Data Science

Overview of Data Science

Definition and Importance

Data Science vs. Data Analytics

Applications of Data Science in Various Industries

Setting Up the Environment

Installing Python

Introduction to Jupyter Notebooks

Installing Required Libraries (NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn)

Basic Python Syntax

Variables and Data Types

Basic Operators

Conditional Statements

Loops

Python Data Structures

Lists

Tuples

Dictionaries

Sets

Functions and Modules

Defining and Calling Functions

Importing Modules

Using Built-in Functions and Libraries

Data Collection

Importing Data from CSV, Excel, and SQL Databases

Web Scraping Basics with BeautifulSoup and Scrapy

Using APIs for Data Collection (requests)

Data Wrangling

Handling Missing Values

Data Cleaning Techniques

Data Transformation and Normalization

Combining and Merging Datasets

Introduction to EDA

Importance of EDA

Steps in EDA

Descriptive Statistics

Measures of Central Tendency (Mean, Median, Mode)

Measures of Dispersion (Standard Deviation, Variance, Range)

Skewness and Kurtosis

Data Visualization

Introduction to Matplotlib and Seaborn

Creating Basic Plots (Line, Bar, Histogram, Box Plot)

Customizing Plots (Titles, Labels, Legends)

Introduction to Pandas

Series and DataFrame Basics

Importing and Exporting Data

Data Manipulation

Filtering and Sorting Data

Grouping and Aggregation

Handling Time Series Data

Applying Functions to DataFrames

Introduction to NumPy

Arrays and Matrices

Basic Operations on Arrays

Advanced NumPy Techniques

Broadcasting

Vectorization

Linear Algebra Operations

Advanced Data Visualization

Heatmaps

Pair Plots

Violin Plots

Faceting with Seaborn

Interactive Visualizations

Using Plotly for Interactive Charts

Creating Dashboards with Plotly Dash

Introduction to Statistics

Probability Theory

Random Variables and Probability Distributions

Hypothesis Testing

Null and Alternative Hypotheses

Types of Tests (T-test, Chi-square test)

P-values and Significance Levels

Overview of Machine Learning

Supervised vs. Unsupervised Learning

Common Machine Learning Algorithms

Implementing Machine Learning Models

Data Preprocessing for Machine Learning

Training and Testing Models

Model Evaluation Metrics

Supervised Learning

Linear Regression

Logistic Regression

Decision Trees

Random Forests

Support Vector Machines (SVM)

Unsupervised Learning

K-Means Clustering

Hierarchical Clustering

Principal Component Analysis (PCA)

Introduction to NLP

Text Preprocessing (Tokenization, Lemmatization, Stopwords Removal)

Bag of Words and TF-IDF

NLP with NLTK and SpaCy

Sentiment Analysis

Named Entity Recognition (NER)

Text Classification

Introduction to Time Series

Components of Time Series Data

Moving Averages and Smoothing

Advanced Time Series Techniques

Autoregressive Integrated Moving Average (ARIMA)

Seasonal Decomposition

Forecasting

Introduction to Deep Learning

Understanding Neural Networks

Overview of Deep Learning Frameworks (TensorFlow, Keras, PyTorch)

Building Neural Networks

Creating a Simple Neural Network with Keras

Training and Evaluating Neural Networks

Introduction to Big Data

Definition and Characteristics of Big Data

Big Data Technologies

Working with Hadoop

Hadoop Ecosystem Overview

HDFS (Hadoop Distributed File System)

MapReduce Basics

Practice Exercises for Each Module

Real-world Problem Solving

Analyzing Real-world Datasets

Interpreting Results and Drawing Conclusions

Building Small Data Science Applications

Implementing Data Processing Pipelines

Comprehensive Project Covering Multiple Modules

Real-world Problem Solving and Implementation