18-Month Data Science Learning Plan

Week 1-2: Python Basics

Project: Fetch and visualize stock market data using Python. Implement basic data retrieval and plotting functionalities.

Week 3-4: Functions, Modules, and Introduction to File I/O

Project: Enhance the previous project by adding modular functions for data processing and integrating file input/output operations.

Week 1-2: File I/O and Exception Handling

Project: Create a contacts management system in Python that includes functionalities for adding, editing, and deleting contacts stored in files.

Week 3-4: Data Structures

Project: Analyze a stock market dataset using Python's data structures. Implement algorithms for data manipulation and perform basic statistical analysis.

Week 1-2: Introduction to Libraries: NumPy and Pandas

Project: Perform exploratory data analysis on a stock market dataset. Use NumPy for numerical computations and Pandas for data manipulation.

Week 3-4: Basic Data Manipulation with Pandas

Project: Extend the EDA project by incorporating advanced data manipulation techniques with Pandas, such as data cleaning, aggregation, and filtering.

Week 1-2: Importing Data (CSV, Excel, Databases)

Project: Develop a data cleaning pipeline in Python to preprocess stock market data sourced from various formats, including CSV files and databases.

Week 3-4: Data Cleaning Techniques

Project: Implement advanced data cleaning techniques, such as handling missing values, outlier detection, and normalization, to prepare data for analysis.

Week 1-2: Introduction to Data Visualization with Matplotlib

Project: Create basic visualizations, such as line plots, scatter plots, and bar charts, to visualize trends and relationships in stock market data.

Week 3-4: Advanced Data Visualization with Seaborn

Project: Enhance the previous project by employing Seaborn for advanced statistical visualizations, including distribution plots and regression analysis.

Week 1-2: Creating Dashboards with Plotly

Project: Design interactive dashboards using Plotly to visualize stock market trends and indicators dynamically.

Week 3-4: Capstone Project 1: Develop a technical analysis dashboard for stock market data

Project: Integrate technical analysis indicators into the dashboard, such as moving averages and RSI, to facilitate decision-making.

Week 1-2: Descriptive Statistics (mean, median, mode, variance)

Project: Compute descriptive statistics for stock market data to gain insights into central tendencies and variability.

Week 3-4: Probability Theory and Distributions

Project: Apply probability theory and distributions, such as normal and binomial distributions, for hypothesis testing in stock market analysis.

Week 1-2: Supervised Learning: Regression (Linear, Logistic)

Project: Implement regression models, such as linear and logistic regression, to predict stock prices and classify market trends.

Week 3-4: Supervised Learning: Classification (Decision Trees, k-NN)

Project: Build classification models using decision trees and k-nearest neighbors (k-NN) for stock market prediction and sentiment analysis.

Week 1-2: Model Evaluation and Cross-Validation

Project: Evaluate machine learning models using cross-validation techniques to assess their performance on different stock market datasets.

Week 3-4: Ensemble Methods (Bagging, Boosting)

Project: Apply ensemble learning techniques, such as bagging and boosting, to improve prediction accuracy and robustness in stock market analysis.

Week 1-2: Unsupervised Learning: Clustering (K-means, Hierarchical)

Project: Cluster stocks based on similar patterns using clustering algorithms like K-means and hierarchical clustering.

Week 3-4: Unsupervised Learning: Dimensionality Reduction (PCA)

Project: Reduce the dimensionality of stock market data using Principal Component Analysis (PCA) to identify key variables driving market trends.

Week 1-2: Time Series Analysis

Project: Perform time series analysis on stock market data to model and forecast price movements using techniques like ARIMA and exponential smoothing.

Week 3-4: Introduction to Natural Language Processing (NLP)

Project: Apply basic NLP techniques to analyze market sentiment from financial news and social media data related to stocks.

Week 1-2: Neural Networks and Deep Learning Basics

Project: Implement feedforward neural networks to predict stock prices based on historical data, exploring different architectures and activation functions.

Week 3-4: Capstone Project 3: Develop a deep learning model for stock market prediction

Project: Build and train a deep learning model, such as LSTM or CNN, for predicting stock prices and analyzing market trends.

Week 1-2: Web Scraping with BeautifulSoup and Scrapy

Project: Extract financial data from websites using BeautifulSoup and Scrapy, focusing on stock market news and company financials.

Week 3-4: Data Engineering: ETL Pipelines

Project: Design and implement ETL (Extract, Transform, Load) pipelines for integrating and processing large-scale stock market datasets.

Week 1-2: Introduction to Big Data and Hadoop

Project: Set up a Hadoop cluster and perform distributed data processing for analyzing massive stock market datasets.

Week 3-4: Apache Spark for Data Analytics

Project: Utilize Apache Spark to perform advanced analytics and machine learning on large-scale stock market data, exploring RDDs and DataFrames.

Week 1-2: Cloud Computing Platforms (AWS, Azure)

Project: Deploy stock market prediction models on cloud platforms like AWS or Azure, focusing on scalability and cost-efficiency.

Week 3-4: Containerization with Docker

Project: Containerize stock market analytics applications using Docker for easy deployment and management across different environments.

Week 1-2: Data Privacy and Security

Project: Develop a data governance framework for handling sensitive stock market data, addressing privacy concerns and regulatory requirements.

Week 3-4: Ethical Considerations in Data Science

Project: Analyze ethical implications of using stock market data for algorithmic trading and decision-making, proposing ethical guidelines.

Week 1-2: Reinforcement Learning

Project: Apply reinforcement learning algorithms to optimize stock market trading strategies, focusing on reward maximization and risk management.

Week 3-4: Natural Language Processing (NLP) for Financial Text Analysis

Project: Use advanced NLP techniques to analyze financial documents and news sentiment for predicting stock price movements.

Week 1-2: Final Capstone Project: Real-world application of data science in finance

Project: Develop a comprehensive data-driven solution or analysis for a financial problem or opportunity, demonstrating mastery of data science skills.

Week 3-4: Portfolio Development and Presentation

Project: Compile and present a portfolio showcasing all completed projects and achievements throughout the 18-month data science learning journey.