Data Science Essential - DSE

About Course

A thorough grasp of statistical analysis, familiarity with huge
dataset processing, and programming languages like Python
or R are all part of the Data Science Essentials. To extract
important insights and make wise judgments, data scientists
use machine learning algorithms and data visualization
approaches. Gaining an understanding of Data Science
Essentials is essential for generating actionable insight and
promoting innovation in a variety of sectors, since it centers
on the extraction of knowledge from raw data

Course Content

1. Introduction to Data Science

Understanding Data Science
00:00
Definition and scope of data science
00:00
Data science lifecycle and processes
00:00
Role of a Data Scientist
00:00
Data Exploration and Preprocessing
00:00
Exploratory Data Analysis (EDA) techniques
00:00
Handling missing data and outliers
00:00
Data cleaning and transformation
00:00
Introduction to Data Visualization
00:00
Importance of data visualization
00:00
Common visualization tools and libraries (e.g., Matplotlib, Seaborn)
00:00
Creating effective visualizations
00:00

2. Statistical Analysis and Machine Learning Basics

3. Supervised Learning

4. Unsupervised Learning and Feature Engineering

5. Advanced Topics in Data Science

6. Final Project

7. Final Exam

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About Course

Course Content

1. Introduction to Data Science

Understanding Data Science

Definition and scope of data science

Data science lifecycle and processes

Role of a Data Scientist

Data Exploration and Preprocessing

Exploratory Data Analysis (EDA) techniques

Handling missing data and outliers

Data cleaning and transformation

Introduction to Data Visualization

Importance of data visualization

Common visualization tools and libraries (e.g., Matplotlib, Seaborn)

Creating effective visualizations

2. Statistical Analysis and Machine Learning Basics

Statistical Analysis for Data Science

Descriptive and inferential statistics

Hypothesis testing

Correlation and Regression Analysis

Introduction to Machine Learning

Overview of machine learning concepts

Types of Machine Learning Algorithms (supervised, unsupervised, and reinforcement learning)

Model training, evaluation, and prediction

Project: Exploratory Data Analysis and Visualization

Applying EDA, data preprocessing, and visualization to a real-world datase

3. Supervised Learning

Regression Analysis

Linear and non-linear regression

Model evaluation metrics for regression

Practical applications of regression

Classification Algorithms

Basics of classification problems

Popular Classification Algorithms (e.g., Decision Trees, SVM)

Model evaluation and metrics for classification

Project: Building a Supervised Learning Model

Implementing a supervised learning model on a provided database

4. Unsupervised Learning and Feature Engineering

Clustering Techniques

K-Means clustering

Hierarchical clustering

Use cases and applications

Dimensionality Reduction

Principal Component Analysis (PCA)

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Feature selection and extraction

: Project: Unsupervised Learning and Feature Engineering

Applying clustering and dimensionality reduction on a real-world dataset

5. Advanced Topics in Data Science

Time Series Analysis

Basics of time series data

Time series visualization and decomposition

Forecasting techniques

Natural Language Processing (NLP)

Introduction to Text Dat

Text preprocessing and tokenization

Building basic NLP models

Capstone Project: Real-world Data Science Application

Comprehensive project integrating various data science concepts on a larger dataset

6. Final Project

7. Final Exam

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