Duration 3 days – 21 hrs
Overview.
This course is designed for fresh graduates aspiring to build a career in Data Science. It introduces the fundamentals of data science, focusing on data analysis, visualization, and basic machine learning concepts using Python. The course provides hands-on practice with real-world datasets, equipping participants with the foundational skills needed to start their data science journey.
Objectives
- Understand the core concepts of Data Science and its applications across industries.
- Learn the basics of Python programming and essential libraries for data analysis.
- Perform data cleaning, exploration, and visualization tasks.
- Gain introductory knowledge of machine learning and build simple models.
- Develop the ability to analyze real-world datasets and derive actionable insights.
Audience
- Fresh graduates from any field aspiring to enter the Data Science domain.
- Students with a background in IT, engineering, mathematics, or related disciplines.
- Beginners looking to learn Data Science fundamentals without prior experience.
Pre- requisites
- Basic familiarity with computers and Microsoft Excel.
- A willingness to learn Python programming (no prior coding experience required).
- A laptop with Python and Jupyter Notebook installed (installation instructions provided).
Course Content
Day 1: Introduction to Data Science and Python Basics
- What is Data Science? Applications and career opportunities.
- Setting up the Python environment (Jupyter Notebook and Anaconda).
- Python fundamentals: Data types, variables, and basic operations.
- Introduction to NumPy for numerical computations.
Day 2: Data Analysis and Visualization
- Introduction to Pandas: DataFrames and series.
- Data cleaning: Handling missing values and duplicates.
- Data exploration: Descriptive statistics and summary reports.
- Data visualization: Creating line plots, bar charts, and scatter plots with Matplotlib and Seaborn.
Day 3: Introduction to Machine Learning and Capstone Project
- Overview of machine learning: Supervised and unsupervised learning.
- Building a simple linear regression model using Scikit-learn.
- Evaluating model performance with basic metrics.
- Hands-on capstone project: Analyzing a real-world dataset and presenting findings.
- This beginner-friendly course ensures that fresh graduates gain the foundational knowledge and confidence to kickstart their Data Science careers.
- Q & A
- Closing & Remarks
