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24-Month Data Science Learning Plan: Step-by-Step Guide



Welcome to your AI Councel Lab Data Science learning roadmap! Here’s a step-by-step, 24-month plan to help you develop the necessary skills to become a proficient Data Scientist. This plan is designed to take you from the basics to advanced topics, while providing practical experience and helping you build a strong portfolio. By the end of two years, you’ll have a solid foundation in data science, machine learning, deep learning, and more.

Months 1-6: Foundation Building

Goal: Master programming fundamentals, data manipulation, and basic statistics.

Focus Areas:

  1. Learn Python (2 months)

    • Basics of Python: variables, loops, conditionals, functions, and data structures (lists, dictionaries, tuples).
    • Key libraries: NumPy, Pandas, Matplotlib, Seaborn.
    • Install and set up Python IDE (Jupyter Notebooks or VS Code).

  2. Mathematics and Statistics (2 months)

    • Linear Algebra: Vectors, matrices, matrix multiplication.
    • Calculus: Derivatives, gradients, optimization.
    • Statistics: Probability, distributions, hypothesis testing, p-values.
    • Learn basic statistical methods for data analysis.

  3. Data Exploration and Preprocessing (2 months)

    • Data cleaning and transformation using Pandas.
    • Handle missing values, outliers, and duplicates.
    • Learn about data types, normalization, and scaling.
    • Data visualization: Use Matplotlib and Seaborn for basic charts, histograms, box plots.

Practical Project:

  • Work on basic data analysis projects, e.g., analyzing a dataset from Kaggle or UCI repository (e.g., Iris Dataset or Titanic dataset).

Months 7-12: Intermediate Concepts and Machine Learning Basics

Goal: Learn machine learning fundamentals, data modeling, and evaluation.

Focus Areas:

  1. Introduction to Machine Learning (ML) (3 months)

    • Understand types of learning: supervised, unsupervised, and reinforcement learning.
    • Supervised Learning: Implement and understand algorithms like Linear Regression, Logistic Regression, Decision Trees, K-Nearest Neighbors (KNN).
    • Unsupervised Learning: Learn Clustering techniques like K-means, Hierarchical Clustering, and PCA (Principal Component Analysis).
    • Model Evaluation: Learn metrics such as accuracy, precision, recall, F1-score, ROC curves, and cross-validation.

  2. Data Visualization and Communication (2 months)

    • Learn to create more advanced visualizations with Seaborn and Plotly.
    • Build interactive dashboards using Tableau or Power BI.
    • Learn to interpret and present results effectively to non-technical audiences.

  3. SQL and Databases (2 months)

    • Learn SQL for querying and managing databases.
    • Master data manipulation in relational databases, including joins, grouping, and aggregation.
    • Work with cloud-based databases like Google BigQuery or AWS RDS.

Practical Project:

  • Build a regression or classification model (e.g., predicting house prices or customer churn).
  • Work on an SQL-based project: Use a database to answer business questions and generate insights from data.

Months 13-18: Deepen Machine Learning Knowledge & Start Working on Real-World Projects

Goal: Gain deeper knowledge of advanced machine learning techniques and start building real-world models.

Focus Areas:

  1. Ensemble Methods and Advanced ML Techniques (3 months)

    • Study and implement Random Forest, Gradient Boosting, and XGBoost.
    • Learn about Support Vector Machines (SVMs) and their use in classification problems.
    • Understand Model Tuning: Hyperparameter tuning using GridSearchCV and RandomizedSearchCV.

  2. Introduction to Deep Learning (3 months)

    • Learn about Neural Networks and Backpropagation.
    • Get familiar with libraries like TensorFlow and Keras.
    • Implement basic neural networks for tasks like image classification and text analysis.
    • Understand the difference between shallow and deep learning models.

  3. Time Series Analysis (2 months)

    • Learn about time series forecasting, ARIMA models, and seasonality.
    • Work with date-time data, handling missing values, and rolling windows for time series data.

Practical Project:

  • Implement ensemble learning to improve the performance of a machine learning model.
  • Work on Deep Learning: Build a neural network for a project like MNIST image classification.
  • Time series project: Forecast stock prices or predict demand for products.

Months 19-24: Mastering Deep Learning & Building Portfolio Projects

Goal: Become proficient in advanced topics like deep learning, natural language processing (NLP), and deploy models to production.

Focus Areas:

  1. Advanced Deep Learning Techniques (3 months)

    • Learn about Convolutional Neural Networks (CNNs) for image processing.
    • Learn about Recurrent Neural Networks (RNNs) and LSTMs for sequence data.
    • Work with Transfer Learning using pre-trained models like ResNet and VGG16 for image-related tasks.

  2. Natural Language Processing (NLP) (3 months)

    • Understand NLP concepts: tokenization, stemming, lemmatization, and stopwords.
    • Learn about advanced NLP techniques like TF-IDF, Word2Vec, GloVe, and BERT.
    • Implement NLP models for tasks like text classification, named entity recognition (NER), and sentiment analysis.

  3. Deploying Machine Learning Models (2 months)

    • Learn about model deployment frameworks like Flask or FastAPI.
    • Deploy models as APIs using Docker and host them on platforms like Heroku or AWS.
    • Understand cloud computing and explore cloud platforms like AWS, Azure, and Google Cloud for model hosting.

Practical Project:

  • Build an NLP project (e.g., sentiment analysis on social media data).
  • Deploy a deep learning model or machine learning model to production.
  • Complete 2-3 end-to-end projects (covering everything from data collection to deployment).

Additional Resources and Tips:

  • Online Courses: Utilize free and paid resources like Coursera, edX, Udemy, and DataCamp.
  • Kaggle: Participate in Kaggle competitions to gain hands-on experience and interact with the data science community.
  • GitHub: Regularly upload your projects to GitHub to build your portfolio.

Conclusion

By following this 24-month roadmap, you’ll gain the knowledge and experience needed to become a proficient Data Scientist. Stay disciplined, practice regularly, and tackle real-world projects to solidify your learning. The key to success is consistency and persistence.

At AI Councel Lab, we’ll be with you every step of the way, providing insights, tutorials, and resources to help you succeed in your Data Science journey. Stay tuned for more content to help you build and grow!

Happy learning!

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