# Data Science with Machine Learning

Unleash the potential of data with the fusion of Data Science and Machine Learning, revolutionizing industries and driving innovation by SCAI

## Skills You Will Gain

NumPy

Pandas

Matplotlib

Scikit-learn

TensorFlow/PyTorch

Seaborn

SciPy

Statsmodels

## This course includes

- 1 : 1 Session
- 100% Placement Assistance

- 24 Weeks
- Real TIme Project Training

## Syllabus Overview

## Data Science with Machine Learning

- Introduction to Python
- What is Python and why use it?
- Setting up the Python environment
- Basic syntax and execution flow
- Writing your first Python script

- Variables and Data Types
- Understanding variables and basic data types (integers, floats, strings)
- Type casting and data type conversion

- Control Flow
- Making decisions with if, elif, and else
- Looping with for and while
- Controlling loop flow with break and continue

- Data Structures (Part 1)
- Lists: Creation, indexing, and list operations
- Tuples: Immutability and tuple operations

- Data Structures (Part 2)
- Sets: Usage and set operations
- Dictionaries: Key-value pairs, accessing, and manipulating data

- Functions
- Defining functions and returning values
- Function arguments and variable scope
- Anonymous functions: Using lambda

- File Handling
- Reading from and writing to files
- Handling different file types (text, CSV, etc.)

- Error Handling and Exceptions
- Try and except blocks
- Raising exceptions
- Using finally for cleanup actions

- Object-Oriented Programming (OOP)
- Classes and objects: The fundamentals
- Encapsulation: Private and protected members
- Inheritance: Deriving classes
- Polymorphism: Method overriding

- Advanced Data Structures
- List comprehensions for concise code
- Exploring the collections module: Counter, defaultdict, OrderedDict

- Decorators and Context Managers
- Creating and applying decorators
- Managing resources with context managers and the with statement

- Concurrency
- Introduction to concurrency with threading
- Understanding the Global Interpreter Lock (GIL)
- Basics of asynchronous programming with asyncio

- Introduction to Excel
- Overview of Excel’s interface and features
- Basic spreadsheet operations: entering data, formatting cells, sorting and filtering
- Introduction to formulas and cell references
- Summarizing data with SUM, AVERAGE, MIN, MAX, COUNT

- Working with Data
- Data types and best practices for data entry
- Using ranges, tables, and data validation
- Understanding date and time functions
- Conditional functions like IF, COUNTIF, SUMIF

- Mastering Excel Functions
- Exploring logical functions: AND, OR, NOT
- Mastering lookup functions: VLOOKUP, HLOOKUP, INDEX, MATCH
- Nesting functions for complex calculations
- Text functions to manipulate strings

- Data Visualization
- Creating and customizing charts
- Using conditional formatting to highlight data
- Introduction to PivotTables for summarizing data
- PivotCharts and slicers for interactive reports

- Advanced Data Analysis Tools
- Exploring What-If Analysis tools: Data Tables, Scenario Manager, Goal Seek
- Solving complex problems with Solver
- Introduction to array formulas for complex calculations

- Introduction to Macros and VBA
- Recording and running macros
- Writing simple VBA scripts to automate repetitive tasks
- Customizing the Excel environment with VBA

- Integration and Power Tools
- Linking Excel with other Office applications
- Using Power Query to import and transform data
- Overview of Power Pivot for data modeling
- An introduction to dashboard creation

- Capstone Project
- Using Excel as part of a data analysis project
- Integrating knowledge from Python and Excel to analyze a dataset
- Presenting insights and telling stories with data

- Introduction to SQL and Database Concepts
- Overview of relational databases
- Basic SQL syntax and setup
- SELECT and FROM clauses to retrieve data
- Sorting and filtering data with ORDER BY and WHERE

- Working with SQL Joins and Aggregations
- Understanding different types of joins: INNER, LEFT, RIGHT, and FULL
- Using aggregate functions like COUNT, SUM, AVG, MIN, and MAX
- Grouping data with GROUP BY
- Filtering grouped data using HAVING

- Advanced SQL Operations
- Subqueries: using subqueries in SELECT, FROM, and WHERE clauses
- Common Table Expressions (CTEs) and WITH clause
- Advanced data manipulation with INSERT, UPDATE, DELETE, and MERGE

- Mastering SQL Functions and Complex Queries
- String functions, date functions, and number functions
- Conditional logic in SQL with CASE statements
- Advanced use of data types and casting

