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Diploma in Data Science in Chennai

Master the foundations of Data Science with a diploma in Data Science in Chennai from Aimore Technologies.
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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

(Monday - Friday)
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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.

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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 for Data Science with R
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Practical exercise on handling file input/output in R
  • Analytical project based on actual stock price data from clients or publicly available sources
  • Applying Box-Jenkins Methodology in Pharmaceuticals
  • Comprehensive Case Study on Pharmaceutical Data
  • Analysis based on publicly available datasets
  • Market Research Data Case Study
  • Analysis based on publicly available datasets
  • Detailed Overview of Supervised and Unsupervised Learning Techniques
  • Introduction to Association Rules in Machine Learning
  • Understanding Segmentation in Machine Learning Contexts
  • 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
  • Practical exercises on Text Analytics using sample data sourced online
  • 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
  • Exploring the Concept of Data Science
  • Basics of Machine Learning
  • Introduction to Deep Learning
  • Fundamentals of Artificial Intelligence
  • Various Forms of Data Analytics
  • Overview of Python
  • The Case for Python in Programming
  • Setting Up Python Environment
  • Exploring Python Integrated Development Environments
  • Tour of Jupyter Notebook
  • 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
  • Introduction to Pandas Library
  • Navigating Numpy Library
  • Overview of Sci-kit Learn
  • Essentials of Mat-plot Library
  • How to Read CSV Files
  • Storing Data in Python
  • Loading Data Structures in Python
  • Writing Data to CSV Format
  • 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
  • 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

  • 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
  • K-Means Clustering Techniques
  • Advanced Clustering with K-Means++
  • Overview and Application of Various ML Algorithms
  • Syllabus for Machine Learning Course
Syllabus for Machine Learning Course
  • 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
  • Variable Types in R
  • Data Structures: Scalars, Vectors, Matrices, Lists, and Data Frames
  • Utilising Functions like c, Cbind, Rbind, attach, and detach
  • 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
  • Methods for Reading Data into R
  • Techniques for Writing Data
  • Executing Basic SQL Queries within R
  • Introduction to Web Scraping with R
  • Utilising Various Plots for Data Visualisation: Box Plots, Histograms, Pareto Charts
  • Creating Graphical Representations: Pie Graphs, Line Charts, Scatterplots
  • 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
  • Introduction to the Process of Data Mining
  • Distinctions Between Supervised and Unsupervised Learning
  • Implementing K-means Clustering
  • Application of Anova in Statistical Analysis
  • Fundamentals of Sentiment Analysis
  • Understanding Decision Trees
  • Fundamentals of Random Forest Algorithms
  • Implementing Random Forest Models
Module - 10

Capstone Project Work

Syllabus for Tableau Course
  • Initial Set-Up and Interface Walkthrough
  • Data Connection Fundamentals
  • Hierarchies and Their Management
  • Understanding and Creating Bins
  • Techniques for Merging Tables
  • Data Blending Principles
  • 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
  • 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
  • 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
  • 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
  • Constructing and Formatting Dashboards
  • Previewing Dashboards for Different Devices
  • Filtering Data within Dashboards
  • Understanding Dashboard Objects
  • Storytelling with Dashboards
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Fundamentals of Apache Spark and its design principles
  • Detailed examination of Spark's features and ecosystem
  • Guidance on setting up the Spark environment
  • Instructions on Spark installation and configuration
  • Addressing common issues in Spark setup
  • Steps for installing Spark in standalone and YARN modes
  • Configuration guidelines for Spark deployment on clusters
  • Recommendations for Spark deployment strategies
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Inspirational Talk
  • Definition of Project Scope
  • 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)
  • 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)
  • 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)
  • 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.
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Aimore’s Data Science Diploma - FAQs

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Here's answers to our most commonly asked questions.