Hyderabad School of Artificial Intelligence With Data Science

Job Outlook

2.7 MillionCareer Opportunities estimated for Data Science and analytical roles in 2020
28.00 %Annual Growth in job opportunities for data scientists, data developers and data engineers, across the globe
Salary Trend
Jobs requiring Data Science skills are paying an average of $114,000. Advertised data scientist and data engineering jobs pay an average of $105,000 and $117,000 respectively.
Top Industries
Prominent economic sectors where data analytics is marking its presence include
- Energy
- Insurance
- Healthcare
- Retail Banking
Job titles include
- Data Scientist
- Data & Analytics Manager
- Data Architect
- Principal Scientist
- Data Engineer

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Statistics and Probability
Course Curriculum
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Course Curriculum

Module 1: Descriptive Statistics

Objective: This session introduces you with how statistics is used in business with basic statistical concepts like levels of data and measures of central tendencies

Topics

Introduction to Statistics
Charts and Graphs
Descriptive Statistics
Probability

Lab Session: You will execute measures of central tendencies and different charts using R programming , and hand based problems on estimating mean , median , mode and various problems on Probability

Module 2: DISTRIBUTIONS AND SAMPLING

Objective: You will get into the deeper aspects of various distributions. You will understand the parameters that define the probability distributions and differences between discrete and continuous distributions.

Topics

Discrete Distributions
Continuous Distributions
Sampling and Sampling Distributions
Probability
Bayes Theorem

Lab Session: We will solve several problems related on Bayes Theorem. Hands-on implementation of Monte Carlo Simulations in R will also be done.

Module 3: MAKING INFERENCES ABOUT POPULATION PARAMETERS

Objective: You will also start making statistical inferences about populations from samples.

Topics

Statistical Inference: Estimation for Single Populations
Statistical Inference: Hypothesis Testing for Single Populations
Statistical Inferences About Two Populations
Analysis of Variance and Design of Experiments

Lab session:

you will learn how to conduct a statistical hypothesis testing In R and will be introduced practical session and various problems and using various methods such as t-test, z-test, F-test and ANOVA methods in detail.

Module 4: NONPARAMETRIC STATISTICS

Objective:Till this point you will have received the complete picture how to understand the data, attributes, distributions, sample versus population, and procedure for statistical testing, etc. While you continue the analysis of a variable, you will extend that understanding to analyse the relationship between categorical variables.

Topics

Analysis of Categorical Data
Nonparametric Statistics

Lab session: Hands on R session on chi-square test and various paper based problems

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R Programming
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Course Curriculum

Module 1: Introduction to R concepts

Objective: In this introduction to R, you will master the basics of this beautiful open source language, including factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis

Topics

Intro to Basics
Vectors
Matrices
Factors
Data Frames
Lists

Module 2: Intermediate R

Objective: The intermediate R course is the logical next stop on your journey in the R programming language. In this R training you will learn about conditional statements, loops and functions to power your own R scripts. Next, you can make your R code more efficient and readable using the apply functions.

Topics

Conditionals and Control Flow
Loops
Functions
Apply () family
Utilities

Module 3: Data Manipulation

Objective: To clean the data we use various Pre-processing Techniques techniques

Topics

Binning
Filling missing values
Standardization & Normalization
type conversions
train-test data split
ROCR1

Note:You will be using R throughout the course by with various algorithms

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Decision Modelling
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Module 1: Linear Regression

Objective: Predictive analytics search for patterns found in historical and transactional data to understand a business problem and predict future events. In many business problems, we try to deal with data on Several variables, sometime more than the number of observations. Regression models help us understand the relationships among these variables and how the relationships can be exploited to make decisions. Primary objective of this module is to understand how regression and causal forecasting models can be used to analyse real-life business problems such as prediction, classification and discrete choice problems. The focus will be case-based practical problem-solving using predictive analytics techniques to interpret model outputs.

