Data science course syllabus for beginners
Below, I have listed the complete course curriculum of our data science training program which
is available in online and offline Hyderabad and Vijayawada training centres:
Module 1
Python Programming
Module 2
Data science course dura on: 6 months | English
Module 3
Sta s cs
Module 4
Machine Learning and machine learning projects for final year students
Module 5
NLP
Module 6
Deep Learning
Module 7
Computer vision
Module 8
Machine Learning and machine learning projects for final-year students
Data science course dra on: 6 months | English
By Codegnan ins tute
Learn more about the data science course fees and dura on before enrolling in any online or
offline course.
Module 1. Python Programming
Learning the basics of Python programming is essen al for data scien sts to manipulate and
visualizing data. This sec on will cover the basic syntax, operators, strings, func ons, and other
essen al details to help you analyse large amounts of data and manipulate them.

Python Introduc on and se ng up the environment

Python Basic Syntax and Data Types

Operators in Python (e.g., Arithme c, Logical, Bitwise)

Strings in Python

Lists

Tuples

Sets

Dic onaries

Python condi onal statements (e.g., if, if-else, if-elif-else)

Loops in Python (e.g., while, for, break, con nue)

Ge ng Started with HackerRank use cases and working on them

List and Dic onaries comprehension

Func ons

Anonymous Func ons (Lambda)

Generators

Modules

Excep ons and Error Handling

Classes and Objects (OOPS) (including different types of methods, inheritance,
polymorphism, operator overloading, overriding)

Date and Time

Regex (e.g., re.search(), re.compile(), re.find(), re.split())

Files (including opening, closing, reading and wri ng files)

APIs the Unsung Hero of the Connected World

Python for Web Development – Flask

Hands-On Projects (Web Scraping, Sending Automated Emails, Building a Virtual
Assistant)
Module 2. Data Analysis
Data analysis helps you in making informed decisions with data explora on and visualiza on
using advanced tools. This sec on will cover the basics along with teaching you how to scrape
data from websites using libraries like Beau fulSoup and handling and storing them in
appropriate formats.

Packages (Working on Numpy, Pandas, and Matplotlib)

Web Scraping (learning about tools, libraries and ethical considera ons)

Exploratory data analysis (EDA) using Pandas and NumPy

Data Visualiza on using Matplotlib, Seaborn, and Plotly

Database Access

Tableau

Power BI
Module 3. Sta s cs
The specific topics covered in the sta s cs sec on give you an overview of descrip ve sta s cs
and inferen al sta s cs that provide the founda on for understanding and analyzing complex
data. Explore the sta s cal founda ons for data signs from this sec on and apply them to data
analysis projects.

Descrip ve Sta s cs (including central tendency, variance, standard devia on,
covariance, correla on, probability)

Inferen al Sta s cs (including Central limit theorem, hypothesis tes ng, one-tailed and
two-tailed test, and Chi-Square test)
Module 4. Machine Learning
This part of the syllabus comprises mathema cal models and algorithms that are needed in
coding machines to adapt them to real-world challenges. The course comprises basic knowledge
of machine learning and its three main types: supervised, unsupervised, and reinforcement
learning among other essen al topics.

Introduc on to Machine Learning

Introduc on to data science and its applica ons

Data Engineering and Preprocessing

Model Evalua on and Hyperparameter Tuning

Supervised Learning – Regression

Supervised Learning – Classifica on

SVM, KNN & Naive Bayes

Ensemble Methods and Boos ng

Unsupervised Learning – Clustering

Unsupervised Learning – Dimensionality Reduc on

Recommenda on Systems

Reinforcement Learning

Developing API using Flask / Webapp with Streamlit

Deployment of ML Models

Project Work and Consolida on
Module 5. NLP
Natural Language Processing NLP helps machines understand and create human language. This
sec on will teach you Named En ty Recogni on, text pre-processing, and text representa on,
along with applica ons ranging from sequen al modelling, and building sen ment analysis.

Natural Language Processing (NLP) (including NER, text representa on, sequen al
model, sen ment analysis)
Module 6. Deep Learning
This sec on allows you to master advanced topics like CNN (Convolu onal Neural Networks),
RNN (Recurrent Neural Networks), Neural Network architecture, and more.

RISE OF THE DEEP LEARNING

Ar ficial Neural Networks

Convolu on Neural Networks

CNN – Transfer Learning

RNN – Recurrent Neural Networks

Genera ve Models and GANs
Module 7. Computer Vision
The computer vision syllabus allows you to understand how to create algorithms for computers
to read and write data sent via images or videos.

Computer Vision (including reading and wri ng images, drawing shapes using OpenCV,
reason eye detec on using OpenCV, VGG, CNN with Keras)
Bonus Module: Projects & Case Study

Real-Time Rain Predic on using ML

Real-Time Drowsiness Detec on Alert System

House Price Predic on using LSTM

Customizable Chabot using OpenAI API

Fire and Smoke Detec on using CNN