Join the 'Data Science' Learning Programme.

Overview of Data Science

Importance and Applications of Data Science

Tools and Technologies in Data Science

Setting Up the Environment: Python and Jupyter Notebooks

Data Types and Data Structures

Introduction to Pandas

  – Loading and Inspecting Data

  – Data Cleaning and Preparation

Data Exploration and Summary Statistics

Handling Missing Data

Importance of Visualization in Data Science

Matplotlib and Seaborn Basics

Creating Line, Bar, and Scatter Plots

Heatmaps and Pair Plots

Interactive Visualizations with Plotly

Descriptive Statistics

Probability Basics

Hypothesis Testing

Correlation and Regression Analysis

Introduction to Time Series Analysis

What is Machine Learning?

Supervised vs. Unsupervised Learning

Train-Test Split and Model Evaluation

Introduction to Scikit-Learn

Implementing Linear and Logistic Regression

Decision Trees and Random Forests

Support Vector Machines (SVM)

Clustering Algorithms: K-Means and DBSCAN

Dimensionality Reduction with PCA

Introduction to Neural Networks

Overview of NLP

Text Preprocessing Techniques

Sentiment Analysis

Bag of Words and TF-IDF

Introduction to Word Embeddings

Basics of Big Data and Hadoop

Introduction to Spark

Cloud Platforms for Data Science: AWS, Google Cloud, Azure

Distributed Data Processing

Building a Data Science Project

Model Deployment Strategies

Using Flask or FastAPI for Model Deployment

Monitoring and Maintaining Models

Define a Real-World Problem

Data Collection and Cleaning

Exploratory Data Analysis

Model Building and Evaluation

Presenting Results and Insights