- Exploring SQL Window Functions
- Introduction to window functions
- Using OVER() with PARTITION BY, ORDER BY
- Functions like ROW_NUMBER(), RANK(), DENSE_RANK(), LEAD(), LAG()

- SQL Performance Tuning
- Understanding indexes, including when and how to use them
- Query optimization techniques
- Using EXPLAIN plans to analyze query performance

- Transaction Management and Security
- Understanding transactions, ACID properties
- Implementing transaction control with COMMIT, ROLLBACK
- Basics of database security: permissions, roles

- Integrating SQL with Other Technologies
- Linking SQL databases with programming languages like Python
- Using SQL data in Excel via ODBC, direct queries
- Introduction to using APIs with SQL databases for web integration

- Advanced Data Analytics Tools in SQL
- Using analytical functions for deeper insights
- Exploring materialized views for performance
- Dynamic SQL for flexible query generation

- Capstone Project
- Designing and implementing a database schema for a real-world application
- Comprehensive data analysis using advanced SQL techniques
- Integrating SQL knowledge with tools like Python and Excel to provide business solutions
- Presenting findings and insights effectively

- Understanding NoSQL
- Overview of NoSQL
- Definition and evolution of NoSQL databases.
- Differences between NoSQL and traditional relational database systems (RDBMS).

- Types of NoSQL Databases
- Key-value stores, document stores, column stores, and graph databases.
- Use cases and examples of each type.

- Overview of NoSQL
- NoSQL Concepts and Data Models
- NoSQL Data Modeling
- Understanding NoSQL data modeling techniques.
- Comparing schema-on-read vs. schema-on-write.

- Advantages of NoSQL
- Scalability, flexibility, and performance considerations.
- When to choose NoSQL over a traditional SQL database.

- NoSQL Data Modeling
- Getting Started with MongoDB
- Installing MongoDB
- Setting up MongoDB on different operating systems.
- Understanding MongoDB’s architecture: databases, collections, and documents.

- Basic Operations in MongoDB
- CRUD (Create, Read, Update, Delete) operations.
- Using the MongoDB Shell and basic commands.

- Installing MongoDB
- Working with Data in MongoDB
- Data Manipulation
- Inserting, updating, and deleting documents.
- Querying data: filtering, sorting, and limiting results.

- Indexing and Aggregation
- Introduction to indexing for performance improvement.
- Basic aggregation operations: $sum, $avg, $min, $max, and $group.

- Data Manipulation

- Introduction to Statistics
- Overview of Statistics in Data Science
- Role of statistics in data analysis and machine learning.
- Differentiation between descriptive and inferential statistics.

- Basic Statistical Measures
- Measures of central tendency (mean, median, mode).
- Measures of dispersion (variance, standard deviation, range, interquartile range).

- Overview of Statistics in Data Science
- Probability Fundamentals
- Probability Concepts
- Basic probability rules, conditional probability, and Bayes’ theorem.

- Probability Distributions
- Introduction to normal, binomial, Poisson, and uniform distributions.

- Probability Concepts
- Hypothesis Testing
- Concepts of Hypothesis Testing
- Null hypothesis, alternative hypothesis, type I and type II errors.

- Key Tests
- t-tests, chi-square tests, ANOVA for comparing group means.

- Concepts of Hypothesis Testing
- Regression Analysis
- Linear Regression
- Simple and multiple linear regression analysis.
- Assumptions of linear regression, interpretation of regression coefficients.

- Logistic Regression
- Understanding logistic regression for binary outcomes.

- Linear Regression
- Multivariate Statistics
- Advanced Regression Techniques
- Polynomial regression, interaction effects in regression models.

- Principal Component Analysis (PCA)
- Reducing dimensionality, interpretation of principal components.

- Advanced Regression Techniques
- Time Series Analysis
- Fundamentals of Time Series Analysis
- Components of time series data, stationarity, seasonality.

- Time Series Forecasting Models
- ARIMA models, seasonal decompositions.

- Fundamentals of Time Series Analysis
- Bayesian Statistics
- Introduction to Bayesian Statistics
- Bayes’ Theorem revisited, prior and posterior distributions.

- Applied Bayesian Analysis
- Using Bayesian methods in data analysis and prediction.

- Introduction to Bayesian Statistics
- Non-Parametric Methods
- Overview of Non-Parametric Statistics
- When to use non-parametric methods, advantages over parametric tests.

