Build a strong programming foundation with Python. Key topics include:

• Variables, Numbers, Strings
• Lists, Dictionaries, Sets, Tuples
• Conditional Statements (if), Loops (for, while)
• Functions and Lambda Functions
• Working with Modules (pip install)
• File Operations: Reading and Writing Files
• Exception Handling
• Object-Oriented Programming: Classes and Objects

Technical Skills:

• Numpy for numerical computations
• Pandas for data handling and preprocessing
• Matplotlib and Seaborn for creating insightful visualizations

Core/Soft Skills:

• Develop an analytical mindset and storytelling skills using data visuals.

Dive into statistical and mathematical foundations essential for data science:

• Descriptive vs. Inferential Statistics
• Data Types: Continuous vs. Discrete, Nominal vs. Ordinal
• Basic Plots: Histograms, Pie Charts, Bar Charts, Scatter Plots
• Measures of Central Tendency: Mean, Median, Mode
• Measures of Dispersion: Variance, Standard Deviation
• Probability Basics: Rules and Applications
• Distributions: Normal Distribution and Applications
• Advanced Topics: Correlation and Covariance Central Limit Theorem Hypothesis Testing (p-value, Confidence Interval, Z-Test, T-Test, ANOVA)

Learn to extract insights and identify patterns from datasets:

• Perform in-depth EDA using tools like Python, Pandas, and visualization libraries.
• Explore example notebooks and projects on Kaggle for hands-on practice.

Gain proficiency in querying relational databases:

• Basics: Relational database concepts and structure
• Core Queries: SELECT, WHERE, LIKE, DISTINCT, BETWEEN, GROUP BY, ORDER BY
• Advanced Queries: CTE, Subqueries, Window Functions
• Joins: Left, Right, Inner, Full Joins
• Focus on queries relevant to data analysis; skip database creation and administration topics.

Data Preprocessing

• Handling missing values and outliers
• Data normalization and encoding techniques (One-hot, Label Encoding)
• Feature Engineering
• Train-Test Split and Cross-Validation

Model Building:

• Types of Machine Learning: Supervised vs. Unsupervised
• Linear Models: Linear Regression, Logistic Regression, Gradient Descent
• Tree-Based Models: Decision Tree, Random Forest, XGBoost

Model Evaluation:

• Regression Metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), MAPE
• Classification Metrics: Accuracy, Precision-Recall, F1 Score, ROC Curve, Confusion Matrix
• Hyperparameter Tuning: GridSearchCV, RandomSearchCV

Apply your knowledge to real-world projects, covering the full ML lifecycle from data to deployment:

Regression Project: Bangalore Property Price Prediction

• Data Cleaning and Feature Engineering
• Model Building and Hyperparameter Tuning
• Create a Flask server as a backend and build a website for price prediction
• Deploy the application to AWS

Classification Project: Sports Celebrity Image Classification

• Data Collection and Cleaning
• Feature Engineering and Model Training
• Build a Flask backend and deploy a user-friendly website

• What is a neural network? Forward propagation, back propagation
• Building multilayer perceptron
• Special neural network architectures
• Convolutional neural network (CNN)
• Sequence models: RNN, LSTM

Many data scientists choose a specialized track which is either NLP or Computer vision. You don't need to learn both.

• Natural Language Processing (NLP)
• Regex
• Text presentation: Count vectorizer, TF-IDF, BOW, Word2Vec, Embeddings
• Text classification: Naïve Bayes
• Fundamentals of Spacy & NLTP library
• One end to end project

Computer Vision

• Basic image processing techniques: Filtering, Edge Detection, Image Scaling, Rotation

Library to use: OpenCV

• Convolutional Neural Networks (CNN) – Already covered in deep learning.
• Data preprocessing, augmentation – Already covered in deep learning.

• More projects
• Online brand building through LinkedIn, Kaggle, Discord, Opensource contribution
• Job application and Success