DataScience & AI

Qmatrix Technologies is a one stop solution for next generation learners of data science and AI technologies. With increased importance for data processing, data science is seeing good growth rates. Hence is the tremendous scope for job opportunities in data science. Most companies expect an authentic certification before they hire candidates. A certification stands as a proof of your expertise in the field of data science and AI technology. The Data Science course is an innovative learning about software technology. This is useful for applying critical analysis, and delivers the ability of developing sophisticated models, driving business insights and creating massive data sets. Our Artificial Intelligence classes are designed to provide a deeper understanding of AI concepts and related programming languages.
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Data Science & AI Training

We are in the middle of the 4th Industrial Revolution (Industry 4.0), which is mostly being driven by Data Science. This ever-growing field involves the collection, analysis, and exchange of data, lots of data! The demand for professionals for various data science jobs is at an all-time high, so is the gap in supply.Taking Training course at QMatrix TechnologiesTechnologies, will help all your profession dreams materializes in the present IT field.

Syllabus

Python

  • Introduction to Python and IDEs – The basics of the Python programming language and how you can use various IDEs for Python development, like Jupyter, Pycharm, etc.
  • Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling, etc.
  • Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
  • Hands-on Sessions And Assignments for Practice – The culmination of all the above concepts with real-world problem statements for better understanding.

Linux

  • Introduction to Linux – Establishing the fundamental knowledge of how Linux works and how, to begin with Linux OS.
  • Linux Basics – File Handling, data extraction, etc.
  • Hands-on Sessions And Assignments for Practice – Strategically curated problem statements for you to start with Linux.

SQL Basics
  • Fundamentals of Structured Query Language
  • SQL Tables, Joins, Variables
Advanced SQL
  • SQL Functions, Subqueries, Rules, Views
  • Nested Queries, string functions, pattern matching
  • Mathematical functions, Date-time functions, etc.
Deep Dive into User-Defined Functions
  • Types of UDFs, Inline table value, multi-statement table.
  • Stored procedures, rank function, triggers, etc.
SQL Optimization and Performance
  • Record grouping, searching, sorting, etc.
  • Clustered indexes, common table expressions.
Hands-on exercise
  • Writing comparison data between the past year to present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data, and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

Python

  • Introduction to Python and IDEs – The basics of the Python programming language and how you can use various IDEs for Python development, like Jupyter, Pycharm, etc.
  • Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling, etc.
  • Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
  • Hands-on Sessions And Assignments for Practice – The culmination of all the above concepts with real-world problem statements for better understanding.

Linux

  • Introduction to Linux – Establishing the fundamental knowledge of how Linux works and how, to begin with Linux OS.
  • Linux Basics – File Handling, data extraction, etc.
  • Hands-on Sessions And Assignments for Practice – Strategically curated problem statements for you to start with Linux.

Extract Transform Load

  • Web Scraping, Interacting with APIs

Data Handling with NumPy

  • NumPy Arrays, CRUD Operations, etc.
  • Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.

Data Manipulation Using Pandas

  • Loading the data, data frames, series, CRUD operations, splitting the data, etc.

Data Preprocessing

  • Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardisation, etc.
  • Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross-validation techniques, train-test split, etc.

Data Visualization

  • Bar charts, scatter plots, count plots, line plots, pie charts, doughnut charts, etc., with Python matplotlib.
  • Regression plots, categorical plots, area plots, etc., with Python seaborn.

Descriptive Statistics

  • A measure of central tendency, a measure of spread, five points summary, etc.

Probability

  • Probability Distributions, Bayes theorem, and central limit theorem.

Inferential Statistics

  • Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

Introduction to Machine Learning

  • Supervised, Unsupervised learning.
  • Introduction to sci-kit-learn, Keras, etc.

Regression

  • Introduction classification problems, Identification of a regression problem, dependent and independent variables.
  • How to train the model in a regression problem.
  • How to evaluate the model for a regression problem.
  • How to optimise the efficiency of the regression model.

Classification

  • Introduction to classification problems, Identification of a classification problem, dependent and independent variables.
  • How to train the model in a classification problem.
  • How to evaluate the model for a classification problem.
  • How to optimise the efficiency of the classification model.

Clustering

  • Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
  • How to train the model in a clustering problem.
  • How to evaluate the model for a clustering problem.
  • How to optimise the efficiency of the clustering model.

Supervised Learning

  • Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardisation, normalisation, etc.
  • Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.
  • Decision Tree – Creating decision tree models on classification problems in a tree-like format with optimal solutions.
  • Random Forest – Creating random forest models for classification problems in a supervised learning approach.
  • Support Vector Machine – SVM or support vector machines for regression and classification problems.
  • Gradient Descent – The gradient descent algorithm that is an iterative optimisation approach to finding the local minimum and maximum of a given function.
  • K-Nearest Neighbors – A simple algorithm that can be used for classification problems.
  • Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.