- Key Non-Parametric Tests
- Mann-Whitney U test, Kruskal-Wallis test, Spearman’s rank correlation.

- Overview of Non-Parametric Statistics

- Introduction to Exploratory Data Analysis
- Overview of EDA
- The importance and objectives of EDA.
- Key steps in the EDA process.

- Overview of EDA
- Data Handling with Pandas
- Getting Started with Pandas
- Introduction to Pandas DataFrames and Series.
- Reading and writing data with Pandas (CSV, Excel, SQL databases).

- Data Cleaning Techniques
- Handling missing values.
- Data type conversions.
- Renaming and replacing data.

- Data Manipulation
- Filtering, sorting, and grouping data.
- Merging and concatenating datasets.
- Advanced operations with groupby and aggregation.

- Getting Started with Pandas
- Numerical Analysis with NumPy
- Introduction to NumPy
- Creating and manipulating arrays.
- Array indexing and slicing.

- Statistical Analysis with NumPy
- Basic statistics: mean, median, mode, standard deviation.
- Correlations and covariance.
- Generating random data and sampling.

- Introduction to NumPy
- Visualization Techniques
- Using Matplotlib
- Basics of creating plots, histograms, scatter plots.
- Customizing plots: colors, labels, legends.

- Advanced Visualization with Seaborn
- Statistical plots in Seaborn: box plots, violin plots, pair plots.
- Heatmaps and clustermaps.
- Facet grids for multivariate analysis.

- Using Matplotlib

- Introduction to Cloud Services
- Cloud Computing Fundamentals
- What is cloud computing?
- Service models: IaaS, PaaS, SaaS.
- Deployment models: public, private, hybrid cloud.

- Cloud Computing Fundamentals
- Cloud Platforms Overview
- Common Cloud Platforms
- Brief overview of AWS, Azure, and Google Cloud Platform.
- Key services from these platforms (e.g., AWS EC2, AWS S3, Azure VMs, Google Compute Engine).

- Common Cloud Platforms

- Introduction to Web Scraping
- What is Web Scraping?
- The legal and ethical considerations of scraping data from websites.
- Common use cases in data analytics and business intelligence.

- What is Web Scraping?
- Tools and Techniques
- Using Python for Scraping
- Introduction to BeautifulSoup and requests library.
- Extracting data from HTML: tags, IDs, classes.

- Handling Web Data*
- Working with APIs using Python.
- Cleaning and storing scraped data.

- Using Python for Scraping

- Introduction to Machine Learning
- Overview of Machine Learning
- Definitions and Significance: Students will explore the fundamental concepts and various definitions of machine learning, understanding its crucial role in leveraging big data in numerous industries such as finance, healthcare, and more.
- Types of Machine Learning: The course will differentiate between the three main types of machine learning: supervised learning (where the model is trained on labeled data), unsupervised learning (where the model finds patterns in unlabeled data), and reinforcement learning (where an agent learns to behave in an environment by performing actions and receiving rewards).

- Overview of Machine Learning
- Supervised Learning Algorithms
- Regression Algorithms
- Linear Regression: Focuses on predicting a continuous variable using a linear relationship formed from the input variables.
- Polynomial Regression: Extends linear regression to model non-linear relationships between the independent and dependent variables.
- Decision Tree Regression: Uses decision trees to model the regression, helpful in capturing non-linear patterns with a tree structure.

- Classification Algorithms
- Logistic Regression: Used for binary classification tasks; extends to multiclass classification under certain methods like one-vs-rest (OvR).
- K-Nearest Neighbors (KNN): A non-parametric method used for classification and regression; in classification, the output is a class membership.
- Support Vector Machines (SVM): Effective in high-dimensional spaces and ideal for complex datasets with clear margin of separation.
- Decision Trees and Random Forest: Decision Trees are a non-linear predictive model, and Random Forest is an ensemble method of Decision Trees.
- Naive Bayes: Based on Bayes’ Theorem, it assumes independence between predictors and is particularly suited for large datasets.

- Regression Algorithms
- Ensemble Methods and Handling Imbalanced Data
- Ensemble Techniques
- Detailed techniques such as Bagging (Bootstrap Aggregating), Boosting, AdaBoost (an adaptive boosting method), and Gradient Boosting will be covered, emphasizing how they reduce variance and bias, and improve predictions.

