Roadmap for a full stack data scientist at a high level

 Roadmap for a full stack data scientist at a high level

Training Plan (Week 22 to be Data Analyst, week 42 to be a data scientist ML, rest are for you to be a full stack data scientist with Mlops(IC role) : Transitioning to Data Roles from ScratchThis plan focuses on building foundational skills in Python, SQL, data analysis, machine learning, NLP, deep learning, and MLOps. It’s not a get-rich-quick scheme but a long marathon for those committed to mastering data science.

### Week 1-4: Introduction to Python Programming

- Week 1-2:

  - Introduction to Python: Syntax, Variables, Data Types

  - Data Structures: Lists, Tuples, Dictionaries, and Sets

  - Conditional Statements and Loops

- Week 3-4:

  - List Comprehensions

  - Functions: Definitions, Lambda Functions

  - Map, Reduce, and Filter

  - Exception Handling in python

### Week 5-8: Advanced Python and Introduction to SQL

- Week 5-6:

  - Introduction to Numpy: Arrays and Operations

Slicing, subsetting, indexing and iterating through arrays, Multidimensional arrays, computation times in numpy and standard python lists. 

  - Introduction to Pandas: DataFrames, Series

  - Data Cleaning and Manipulation in Pandas

- Week 7-8:

  - SQL Basics: Introduction, SELECT Statements

  - Data Retrieval and Manipulation: INSERT, UPDATE, DELETE

  - String and Date Manipulation in SQL. 

Changing constraints (primary key). 

Window functions, query optimisation. 

### Week 9-12: Data Warehousing Concepts and Python Visualization

- Week 9-10:

  - Data Warehousing Fundamentals: OLAP vs OLTP

  - Star Schema and Industry Example

  - Updating Tables: Adding, Deleting, Renaming Columns

- Week 11-12:

  - Data Visualization in Python: Seaborn and Matplotlib

  - Plotting Data Distributions: Univariate and Bivariate Analysis

  - Plotting Categorical and Time Series Data

### Week 13-16: Exploratory Data Analysis and Inferential Statistics

- Week 13-14:

  - Exploratory Data Analysis (EDA): Data Sourcing and Cleaning

  - Univariate and Bivariate Analysis

  - Derived Metrics

- Week 15-16:

  - Basics of Probability and Probability Distributions

  - Central Limit Theorem and Sampling Distributions

  - Estimating Mean Using CLT

### Week 17-20: BI Tool Development with Tableau

- Week 17-18:

  - Introduction to Tableau: Connecting Data, Ingesting Data

  - Data Preparation, Hierarchies, Drill-Down Visualizations

- Week 19-20:

  - Charting Visualizations: Bar, Pie, Scatter, Tree Maps, Dual Axes Charts

  - Calculations in Tableau

  - Dashboarding and Publishing

### Week 21-24: Hypothesis Testing and Machine Learning Introduction

- Week 21-22:

  - Hypothesis Testing: Null and Alternate Hypotheses

  - Critical Value Method and p-value Method

  - Types of Errors and t-distributions

  - p value method and error types, T distribution,Two sample mean test, A/B testing demo. 

- Week 23-24:

  - Introduction to Machine Learning: Supervised and Unsupervised Learning

  - Linear Regression: Regression Line, Best Fit Line

  - Building a Linear Model in Python

### Week 25-28: Advanced Regression Techniques

- Week 25-26:

  - Multiple Linear Regression: Assumptions, Multicollinearity

  - Dealing with Categorical Variables

  - Model Assessment and Comparison

- Week 27-28:

  - Building MLR in Python with Industry Use Case

  - Feature Selection: Forward/Backward Techniques, RFE

### Week 29-32: Logistic Regression and Clustering

- Week 29-30:

  - Univariate Logistic Regression: Binary Classification, Sigmoid Curve

  - Building Logistic Regression Model in Python

  - Model Evaluation Metrics: Confusion Matrix, ROC Curve

- Week 31-32:

  - K-means Clustering: Steps, Practical Considerations

  - Executing K-means in Python

  - Hierarchical Clustering and Other Clustering Methods

### Week 33-36: Advanced Model Selection and SVM

- Week 33-34:

  - Model Selection Principles: Bias-Variance Tradeoff, Overfitting

  - Regularization Techniques: Ridge and Lasso Regression

  - Cross-Validation Methods

- Week 35-36:

  - Support Vector Machines (SVM): Maximal Margin Classifier, Hyperplane

  - Kernel Tricks, Feature Transformation

  - SVM Implementation in Python with Use Case

### Week 37-40: Tree Models and Boosting Techniques

- Week 37-38:

  - Decision Trees: Gini Index, Entropy, Information Gain

  - Random Forests: Hyperparameter Tuning, OOB Method

- Week 39-40:

  - Introduction to Boosting: Weak Learners, AdaBoost Algorithm

  - AdaBoost Distribution and Parameter Calculation

  - Gradient Boosting: Understanding Gradient Boosting, Gradient Boosting Algorithm

