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Comprehensive Data Analytics Syllabus.

Designed for Industry Readiness with Practical, Hands-on Implementation

Data Analytics Course in Pune – By Advanto Infotech with 100% Unilmited Placement Calls

Join the leading Data Analytics Course in Pune and learn how to tackle real-world business challenges using data-driven insights. This 100% Unlimited Placement Calls.

Data Analytics Course is perfect for recent graduates and professionals aiming to launch a successful career in data analytics. Gain practical knowledge of data analytics applications in enterprises and prepare yourself to become a skilled data analytics professional in a fast-growing industry.

At Advanto Infotech, we offer an integrated learning experience with ongoing career support to help you shape your future innovatively. Our unique training approach lets you work on projects from day one, alongside expert-led sessions. Each batch receives continuous guidance from experienced mentors throughout the course.
Our training is delivered by highly experienced and qualified professionals who have in-depth industry expertise. They are committed to providing relevant, hands-on knowledge to help students excel in their careers.

Course Fees and Duration

Advanto provides an affordable Data Analytics course with flexible payment plans, offering the best value among institutes in Pune.

The course duration is typically 100 hours (weekends), ideal for students and working professionals.

Open to any graduate (BE/B.Tech, M.Sc, MCA, ME/M.Tech, BCS, BCA). Final year candidates are also eligible to apply.

Visit our office to get detailed information on fees, course duration, and other specifics about the Data Analytics course in Pune.

100% Placement Assistance

Advanto infotech is one of Pune’s most trusted Java Full Stack Developer training institutes with a proven track record. We provide 100% unlimited placement Calls .

Unlimited placement calls
Personalized interview preparation
Resume building and mock sessions

With over 12 years of training experience, we’ve successfully placed hundreds of students in top MNCs and IT companies. Completing this course gives you a near-guaranteed pathway to employment in the tech industry.

Data Analytics Course Content.

Designed for Industry Readiness with Practical, Hands-on Implementation

Foundation

Introduction to Programming
Variables & Arithmetic Expressions
Functions
Data Types
Conditions and Conditional statements
Lists
OOPS

Core Track MS-Excell

Intro to Excel
Importing data
Formatting in Excel
Excel Formulae
Data Validation
Calculations

Reporting using Excell

Lookup and Reference
Pivot Tables
Charts
What-if Analysis
Intro to Macros

SQl Basic SQL

Introduction to SQL
DDL Statements
DML Statements
DQL Statements

Advance SQL – Part 1

Aggregate Functions
Date functions
Union, Union All & Intersect Operators
Joins

Advance SQL – Part 2

Views & Indexes
Sub-Queries
Exercise on SQL

Introduction to Python

Python Introduction
Variables
Functions
Python Operators
Python Flow Controls
Conditional Statements
Loops

Python Collection Objects & Comprehensions

1. Strings
2. List
3. Tuple
4. Dictionary
List Comprehension

User Defined and Lambda Functions

User-defined Functions
Function Arguments
Lambda Functions

NumPy

Introduction to NumPy
NumPy Array
Creating NumPy Array
Array Attributes
Array Methods
Array Indexing
Slicing Arrays
Array Operation
Iteration through Arrays

Loops

For Loop
While Loop
Do While Loop
Break Statements

Data Frame Manipulation

Pandas Data frame – Introduction
Data frame Creation
Reading Data from Various Files
Understanding Data
Accessing Data frame Elements using Indexing Data frame Sorting
Ranking in Data frame
Data frame Concatenation
Data frame Joins
Data frame Merge
Reshaping Dataframe
Pivot Tables
Cross Tables
Dataframe Operations
Checking Duplicates
Dropping Rows and Columns
Replacing Values
Grouping Dataframe
Missing Value Analysis & Treatment

Visualization – Part 1

Visualisation using Matplotlib
Plot Styles & Settings
Line Plot
Multiline Plot
Matplotlib Subplots
Histogram
Boxplot
Pie Chart
Scatter Plot

Inter Object Communication

Message
Message Passing
Message Sender
Message Receiver

Visualization – Part 2

Visualisation using Seaborn
Strip Plot
Distribution Plot
Joint Plot
Violin Plot
Swarm Plot
Pair Plot
Count Plot
Heatmap

EDA

Summary Statistics
Missing Value Treatment
Dataframe Analysis using Groupby
Advanced Data Explorations