- Strategies for Imbalanced Data
- Techniques such as Oversampling, Undersampling, and Synthetic Minority Over-sampling Technique (SMOTE) are discussed to handle imbalanced datasets effectively, ensuring that the minority class in a dataset is well-represented and not overlooked.

- Ensemble Techniques
- Unsupervised Learning Algorithms
- Clustering Techniques
- K-Means Clustering: A method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters.
- Hierarchical Clustering: Builds a tree of clusters and is particularly useful for hierarchical data, such as taxonomies.
- DBSCAN: Density-Based Spatial Clustering of Applications with Noise finds core samples of high density and expands clusters from them.

- Association Rule Learning
- Apriori and Eclat algorithms: Techniques for mining frequent item sets and learning association rules. Commonly used in market basket analysis.

- Clustering Techniques
- Model Evaluation and Hyperparameter Tuning
- Evaluation Metrics
- Comprehensive exploration of metrics such as Accuracy, Precision, Recall, F1 Score, and ROC-AUC for classification; and MSE, RMSE, and MAE for regression.

- Hyperparameter Tuning
- Techniques such as Grid Search, Random Search, and Bayesian Optimization with tools like Optuna are explained. These methods help in finding the most optimal parameters for machine learning models to improve performance.

- Evaluation Metrics

- Introduction to Natural Language Toolkit (NLTK)
- Getting Started with NLTK*
- Installation and setup of NLTK.
- Overview of NLTK’s features and capabilities for processing text.

- Basic Text Processing with NLTK
- Tokenization: Splitting text into sentences and words.
- Text normalization: Converting text to a standard format (case normalization, removing punctuation).
- Stopwords removal: Filtering out common words that may not add much meaning to the text.

- Getting Started with NLTK*
- Basics of OpenCV
- Introduction to OpenCV
- Installing and setting up OpenCV.
- Understanding how OpenCV handles images.

- Basic Image Processing Techniques
- Reading, displaying, and writing images.
- Basic operations on images: resizing, cropping, and rotating.
- Image transformations: Applying filters and color space conversions.

- Introduction to OpenCV
- Basics of Convolutional Neural Networks (CNN)
- Understanding CNNs
- The architecture of CNNs: Layers involved (Convolutional layers, Pooling layers, Fully connected layers).
- The role of Convolutional layers: Feature detection through filters/kernels.

- Implementing a Simple CNN
- Building a basic CNN model for image classification.
- Training a CNN with a small dataset: Understanding the training process, including forward propagation and backpropagation.

- Understanding CNNs
- Basics of Recurrent Neural Networks (RNN)
- Introduction to RNNs
- Why RNNs? Understanding their importance in modeling sequences.
- Architecture of RNNs: Feedback loops and their role.

- Challenges with Basic RNNs
- Exploring issues like vanishing and exploding gradients.

- Introduction to LSTMs
- How Long Short-Term Memory (LSTM) networks overcome the challenges of traditional RNNs.
- Building a simple LSTM for a sequence modeling task such as time series prediction or text generation.

- Introduction to RNNs
- Capstone Project
- Applying Deep Learning Skills
- Choose between a natural language processing task using NLTK, an image processing task using OpenCV, or a sequence prediction task using RNN/LSTM.
- Implement the project using the techniques learned over the course.

- Presentation of Results
- Summarize the methodology, challenges faced, and the insights gained.
- Demonstrate the practical application of deep learning models in solving real-world problems.

- Applying Deep Learning Skills

- Introduction to Flask
- Overview of Flask
- What is Flask? Understanding its microframework structure.
- Setting up a Flask environment: Installation and basic configuration.

- First Flask Application
- Creating a simple app: Routing and view functions.
- Templating with Jinja2: Basic templates to render data.

- Overview of Flask
- Flask Routing and Forms
- Advanced Routing
- Dynamic routing and URL building.
- Handling different HTTP methods: GET and POST requests.

- Working with Forms
- Flask-WTF for form handling: Validations and rendering forms.
- CSRF protection in Flask applications.

- Advanced Routing
- Flask and Data Handling
- Integrating Flask with SQL Databases
- Using Flask-SQLAlchemy: Basic ORM concepts, creating models, and querying data.

- API Development with Flask
- Creating RESTful APIs to interact with machine learning models.
- Using Flask-RESTful extension for resource-based routes.

