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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)
(Monday - Friday)
(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.

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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
  • Data Types
  • Introduction to Data Science Tools
  • Statistics
  • Approach to Business Problems
  • Numerical Categorical
  • R, Python, WEKA, RapidMiner
  • 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
  • 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
  • Introduction to Logistic Regression
  • Logit Function
  • Training-Validation approach
  • Lift charts
  • Decile Analysis
  • Using the UCI ML repository dataset or Built-in R dataset
  • 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
  • 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
  • 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
  • Live example/ live project
  • Using client-given stock prices / taking stock price data
  • Box – Jenkins Methodology
  • Case Study with the Data
  • Based on open set data
  • Case Study with the Data
  • Based on open set data
  • Supervised Learning Techniques
  • Conceptual Overview
  • Unsupervised Learning Techniques
  • Association Rule Mining Segmentation
  • Fraud Identification Process in Parts Procuring
  • Sample data from online
  • Text Analytics
  • Sample text from online
  • Social Media Analytics
  • Sample text from online
Syllabus of Data Science with Python Course
  • What is Data Science?
  • What is Machine Learning?
  • What is Deep Learning?
  • What is AI?
  • Data Analytics & its types
  • What is Python?
  • Why Python?
  • Installing Python
  • Python IDEs
  • Jupyter Notebook Overview
  • Python Basic Data types
  • Lists
  • Slicing
  • IF statements
  • Loops
  • Dictionaries
  • Tuples
  • Functions
  • Array
  • Selection by position & Labels
  • Pandas
  • Numpy
  • Sci-kit Learn
  • Mat-plot library
  • Reading CSV files
  • Saving Python data
  • Loading Python data objects
  • Writing data to CSV file
  • Selecting rows/observations
  • Rounding Number
  • Selecting columns/fields
  • Merging data
  • Data aggregation
  • Data munging techniques
  • 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

  • Linear Regression
  • Linear Equation
  • Slope<
  • Intercept
  • R square value
  • Logistic regression
  • ODDS ratio
  • Probability of success
  • Probability of failure
  • ROC curve
  • Bias Variance Tradeoff
  • K-Means
  • K-Means ++
  • Hierarchical Clustering
  • Title
  • Base
  • Link
  • Style s
  • Script
Syllabus of Machine Learning Course
  • 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
  • Variables in R
  • Scalars
  • Vectors
  • Matrices
  • List
  • Data frames
  • Using c, Cbind, Rbind, attach and detach functions in R
  • Factors
  • Data sorting
  • Find and remove duplicates record
  • Cleaning data
  • Recoding data
  • Merging data
  • Slicing of Data
  • Merging Data
  • Apply functions
  • Reading Data
  • Writing Data
  • Basic SQL queries in R
  • Web Scraping
  • Box plot
  • Histogram
  • Pareto charts
  • Pie graph
  • Line chart
  • Scatterplot
  • Developing Graphs
  • Basics of Statistics
  • Inferential statistics
  • Probability
  • Hypothesis
  • Standard deviation
  • Outliers
  • Correlation
  • Linear & Logistic Regression
  • Introduction to Data Mining
  • Understanding Machine Learning
  • Supervised and Unsupervised Machine Learning Algorithms
  • K- means clustering
  • Anova
  • Sentiment Analysis
  • Decision Tree
  • Concepts of Random Forest
  • Working on Random Forest
  • Features of Random Forest
Module 10:

Project work

Syllabus of Tableau Course
  • 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
  • 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
  • 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
  • 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
  • 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
  • Create a Dashboard
  • Format Dashboard Layou
  • Create a Device Preview of a Dashboard
  • Create Filters on the Dashboard
  • Dashboard Objects
  • Create a Story
  • Tableau online.
  • Overview of Tableau Server.
  • Publishing Tableau objects and scheduling/subscription.
Syllabus of Big Data Hadoop with Spark Developer
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Introduction to Apache Spark
  • Architecture and design principles of Apache Spark
  • Spark features and characteristics
  • Apache Spark Ecosystem components and their insights
  • Spark environment setup
  • Installing and configuring prerequisites
  • Installation of Spark in local mode
  • Troubleshooting encountered problems
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Ignite Talk
  • Statement of work
  • Milestone #1 Presentation
  • Summary Report + technical report
  • Self-/peer- evaluation
  • Review another group's reports
  • Code (runs as advertised)
  • Milestone #2 Presentation ("Midterm")
  • Summary Report + technical report
  • Self-/peer- evaluation
  • Review another group's reports
  • Code (runs as advertised)
  • Milestone #3 Presentation
  • Summary Report + technical report
  • Self-/peer- evaluation
  • Review another group's reports
  • Code (runs as advertised)
  • 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.
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Aimore’s Data Science Diploma - FAQs

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