Data Science With Python

Curriculum

The Course Curriculam

Week 1: Introduction to Data Science and Python

What is data science? Overview of the data science lifecycle.
Introduction to Python programming: Syntax, variables, and data types.
Setting up the Python environment: Jupyter Notebook, Anaconda, and IDEs.
Hands-on Practice: Write basic Python scripts and run them.

Week 2: Python Essentials for Data Science

Week 3: Data Cleaning and Preprocessing

Week 4: Data Visualization Basics

Week 5: Exploratory Data Analysis (EDA)

Week 6: Introduction to Machine Learning with Python

Week 7: Advanced Machine Learning Concepts

Week 8: Final Project and Portfolio Building

₹1000

Course Information

Instructor: vedupskilling@gmail.com
Lesson: 28
Duration: 40h
Quizze: 0

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Data Science With Python

The Course Curriculam

Week 1: Introduction to Data Science and Python

What is data science? Overview of the data science lifecycle.

Introduction to Python programming: Syntax, variables, and data types.

Setting up the Python environment: Jupyter Notebook, Anaconda, and IDEs.

Hands-on Practice: Write basic Python scripts and run them.

Week 2: Python Essentials for Data Science

Python control structures: Loops, conditionals, and functions

Working with Python libraries: NumPy and Pandas.

Data manipulation: Indexing, filtering, grouping, and aggregations.

Hands-on Practice: Manipulate and analyze datasets using Pandas

Week 3: Data Cleaning and Preprocessing

Understanding data quality: Missing values, duplicates, and outliers.

Techniques for data cleaning and transformation.

Hands-on Practice: Clean a messy dataset and prepare it for analysis

Week 4: Data Visualization Basics

Introduction to Matplotlib and Seaborn for visualization.

Creating charts: Bar, line, scatter, and histograms.

Visualizing relationships and distributions in data.

Hands-on Practice: Create a dashboard with multiple visualizations.

Week 5: Exploratory Data Analysis (EDA)

Techniques for exploring and summarizing data.

Detecting patterns, trends, and correlations

Hands-on Practice: Perform EDA on a public dataset (e.g., Titanic or Iris dataset).

Week 6: Introduction to Machine Learning with Python

Overview of machine learning: Supervised vs. unsupervised learning.

Linear regression and logistic regression using Scikit-learn

Hands-on Practice: Build a simple regression model to predict outcomes.

Week 7: Advanced Machine Learning Concepts

Decision trees and random forests.

Introduction to clustering algorithms (e.g., K-Means).

Model evaluation and validation techniques.

Hands-on Practice: Train, test, and evaluate models on real-world datasets.

Week 8: Final Project and Portfolio Building

Final Project: Solve a data science problem, including data cleaning, analysis, modeling, and visualization.

Portfolio preparation: Documenting and presenting your project

Review and feedback session for improvement.