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.

DATA WRANGLING WITH SQL

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.