Data Sciences

Module 1. Python Programming

Learning the basics of Python programming is essential for data scientists to manipulate and visualizing data. This section will cover the basic syntax, operators, strings, functions, and other essential details to help you analyse large amounts of data and manipulate them.

Python Introduction and setting up the environment

    Python Basic Syntax and Data Types

    Operators in Python (e.g., Arithmetic, Logical, Bitwise)

    Strings in Python

    Lists

    Tuples

    Sets

    Dictionaries

    Python conditional statements (e.g., if, if-else, if-elif-else)

    Loops in Python (e.g., while, for, break, continue)

    Getting Started with HackerRank use cases and working on them

    List and Dictionaries comprehension

    Functions

    Anonymous Functions (Lambda)

    Generators

    Modules

Exceptions and Error Handling

Classes and Objects (OOPS) (including different types of methods, inheritance, polymorphism, operator overloading, overriding)

    Date and Time

    Regex (e.g., re.search(), re.compile(), re.find(), re.split())

    Files (including opening, closing, reading and writing files)

    APIs the Unsung Hero of the Connected World

    Python for Web Development – Flask

    Hands-On Projects (Web Scraping, Sending Automated Emails, Building a Virtual Assistant)

Module 2. Data Analysis

Data analysis helps you in making informed decisions with data exploration and visualization using advanced tools. This section will cover the basics along with teaching you how to scrape data from websites using libraries like BeautifulSoup and handling and storing them in appropriate formats.

Packages (Working on Numpy, Pandas, and Matplotlib)

    Web Scraping (learning about tools, libraries and ethical considerations)

    Exploratory data analysis (EDA) using Pandas and NumPy

    Data Visualization using Matplotlib, Seaborn, and Plotly

    Database Access

    Tableau

    Power BI

Module 3. Statistics

The specific topics covered in the statistics section give you an overview of descriptive statistics and inferential statistics that provide the foundation for understanding and analyzing complex data. Explore the statistical foundations for data signs from this section and apply them to data analysis projects.

  Descriptive Statistics (including central tendency, variance, standard deviation, covariance, correlation, probability)

    Inferential Statistics (including Central limit theorem, hypothesis testing, one-tailed and two-tailed test, and Chi-Square test)

Module 4. Machine Learning

This part of the syllabus comprises mathematical models and algorithms that are needed in coding machines to adapt them to real-world challenges. The course comprises basic knowledge of machine learning and its three main types: supervised, unsupervised, and reinforcement learning among other essential topics.

    Introduction to Machine Learning

    Introduction to data science and its applications

    Data Engineering and Preprocessing

    Model Evaluation and Hyperparameter Tuning

    Supervised Learning – Regression

    Supervised Learning – Classification

    SVM, KNN & Naive Bayes

    Ensemble Methods and Boosting

    Unsupervised Learning – Clustering

    Unsupervised Learning – Dimensionality Reduction

    Recommendation Systems

    Reinforcement Learning

    Developing API using Flask / Webapp with Streamlit

    Deployment of ML Models

    Project Work and Consolidation

Module 5. NLP

Natural Language Processing NLP helps machines understand and create human language. This section will teach you Named Entity Recognition, text pre-processing, and text representation, along with applications ranging from sequential modelling, and building sentiment analysis.

    Natural Language Processing (NLP) (including NER, text representation, sequential model, sentiment analysis)

Module 6. Deep Learning

This section allows you to master advanced topics like CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks), Neural Network architecture, and more.

    RISE OF THE DEEP LEARNING

    Artificial Neural Networks

    Convolution Neural Networks

    CNN – Transfer Learning

    RNN – Recurrent Neural Networks

    Generative Models and GANs

Module 7. Computer Vision

The computer vision syllabus allows you to understand how to create algorithms for computers to read and write data sent via images or videos.

    Computer Vision (including reading and writing images, drawing shapes using OpenCV, reason eye detection using OpenCV, VGG, CNN with Keras)

Bonus Module: Projects & Case Study

Real-Time Rain Prediction using ML

Real-Time Drowsiness Detection Alert System

House Price Prediction using LSTM

Customizable Chabot using OpenAI API

Fire and Smoke Detection using CNN

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