How long is the duration of the course?

The duration of this course is for 6 months with certification.

What is the mode of training?

We provide training in both online and classroom sessions.

What are the certifications provided?

Certifications from IBM and UTM Malaysia are provided.

What are the tools that are covered in Data Science?

The main tools are Python, R, and Python libraries are explained thoroughly.

What is the eligibility?

A basic degree is enough. There is no qualification exam required to enroll in Data Science course.

Data Science Course in Hyderabad | Best Data Science Training in Hyderabad

How Data Science Training, Hyderabad will be beneficial to you?

Data Science Training in Hyderabad provides students with abundant and beneficial facilities that help them to achieve their goals. The training program is meticulously designed to meet the requirements of the participants belonging to various backgrounds( like students, freshers, working professionals). The curriculum is formulated by industry experts taking the inputs from the constantly changing market trends. From training to getting placed in big companies, our trainers, mentors, and career coach will support you. This training will enhance your skills and knowledge pertaining to the concepts and applications of various tools through real-time projects and assignments. The main objective of this training is not just about getting a job, but overall developing the aspirant to face the real challenges with the right perspective and confidence.

Why should I learn a Data Science course?

Data Scientist job is considered as the sexiest job of the era. We believe that you have read this line on many websites and other sources, but we want to make you believe that it is the fact. The world is shifting towards digitization, which is creating tons of data, which has to be processed, analyzed, and optimized to draw valuable insights to improve the productivity and efficiency of the organization. This crucial task can be accomplished by Data Science professionals. The emergence of tons of data that has to be analyzed to extract valuable insights is creating millions of Data Science jobs in Hyderabad. But, as per the analyst’s evaluations, there is a huge gap between demand and supply, which is paving a way for ample job opportunities with lucrative salaries. Data scientists can earn more than a software engineer and this is a long-lasting and rewarding career as the production of data is endless. Here are the few facts about Data Science career-

Harvard Business Review - ‘Data Scientist is the sexiest job of the 21st century’.

NASSCOM - About 1.3 Lakh jobs are open in Data Science, Big Data and Artificial Intelligence.

Glassdoor - Data Science is the best job(2018 rankings).

Talent Supply Index - Demand for Professional Data Scientists will rise by 416.5% in India.

Is the certification necessary for the Data Science course?

Yes, Data Science is generating sundry opportunities globally. Every organization is moving towards digitization and they have realized the importance of data which has to be analyzed to explore various beneficial insights to gain a competitive advantage. This analysis of data to make data-driven decisions is leading to create enumerate job opportunities for the Data Scientists. Certification from reputed industries will add value to your resume and to your efforts you have put in to gain the required essential skills pertaining to Data Science. This adds worth and contributes to getting high paid jobs in top-notch companies across the world. Certified Data Scientists are considered to be more efficient and skilled in making data-driven decisions in the organizations increasing profits and production. Many top organizations around the world are adopting the latest technologies to strive for excellence and coming forward to hire Certified Professional Data Scientists. Data Science Training in Hyderabad delivers a Data Science certification course, with certification from reputed industries/Universities that include IBM, UTM, Panasonic, CareerEx, etc. Choose a Data Science course in Hyderabad that gives certification from reputed universities/companies to build your career perfectly.

Who should pursue this course?

Well, if you are in a dilemma whether to take up Data Science as a career or not, here is a self-assessment test that vanishes your confusion.
Can you think analytically and logically?
Do you have basic knowledge of Mathematical Science?
Want to play with numbers/Data?
Do you have little knowledge of statistics?
Want to excel in your career and reach top positions?
Are you a fresher with any basic degree( from any stream)?
Are you a working professional from Data warehousing, Business Intelligence background?
Are you a Doctor or Dentist or from a Science background who wants to analyze data or discover new tools with the help of the latest technologies like AI or Machine Learning?
Do you believe that Data Science is the next big wave in software development?

For the above questions, if your answer is maximum yes, then bang on! you can definitely pursue your career in Data Science and see yourself rising to the top position compared to your peers. Our career counselors will guide if you have any further queries. We shall be a part of your mission and help you in achieving your goal diligently.

