Boost Your Data Science Skills With Aimore's Diploma Program

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

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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, the best software training institute in Chennai 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, and have secured an average score of 50 percent 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 contextualize the course content with real-world scenarios and magnify the learning impact.

Data Science Syllabus

Syllabus for Data Science with R

Module - 1

Foundations of Data Science

Exploring Different Data Types
Overview of Tools in Data Science
Basic Statistical Principles
Tackling Business Issues Analytically
Distinction Between Numerical and Categorical Data
Introduction to R, Python, WEKA, RapidMiner

Module - 2

Understanding Correlations and Regression

Basics of Correlation and Spearman Rank Correlation
Introduction to Ordinary Least Squares (OLS) Regression - Simple and Multiple
Analysis of Categorical Variables and Dummy Variable Implementation
Multiple Regression Techniques
Addressing Assumptions Violations with Maximum Likelihood Estimation (MLE)
Utilising either UCI ML repository dataset or R’s built-in datasets for analysis

Module - 3

Preparing Data for Analysis

Data Preparation Techniques and Variable Selection
Enhancing Regression Analysis
Estimating and Interpreting Parameters
Employing Robust Regression Methods
Achieving Accuracy in Parameter Estimation
Engaging with the UCI ML repository dataset or R’s built-in datasets

Module - 4

Exploring Logistic Regression

Fundamentals of Logistic Regression and the Logit Model
Training and Validation Methods
Creating and Interpreting Lift Charts
Analysing Data by Deciles
Practical exercises on real-world logistic regression scenarios using the UCI ML repository dataset or R’s built-in datasets

Module - 5

Cluster Analysis and Classification Techniques

Basics of Clustering Methods
Exploring Distance Measurement Techniques
Hierarchical and Non-Hierarchical Clustering Approaches
Application of K-Means Clustering
Decision Trees and Segmentation with a Business Scenario Study
Analysis using the UCI ML repository dataset or R’s built-in datasets

Module - 6

Forecasting Principles and Methods

Time Series Analysis Fundamentals
Breaking Down Time Series Components
Identifying and Forecasting Trends and Seasonal Variations
Application of Exponential Smoothing Techniques
Constructing Time Series Datasets in R
Conducting a Sales Forecasting Business Scenario Study

Module - 7

AdvancedApproaches in Time Series Analysis

Detailed Explanation of the Box-Jenkins Method
Delving into Auto Regression, Moving Averages, ACF, and PACF
Identifying ARIMA Model Orders
Seasonal ARIMA Model Implementation
Basics of Multivariate Time-Series Analysis
Using R’s extensive built-in datasets for in-depth exercises

Module - 8

Data Input/Output Operations

Practical exercise on handling file input/output in R
Analytical project based on actual stock price data from clients or publicly available sources

Module - 9

Data Science in the Pharmaceutical Sector

Applying Box-Jenkins Methodology in Pharmaceuticals
Comprehensive Case Study on Pharmaceutical Data
Analysis based on publicly available datasets

Module - 10

Data Science in Market Research

Market Research Data Case Study
Analysis based on publicly available datasets

Module - 11

Machine Learning Fundamentals

Detailed Overview of Supervised and Unsupervised Learning Techniques
Introduction to Association Rules in Machine Learning
Understanding Segmentation in Machine Learning Contexts

Module - 12

Analytics in Fraud Detection

Detailed Process of Identifying Fraud in Parts Procurement
Engaging with sample data from an online source for practical analysis
In-depth look at Text Analytics for Fraud Detection

Module - 13

Insights through Text Analytics

Practical exercises on Text Analytics using sample data sourced online

Module - 14

Analysing Social Media Data

Detailed Study of Social Media Analytics Techniques
Practical exercises on Social Media Analytics using sample data sourced online

Syllabus for Data Science with Python Course

Module - 1

Foundations of Data Science

Exploring the Concept of Data Science
Basics of Machine Learning
Introduction to Deep Learning
Fundamentals of Artificial Intelligence
Various Forms of Data Analytics

Module - 2

Getting Started with Python

Overview of Python
The Case for Python in Programming
Setting Up Python Environment
Exploring Python Integrated Development Environments
Tour of Jupyter Notebook

Module - 3

Core Python Programming

Basic Data Types in Python
Understanding and Creating Lists
Utilising Slicing Techniques
Employing IF Conditions
Iteration with Loops
Organising Data with Dictionaries
Utilising Tuples
Crafting Functions
Arrays in Python
Data Selection Techniques

Module - 4

Python Libraries

Introduction to Pandas Library
Navigating Numpy Library
Overview of Sci-kit Learn
Essentials of Mat-plot Library

Module - 5

Data Import Techniques

How to Read CSV Files
Storing Data in Python
Loading Data Structures in Python
Writing Data to CSV Format

