Diploma In Data Science In Chennai

The image illustrates mastering the foundations of Data Science with a diploma in Data Science.

Aimore’s Data Science Training - An Overview

Data Science is a cross-disciplinary field that leverages scientific methodologies, systems, and processes to extract insightful knowledge from diverse data sets. Our Postgraduate (PG) Diploma in Data Science in Chennai is tailored for individuals seeking to enhance their expertise in this dynamic field.

Aimore’s robust Data Science diploma course empowers learners with both conceptual understanding and the technical prowess essential for a leading role in the analytics sector. Learners are introduced to the world of business analytics through sought-after technologies like Python and R, with thorough instruction on data visualisation, hypothesis testing, and exploration. A special emphasis is placed on Machine Learning techniques for classification, clustering, and regression.

Whether you're new to the field or an experienced professional aiming for advancement, our guided data science course is your stepping stone to mastery. It's meticulously crafted to ensure both beginners and seasoned professionals can elevate their data science skills effectively.

Features of Aimore’s Data Science Course

Assured Placements

Aimore guarantees hands-on training experience, promising 100% placement in top-tier MNCs.

Practical Exposure

Experience real-world scenarios through our intensive lab sessions, enhancing practical skills.

Expert Guidance

Learn best practices and technical skills from accredited instructors. Opt for either virtual or in-person sessions.

Certified Credentials

Earn industry-recognized credentials to boost your credibility, available virtually via online proctoring or in a physical testing centre.

Flexible Learning

Follow a recommended learning plan for specific domains or job roles, with the flexibility to deviate as needed.

Self-Paced Learning

Learn at your convenience with our self-paced training modules.

Comprehensive Instruction

Benefit from 55 hours of dedicated, instructor-led training.

Lifetime Access

Attend as many batches as you want for a lifetime, reaffirming knowledge at your pace.

Upcoming Batches

Weekdays

Wednesday
(Monday - Friday)

Weekdays

Friday
(Monday - Friday)

Weekend

Saturday
(Saturday - Sunday)

Your Journey Begins at Aimore

Kickstart your Data Science journey with our comprehensive course at Aimore Technologies- a pathway designed for those passionate about carving a successful career.

Enrol Today

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Prerequisites of Aimore’s Data Science Course

At Aimore Technologies, we maintain the highest standard for our Professional Certificate in Data Science program to ensure the utmost quality of education and training.

To be eligible for enrolment, candidates must hold a Bachelor's degree, having secured an average score of 50 per cent or higher. Additionally, prior exposure to programming and mathematics is mandatory, reflecting the program's technical nature.

While not compulsory, we strongly recommend applicants possess at least two years of formal work experience. This helps you better contextualise the course content with real-world scenarios and magnify the learning impact.

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Data Science Syllabus

Syllabus of Data Science with R

Module 1:

Introduction to Data Science Methodologies

Data Types
Introduction to Data Science Tools
Statistics
Approach to Business Problems
Numerical Categorical
R, Python, WEKA, RapidMiner

Module 2:

Correlation / Association / Regression / Categorical variables

Introduction to Correlation Spearman Rank Correlation
OLS Regression – Simple and Multiple Dummy variables
Multiple regression
Assumptions violation – MLE estimates
Using the UCI ML repository dataset or Built-in R dataset

Module 3:

Data Preparation

Data preparation & Variable identification
Advanced regression
Parameter Estimation / Interpretation
Robust Regression
Accuracy in Parameter Estimation
Using the UCI ML repository dataset or Built-in R dataset

Module 4:

Logistic Regression

Introduction to Logistic Regression
Logit Function
Training-Validation approach
Lift charts
Decile Analysis
Using the UCI ML repository dataset or Built-in R dataset

Module 5:

Cluster Analysis Classification Models

Introduction to Cluster Techniques
Distance Methodologies
Hierarchical and Non-Hierarchical Procedure
K-Means clustering
Introduction to decision trees/segmentation with Case Study
Using the UCI ML repository dataset or Built-in R dataset

Module 6:

Introduction and to Forecasting Techniques

Introduction to Time Series
Data and Analysis
Decomposition of Time Series
Trend and Seasonality detection and forecasting
Exponential Smoothing
Building R Dataset
Sales Forecasting Case Study

Module 7:

Advanced Time Series Modeling

Box – Jenkins Methodology
Introduction to Auto Regression and Moving Averages, ACF, PACF
Detecting the order of ARIMA processes
Seasonal ARIMA Models (P, D, Q)(p,d,q)
Introduction to Multivariate Time-series Analysis
Using built-in R datasets

Module 8:

File Input/Output

Live example/ live project
Using client-given stock prices / taking stock price data

Module 9:

Pharmaceuticals

Box – Jenkins Methodology
Case Study with the Data
Based on open set data

Module 10:

Market Research

Case Study with the Data
Based on open set data

Module 11:

Machine Learning

Supervised Learning Techniques
Conceptual Overview
Unsupervised Learning Techniques
Association Rule Mining Segmentation

Module 12:

Fraud Analytics

Fraud Identification Process in Parts Procuring
Sample data from online
Text Analytics

Module 13:

Text Analytics

Sample text from online

Module 14:

Social Media Analytics

Social Media Analytics
Sample text from online

Syllabus of Data Science with Python Course

Module 1:

Introduction to Data Science

What is Data Science?
What is Machine Learning?
What is Deep Learning?
What is AI?
Data Analytics & its types

Module 2:

Introduction to Python

What is Python?
Why Python?
Installing Python
Python IDEs
Jupyter Notebook Overview

Module 3:

Python Basics

Python Basic Data types
Lists
Slicing
IF statements
Loops
Dictionaries
Tuples
Functions
Array
Selection by position & Labels

Module 4:

Python Packages

Pandas
Numpy
Sci-kit Learn
Mat-plot library

Module 5:

Importing data

Reading CSV files
Saving Python data
Loading Python data objects
Writing data to CSV file

Module 6:

Manipulating Data

Selecting rows/observations
Rounding Number
Selecting columns/fields
Merging data
Data aggregation
Data munging techniques

Module 7:

Statistics Basics

Central Tendency
Mean
Median
Mode
Skewness
Normal Distribution
Probability Basics
What does probability mean by probability?
Types of Probability
ODDS Ratio?
Standard Deviation
Data deviation & distribution
Variance
Bias variance Trade off
Underfitting
Overfitting
Distance metrics
Euclidean Distance
Manhattan Distance
Outlier analysis
What is an Outlier?
Inter Quartile Range
Box & whisker plot
Upper Whisker
Lower Whisker
Catter plot
Cook’s Distance
Missing Value treatments
What is a NA?
Central Imputation
KNN imputation
Dummification
Correlation
Pearson correlation
Positive & Negative correlation
Error Metrics
Classification
Confusion Matrix
Precision
Recall
Specificity
F1 Score
Regression
MSE
RMSE
MAPE

Module 8:

Machine Learning

Module 9:

Supervised Learning

Linear Regression
Linear Equation
Slope<
Intercept
R square value
Logistic regression
ODDS ratio
Probability of success
Probability of failure
ROC curve
Bias Variance Tradeoff

Module 10:

Unsupervised Learning

K-Means
K-Means ++
Hierarchical Clustering

Module 11:

Other Machine Learning Algorithms

Title
Base
Link
Style s
Script

Syllabus of Machine Learning Course

Module 1:

Introduction to Data Analytics

Business Analytics, Data, Information
Understanding Business Analytics and R
Compare R with other software in analytics
Install R
Perform basic operations in R using the command line
Learn the use of IDE R Studio
Use the ‘R help’ feature in R

Module 2:

Introduction to R Programming

Variables in R
Scalars
Vectors
Matrices
List
Data frames
Using c, Cbind, Rbind, attach and detach functions in R
Factors

Module 3:

Data Manipulation in R

Data sorting
Find and remove duplicates record
Cleaning data
Recoding data
Merging data
Slicing of Data
Merging Data
Apply functions

Module 4:

Data Import Techniques in R

Reading Data
Writing Data
Basic SQL queries in R
Web Scraping

Module 5:

Exploratory Data Analysis

Box plot
Histogram
Pareto charts
Pie graph
Line chart
Scatterplot
Developing Graphs

Module 6:

Basics of Statistics & Linear & Logistic Regression

Basics of Statistics
Inferential statistics
Probability
Hypothesis
Standard deviation
Outliers
Correlation
Linear & Logistic Regression

Module 7:

Data Mining: Clustering Techniques, Regression & Classification

Introduction to Data Mining
Understanding Machine Learning
Supervised and Unsupervised Machine Learning Algorithms
K- means clustering