What is Data Science? Who is a Data Scientist?

Data Science is about extracting valuable data from historical data by collecting, segregating, and analyzing the different patterns of data. It can be from behavior patterns, trends, search histories, etc. Valuable information from the extracted data enables businesses to make decisions that enhance their performance and production.

The professionals who execute these activities are called Data Scientists. His role is considered to be the most prominent in organizations where everyone looks up to his decisions. A Data Scientist job is one of the most demanding and highly paid jobs of this era.

Degree	Subjective knowledge	Statistical Knowledge
Any degree- Bsc, Bcom, Btech, etc	Basics of Maths	Basic programming skills are necessary but not mandatory

Become Highly Demanding Data Science Professional

Which Companies are Hiring?

Data Science is bringing a lot of opportunities and is going to stay for a long period. As most of the companies have realized the multiple benefits of Data Science, they are very keen and showing interest in hiring Data Scientists in their companies to improve their efficiency in production and revenue generation. There is a big demand for Professional Data Scientists worldwide and prime companies are offering them high salaries. As per Glassdoor, Data Scientists earn an average of $116,200 per annum. This makes Data Science a highly lucrative career option.

Amazon, IBM, HCL technologies, Pepsico, Novartis Healthcare, Franklin Templeton, Egnify technologies are some of the top companies that are hiring professional Data Scientists.

Curriculum in Detail

Module 1 - Data Science Project Management Methodology

Introduction to Big Data
Data, Data, Data everywhere
Data and its uses – A case study (Grocery store)
Interactive Marketing using Data & IoT – A case study
Stages of Analytics
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
Machine Learning Categories
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Data Science Project Lifecycle
Frameworks for Building Machine Learning Systems
- Knowledge Discovery Databases (KDD)
- SEMMA (Sample, Explore, Modify, Model, Assess)
- Cross-Industry Standard Process for Data Mining
- KDD vs. CRISP-DM vs. SEMMA
CRISP-DM
- Business Understanding
  - Deﬁne Business Problem – Objective and Constraints
  - Assess and Analyze Scenarios
  - Deﬁne Data Mining Problem
  - Project Plan
- Data Understanding
  - Data Collection
  - Data Description
  - Exploratory Data Analysis
  - Data Quality Analysis
- Data Preparation
  - Data Integration
  - Data Wrangling
  - Feature Extraction and Engineering
  - Attribute Generation and Selection
- Modeling
  - Selecting Modeling Methods
  - Model Training
  - Model Evaluation and Improving by Tuning
  - Model Assessment
- Evaluation
- Deployment

Module 2 - Data Understanding: Exploratory Data Analytics (EDA) / Descriptive Analytics

Common Data Formats
- CSV
- JSON
- XML
- HTML
- SQL (Databases)
Data Types
- Numeric (Quantitative)
- Categorical (Qualitative)
- Continuous
- Discrete
- Count
- Text
- Measurement Scales
  - Nominal
  - Ordinal
  - Interval
  - Ratio Types
Data Collection
- Primary Sources
  - Surveys
  - Simulations
  - Sensors Data
  - Design of Experiments, etc.
- Secondary Sources
  - Data Warehouses
  - Data Lakes
  - Databases (SQL, NoSQL, etc.)
Data and Datasets
- Structured Data vs. Unstructured Data
- Big Data vs. Regular Size Data
- Cross-Sectional Data vs. Time Series Data
- Balanced vs. Imbalanced Data
- Offline vs. Real-Time Data
Population and Sample
- Sampling Techniques
  - Probability Sampling (Unbiased)
  - Non-Probability Sampling (Biased)
Sampling Techniques for handling Balanced vs. Imbalanced Datasets
- Random Resampling - Under & Over Sampling
- K-fold Cross-Validation
- SMOTE - Synthetic Minority Oversampling Technique
- MSMOTE - Modiﬁed SMOTE
- Cluster-Based Sampling
Sampling Funnel and its Components
- Population
- Sampling Frame
- Simple Random Sampling
- Sample
Data Cleansing/ Preparation/ Wrangling/ Munging
- Outlier Analysis / Treatment
- Missing Values Handling / Imputation
- Data Filtering
- Typecasting
- Transformations
- Duplicate Data Handling
- Managing Categorical Data
- Standardizing and Normalizing the Data
- Zero and Near-Zero Variance Feature
Random Variable and its Deﬁnition
Probability & Probability Distribution
- Continuous Probability Distribution/ Probability Density Function
- Discrete Probability Distribution/ Probability Mass Function