  - XGBoost and Kaggle Practice Exercise

### Week 41-44: Time Series Analysis

- Week 41-42:

  - Time Series Analysis: Components, Stationary Data, Detrending Methods

  - ARIMA, SARIMA, Exponential Smoothing

  - Model Evaluation: MAPE, Smoothing Techniques

- Week 43-44:

  - Time Series Differencing and Smoothing Techniques

  - Building and Evaluating Time Series Models

  - Time Series Project and Presentation

### Week 45-48: Big Data Analytics

- Week 45-46:

  - Introduction to Big Data and Big Data Storage in Hadoop

  - Hadoop Terminologies: Master-Slave, HDFS

  - Introduction to MapReduce, Big Data Injection with Hive and Sqoop

- Week 47-48:

  - Introduction to Apache Hive: Key Features, Use Cases

  - Hive Metastore, Hive Data Models

  - Big Data Processing using Apache Spark

### Week 49-52: Unsupervised Learning and Recommendation Systems

- Week 49-50:

  - Introduction to Recommendation Systems: Content-Based and Collaborative Filtering

  - Building Recommendation Systems from Scratch

  - Industry Relevant Case Study: Recommendation Systems

- Week 51-52:

  - Advanced Supervised Algorithms: K-means++, Anomaly Detection, Outlier Detection

  - High Dimensionality Reduction: PCA, t-SNE, UMAP

  - Implementing High Dimensionality Reduction Techniques

### Week 53-56: Natural Language Processing (NLP)

- Week 53-54:

  - Lexical Processing:

    - Text Encoding and Regular Expressions: Anchors and Wildcards, Character Sets, Greedy vs. Non-Greedy Search, Commonly Used Regular Expression Functions, Grouping in Regular Expressions

    - Basic Lexical Processing: Word Frequencies and Stopwords, Tokenization, Stemming, Lemmatization

    - TF-IDF Representation: Use Case and Building a Spam Detector

    - Word2Vec Libraries for Vectorizing

- Week 55-56:

  - Syntactic Processing:

    - Parsing, Parts of Speech, Rule-Based POS Tagging

    - Markov Chain and HMM

    - Naive Bayes using Named Entity Recognition

    - Decision Tree Classifiers for Named Entity Recognition

  - Semantic Processing:

    - Using SpaCy and Hugging Face Libraries

    - Building a Chatbot using Rasa and DialogFlow

### Week 57-60: Deep Learning

- Week 57-58:

  - Introduction to Neural Networks: Perceptron, Neurons

  - Assumptions Made to Simplify Neural Networks

  - Parameters and Hyperparameters of Neural Networks

  - Activation Functions in Neural Networks

- Week 59-60:

  - Feedforward in Neural Networks: Information Flow, Image Recognition

  - Understanding Vectorized Feedforward Implementation

  - Backpropagation in Neural Networks: Training, Complexity of Loss Function

  - Updating Weights and Bias, Sigmoid Backpropagation

  - Training in Batches, Loss Functions, Gradient Descent

  - Hyperparameter Tuning, Dropouts, Bayesian Approach

### Week 61-64: Convolutional Neural Networks and Recurrent Neural Networks

- Week 61-62:

  - Convolutional Neural Networks (CNNs): Introduction and Comprehension of VGG-16 Architecture

  - Building CNNs in Keras on an MNIST Dataset

  - CNN Architectures: Overview, Transfer Learning

  - Using AlexNet, VGG-Net, GoogleNet, ResidualNet

  - Introduction to Transfer Learning, Use Cases of Transfer Learning

  - Transfer Learning with Pre-trained CNNs

  -Understanding RNN Architecture: LSTM, Bidirectional, and Vanilla Algorithms. 

### Week 65-68: MLOps

- Week 65-66:

  - Introduction to Git and GitHub Setup for MLOps

  - Building a CARS-24 ML Tool using Streamlit

  - Developing Web APIs using Flask

  - Containerization with Docker and Docker Hub

- Week 67-68:

  - Deploying APIs on AWS using ECS

  - GitHub Actions: Setting Up CI Pipelines

  - GitHub Actions: Setting Up CD Pipelines

  - Experiment Tracking and Data Management using MLflow

  - ML System Design and Business Case Review

  - ML Pipelines with AWS SageMaker

###Week 69-70: Generative AI

Week 69-70:GenAI: Architecture of LLM, Multimodal LLMs, VectorDB (OpenAI, Llama), Similarity Search, FAISS, RAG (Retrieval Augmented Generation)

Open Source LLM: Langchain, Llama2, Hugging Face, Capstone Projects in Finance and E-commerce. 

This plan is designed to build a deep understanding and practical skills over an extended period, ensuring thorough knowledge and experience in various aspects of data science, machine learning, big data, Natural language processing,deep learning and MLOps, with a GenAi.

This could be a roadmap for a full stack data scientist at a high level