Data Science using Python

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About Course

Our Python-based Data Science course equips you with essential skills for analyzing data, building predictive models, and deriving insights. From data manipulation with Pandas to visualization with Matplotlib, Seaborn, and Plotly, you’ll learn the core libraries. Dive into machine learning with scikit-learn, covering regression, classification, clustering, and more. Practical projects ensure hands-on experience, making you proficient in data science with Python.

Data manipulation with Pandas for efficient data handling.
Data visualization using Matplotlib, Seaborn, and Plotly for insightful representation.
Exploratory data analysis (EDA) techniques to understand data distributions and patterns.
Machine learning algorithms implementation using scikit-learn for regression, classification, and clustering tasks.
Model evaluation and selection to ensure optimal performance.
Practical projects to apply your skills in real-world scenarios, solidifying your understanding of Python for data science.

Course Content

Introduction to Data Science

What is Data Science?
Applications of Data Science
Role of Data Scientists

Python Programming

Data Manipulation with Pandas

Data Analysis with NumPy

Data Visualization with Matplotlib

Interactive Data Visualization with Seaborn and Plotly

MySQL Database

Advanced Data Analysis Techniques

Machine Learning with Scikit-Learn

Introduction to PowerBi

Advanced PowerBi Techniques

Final Assessment

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About Course

What Will You Learn?

Course Content

Introduction to Data Science

What is Data Science?

Applications of Data Science

Role of Data Scientists

Python Programming

Get Started With Python

Python Statements

Python Comments

Python Keywords

Data Manipulation with Pandas

Introduction to Pandas Library

DataFrames and Series

Data Cleaning and Preprocessing

Data Aggregation and Grouping

Data Analysis with NumPy

Introduction to NumPy Library

Arrays and Matrices

Array Operations and Functions

Statistical Analysis with NumPy

Data Visualization with Matplotlib

Introduction to Matplotlib Library

Basic Plots: Line, Bar, Scatter, Histogram

Advanced Plot Customization

Interactive Data Visualization with Seaborn and Plotly

Introduction to Seaborn and Plotly Libraries

Advanced Visualization Techniques

Interactive Dashboards and Plots

MySQL Database

MySQL Installation and Setup

MySQL Datatypes

Data Definition Language (DDL)

Data Manipulation Language (DML)

Data Querying and Filtering with SQL

Advanced Data Analysis Techniques

Time Series Analysis

Dimensionality Reduction Techniques

Clustering Algorithms: K-Means, Hierarchical Clustering

Introduction to Machine Learning Concepts

Machine Learning with Scikit-Learn

Supervised Learning: Regression, Classification

Unsupervised Learning: Clustering, Dimensionality Reduction

Model Evaluation and Selection

Introduction to PowerBi

Overview of PowerBi Features and Components

Data Import and Transformation in PowerBi

Creating Visualizations: Charts, Graphs, Maps

Building Interactive Dashboards

Advanced PowerBi Techniques

DAX (Data Analysis Expressions) Language

PowerBi Data Modeling and Relationships

Power Query: Data Preparation and Cleansing

PowerBi Service: Sharing and Collaboration

Final Assessment

Final Assessment (copy)

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