Module 3 - Statistical Data Business Intelligence & Data Visualization

Measures of Central Tendency
- Mean/Average
- Median
- Mode
Measures of Dispersion
- Variance
- Standard Deviation
- Range
Measure of Skewness
Measure of Kurtosis
Spread of the Data
Various Graphical Techniques to Understand Data
- Univariate
  - Line Charts
  - Bar Plots
  - Dot Charts
  - Histograms / Frequency Distribution
  - Box Plots / Box and Whisker Plots
  - Density Plots
  - Q-Q Plots / Normal Quantile – Quantile Plots
- Bivariate
  - Scatter Plots
- Multivariate
  - Pair Plots
  - Heat Maps
  - Correlation Matrix

Module 4 - Feature Engineering and Selection

Feature Engineering
Binarization
Rounding
Interactions
Binning
- Fixed-Width Binning
Adaptive Binning
Transformations
- Log Transform
- Box-Cox Transform
Feature Engineering on Numeric Data
Feature Engineering on Categorical Data
- Transforming Nominal Features
- Transforming Ordinal Features
Encoding Categorical Features
- One Hot Encoding Scheme
- Dummy Coding Schema
- Effect Coding Schema
- Bin-Counting Schema
- Feature Hashing Schema
Feature Engineering on Text Data
Feature Engineering on Temporal Data
Feature Engineering on Image Data
Feature Scaling
- Standardized Scaling
- Min-Max Scaling
- Robust Scaling
Feature Selection Techniques
- Threshold-Based Methods
- Statistical Methods
- Recursive Feature Elimination
- Model-Based Selection

Module 5 - Probability & Probability Distribution (Continuous & Discrete)

Discrete Probability Distribution - Binomial Distribution
Continuous Probability Distribution - Normal Distribution
Standard Normal Distribution / Z-Distribution
Z scores and the Z table
QQ Plot / Quantile - Quantile plot
Sample Statistics
Population Parameters
Inferential Statistics
Sampling Variation
Central Limit Theorem
Conﬁdence Interval - Concept
Conﬁdence Interval with Sigma
t-Distribution / Student's-t Distribution
Conﬁdence Interval without Sigma
- Population Parameter Standard Deviation Known
- Population Parameter Standard Deviation Not Known

Module 6 - Confirmatory Analysis - Hypothesis Testing

Business Understanding
Formulating a Hypothesis Statements
(Ho) Null Hypothesis – Default Condition / Current Condition / Status Quo
(Ha/H1) Alternative Hypothesis – Action Condition
Type I – (Alpha) – Caused by Rejection of a True Ho
Type II Errors – Caused by No Rejectionof a False Ho
Comparative Study using Hypothesis testing
Parametric vs. Non-Parametric Test Cases
Hypothesis Test Cases Based on Variable of Interest being Evaluated
- Y is Continuous
- Y is Discrete
1 Sample z-test
2 Sample t-test
Mann-Whitney Test
Paired t-test
ANOVA
ANOVA vs. ANOM
2 Proportion Tests
Chi-Square Test
Tukey Test

Module 7 - Data Mining & Supervised Learning - Regression Analysis

Scatter Diagram
- Correlation Analysis – Direction, Strength, Linearity
- Correlation vs. Covariance
Correlation and Causation
Correlation Coefﬁcient (r)
Principles of Regression
Ordinary Least Squares – Unbiased Technique
Interpretation of Regression Output
- Coefﬁcients
- p-values for signiﬁcance
- Residuals
- Coefﬁcient of Determination (R2)
Simple Linear Regression
Non-Linear Regression Techniques
- Exponential Regression
- Logarithmic Regression
- Polynomial Regression
- Power Regression
Zero Intercept Model
Model Evaluation
- Loss Function
- Cost Function
- Error Function