- Integrating Flask with SQL Databases
- Introduction to FastAPI
- Why FastAPI?
- Advantages of FastAPI over other Python web frameworks, especially for async features.
- Setting up a FastAPI project: Installation and first application.

- FastAPI Routing and Models
- Path operations: GET, POST, DELETE, and PUT.
- Request body and path parameters: Using Pydantic models for data validation.

- Why FastAPI?
- Building APIs with FastAPI
- API Operations
- Advanced model validation techniques and serialization.
- Dependency injection: Using FastAPI’s dependency injection system for better code organization.

- Asynchronous Features
- Understanding async and await keywords.
- Asynchronous SQL database interactions using databases like SQLAlchemy async.

- API Operations
- Serving Machine Learning Models
- Integrating ML Models
- Building endpoints to serve predictions from pre-trained machine learning models.
- Handling asynchronous tasks within FastAPI to manage long-running ML predictions.

- Security and Production
- Adding authentication and authorization layers to secure APIs.
- Tips for deploying Flask and FastAPI applications to production environments.

- Integrating ML Models

- Introduction to Redis (45 minutes)
- Overview of Redis
- What is Redis and why is it used? Understanding its role as an in-memory data structure store.
- Key features of Redis: speed, data types, persistence options, and use cases.

- Installation and Setup
- Quick guide on installing Redis on different operating systems (Windows, Linux, macOS).
- Starting the Redis server and basic commands through the Redis CLI.

- Overview of Redis
- Redis Data Types and Basic Commands (1 hour)
- Key-Value Data Model
- Introduction to Redis’ simple key-value pairs; commands like SET, GET, DEL.

- Advanced Data Types
- Lists, Sets, Sorted Sets, Hashes, and their associated operations.
- Practical examples to demonstrate each type: e.g., creating a list, adding/removing elements, accessing elements.

- Key-Value Data Model
- Redis in Application – Caching (1 hour)
- Caching Concepts
- Explaining caching and its importance in modern applications.
- How Redis serves as a cache: advantages over other caching solutions.

- Implementing Basic Caching
- Setting up a simple cache: handling cache hits and misses.
- Expiration and eviction policies: how to manage stale data in Redis.

- Caching Concepts

- Week 1: Introduction to ChatGPT and AI Principles
- Overview of AI and Natural Language Processing
- Introduction to the field of artificial intelligence, focusing on the subset of natural language processing (NLP).
- Overview of the evolution and capabilities of language models like GPT (Generative Pre-trained Transformer).

- Introduction to ChatGPT
- Understanding what ChatGPT is and how it works.
- Discussing the development of ChatGPT by OpenAI and its foundational technologies.

- Overview of AI and Natural Language Processing
- Setting Up ChatGPT
- Accessing ChatGPT
- How to access ChatGPT, including API setup for programming interfaces.
- Basic commands and interaction patterns with ChatGPT.

- Hands-On Practice
- Simple exercises to interact with ChatGPT: asking questions, receiving answers, and understanding the responses in the context of data science.

- Accessing ChatGPT
- Using ChatGPT for Data Insights
- ChatGPT in Data Analysis
- Using ChatGPT to assist in exploratory data analysis (EDA): generating descriptive statistics, proposing data visualizations, and suggesting preliminary insights.
- Practical examples where students use ChatGPT to ask questions about given datasets.

- ChatGPT in Data Analysis
- Enhancing Data Science Workflows with ChatGPT
- Automation of Repetitive Tasks*
- How ChatGPT can automate repetitive tasks in data science workflows, such as data cleaning descriptions and basic analysis reporting.
- Example tasks where ChatGPT can generate reports or summaries based on dataset characteristics.

- Automation of Repetitive Tasks*
- Ethics and Limitations
- Understanding Ethical Implications
- Discussion on the ethical considerations when using AI tools like ChatGPT in data science, including data privacy, model biases, and transparency.

- Limitations of ChatGPT
- Understanding the limitations of ChatGPT in the context of accuracy, reliability, and when not to rely solely on AI-generated information.

- Understanding Ethical Implications
- Capstone Project
- Applying ChatGPT in a Practical Scenario
- A simple project where students use ChatGPT to facilitate a data science task, such as creating a data report or conducting preliminary data analysis.
- Students will document their process, outcomes, and evaluate the effectiveness and challenges of using AI in their project.

- Applying ChatGPT in a Practical Scenario

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