Data Science Training Syllabus :
This course equips participants with skills in data analysis,
manipulation, and visualization using Python and libraries such as
Pandas, NumPy, and Matplotlib. The program also covers machine learning
and real-world case studies.
Introduction and Setting up Your Data Science Environment
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Lesson 1.
Introduction to Data Science: Overview of data science and its
applications.
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Lesson 2.
Python Basics: Setting up Python for data science.
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Lesson 3.
Data Manipulation with Pandas: Data wrangling and manipulation
using Pandas.
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Lesson 4.
Data Visualization: Using Matplotlib and Seaborn for data
visualization.
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Lesson 5.
Numerical Computing with NumPy: Performing numerical operations
with NumPy.
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Lesson 6.
Exploratory Data Analysis (EDA): Techniques for exploring and
understanding datasets.
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Lesson 7.
Supervised Learning: Building classification and regression
models.
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Lesson 8.
Unsupervised Learning: Implementing clustering and dimensionality
reduction
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Lesson 9.
Real-World Case Studies: Solving real-world problems using data
science tools.
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Lesson 10.
SQL for Data Science: Querying and managing databases using
SQL.
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Lesson 11.
Capstone Project: Building a data science project from
scratch.
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Lesson 12.
Exploratory Data Analysis (EDA): Techniques for exploring and
understanding datasets.