Module 8 - Predictive Modelling - Multiple Linear Regression

Multivariate Regression
LINE assumption
- Linearity
- Collinearity (Variance Inflation Factor)
- Independent Errors
- Auto Correlation
- Normality
- Homoscedasticity / Equal Variance
- Heteroscedasticity
Multiple Linear Regression
Model Quality Metrics
Deletion Diagnostics
Influence Plot
Added Variable Plots
Cook’s Distance
Leverage
Residuals vs. Predicting Variables Plots
Fitted vs. Residuals Plot
Histogram of the Normalized Residuals
Q-Q plot of the Normalized Residuals
Shapiro-Wilk Normality Test on the Residuals
Cook’s Distance Plot of the Residuals
Testing a Subset of Regression Coefﬁcients
- AIC
- BIC
- Step AIC
- Forward Selection
- Backward Elimination
- Stepwise Method

Module 9 - Lasso and Ridge Regressions

Multiple R2 and Adjusted R2
Understanding Overﬁtting (Variance) vs. Underﬁtting (Bias)
Generalization Error
Regularization Techniques
- L1 Norm
- L2 Norm
Penalty Term for Cost Function
LASSO (Least Absolute Shrinkage and Selection Operator) Regression
Ridge Regression / Tikhonov Regularization
Elastic Net Regression
Finding Optimized Alpha

Module 10 - Logistic Regression - Binary Value Prediction, MLE

Principles of Logistic Regression
Logit Function
Types of Logistic Regression
Assumption & Steps in Logistic Regression
Analysis of Simple Logistic Regression Results
Multiple Logistic Regression
Confusion Matrix
- False Positive, False Negative
- True Positive, True Negative
Performance Metrics
- Precision
- Sensitivity / Recall
- Speciﬁcity
- F1 Ratio
Receiver Operating Characteristics Curve (ROC curve)
Area Under Curve (AUC)
Lift Charts and Gain Charts
Finding the best Cutoff Value
Risk-Taking vs. Risk-Averse Strategies

Module 11 - Multinomial & Ordinal Logistic Regression

Logit and Log-Likelihood
Category Baselining
Modeling (Multi) Nominal Categorical Data
Modeling Ordinal Categorical Data
Multilogit Function
Residual Deviance
Interpretation of p-value’s
Exponential Family of Distributions
- Bernoulli
- Dirichlet
- Gamma
- Geometric

Module 12 - Multinomial & Ordinal Logistic Regression

Over Dispersion
Discrete Probability Distribution
- Negative Binomial Distribution
- Poisson Distribution
Poisson Regression
Poisson Regression with Offset
Negative Binomial Regression
Model Fit Test with Residual Deviance
Interpretation of Negative Binomial Regression Coefﬁcients
Interpretation of Poisson Regression Coefﬁcients
Saturated Models
Effects of Interaction Variables
Effects of Moderation Variables
Link Functions
- Identity Link
- Log Link
- Logit Link
- Probit Link
- Log-Log Link
Treatment of Data with Excessive Zeros'
- Zero-Inflated Poisson
- Zero-Inflated Negative Binomial
- Hurdle Model

Module 13 - Data Mining Supervised Learning - Machine Learning - KNN Classifier

Parametric Learning
Building a KNN Model by Splitting the Data
Calculating Distance
Bias-Variance Tradeoff
Weighted Voting Process
Deciding the best K value
Understanding various generalization and regulation techniques to avoid Over Fitting and Under Fitting
Improving Model Performance through Standardization