Module - 6

Data Handling and Processing

Techniques for Selecting Rows
Methods for Rounding Numbers
Strategies for Selecting Columns
Combining Data from Different Sources
Grouping and Summarising Data
Methods of Data Transformation

Module - 7

Statistics and Probability Essentials

Measures of Central Tendency: Mean, Median, Mode
Understanding Skewness
The Concept of Normal Distribution
The Basics of Probability
Different Types of Probability
Understanding the Odds Ratio
Computing Standard Deviation
Data Spread and Distribution
Variance Concepts
Trade-offs Between Bias and Variance
Understanding Underfitting and Overfitting
Metric Distances: Euclidean and Manhattan
Identifying and Analysing Outliers
Visualising Data with Box & Whisker Plots
Detecting Influential Points with Cook’s Distance
Addressing Missing Values
Methods of Imputation
Introduction to Correlation Metrics
Error Metrics for Classification and Regression Models

Module - 8:

Introduction to Machine Learning Concepts

Module - 9

Supervised Learning Methods

Principles of Linear Regression
Understanding the Components of Linear Models
Examining Logistic Regression
Analysing Success and Failure Probabilities
ROC Curve Interpretation
Exploring the Bias-Variance Tradeoff

Module - 10

Exploring Unsupervised Learning

K-Means Clustering Techniques
Advanced Clustering with K-Means++

Module - 11

Diverse Machine Learning Algorithms

Overview and Application of Various ML Algorithms

Module - 12

Project Implementation and Analysis

Syllabus for Machine Learning Course

Syllabus for Machine Learning Course

Module - 1

Data Analytics Overview

Defining Business Analytics and Its Importance
Data and Its Business Implications
Introduction to R and Its Comparison with Other Analytics Software
Installation and Configuration of R
Basic Operations in R Using Commands
The Utility of R Studio and Its Interface
Leveraging 'R help' for Assistance

Module - 2

R Programming Foundations

Variable Types in R
Data Structures: Scalars, Vectors, Matrices, Lists, and Data Frames
Utilising Functions like c, Cbind, Rbind, attach, and detach

Module - 3

Data Operations in R

Techniques for Data Sorting
Duplicate Identification and Removal
Data Cleansing Processes
Recoding Data for Analysis
Data Merging Strategies
Data Slicing Methods
Application of Functions for Data Manipulation

Module - 4

Importing Data into R

Methods for Reading Data into R
Techniques for Writing Data
Executing Basic SQL Queries within R
Introduction to Web Scraping with R

Module - 5

Exploratory Data Analysis Techniques

Utilising Various Plots for Data Visualisation: Box Plots, Histograms, Pareto Charts
Creating Graphical Representations: Pie Graphs, Line Charts, Scatterplots

Module - 6

Fundamentals of Statistics and Regression Analysis

Introduction to Statistical Concepts
Inferential Statistics and Its Application
Exploring Probability Theory
Hypothesis Testing Basics
Identifying and Managing Outliers
Establishing Correlation
Principles of Linear and Logistic Regression

Module - 7

Data Mining Concepts and Techniques

Introduction to the Process of Data Mining
Distinctions Between Supervised and Unsupervised Learning
Implementing K-means Clustering

Module - 8

Advanced Analysis Techniques

Application of Anova in Statistical Analysis
Fundamentals of Sentiment Analysis

Module - 9

Decision Making Models in Data Mining

Understanding Decision Trees
Fundamentals of Random Forest Algorithms
Implementing Random Forest Models

Module - 10

Capstone Project Work

Syllabus for Tableau Course

Module - 1

Introduction to Tableau Interface

Initial Set-Up and Interface Walkthrough
Data Connection Fundamentals
Hierarchies and Their Management
Understanding and Creating Bins
Techniques for Merging Tables
Data Blending Principles

Module - 2

Crafting Basic Tableau Reports

Utilising Parameters in Reports
Grouping Data: Practical Examples
Editing and Managing Groups
Sets and Their Applications
Generating an Initial Report
Sorting and Organising Data
Incorporating Various Totals in Reports

Module - 3

Tableau Charting Techniques

Creating Various Charts: Area, Bar, Box Plot, and More
Dual Chart Combinations
Funnel Chart Variations
Gantt and Heatmap Implementation
Utilising Histograms and Lollipop Charts
Constructing Pie Charts and Scatter Plots
Developing Text Labels and Tree Maps

Module - 4

Advanced Reporting in Tableau

Employing Dual Axis for Complex Reports
Using Blended and Individual Axes
Adding Reference Lines and Distributions
Building Basic Maps and Symbol Maps
Integrating Google and Mapbox Maps
Utilising WMS Servers for Maps

