Data Science for Beginners

Inquire now

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 
Inquire now

Best selling courses

This site uses cookies to offer you a better browsing experience. By browsing this website, you agree to our use of cookies.