Module 14 - Decision Tree

Elements of Classiﬁcation Tree:
- Root Node
- Child Node
- Leaf Node, etc.
The decision to build a Tree
The decision on when to stop the growth of a Tree
Greedy Algorithm
Measure of Entropy
Gini Index, Chi-Squared Statistic, Gain Ratio
Attribute Selection using Information Gain
Developing a Tree using Information Gained Technique
Decision Tree C5.0
Pruning
- Pre-Pruning
- Post-Pruning
Grafting Branches
- Sub-Tree Raising
- Sub-Tree Replacement
Strengths and Weakness of the Decision Tree
Devising Cost Matrix

Module 15 - Ensemble Techniques - Bagging & Boosting

Overﬁtting
Underﬁtting
Bias vs. Variance
Voting
- Soft Voting
- Hard Voting
Meta-Learning Methods
Allocation Functions, Combination Functions
Stacking / Stack Generalization
Parallel Model Training - Bagging (Bootstrap Aggregation)
Sequential Model Training – Boosting
The culmination of Multiple Trees - Random Forest / Decision Tree Forest
Variable Importance Plot
Out-of-Bag Error Rate
Random Forest with k-Fold Validation
Strategies of Random Feature Selection
Ensemble Learning for Regression
Ensemble Learning for Classiﬁcation

Module 16 - AdaBoost & Extreme Gradient Boosting

AdaBoost / Adaptive Boosting
Gradient Boosting
Extreme Gradient Boosting (XGB)
- Cross-Validation
  - Leave One Out CV
  - K-Fold CV
  - Stratiﬁed K-Fold CV

Module 17 - Introduction to Neural Network

Neurons of a Biological Brain
Artiﬁcial Neuron
Perceptron
Perceptron Algorithm
Iterative Approach
- Threshold Error
- Predeﬁned Iterations
Use Case to Classify a Linearly Separable Data
Multilayer Perceptron to Handle Non-Linear Data

Module 18 - Building Blocks of Neural Network

Integration Functions
Activation Functions
Weights
Bias
Learning Rate (eta)
Error Functions
- Mean Squared Error
- Binary Cross-Entropy
- Cross-Entropy

Module 19 - Deep Learning Black Box Technique - Neural Network

Artiﬁcial Neural Networks
ANN Structure
Activation Functions
Error Surface
Gradient Descent Algorithm
Backward Propagation
Network Topology
Principles of Gradient Descent (Manual Calculation)
Learning Rate (eta)
- Momentum
- Constant Learning Rate
- Shrinking Learning Rate
Batch Gradient Descent
Stochastic Gradient Descent
Minibatch Stochastic Gradient Descent
Optimization Methods: Adagrad, Adadelta, RMSprop, Adam

Module 20 - Deep Learning Algorithms for Videos, Images, Text

Convolution Neural Network (CNN)
- ImageNet Challenge – Winning Architectures
- Parameter Explosion with MLPs
- Convolution Networks
- Convolution Layers with Filters and Visualizing Convolutio Layers
- Pooling Layer, Padding, Stride
- Properties of CNN
- Adversaries
Recurrent Neural Network
- Language Models
- Traditional Language Model
Disadvantages of MLP
Back Propagation Through Time
Long Short-Term Memory (LSTM)
LSTM – Architecture
- Cell State
- Input Gate
- Output Gate
- Forget Gate
- Sigmoid and Tanh
Gated Recurrent Network (GRU)
Architecture & Gates
Final Memory at Current Timestep

Module 21 - Kernel Method – SVM

Support Vector Machines / Large-Margin Max-Margin Classiﬁer
Hyperplanes
Best Fit "boundary"
Linear Support Vector Machine using Maximum Margin
SVM for Noisy Data
Non- Linear Space Classiﬁcation
Non-Linear Kernel Tricks
- Linear Kernel
- Polynomial
- Sigmoid
- Gaussian RBF
SVM for Multi-Class Classiﬁcation
- One vs. All
- One vs. One
Directed Acyclic Graph (DAG) SVM

Module 22 - Text Mining & Natural Language Processing (NLP)