Unsupervised Learning

  • K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
  • Dimensionality reduction – Handling multi-dimensional data and standardising the features for easier computation.
  • Linear Discriminant Analysis – LDA or linear discriminant analysis to reduce or optimise the dimensions in the multidimensional data.
  • Principal Component Analysis – PCA follows the same approach in handling multidimensional data.

Performance Metrics

  • Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.
  • Confusion matrix – To evaluate the true positive/negative and false positive/negative outcomes in the model.
  • r2, adjusted r2, mean squared error, etc.

Artificial Intelligence Basics

  • Introduction to Keras API and TensorFlow

Neural Networks

  • Neural networks
  • Multi-layered Neural Networks
  • Artificial Neural Networks

Deep Learning

  • Deep neural networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • GPU in deep learning
  • Autoencoders restricted Boltzmann machine

Power BI Basics

  • Introduction to PowerBI, Use cases and BI Tools, Data Warehousing, Power BI components, Power BI Desktop, workflows and reports, Data Extraction with Power BI.
  • SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
  • Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data, M Query and Hierarchies in Power BI.

DAX

  • Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

Data Visualization with Analytics

  • Slicers, filters, Drill Down Reports
  • Power BI Query, Q & A and Data Insights
  • Power BI Settings, Administration and Direct Connectivity
  • Embedded Power BI API and Power BI Mobile
  • Power BI Advance and Power BI Premium

Hands-on Exercise

  • Creating a dashboard to depict actionable insights in sales data.

Introduction to MLOps

  • MLOps lifecycle
  • MLOps pipeline
  • MLOps Components, Processes, etc

Deploying Machine Learning Models

  • Introduction to Azure Machine Learning
  • Deploying Machine Learning Models using Azure

Version Control

  • What are version control, types, and SVN?

GIT

  • Git Lifecycle, Common Git commands, Working with branches in Git
  • Github collaboration (pull request), GitHub Authentication (ssh and Http)
  • Merging branches, Resolving merge conflicts, Git workflow

The Data Science capstone project focuses on establishing a solid hold of analysing a problem and coming up with solutions based on insights from the data analysis perspective. The capstone project will help you master the following verticals:

  • Extracting, loading and transforming data into a usable format to gather insights.
  • Data manipulation and handling to pre-process the data.
  • Feature engineering and scaling the data for various problem statements.
  • Model selection and model building on various classification and regression problems using supervised/unsupervised machine learning algorithms.
  • Assessment and monitoring of the model created using the machine learning models.
  • Recommendation Engine – The case study will guide you through various processes and techniques in machine learning to build a recommendation engine that can be used for movie recommendations, restaurant recommendations, book recommendations, etc.
  • Rating Predictions – This text classification and sentiment analysis case study will guide you towards working with text data and building efficient machine learning models that can predict ratings, sentiments, etc.
  • Census – Using predictive modelling techniques on the census data, you will be able to create actionable insights for a given population and create machine learning models that will predict or classify various features like total population, user income, etc.
  • Housing – This real estate case study will guide you towards real-world problems, where a culmination of multiple features will guide you towards creating a predictive model to predict housing prices.
  • Object Detection – A much more advanced yet simple case study that will guide you towards making a machine-learning model that can detect objects in real time.
  • Stock Market Analysis – Using historical stock market data, you will learn about how feature engineering and feature selection can provide you with some really helpful and actionable insights for specific stocks.
  • Banking Problem – A classification problem that predicts consumer behaviour based on various features using machine learning models.
  • AI Chatbot – Using the NLTK python library, you will be able to apply machine learning algorithms and create an AI chatbot.

Text Mining, Cleaning, and Pre-processing

  • Various Tokenizers, Tokenization, Frequency Distribution, Stemming, POS Tagging, Lemmatization, Bigrams, Trigrams & Ngrams, Lemmatization Entity Recognition.

Text classification, NLTK, sentiment analysis, etc

  • Overview of Machine Learning, Words, Term Frequency, Countvectorizer, Inverse Document Frequency, Text Conversion, Confusion Matrix, Naive Bayes Classifier.

Sentence Structure, Sequence Tagging, Sequence Tasks, and Language Modeling

  • Language Modeling, Sequence Tagging, Sequence Tasks, Predicting Sequence of Tags, Syntax Trees, Context-Free Grammars, Chunking, Automatic Paraphrasing of Texts, Chinking.