Module - 5

Tableau Calculations and Filters

Building Calculated Fields
Ranking Calculations
Implementing Running Totals
Filters Overview and Application
Employing Quick and Dimensional Filters
Conditional and Measure-Based Filtering
Contextual and Data Source Filtering

Module - 6

Dashboard Creation in Tableau

Constructing and Formatting Dashboards
Previewing Dashboards for Different Devices
Filtering Data within Dashboards
Understanding Dashboard Objects
Storytelling with Dashboards

Module - 7

Tableau Server and Online Integration

Overview of Tableau Online and Server Features
Publishing and Managing Tableau Resources
Scheduling and Subscription Functions

Syllabus for Data Science with a Focus on Big Data and Spark Development

Module - 1

Overview of Big Data

Essential role of Big Data and Hadoop within the sector
Transition to Big Data tools and its influence on the industry
Exploring the different aspects of Big Data
Surge in Big Data across the industry
Implementations of Big Data across various sectors
Technologies employed in managing Big Data
Limitations of conventional systems in handling Big Data
Prospective developments in Big Data within the IT sector

Module - 2

Understanding Hadoop

Importance of Hadoop in Big Data solutions
Fundamentals of Big Data Hadoop architecture
Core components of Hadoop and their functionality
Data management and flow within the Hadoop ecosystem
Overview of the components in the Hadoop ecosystem
Examination of different Hadoop distributions

Module - 3

Hadoop Configuration and Management

Setting up the Hadoop environment and necessary prerequisites
Steps in installing and configuring Hadoop
Configuring Hadoop for pseudo-distributed operations
Approaches to solving common Hadoop setup issues
Establishing Hadoop on cloud platforms such as AWS
Node setup for Hadoop clusters
Managing and operating Hadoop in a distributed environment

Module - 4

Understanding HDFS and MapReduce

Justification for a distributed processing framework
Historical issues solved by MapReduce
Concepts behind list processing
Key elements of the MapReduce framework
Understanding MapReduce terminology
Process flow of MapReduce operations
Executing MapReduce operations
Strategies to manage mapper and reducer workflows
Techniques to enhance MapReduce job efficiency
Features such as fault tolerance and data locality in Hadoop
Methods for operating map-only jobs
Introduction to using Combiners in MapReduce for optimization

Module - 5

Advanced MapReduce Techniques

Detailed study of MapReduce components
MapReduce data types and custom type development
Advanced input methods in MapReduce
Partitioning and custom RecordReader implementation
Mechanisms of data shuffling and sorting from mapper to reducer
Use of Distributed Cache and chaining jobs
Hadoop case scenarios for component customisation
Management of job scheduling within MapReduce

Module - 6

Data Warehousing with Hive

Reasons for Hive’s existence as an SQL-like solution
Introduction and structural design of Hive
Hands-on experience with HiveQL
Hive data definition and manipulation
Workflow of Hive executions
Discussion on schema design approaches in Hive
Hive metastore management and database integration
Performance tuning in Hive through partitioning
Illustrations of Hive’s application in real-world scenarios

Module - 7

Data Flow Scripting with Pig

Justification for a higher-level language in Apache Pig
Role of Pig scripts in complementing Hadoop functionality
Fundamentals of Apache Pig
Operational flow within Pig
Essential operations in Pig such as filter and join commands
Compilation of Pig Latin scripts into MapReduce tasks
Comparative study of Pig and MapReduce functionalities

Module - 8

NoSQL Databases and HBase

Rationale behind NoSQL database adoption in the industry
Core concepts of Apache HBase
In-depth look into HBase architecture
Understanding HBase’s master-slave dynamics
Features of HBase as a columnar database
Techniques for data modeling in HBase
Ensuring data is highly available and reliable
Comparative analysis of HBase with HDFS and RDBMS
Various methods of data interaction in HBase
Using HBase through command-line interface

Module - 9

Data Ingestion with Sqoop

Purpose and functionality of Apache Sqoop
Fundamentals of Sqoop operations
Methods for importing data from relational databases to HDFS
Exporting data from HDFS back to relational databases
Conversion of data transfer processes into MapReduce jobs

Module - 10

Data Collection with Flume

Introduction to Apache Flume and its relevance
Detailed Flume architecture and data flow
Exploration of Flume components including sources and sinks
Buffering events with Flume channels
Data collection methodologies that are scalable and reliable
Strategies for aggregating and separating data streams
Detailed study of Flume’s agent architecture and its deployment
Implementing Flume for large-scale data collection into HDFS

Module - 11

Apache YARN and Advanced Hadoop Concepts

The evolution and necessity of YARN in Big Data
YARN’s ecosystem and architecture
Deep dive into YARN's daemon architecture
Exploring the roles of Resource Manager and Node Manager in YARN
Process of resource requests in YARN applications
Introduction to dynamic resource allocation with YARN
Understanding the execution of applications on YARN
Insights into Hadoop Federation and high-availability features