Introduction to Machine Learning

Introduction to Machine Learning
Machine Learning Modelling Flow
Parametric and Non-parametric Algorithms
Types of Machine Learning

Linear Regression Using OLS

Introduction of Linear Regression
Types of Linear Regression
OLS Model
Math behind Linear Regression Decomposition Variability
Metrics to Evaluate Model
Feature Scaling
Feature Selection
Regularisation Techniques

Project on Linear Regression

Project – Property Price Prediction
Class Assessment on Linear Regression

Logistic Regression

Intro to Logistic Regression
Maximum Likelihood Estimation
Performance Metrics

Model Tuning Techniques

- - - - Performance Measures
      - Bias-Variance Tradeoff
      - Overfitting and Underfitting Problems
      - Cross Validation

Project on Logistic Regression

Project – Vaccine Usage Prediction
Home Assignment on Logistic Regression

Decision Trees

Introduction to Decision Tree
Entropy
Information Gain
Greedy Algorithm
Decision Tree: Regression
Gini Index
Tuning of Decision Tree-Pruning
Project – Heart Disease Prediction

Random Forest

Introduction to Random Forests
Averaging
Bagging
Random Forest – Why & How?
Feature Importance
Advantages & Disadvantages
Project on random forest – Taxi Fare Prediction
Class Assessment on Classification

Inner Classes

Inner Class
Nested Class
Different types of Nested Classes

K-means Clustering

What is Clustering?
Prerequisites
Cluster Analysis
K-means
Implementation of K-means
Pros and Cons of K-means
Application of K-means
Project on K-means Clustering – E-commerce Customer Segmentation

Hierarchical Clustering

Introduction to Hierarchical Clustering
Types of Hierarchical Clustering
Dendrogram
Pros and Cons of Hierarchical Clustering
Project on Hierarchical Clustering – Travel Review Segmentation
Home Assignment on Clustering

Principal Components Analysis

Prerequisites
Introduction to PCA
Principal Component
Implementation of PCA
Case study
Applications of PCA
Project on PCA – Real Estate Data Analysis using PCA

Time Series Modelling

Understand Time Series Data
Visualising Time Series Components
Exponential Smoothing
Holt’s Model
Holt-Winter’s Model
ARIMA
Project – Forecasting the Sales of a Furniture Store

Introduction to Statistics

Introduction to Statistics
Random Variables
Descriptive Statistics
Measure of Central Tendency
Measure of Dispersion
Skewness and Kurtosis
Covariance and Correlation

Probability Theory

What is Probability?
Events and Types of Events
Sets in Probability
Probability Basics using Python
Conditional Probability
Expectation and Variance

Probability Distributions

Probability Distributions
Discrete Distributions
– Uniform
– Bernoulli
– Binomial
– Poisson
Continuous Distributions
– Uniform
– Normal
Probability Distributions using Python

Hypothesis Testing

Introduction to Hypothesis Testing
Terminologies used in Hypothesis Testing
Procedure for testing a Hypothesis
Test for Population Mean
Small Sample Tests
Large Sample Tests

Statistical Tests

One-way ANOVA
Assumptions
ANOVA Hypothesis
Post Hoc Test
Chi-Square Test
Chi-Square Test Steps
Chi-Square Example

Basics of Cloud

Machine Learning on Cloud
Deploying ML models on CloudData visualisation with Tableau and Power BI

Tableau Part – 1

Introduction to Tableau
Data Connection
Tableau Interface and Basic Chart Types
Working with Metadata
Visual Analytics

Tableau Part – 2

Mapping
Calculations
Dashboard and Stories

Power BI – Part 1

Introduction
Interface
Data Connections
Data Transformation
Advance Data Transformation
Project – Stock Data analysis
Exam – Tableau and Power BI Exam

Ensemble Modeling Techniques

Ensemble Techniques
What is Ensembling?
Bootstrap Method
Bagging
Boosting
XGBoost
AdaBoost

K Nearest Neighbours

Introduction to KNN
Working of KNN
Applications of KNN
Advantages and disadvantages of KNN

Projects

Analytics in Healthcare/Finance
Some detailed project based on Classification
Analytics in Marketing/Sales
Some detailed project based on Regression
Some detailed project based on SQL, Tableau
Some detailed project based on Excel, Power BI

Linux Commands

- Introduction to Linux
- Linux Commands

Career Services

- Resume-building
- GitHub Project Portfolio
- Interview Preparation
- Mock Interviews
- Career Mentorship