AI Chatbots and Recommendations Engine

  • Using the NLP concepts, build a recommendation engine and an AI chatbot assistant using AI.

RBM and DBNs & Variational AutoEncoder

  • Introduction rbm and autoencoders
  • Deploying rbm for deep neural networks, using rbm for collaborative filtering
  • Autoencoders features and applications of autoencoders.

Object Detection using Convolutional Neural Net

  • Constructing a convolutional neural network using TensorFlow
  • Convolutional, dense, and pooling layers of CNNs
  • Filtering images based on user queries

Generating images with Neural Style and Working with Deep Generative Models

  • Automated conversation bots leveraging
  • Generative model, and the sequence-to-sequence model (lstm).
  • Distributed & Parallel Computing for Deep Learning Models

Parallel Training, Distributed vs Parallel Computing

  • Distributed computing in Tensorflow, Introduction to tf. distribute
  • Distributed training across multiple CPUs, Distributed Training
  • Distributed training across multiple GPUs, Federated Learning
  • Parallel computing in Tensorflow

Reinforcement Learning

  • Mapping the human mind with deep neural networks (dnn)
  • Several building blocks of artificial neural networks (anns)
  • The architecture of dnn and its building blocks
  • Reinforcement learning in dnn concepts, various parameters, layers, optimisation algorithms in dnn, and activation functions.

Deploying Deep Learning Models and Beyond

  • Understanding model Persistence, Saving and Serializing Models in Keras, Restoring and loading saved models.
  • Introduction to Tensorflow Serving, Tensorflow Serving Rest, Deploying deep learning models with Docker & Kubernetes, Tensorflow Serving Docker, Tensorflow Deployment Flask.
  • Deploying deep learning models in Serverless Environments
  • Deploying Model to Sage Maker
  • Explain Tensorflow Lite Train and deploy a CNN model with TensorFlow

Introduction to Big Data And Spark

  • Apache spark framework, RDDs, Stopgaps in existing computing methodologies

RDDs

  • RDD persistence, caching, General operations: Transformation, Actions, and Functions.
  • Concept of Key-Value pair in RDDs, Other pair, two pair RDDs
  • RDD Lineage, RDD Persistence, WordCount Program Using RDD Concepts
  • RDD Partitioning & How it Helps Achieve Parallelization

Advanced Concepts & Spark-Hive

  • Passing Functions to Spark, Spark SQL Architecture, SQLContext in Spark SQL
  • User-Defined Functions, Data Frames, Interoperating with RDDs
  • Loading Data through Different Sources, Performance Tuning
  • Spark-Hive Integration
  • Job Search Strategy
  • Resume Building
  • LinkedIn Profile Creation
  • Interview Preparation Sessions by Industry Experts
  • Mock Interviews
  • Placement opportunities with X+ hiring partners upon clearing the Placement Readiness Test.

Category

Datascience

Training Mode

On/Offline

Duration

3-6 month

Availability

Weekdays/Weekend

Course Features

Placement Support

RealTime Projects

Free Career Guidance

Experience Trainers

Practical Classes

Free Demo Classes

Course Certification

Flexible Schedule

Our Student Reviews

Recently Asked Questions?

Modern technical industry uses data science to analyse data and make smart predictions. It is a relatively new field with immense potential. With the right skill set, data scientists can find lucrative careers in a variety of industries. The field is expected to grow exponentially in the coming years, making it a great option for those looking to start or further their career.

Our data science training prepares you with:

  • Comprehensive knowledge of Data Science
  • Fluency in Statistics and Data Mining
  • Ability to create a decision tree
  • Ability to explore big data concepts
  • Expertise in using Map Reduce and Tableau

A job in data science involves working with massive volumes of data. This is where your mathematical skills and knowledge come into play. Your expertise in linear algebra, statistical hypotheses, and calculus is inherent in helping you accomplish business analyst tasks. Mastering these skills may be challenging. However, consistent practice will help you hone your mathematical skills.

To excel in your data science career, you will need:

  • Programming language skills
  • Higher educational degrees and certifications
  • Knowledge of Hadoop and Apache Spark
  • Knowledge of AI and ML
  • Understanding of Natural Language Processing
  • Knowledge of Computer Vision

Aimore’s data science training prepares you for all this and more.

Qmatrix Technologies provides the most comprehensive data science training in Chennai. In addition to teaching ML, AI, R programming, and Python, machine learning, we provide our students with hands-on training from qualified professionals.

You will gain a comprehensive understanding of the concepts and tools related to data science in this course. By the end of this course, you’ll be able to analyze and manipulate data, create predictive models, and convey your findings in an effective manner. Moreover, you’ll have the chance to showcase your data science talents in real-time projects.

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