Topics

Covariance
Correlation
Simple linear Regression
Multiple Linear Regression
Coefficient of determination
StepAIC
Multicollinearity check
Model diagnostics
Model Validations

Lab session : On given Data Set hands on training will be given to execute various steps to perform linear regression using R Programming . You will be given one more data set as home work assignment to be submitted

Module 2: Logistic Regression

Objective: Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification

Topics

Logistic function
Estimation of probability using logistic regression
Model Evaluation
Confusion Matrix
Naive Bayes classifier

Lab session: On given Data Set hands on training will be given to execute various steps to perform Logistic regression using R Programming . You will be given one more data set as home work assignment to be submitted

Module 3: Feature Engineering and Regularization

Objective: One of the major aspects of training your machine learning model is avoiding overfitting. The model will have a low accuracy if it is overfitting. This happens because your model is trying too hard to capture the noise in your training

Topics

PCA
EDA
Lasso
Ridge Regression
Over fitting

Lab session : You will be using all the Dimensionality Reduction Techniques on a given data set and will be asked to apply any one of linear or logistic regression using R

Module 4: Time series Analysis

Objective: A powerful approach to analyze financial data and other forms of data based on their time dependent past values is known as the Time series analysis.

Topics

Trend analysis
Cyclical and Seasonal analysis
Smoothing
Moving averages
Auto-Correlation
ARIMA

Lab session: On given Data Set hands on training will be given to execute various steps to perform Time Series Analysis using R Programming . You will be given one more data set as home work assignment to be submitted

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Machine Learning
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Module 1: Clustering

Objective: Learn about unsupervised learning and various types of clustering that can be used to analyze the data

Topics

Distance Calculation
Hierarchical Clustering
K means Clustering
K medoids Clustering
Model Based Clustering

Lab session : On given Data Set hands on training will be given to execute various steps to perform Clustering using R Programming . You will be given one more data set as home work assignment to be submitted

Module 2: Recommendation Systems

Objective: Learn how to build a powerful recommendation engine using various Methods

Topics

Collaborative Filtering
Hybrid recommendation
Association Rules
Apriori Algorithm
User - Based Method
Item-Based Methods
Different Methods of Recommending
Evaluation Goals

Lab session: On given Retail data set we perform various methods of recommending items by building recommendation engine using R . You will be given one more data set as home work assignment to be submitted

Module 3: Decision Tree

Objective: This Algorithm is meant to learn tree based modeling. After the successful completion of this Algorithm, one is expected to become proficient at using tree based algorithms and build predictive models. They are adaptable at solving any kind of problem at hand (classification or regression).

Topics

Sample Decision Tree
CART
C5.0
Entropy
Information Gain
Building Regression

Lab session: Implementing a Decision tree Model on given data set for both classification and Prediction . You will be given one more data set as home work assignment to be submitted

Module 4: Ensemble Methods

Objective: In machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any one single algorithm. The basics of ensembles are bagging & boosting that will be covered in detail and later progress with machine learning methods that use either or both approaches to build ensemble models.

Topics

Bagging and Boosting
Random Forest
GBM
XGBoost
Stacking

Lab session : Performing Multiple combination of models on a given dataset using R . You will be given one more data set as home work assignment to be submitted

Module 5: KNN Algorithm

Objective: KNN can be used for both classification and regression problems! It’s far more popularly used for classification problems

Topics

Distance Calculation
Computational geometry
Voronoi Diagrams
Delaunay Triangulations
Wilson editing and triangulations

Labsession : Implementing a KNN Model on given data set for both classification and Prediction . You will be given one more data set as home work assignment to be submitted

Module 6: Support vector machines

Objective: Support Vector Machines (SVM) is the most elegant technique developed in the last two decades. You will also learn another powerful technique Hidden Markov Models (HMM) that has a very rich mathematical structure and forms a very strong theoretical basis for a wide range of applications.

Topics

SVM Algorithm
Linear learning machines
Kernel space
Making Kernels and working in feature space
Svm Classification
Svm Regression

Labsession : Implementing a SVM Model on given data set for both classification and Prediction . You will be given one more data set as home work assignment to be submitted

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Data Visualization
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Python Programming
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Deep Learning
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Course Curriculum

Module 1: Artificial Neural Networks

Objective: Deep learning techniques are so powerful because they learn with layers the best way to represent the problem while learning how to solve the problem.

Deep architecture
ANN Initution
Single Layer Perceptron
Multi Layer Perceptron
Back propagation
Keras

Labsession : On a Given Data set We execute Ann Algorithm with both layers based on Data set using keras Package of python . Home assignment will be given with another data set

Module 2: Auto Encoders

Objective: Generally, you can consider autoencoders as an unsupervised learning technique, since you don’t need explicit labels to train the model on. All you need to train an autoencoder is raw input data.

