This document outlines the topics covered in a data science course, including introduction to data science, analytics using Excel and statistics, Python programming and visualization, SQL, machine learning, and deep learning. The course covers core data science concepts like linear regression, probability distributions, data visualization with Matplotlib and Pandas, SQL, machine learning algorithms for regression, classification and clustering. It includes over 20 mini projects and full projects for students to apply the concepts learned.

1. Course:
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Data
Science

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topics
1 Introduction
2 Analytics using Excel
3 Statistics
4 Python & Visualization
5 SQL
6 Power BI
7 Machine Learning
8 Deep Learning

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1. INTRODUCTION
Problem Solving Approach
Deterministic v Non Deterministic Approach
Machine Learning Philosophy
2. ANALYTICS
Excel Formula
Excel Functions
What-if Analysis
Linear Programming
Linear Regression

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3. STATISTICS
Data and Types
Central Tendency: Mean, Median, Mode
Deviation: Range, Variance, Standard Deviation
Box Plot and its importance
Frequency distribution and its importance
Scatter Plots and its importance
Probability
Discrete Probability Distribution:
Binomial Distribution, Poisson
Continuous Probability Distribution:
Normal, t distribution, Exponential
Correlation
4 Mini Projects

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4. PYTHON
Python Basics
Python Variables: int, float, string, bool, complex
Conditional statements
Loops
Python Collections: List, Tuple, Dictionary, Set, Frozen-
set
Mini Python Project 1
Functions and Methods
Class & Objects
Mini Python Project 2
Numpy
Working with CSV and Text files
Error Handling
Python Project 1: Using All Python concepts

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5. DATA VISUALIZATION
Story Telling
Scipy
Pandas
Visualization Mini Project - 1
Matplotlib: basic plots and advanced plots
Visualization Mini Project - 2
NLP: N-gram models of language
NLP Project
Web Scrapping
Project: Web scraping and visualization
6. POWER BI
Connecting to Data
Transforming Data
Working with calculations and expressions
Visualizing Data
Building Graphs and Dashboards

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7. SQL PROGRAMMING
OLTP v OLAP
Database Objects
Manipulating Data
Working with the SELECT Statement
Querying Multiple Tables
Employing Functions in Data Retrieval
Constructing Nested Queries
8. MACHINE LEARNING
Regression: Simple linear, multiple linear, ridge, lasso,
decision tree, random forest
Classification: svm, decision tree, random forest, naïve
bayes, bagging, boosting
Clustering: K means, Hierarchical
Projects
Association: Market Basket Analysis
Mini Project
12 more projects to practice

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9. DEEP LEARNING
ANN
CNN
RNN
LSTM
Examples to practice