Module 8:

Anova & Sentiment Analysis

Anova
Sentiment Analysis

Module 9:

Data Mining: Decision Trees and Random Forest

Decision Tree
Concepts of Random Forest
Working on Random Forest
Features of Random Forest

Module 10:

Project work

Syllabus of Tableau Course

Module 1:

Tableau Course Material

Start Page
Show Me
Connecting to Excel Files
Connecting to Text Files
Connect to Microsoft SQL Server
Connecting to Microsoft Analysis Services
Creating and Removing Hierarchies
Bins
Joining Tables
Data Blending

Module 2:

Learn Tableau Basic Reports

Parameters
Grouping Example 1
Grouping Example 2
Edit Groups
Set
Combined Sets
Creating a First Report
Data Labels
Create Folders
Sorting Data
Add Totals, Sub Totals and Grand Totals to Report

Module 3:

Learn Tableau Charts

Area Chart
Bar Chart
Box Plot
Bubble Chart
Bump Chart
Bullet Graph
Circle Views
Dual Combination Chart
Dual Lines Chart
Funnel Chart
Traditional Funnel Charts
Gantt Chart
Grouped Bar or Side by Side Bars Chart
Heatmap
Highlight Table
Histogram
Cumulative Histogram
Line Chart
Lollipop Chart
Pareto Chart
Pie Chart
Scatter Plot
Stacked Bar Chart
Text Label
Tree Map
Word Cloud
Waterfall Chart

Module 4:

Learn Tableau Advanced Reports

Dual Axis Reports
Blended Axis
Individual Axis
Add Reference Lines
Reference Bands
Reference Distributions
Basic Maps
Symbol Map
Use Google Maps
Mapbox Maps as a Background Map
WMS Server Map as a Background Map

Module 5:

Learn Tableau Calculations & Filters

Calculated Fields
Basic Approach to Calculate Rank
Advanced Approach to Calculate Ra
Calculating Running Total
Filters Introduction
Quick Filters
Filters on Dimensions
Conditional Filters
Top and Bottom Filters
Filters on Measures
Context Filters
Slicing Filters
Data Source Filters
Extract Filters

Module 6:

Learn Tableau Dashboards

Create a Dashboard
Format Dashboard Layou
Create a Device Preview of a Dashboard
Create Filters on the Dashboard
Dashboard Objects
Create a Story

Module 7:

Server

Tableau online.
Overview of Tableau Server.
Publishing Tableau objects and scheduling/subscription.

Syllabus of Big Data Hadoop with Spark Developer

Module 1:

The big picture of Big Data

DataThe Necessity of Big Data and Hadoop in the Industry
Paradigm shift - why the industry is shifting to Big Data tools
Different Dimensions of Big Data
Data explosion in the Big Data industry
Various implementations of Big Data
Different technologies to handle Big Data
Traditional systems and associated problems
Future of Big Data in the IT Industry

Module 2:

Demystifying Hadoop

Why Hadoop is at the heart of every Big Data solution
Introduction to the Big Data Hadoop Framework
Hadoop architecture and design principles
Ingredients of Hadoop
Hadoop characteristics and data-flow
Components of the Hadoop Ecosystem
Hadoop Flavors – Apache, Cloudera, Hortonworks, and more

Module 3:

Setup and Installation of Hadoop

Hadoop environment setup and pre-requisites
Hadoop Installation and Configuration
Working with Hadoop in a pseudo-distributed mode
Troubleshooting encountered problems
Hadoop environment setup on the cloud (Amazon cloud)
Installation of Hadoop pre-requisites on all nodes
Configuration of masters and slaves on the cluster
Playing with Hadoop in distributed mode

Module 4:

HDFS – The Storage Layer

The need for a distributed processing framework
Issues before MapReduce and its evolution
List processing concepts
Components of MapReduce – Mapper and Reducer
MapReduce terminologies- keys, values, lists, and more
Hadoop MapReduce execution flow
Mapping and reducing data based on keys
MapReduce word-count example to understand the flow
Execution of Map and Reduce together
Controlling the flow of mappers and reducers
Optimisation of MapReduce Jobs
Fault tolerance and data locality
Working with map-only jobs
Introduction to Combiners in MapReduce
How MR jobs can be optimised using combiners

Module 5:

MapReduce - Advanced Concepts

Anatomy of MapReduce
Hadoop MapReduce data types
Developing custom data types using Writable & WritableComparable
InputFormats in MapReduce
InputSplit as a unit of work
How Partitioners partition data
Customisation of RecordReader
Moving data from mapper to reducer – shuffling & sorting
Distributed cache and job chaining
Different Hadoop case studies to customise each component
Job scheduling in MapReduce

Module 6:

Hive – Data Analysis Tool

The need for an adhoc SQL based solution – Apache Hive
Introduction to and architecture of Hadoop Hive
Playing with the Hive shell and running HQL queries
Hive DDL and DML operations
Hive execution flow
Schema design and other Hive operations
Schema-on-Read vs Schema-on-Write in Hive
Meta-store management and the need for RDBMS
Limitations of the default meta-store
Using SerDe to handle different types of data
Optimisation of performance using partitioning
Different Hive applications and use cases

Module 7:

Pig - Data Analysis Tool

The need for a high level query language - Apache Pig
How Pig complements Hadoop with a scripting language
What is Pig
Pig execution flow
Different Pig operations like filter and join
Compilation of Pig code into MapReduce
Comparison - Pig vs MapReduce

Module 8:

NoSQL Database - HBase

NoSQL databases and their need in the industry
Introduction to Apache HBase
Internals of the HBase architecture
The HBase Master and Slave Model
Column-oriented, 3-dimensional, schema-less datastores
Data modeling in Hadoop HBase
Storing multiple versions of data
Data high-availability and reliability
Comparison - HBase vs HDFS
Comparison - HBase vs RDBMS
Data access mechanisms
Work with HBase using the shell

Module 9:

Data Collection using Sqoop

The need for Apache Sqoop
Introduction and working of Sqoop
Importing data from RDBMS to HDFS
Exporting data to RDBMS from HDFS
Conversion of data import/export queries into MapReduce jobs

Module 10:

Data Collection using Flume

What is Apache Flume
Flume architecture and aggregation flow
Understanding Flume components like data Sources and Sinks
Flume channels to buffer events
Reliable & scalable data collection tools
Aggregating streams using Fan-in
Separating streams using Fan-out
Internals of the agent architecture
Production architecture of Flume
Collecting data from different sources to Hadoop HDFS
Multi-tier Flume flow for collection of volumes of data using AVRO

Module 11:

Apache YARN & advanced concepts in the latest version

The need for and the evolution of YARN
YARN and its eco-system
YARN daemon architecture
Master of YARN – Resource Manager
Slave of YARN – Node Manager
Requesting resources from the application master
Dynamic slots (containers)
Application execution flow
MapReduce version 2 application over Yarn
Hadoop Federation and Namenode HA

Module 12:

Exploring Scala

Introducing Scala
Installation and configuration of Scala
Developing, debugging, and running basic Scala programs
Various Scala operations
Functions and Procedures in Scala
Scala APIs for common operations
Loops and collections- Array, Map, List, Tuple
Pattern-matching and Regex
Eclipse with Scala plugin

Module 13:

Object-Oriented and Functional Programming

Introduction to OOP - object oriented programming
Different oops concepts
Constructors, getters, setters, singletons; overloading and overriding
Nested Classes and visibility Rules
Functional Structures
Functional programming constructs
Call by Name, Call by Value

Module 14:

Big Data and the need for Spark

Problems with older Big Data solutions
Batch vs Real-time vs in-Memory processing
Limitations of MapReduce
Apache Storm introduction and its limitations
Need for Apache Spark

Module 15:

A deep dive into Apache Spark

Introduction to Apache Spark
Architecture and design principles of Apache Spark
Spark features and characteristics
Apache Spark Ecosystem components and their insights

Module 16:

Deploying Spark in local mode

Spark environment setup
Installing and configuring prerequisites
Installation of Spark in local mode
Troubleshooting encountered problems

Module 17:

Apache Spark deployment in different modes

Spark installation and configuration in standalone mode
Installation and configuration of Spark in YARN mode
Installation and configuration of Spark on a real cluster
Best practices for Spark deployment

Module 18:

Demystifying Apache Spark

Working on the Spark shell
Executing Scala and Java statements in the shell
Understanding SparkContext and the driver
Reading data from local file-system and HDFS
Caching data in memory for further use
Distributed persistence
Spark streaming
Testing and troubleshooting

Module 19:

Learning RDDs in Spark

Introduction to Spark RDDs
How RDDs make Spark a feature rich framework
Transformations in Spark RDDs
Spark RDDs action and persistence
Lazy operations and fault tolerance in Spark
Loading data and how to create RDD in Spark
Persisting RDD in memory or disk
Pairing operations and key-value in Spark
Hadoop integration with Spark
Apache Spark practicals and workshops

Module 20:

Spark Streaming

The need for stream analytics
Comparison with Storm and S4
Real-time data processing using streaming
Fault tolerance and checkpointing in Spark
Stateful Stream Processing
DStream and window operations in Spark
Spark Stream execution flow
Connection to various source systems
Performance optimizations in Spark

Module 21:

Spark MLlib and Spark GraphX

Introducing Scala
Installation and configuration of Scala
Developing, debugging, and running basic Scala programs
Various Scala operations
Functions and procedures in Scala
Scala APIs for common operations
Loops and collections- Array, Map, List, Tuple
Pattern-matching and Regex
Eclipse with Scala plugin

Module 22:

Spark SQL

Introduction to Spark SQL
Apache Spark SQL Features and Data flow
Architecture and components of Spark SQL
Hive and Spark together
Data frames and loading data
Hive Queries through Spark
Various Spark DDL and DML operations
Performance tuning in Spark

Module 23:

Real Life Hadoop & Spark Project

Live Apache Spark & Hadoop project using Spark & Hadoop components to solve real-world Big Data problems in Hadoop & Spark.

Syllabus of Data Science Capstone Course

Module 1:

Ignite Talk
Statement of work

Module 2:

Milestone #1 Presentation
Summary Report + technical report
Self-/peer- evaluation
Review another group's reports
Code (runs as advertised)

Module 3:

Milestone #2 Presentation ("Midterm")
Summary Report + technical report
Self-/peer- evaluation
Review another group's reports
Code (runs as advertised)

Module 4:

Milestone #3 Presentation
Summary Report + technical report
Self-/peer- evaluation
Review another group's reports
Code (runs as advertised)

Module 5:

Final Presentation to class
Final write-up via blog
Poster and video recording
Self-/peer- evaluation
Code (runs, is organized and readable)

Advantages of Aimore's

Data Science Certification

Elevate your professional journey with our sought-after Data Science Diploma Certification. Here's how you benefit:

Financial Accessibility

Our commitment to education extends to offering multiple financing options, making the program budget-friendly for all.

Cohort Diversity

Gain from enriched interactions and discussions courtesy- our diverse cohort from varied sectors and experiences.

Real-world Experience

Enhance practical skills by working on simple to complex projects involving both structured and unstructured data.

Career Advancement

Propel your career filling roles as Data Scientists and Machine Learning Scientists at data-driven firms like Amazon, LinkedIn, Google, and more.

Placement Assistance

Upon course completion, gain access to our placement pool and get noticed by our 100+ hiring partners for potential employment opportunities.

Dedicated Support

Aimore's team of career advisors provide personalized support, assisting with CV development, and, interview arrangements.

A Fruitful Career Beckons You

Join the ranks of successful Data Scientists with top-tier training. Join Aimore’s diploma in Data Science in Chennai.

Join Today

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Aimore’s Data Science Diploma - FAQs

Have a little question?
Here's answers to our most commonly asked questions.

Why should I choose Aimore’s Data Science course?

In today's world, where businesses are increasingly data-driven, the demand for professionals with robust Data Science skills is escalating rapidly. Our online Data Science training empowers you with proficiency in the R programming language, big data, data analysis, and more, helping to accelerate your career to new heights in this burgeoning domain.

What is the timeline for becoming a Data Scientist?

Becoming a Data Scientist with us is an efficient process. Once you successfully complete our Data Science training, execute all the associated projects, and meet all necessary requirements, you will earn an industry-recognized Data Science course completion certificate. Our dedicated placement team will then assist with resume preparation and conduct mock interviews, ensuring you are well-prepared to secure a high-paying position as a Data Scientist.

How long is the Data Science Certification valid?

The Data Science certification you receive from us is valid for a lifetime and recognised by top organisations globally.

What job roles can a Data Scientist pursue?

As a certified Data Scientist, a wealth of opportunities await you in roles such as Data Analyst, Business Analyst, Big Data Engineer, Statistician, Data Scientist, Data Engineer, Data Architect, and Machine Learning Engineer.