Sources of Data
Bag of Words
Pre-Processing, Corpus
Document Term Matrix (DTM) & TDM
Stemming
Lemmatization
TF / TF-IDF
Word Clouds, Lexical Dispersion Plot
Co-occurrence Matrix
Corpus Level Word Clouds
- Sentiment Analysis
- Positive Word Clouds
- Negative word Clouds
- Unigram, Bigram, Trigram
Semantic Network
Clustering
Extract User Reviews of the Product/Services from Amazon, Snapdeal and Trip Advisor
Extraction and Text Analytics in Python
Latent Dirichlet Allocation (LDA)
Topic Modelling
Parts of Speech Tagging
Sentiment Extraction
Lexicons & Emotion Mining

Module 23 - Machine Learning Classifier Technique - Naive Bayes

Probability, Joint Probability, Conditional Probability
Bayes Rule
Naïve Bayes Classiﬁer / Probabilistic Classiﬁcation
Prior Probability
- Data Prior
- Class Prior
- Marginal Likelihood
Posterior Probability
MAP Rule
Practical Issue in Handling Continuous Attributes
Underflow Prevention
Laplace Estimator
Strengths and Weakness of Naïve Bayes
Text Classiﬁcation using Naive Bayes
Hidden Markov Models

Module 24 - Data Mining Unsupervised Learning - Clustering Topics

Data Mining Process
Supervised vs Unsupervised Learning
Measures of Distance
- Numeric - Euclidean, Manhattan, Mahalanobis
- Categorical - Binary Euclidean, Simple Matching Coefﬁcient, Jaquard's Coefﬁcient
- Mixed - Gower's General Dissimilarity Coefﬁcient
Types of Linkages
- Single Linkage / Nearest Neighbor
- Complete Linkage / Farthest Neighbor
- Average Linkage
- Centroid Linkage
Hierarchical Clustering / Agglomerative Clustering
Non-Hierarchical Clustering / K- Means Clustering
- Measurement Metrics of Clustering
  - Within the Sum of Squares
  - Between the Sum of Squares
  - Total Sum of Squares
- Choosing the Ideal K value using Screeplot / Elbow Curve
K-Medians
K-Medoids
K-Modes
Clustering Large Application (Clara)
Partitioning Around Medoids (PAM)
Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
Ordering Points to Identify the Clustering Structure (OPTICS)

Module 25 - Data Mining Unsupervised Learning Dimension Reduction

High Dimensional Data
Factor Analysis
Dimension Reduction
Advantages of PCA
Calculation of PCA Weights
Basics of Matrix Algebra
2D Visualization using Principal Components
Linear Discriminant Analysis
Singular Value Decomposition

Module 26 - Data Mining Unsupervised Learning - Association Rules

Market Basket / Afﬁnity Analysis / Relationship Mining
If-Then Probabilistic Statements – Classiﬁcation Rules
Measure of Association
- Support
- Conﬁdence
- Lift Ratio
Frequent Item Sets
Drawbacks of Measures of Association Techniques
Sparse Matrix and Density Calculation
Apriori Algorithm
Visualizing Transaction Data
3 Categories of Association Rules
- Actionable
- Trivial
- Inexplicable
Sequential Pattern Mining

Module 27 - Recommendation Engine

User-Based Collaborative Filtering
The measure of Distance/Similarity between users
Driver for Recommendation
Computation Reduction Techniques
Item to Item Collaborative Filtering
Search-Based Methods
Content-Based Filtering
Hybrid-Recommendation Engine
Popularity Based Recommendation Engine
SVD in Recommendation
Matrix Factorization Based Recommendation Engine
The vulnerability of Recommender Systems

Module 28 - Network / Graph Analytics

Deﬁnition of a Network / Graph
Vertices / Nodes
Edges / Connections / Links
- Adjacency Matrix
- Unidirectional
- Bidirectional
Node Properties
- Degree Centrality
- Closeness Centrality
- Eigenvector Centrality
- Betweenness Centrality
- Google Page Ranking
- Diffusion Centrality
Centrality as Predictors
Entity Resolution
Network Properties
- Path
- Shortest Path
- Diameter
- Average Path Length
- Density
- Cluster Coefﬁcient
Community Detection Algorithm
- Edge Betweenness
- Fast Greedy
- Leading Eigenvector

