This repo contains roadmaps on various topics suggested by various experts on social media and Open source Projects
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| Topics | Download Free Book | Popular Free Courses | |
|---|---|---|---|
| Intro: Python & Data Science | Day 0: Python Installation + basic Syntax Day 1: Variables, Data Types, Operators Day 2: Control statements & Loops Day 3: Functions and Libraries Day 4: Data Science Intro |
Python Crash Course by Eric Matthes | Python for Everybody by Dr. Charles Severance on edX |
| Data Analysis with Pandas | Day 5: Pandas Intro & Data structures Day 6: Read & Write Data from various sources Day 7: Data cleaning & Preprocessing Day 8: Data wrangling & Transformation Day 9: Data aggregation & Group by operations |
Python for Data Analysis by Wes McKinney | Easier data analysis in Python with pandas by Kevin Markham |
| Data Visulaization with Matplotlib & Seaborn | Day 10: Data Visualization & Matplotlib Intro Day 11: Basic Plots & Charts Day 12: Advanced Plots & charts Day 13: Intro of Seaborn & Plotting Functions Day 14: Advance visualizations with Seaborn |
Python Data Science Handbook by Jake VanderPlas | Visualizing Data with Python |
| Probability and Statistics | Day 15: Intro to probability & its concepts Day 16: Descriptive statistics & summary metrics Day 17: Inferential statistics & hypothesis testing Day 18: Probability distributions & their applications Day 19: Bayesian statistics and its applications |
Think Stats -Allen B Downey | Intro to statistics Intro to Descriptive Statistics Intro to Inferential Statistics Bayesian Statistics: From Concepts to Data Analysis |
| Machine Learning with Scikit-Learn | Day 20: Introduction to machine learning Day 21: Supervised learning algorithms in Scikit-Learn Day 22: Unsupervised learning algorithms in Scikit-Learn Day 23: Model selection and validation techniques Day 24: Hyperparameter tuning and optimization techniques |
Book | Course |
| Linear Algebra and Calculus for Data Science | Day 25: Introduction to linear algebra and its concepts Day 26: Vectors, matrices, and their operations Day 27: Linear transformations and their applications Day 28: Introduction to calculus and its concepts Day 29: Applications of calculus in data science |
Linear Algebra | Liner Algebra by Gilbert |
| Deep Learning with TensorFlow or PyTorch | Day 30: Introduction to deep learning and neural networks Day 31: Building and training simple neural networks with TensorFlow or PyTorch Day 32: Convolutional neural networks for image classification Day 32: Recurrent neural networks for sequence Modeling Day 33: Advanced topics in deep learning, such as transfer learning and reinforcement learning |
Deep Learning with Python by Francois | Course |
| Natural Language Processing (NLP) with NLTK | Day 34: Introduction to NLP and NLTK Day 35: Text preprocessing and normalization with NLTK Day 36: Part-of-speech tagging and named entity recognition with NLTK Day 37: Sentiment analysis and text classification with NLTK Day 38: Advanced topics in NLP, such as text summarization and machine translation |
Book | |
| Big Data Processing with Apache Spark | Day 39: Introduction to big data processing and Apache Spark Day 40: Working with Spark DataFrames and SQL Day 41: Distributed computing with Spark RDDs Day 42: Machine learning with Spark MLlib Day 43: Streaming and real-time processing with Spark Streaming |
Learning Spark By Holden | Big Data Analytics |
| Advanced Topics in Data Science | Day 44: Dimensionality reduction and feature selection Day 45: Ensemble methods and model stacking Day 46: Time Series Analysis and Forecasting Day 47: Clustering and unsupervised learning techniques Day 48: Model interpretation and explainability techniques |
ML By Andrew | Coursera |
| Data Engineering and Pipeline Development | Day 49: Introduction to data engineering and pipeline development Day 50: Data ingestion and processing with Apache Kafka and Apache NiFi Day 51: ETL (extract, transform, load) techniques with Apache Airflow Day 52: Data warehousing and storage with Apache Hadoop and Hive Day 53: Building scalable data pipelines with cloud services, such as AWS and GCP |
Design Data Intensive | |
| Projects | Day 54: Designing and implementing a data science project Day 55: Working on the final project and incorporating all the skills learned Day 56: Final Project |
Data Science Projects with python- Stephen Kiosterman | Youtube |