Module - 12

Scala Programming

Basics and set up of the Scala programming language
Developing and running simple Scala programs
Exploration of Scala operations
Functionalities of functions and collections in Scala
Utilisation of pattern matching and regular expressions
Integration of Scala within the Eclipse IDE

Module - 13

OOP and Functional Programming Foundations

Core principles of object-oriented programming
Discussion of OOP concepts including constructors and class members
Visibility rules within nested classes
Introduction to functional programming concepts
Differences between calling by name and calling by value

Module - 14

Introduction to Spark and its Ecosystem

Challenges with older Big Data frameworks
Differentiating batch, real-time, and in-memory processing
Understanding the limitations of the MapReduce model
Brief overview of Apache Storm and its limitations
Establishing the requirement for Apache Spark

Module - 15

Mastering Apache Spark

Fundamentals of Apache Spark and its design principles
Detailed examination of Spark's features and ecosystem

Module - 16

Setting Up Spark Locally

Guidance on setting up the Spark environment
Instructions on Spark installation and configuration
Addressing common issues in Spark setup

Module - 17

Deploying Spark in Various Modes

Steps for installing Spark in standalone and YARN modes
Configuration guidelines for Spark deployment on clusters
Recommendations for Spark deployment strategies

Module - 18

In-depth Analysis of Apache Spark

Experiential learning with the Spark shell
Scala and Java integration in Spark operations
Deep understanding of SparkContext and its driver program
Data operations with Spark from various sources
In-memory caching and data persistence in Spark
Debugging and problem-solving within Spark

Module - 19

Exploring RDDs in Spark

Introduction to resilient distributed datasets (RDDs)
Transformation operations in Spark RDDs
Action and persistence in Spark RDDs
Lazy evaluation and fault tolerance in RDD operations
Techniques for data loading and RDD creation
Performing pair operations and understanding key-value pairs
Integration of Hadoop with Spark
Interactive workshops on Spark practicals

Module - 20

Real-time Processing with Spark Streaming

The landscape of stream analytics
Comparative analysis between Spark, Storm, and S4
Stream data processing fundamentals in Spark
Mechanisms for fault tolerance and stateful stream processing
API walkthrough for Spark Streaming
Developing applications using Spark Streaming
Handling live data streams with DStreams
Integration with Kafka and Flume

Module - 21

Advanced Analytics and Machine Learning with Spark

The rise of machine learning and predictive analytics
Machine learning library (MLlib) in Spark
Introduction to algorithms in MLlib
Techniques for collaborative filtering, classification, and regression
Employing MLlib for clustering and dimensionality reduction
Best practices for machine learning in the context of big data

Module - 22

Visualisation and Analysis with Spark SQL

Background of SQL and its applicability in Big Data
SQL and DataFrame API in Spark
Handling structured data operations with Spark SQL
Executing SQL queries over structured data in Spark
Strategies for optimising Spark SQL performance

Module - 23

DataFrames and Dataset Operations in Spark

The advantages of DataFrames and Datasets over RDDs
Creating and manipulating DataFrames and Datasets
Handling unstructured data using Spark's DataFrames
Operations and transformations using DataFrames and Datasets
Performance tuning with Catalyst optimizer and Tungsten execution engine

Module - 24

Project and Case Study

Real-world Big Data project scenarios
Developing a Big Data solution using Spark
Practical implementation of the course content
Project work and peer review
Case study discussions and learning from industry experiences

Module - 25

Certification Exam and Evaluation

Revision sessions and practice exams
Certification examination guidelines and tips
Evaluation process and feedback
Career guidance and opportunities post-certification

Data Science Capstone Course Syllabus

Module - 1

Course Introduction

Inspirational Talk
Definition of Project Scope

Module - 2

First Evaluation Point

Presentation of Milestone #1
Compilation of an Executive Summary and a Detailed Technical Document
Mutual and Self-Assessment
Critical Analysis of a Peer Group's Documentation
Submission of Code (functional as per requirements)

Module - 3

Mid-Course Assessment

Presentation of Milestone #2 (Deemed as "Midterm")
Production of an Executive Summary along with an In-Depth Technical Report
Process of Mutual and Self-Assessment
Constructive Criticism of a Peer Group's Documentation
Submission of Code (functional and verified)

Module - 4

Pre-Final Evaluation

Presentation of Milestone #3
Development of an Executive Summary and an Extensive Technical Report
Mutual and Self-Evaluation Activities
Examination and Critique of Another Team's Submissions
Code Submission (functional as per specifications)

Module - 5

Course Culmination

Final Presentation to the Cohort
Comprehensive Final Report Published on a Blog
Design and Presentation of a Research Poster and a Video Synopsis
Mutual and Self-Evaluation Exercises
Code Submission (functional, well-structured, and documented)

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.