.kaggle.com/datasets/cedricaubin/linkedin-data-analyst-jobs-listings
💡More than 8400 rows of data analyst jobs from USA, Canada and Africa.

Join for more -> https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5

ENJOY LEARNING 👍👍

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://news.1rj.ru/str/DataPortfolio/2

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

▎Essential Data Science Concepts Everyone Should Know:

1. Data Types and Structures:

• Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)

• Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)

• Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)

2. Denoscriptive Statistics:

• Measures of Central Tendency: Mean, Median, Mode (describing the typical value)

• Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)

• Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)

3. Probability and Statistics:

• Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)

• Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)

• Confidence Intervals: Estimating the range of plausible values for a population parameter

4. Machine Learning:

• Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)

• Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)

• Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)

5. Data Cleaning and Preprocessing:

• Missing Value Handling: Imputation, Deletion (dealing with incomplete data)

• Outlier Detection and Removal: Identifying and addressing extreme values

• Feature Engineering: Creating new features from existing ones (e.g., combining variables)

6. Data Visualization:

• Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)

• Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)

7. Ethical Considerations in Data Science:

• Data Privacy and Security: Protecting sensitive information

• Bias and Fairness: Ensuring algorithms are unbiased and fair

8. Programming Languages and Tools:

• Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn

• R: Statistical programming language with strong visualization capabilities

• SQL: For querying and manipulating data in databases

9. Big Data and Cloud Computing:

• Hadoop and Spark: Frameworks for processing massive datasets

• Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)

10. Domain Expertise:

• Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis

• Problem Framing: Defining the right questions and objectives for data-driven decision making

Bonus:

• Data Storytelling: Communicating insights and findings in a clear and engaging manner

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

ENJOY LEARNING 👍👍

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

Join @free4unow_backup for more free courses

ENJOY LEARNING👍👍

Machine learning powers so many things around us – from recommendation systems to self-driving cars!

But understanding the different types of algorithms can be tricky.

This is a quick and easy guide to the four main categories: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.

𝟏. 𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data.

𝐒𝐨𝐦𝐞 𝐜𝐨𝐦𝐦𝐨𝐧 𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:

➡️ Linear Regression – For predicting continuous values, like house prices.
➡️ Logistic Regression – For predicting categories, like spam or not spam.
➡️ Decision Trees – For making decisions in a step-by-step way.
➡️ K-Nearest Neighbors (KNN) – For finding similar data points.
➡️ Random Forests – A collection of decision trees for better accuracy.
➡️ Neural Networks – The foundation of deep learning, mimicking the human brain.

𝟐. 𝐔𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
With unsupervised learning, the model explores patterns in data that doesn’t have any labels. It finds hidden structures or groupings.

𝐒𝐨𝐦𝐞 𝐩𝐨𝐩𝐮𝐥𝐚𝐫 𝐮𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:

➡️ K-Means Clustering – For grouping data into clusters.
➡️ Hierarchical Clustering – For building a tree of clusters.
➡️ Principal Component Analysis (PCA) – For reducing data to its most important parts.
➡️ Autoencoders – For finding simpler representations of data.

𝟑. 𝐒𝐞𝐦𝐢-𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
This is a mix of supervised and unsupervised learning. It uses a small amount of labeled data with a large amount of unlabeled data to improve learning.

𝐂𝐨𝐦𝐦𝐨𝐧 𝐬𝐞𝐦𝐢-𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:

➡️ Label Propagation – For spreading labels through connected data points.
➡️ Semi-Supervised SVM – For combining labeled and unlabeled data.
➡️ Graph-Based Methods – For using graph structures to improve learning.

𝟒. 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
In reinforcement learning, the model learns by trial and error. It interacts with its environment, receives feedback (rewards or penalties), and learns how to act to maximize rewards.

𝐏𝐨𝐩𝐮𝐥𝐚𝐫 𝐫𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:

➡️ Q-Learning – For learning the best actions over time.
➡️ Deep Q-Networks (DQN) – Combining Q-learning with deep learning.
➡️ Policy Gradient Methods – For learning policies directly.
➡️ Proximal Policy Optimization (PPO) – For stable and effective learning.

ENJOY LEARNING 👍👍

You don't need to buy a GPU for machine learning work!

There are other alternatives. Here are some:

1. Google Colab
2. Kaggle
3. Deepnote
4. AWS SageMaker
5. GCP Notebooks
6. Azure Notebooks
7. Cocalc
8. Binder
9. Saturncloud
10. Datablore
11. IBM Notebooks
12. Ola kutrim

Spend your time focusing on your problem.💪💪

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