Topics

simple autoencoder based on a fully-connected layer
a sparse autoencoder
a deep convolutional autoencoder
an image denoising model
a sequence-to-sequence autoencoder
a variational autoencoder

Module 3: Optimization Techniques

Objective: At the end of the Module, you will be able to answer questions like “Should I outsource a service or do it in-house?”, “How to optimize a supply chain?”, “How to price a product when faced with demand uncertainty?” and “How to price a derivative product using a Monte Carlo simulation

Topics

Linear Programming
Monte Carlo simulation
Genetic Algorithms
Gradient Descent
Markov chains

Lab Sessions: You will be given Transportation problem and finance Problem dataset. Based on the problem we use different types of techniques in R and Python

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Computer vision
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Natural Language Processing
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Intelligent Systems
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Big Data With Hadoop Ecosystem
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Master Program Certificate

Applicable for learners enrolling for entire learning path

Hyderabad School of Artificial Intelligence With Data Science | Full time Certificate Holders work at companies like :

About the Program

This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles.

Course 01/09

R Programming
01 Weeks
03 Modules

01Introduction to R concepts
02Intermediate R
03Data Manipulations
04Lab Sessions

01Introduction to R concepts
02Intermediate R
03Data Manipulations
04Lab Sessions

In this course you will learn how to program in R and how to use R for effective data analysis.
The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.

Course 02/09

Statistics and Probability
02 Weeks
04 Modules

01Descriptive statistics
02Distributions and sampling
03Making inferences about population parameters
04Nonparametric statistics

01Descriptive statistics
02Distributions and sampling
03Making inferences about population parameters
04Nonparametric statistics

This session introduces you with how statistics is used in business with basic statistical concepts like levels of data and measures of central tendencies
You will get into the deeper aspects of various distributions. You will understand the parameters that define the probability distributions

Course 03/09

Decision Modeling
02 Weeks
04 Modules

01Linear Regression
02Logistic Regression
03Feature Engineering
04Time series Analysis

01Linear Regression
02Logistic Regression
03Feature Engineering
04Time series Analysis

This training helps you identify predictions problems and classification problems also covers Dimensionality Reduction techniques
This Training helps you deal with stock market data and financial data problems with time series analysis

Course 04/09

Machine Learning
03 Weeks
06 Modules

01Clustering
02Recommendation Systems
03Decision Tree
04Ensemble Methods
04KNN Algorithm
04Support vector machines

01Clustering
02Recommendation Systems
03Decision Tree
04Ensemble Methods
04KNN Algorithm
04Support vector machines

This Training helps you gain expertise in machine learning algorithms like Unsupervised and Supervised Algorithms
This Training Helps you gain expertise in Creating Powerful Recommendation Engine Systems

Course 05/09

Python programming
01 Weeks
02 Modules

01Introduction python
02Python Libraries

01Introduction python
02Python Libraries

You will be able to perform various python libraries for Different machine Learning and Text mining tasks

Course 06/09

Deep learning
02 Weeks
03 Modules

01Artificial Neural Networks
02Auto Encoders
02Optimization Techniques

01Artificial Neural Networks
02Auto Encoders
02Optimization Techniques

This training helps you gain expertise in prediction and Classification problems using Artificial Neural Networks with keras Tensorflow Api

Course 07/09

Computer vision
01 Weeks
02 Modules

01CNN
02RNN : LSTM

01CNN
02RNN : LSTM

This training helps you gain expertise in Image detection and Image Augmentation using Various algorithms S

Course 08/09

Natural Language
processing
02 Weeks
03 Modules

01Text Preprocessing
02Relevance Ranking
02Sentiment Analysis

01Text Preprocessing
02Relevance Ranking
02Sentiment Analysis

This Training helps you gain expertise in various NLP Tasks such as Text Classification , text generation , Text Summarization

Course 09/09

Big Data with Hadoop
and Apache spark
02 Weeks
04 Modules

01Hadoop Eco system
02Data Ingestion
02No Sql
02Apache Spark

01Hadoop Eco system
02Data Ingestion
02No Sql
02Apache Spark

This training helps you work with streaming Data by applying all the machine learning algorithms using Apache spark or Hadoop

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Sample Placement MOU and Sample Certificate

Course Evaluation

Every Alternate weeks there would be an Assignment test . Total Assignments conducted would be around 8 - 10 Tests . Average of All assignments score is considered Percentage Count : 25%