Module 29 - Survival Analytics

Examples of Survival Analysis
Time to Event/ Duration Analysis
Censoring
- Right Censored
- Left Censored
- Interval Censored
Survival, Hazard, Cumulative Hazard Functions
Introduction to Parametric and Non-Parametric Functions
Kaplan-Meier Survival Function and Curve

Module 30 - Forecasting/Time Series - Model Driven Algorithms

Introduction to Time Series Data
Steps to Forecasting
Components to Time Series Data
Scatter Plot and Time Plot
Lag Plot
ACF - Auto-Correlation Function / Correlogram
Visualization Principles
Naïve Forecast Methods
Errors in the Forecast
- Mean Error
- Mean Absolute Error
- Mean Square Error
- Root Mean Square Error
- Mean Percentage Error
- Mean Absolute Percentage Error
Model-Based Approaches
- Linear Model
- Exponential Model
- Quadratic Model
- Additive Seasonality
- Multiplicative Seasonality
Model-Based Approaches Continued
AR (Auto-Regressive) Model for Errors
Random Walk

Module 31 - Forecasting/Time Series - Data Driven Algorithms

ARMA (Auto-Regressive Moving Average), Order p and q
ARIMA (Auto-Regressive Integrated Moving Average), Order p, d, and q
Data-Driven Approach to Forecasting
Smoothing Techniques
- Moving Average
  - Centered Moving Average
  - Training Moving Average
- Exponential Smoothing
- Holts / Double Exponential Smoothing
- Winters / Holt-Winters
De-Seasoning and De-Trending
- Differencing
- Seasonal Index
Econometric Models
ARCH and GARCH for High-Frequency Data

Module 32 - AutoML

AutoML Methods
- Meta-Learning
  - Transfer Learning
  - Few Shot Learning
- Hyperparameter Optimization
  - - Grid Search
  - - Randomized Search
  - - Bayesian Optimization
- Neural Architecture Search
- Network Architecture Search
AutoML Systems
- Auto-WEKA
- Hyperopt – sklearn
- Auto – sklearn
- Auto-Net 1.0 & 2.0
- TPOT
- Hyperras - keras
AutoML on Cloud - AWS
- Amazon SageMaker
- Sagaemaker Notebook Instance for Model Development, Training and Deployment
- XG Boost Classiﬁcation Model
- Training Jobs
- Hyperparameter Tuning Jobs
AutoML on Cloud - Azure
- Workspace
- Environment
- Compute Instance
- Compute Targets
- Automatic Featurization
- AutoML and ONNX
AutoML on Cloud - GCP
- AutoML Natural Language Performing Document Classiﬁcation
- AutoML Version API's For Image Classiﬁcation
- Performing Sentiment Analysis using AutoML Natural Language API
- Tensor-Flow Models Using Cloud ML Engine
- Cloud ML Engine and Its Components
- Training and Deploying Applications on Cloud ML Engine
- Choosing Right Cloud ML Engine for Training Jobs

Data Science Salaries in Hyderabad

Hyderabad is emerging as a hub for software development and providing abundant opportunities. Data Science is one of the trending courses of this era, which has a lot of scopes. It offers multiple opportunities for aspirants who want to excel successfully in their careers. Below are the average salaries per annum for a few job roles in Hyderabad.

Job role	As per Glassdoor	As per Payscale in Hyderabad
Data Scientist	Rs. 8,62,000	Rs. 8,16,000
Data Analyst	Rs. 5,23,000	Rs. 4,20,000
Data Engineer	Rs. 12,87,865	Rs. 8,67,951
Machine Learning Engineer	Rs. 10,45,561	Rs. 674,074
Data Architect	Rs. 1,746,737	Rs. 1,946,637
Business Analyst	Rs. 6,75,618	Rs. 9,94,715

The salaries mentioned here are for reference only, it is not accurate. Salaries vary accordingly with skills and experience.

