top of page
ai 5.avif

DATA SCIENCE COURSE

(4.0) reviews

  • Foundations & Tools: Learn Python, data handling, statistics, and essential libraries like Pandas, NumPy, Matplotlib, and Git for version control.

  • Core Data Science & ML: Master EDA, machine learning, deep learning, NLP, and model evaluation techniques with real-world applications.

  • Deployment & Capstone: Build, deploy, and present a complete end-to-end project using cloud platforms, APIs, Docker, and GitHub.

Course 
Syllabus

Data Science Features

ds features.jpg
ac.jpg

Our Services

1

Introduction to Data Science

  1. Overview of Data Science

  2. Applications of Data Science

  3. Understanding Data, Information, and Knowledge

  4. Role of Data Scientist

  5. Data Science Process

2

Python for Data Science

  1. Python Basics

  2. Data Types and Variables

  3. Control Structures (If, Else, For, While)

  4. Functions and Modules

  5. Working with Libraries: NumPy, Pandas, Matplotlib, Seaborn

3

Data Collection and Data Cleaning

  1. Importing Data from CSV, Excel, Database

  2. Data Cleaning Techniques

  3. Handling Missing Values

  4. Data Transformation

  5. Feature Engineering

4

Exploratory Data Analysis (EDA)

  1. Descriptive Statistics

  2. Data Visualization Techniques

  3. Correlation and Covariance

  4. Outlier Detection and Handling

5

Statistics for Data Science

  1. Probability and Probability Distributions

  2. Measures of Central Tendency and Dispersion

  3. Hypothesis Testing

  4. Statistical Tests (T-test, Chi-square, ANOVA)

6

Machine Learning Algorithms

  1. Supervised vs Unsupervised Learning

  2. Linear Regression

  3. Logistic Regression

  4. Decision Trees and Random Forest

  5. Support Vector Machines (SVM)

  6. K-Means Clustering

  7. Principal Component Analysis (PCA)

7

Model Evaluation and Improvement

  1. Model Validation​

  2. Cross Validation Techniques

  3. Performance Metrics (Accuracy, Precision, Recall, F1 Score)

  4. Hyperparameter Tuning

  5. Model Optimization

8

Deep Learning Basics

  1. Introduction to Neural Networks

  2. Forward and Backward Propagation

  3. Activation Functions

  4. Introduction to TensorFlow and Keras

  5. Building and Training Neural Networks

9

Natural Language Processing (NLP)

  1. Text Preprocessing (Tokenization, Lemmatization, Stopwords)

  2. Word Embeddings (Word2Vec, GloVe)

  3. Sentiment Analysis

  4. Text Classification

  5. Chatbots and Virtual Assistants

10

Model Deployment and Version Control

  1. Introduction to Model Deployment

  2. Deployment on Cloud Platforms (Streamlit, Gradio, Flask)

  3. Building APIs using Flask/FastAPI

  4. Containerization using Docker

  5. Version Control with Git and GitHub

  6. Continuous Integration and Continuous Deployment (CI/CD)

11

Git and Version Control

  1. Purpose of Version Control

  2. Popular Version Control Tools

  3. Git Distribution Version Control

  4. Terminologies

  5. Git Workflow and Architecture

  6. Creating New Repositories (Init, Clone)

  7. Code Commits, Pull, Fetch, and Push

  8. Handling Merge Conflicts

  9. Working with GitHub and Bitbucket

  10. Creating GitHub Account

  11. Collaborating with Developer

12

Capstone Project

  1. End-to-End Data Science Project

  2. Problem Statement Definition

  3. Data Collection and Cleaning

  4. Model Building and Evaluation

  5. Deployment of Model

bottom of page