After Completion of Machine Learning one Test, After completion of Deep learning one more Test , After Completion of Natural language processing one more Test . Total number of Tests would be 3 . Average of all three tests would be considered . percentage Count : 25%

Every Week there would be presentation that could be on technical concepts or business cases studies . percentage Count : 15%

Class Attendance and class participation as well as lab session would be considered for Evaluation . Percentage Count : 5%

End to End data science application and project presentation is considered . percentage Count: 30%

Projects and case Studies

| Early Diagnosis | Readmissions | Fraud and Failure analysis of clinical trails |

| Customer churn Prediction | Plan Recommendation |

| Employee churn predection | Job Recommendation | Ticket Clustering |

| Forecasting |

| Root Optimization | Customer Transaction Behaviour | Dynamic Offers |

| TV Impressions | TRP Predictions | Ad Targeting

| Sentimental Analysis |

| Uber Dynamic Price Prediction |

| Hand Digit Recognition | facial recognition |

| Intrusion Detection |

Placement FAQ

How is Job Guarantee

Job Guarantee Process : After completion of course In 4th Month Interviews will be started If You Perform well in interviews which we provide You will be getting Direct Job offer letter . If You do not perform well in 3 Interviews we will be sending You as Internship without any interviews . Based on performance in internship company converts into Full time Employee Finally You will be in any one of the company without being Jobless.

What are Refund Policies

In case if any of the student satisfies the evaluation criteria but fails to get placement without any opportunity provided by Institute in different companies, then the institute will refund a fee of 8% with grace period of 45-60 Days of after course completion if at all the total fee has already been paid by the student.

What is Placement MOU?

It’s an agreement between student and institution that we will provide guaranteed Job Or Internship based on certain terms Candidate who paid full fee.

Attendance of classes consideration.
12 Assignments Submission with at least 50%.
12 Seminar Presentation score should be at least 50%.
12 conceptual exams and viva scores should be at least 50%.
Final Project is considered with 60%
No Location Constraint on Job Offers

NOTE : If any candidate does not satisfy the above criteria will not be eligible for Refund and Placements.

How do you conduct exams and assignments?

2 Types of exams will be conducted. i.e, through classrooms and other is in the form of home assignments.
NOTE: Submission of others assignment is considered as abort and lapse and assignment will not be considered.

How many interviews you provide on an average?

We provide on an average of 5 interviews per an individual. If they are failed to get placed in any one of them. We still help them to get placed in others.

What is Interview Process?

It includes

a) Telephonic Round/Skype
b) Case Study Presentation
c) Technical Round
d) HR Round

NOTE: No Aptitude, No JAM, No Group Discussion.

What would be the average salary?

It would be as

5-6.5 LPA as a fresher
60-80% hike for experienced candidate

It varies based on location, company and based on performance of the candidate in interviews.

How many months does it take to get into the placements?

You will get into the placements immediately after completion of the course or maximum within 45days - 60 days

At, what conditions, you will not Refund?

The candidates/Students who doesn’t attend the classes properly.
The candidates who failed to pay the fee.
The candidates who clone the assignments that were conducted during the course.
The candidates who are failed to complete the reattempt of assignments.
The candidates who are not able to attend the interviews which were given.
The candidates who have constraints with their location.

Will I get a refund if i drop from my course ?

Cancellation ≤ 3 days from the commencement of the classes: 80% of 1st Instalment paid amount.
Cancellation ≥ 3 days after the commencement of the classes: NIL
Refunds, if any, will be processed within 40 working days.
In all the above cases involving course fee, the student can choose to postpone the admission to the next batch. In such a case, the Institution reserves the right to postpone admission at the current fee structure at which the fee is paid or at the prevailing fee structure of the next batch.

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Job Outlook

Salary Trend

Top Industries

Job titles include

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Master Program Certificate

About the Program

Sample Placement MOU and Sample Certificate

Course Evaluation

Projects and case Studies

Placement FAQ

How is Job Guarantee

What are Refund Policies

What is Placement MOU?

NOTE : If any candidate does not satisfy the above criteria will not be eligible for Refund and Placements.

How do you conduct exams and assignments?

How many interviews you provide on an average?

What is Interview Process?

What would be the average salary?

How many months does it take to get into the placements?

At, what conditions, you will not Refund?

Will I get a refund if i drop from my course ?

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