Other Benefits

We provide a first of its kind benefit for all the participants, where they can again attend the training sessions for free if they couldn’t complete their previous training program for various reasons.

Students will be provided with LMS access enabled with Artificial Intelligence.
Students will be given the opportunity to attend webinars, sessions on trending topics to upgrade their knowledge. This access is given for free for a lifetime.
Students are provided with internship programs to expose them to real-time projects and face business challenges.

Data Science Interview Questions

What is meant by Stemming?

Stemming will help in converting the word into normalizing to the base root form, this algorithm works on cutting the beginning or end of the word by taking prefix or suffix that can be found in the intellective word.

Ex: Effective, Effecting, Effected, Effects

After Stemming applied: Effect

What is mean by Tokenization?

Tokenization is a process of breaking strings into tokens that will return into small structures or units that can be used as tokenization.

Ex: I love to learn in 360DigiTMG institute

After Tokenization applied: I – love – to – Learn – in – 360DigiTMG – institute (each word divided into different tokens, overall 7 tokens created based on given sentence)

What is Lemmatization?

Lemmatizations work based on logical analysis of the word, to do so it should have a detailed dictionary which algorithm to link back to its original word or root word which is called Lema.

Ex: Mapping of Going, Gone, and Went as Go

What are POS tags in NLP?

Generally, the grammatical type of the word is referred to as POS tags or parts of speech be it a noun, adjective, adverb, verb, etc. It means how a word functions in meaning and grammatically in a sentence. A word can have one part of speech based on the context in which it is used.

Ex: ‘Google’ something on the internet

Google – verb and also a proper noun

What is Name Entity Recognition?

It is a process of detecting the named entity such as person names, company names, quantities or the locations, etc. It has three steps 1) Noun face identification, 2) Phrase classification, and 3) Entity disambiguation.

EX: Google CEO Sundar Pichai introduced the new pixel3 at New York Central Mall

Google – Organization

Sundar Pichai – Person

New York – Location

Central Mall – Organization

What is Referential Ambiguity?

Referential ambiguity will arise when we are referring to something using pronouns.

Ex: “The girl told his mother about her friend. She is happy” – Now, who is “She” in the given statement, is it the girl or mother or friend?

What are Stop Words?

We know that there are several words in the English language such as I, the, is, a, above, below, if, are, etc. This is very helpful for the formation of sentences and without it sentences wouldn’t make any sense but these words do not provide any help in natural language processing and this list of words also known as stop words.

What is a Syntax in NLP?

The linguistic syntax is the set of rules, principles, and the processes that govern the structure of a given sentence in a given language .The term syntax is also used to refer to a study of such principles and processes so what we have are certain rules as what part of the sentence should come at what position with these rules one can create a syntax tree whenever there is a sentence input.

What is the Syntax tree in NLP?

The tree represents the syntactic structure of the sentence of the strings, it is the way of representing the syntax of a programming language as a hieratical tree structure. This structure is used for generating symbols, tables, compilers, and later code generation.The tree represents all the constructs in the language and their subsequent roots.

Syntactic Analysis in NLP. Define?

Syntactic analysis studies about arrangements of words in sentences to derive the meaning from them, it is based on grammar rules. Some of the techniques used for syntactic analysis are parsing, word segmentation, sentence breaking, morphological segmentation, stemming, lemmatization

What are some of the NLP libraries?

Some of the commonly used NLP libraries are NLTK – Lots of third party extensions and support many languages likewise we have spacy, sci-kit-learn, Gensim, Pattern, Polyglot.

What are the major steps in preprocessing the data in NLP?

There are a lot of steps involved in the pre-processing of text data but mainly there are three steps Segmentation, Tokenization, and Normalization. Segmentation divides the large paragraphs into sentences and Tokenization is used for splitting the sentence into words. Normalization involves a lot of small steps like (case conversion, remove punctuations, white spaces, stop words, stemming, converting all into the lower case or upper case, etc.), and finally removing noise from the data before processing the data.

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