Data Science Course in Yelahanka, Bangalore

The Data Science course in Yelahanka provides you the most comprehensive training which helps you to learn and discover valuable technical skills. Be a winner in this competitive race by amplifying the required skillsets.

  • Classroom/Online Virtual Live Instructor-led Sessions
  • Get IBM Certification
  • 1:1 Mentorship
  • Work on Industry Live Projects
  • Industry Placement Training

data science learning path

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

Data Science Training in Yelahanka 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.

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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 Yelahanka. 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 Yelahanka 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 Yelahanka 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.

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Course Overview

The Data Science training program in Yelahanka is a job-oriented training program that ensures students to be placed in top-notch companies. This program is designed to empower students with the required technologies that include Artificial Intelligence, Machine Learning, Data Analytics, Data mining, Predictive Analysis, and Data Visualization.

The objective of Data Science training in Yelahanka is to prepare students for job-ready by learning the Data Science Course with real-time projects. The curriculum of this program is designed meticulously that meets the needs of students, freshers, and working professionals. Each topic in this course is much emphasized and elucidated thoroughly covering all the details. Through this course, students will be able to build models, analyze data, and understand the applications of various tools and techniques. This course with various benefits helps students in accelerating their careers and accomplish their goals. Enroll now for this course and start your mission to reach heights in Data Science and become an expert.

What are the Prerequisites?

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

Training Methodology

Best training in Data Science is given by online training and classroom sessions. The session timings are scheduled as per the flexibility of the participants. Individual attention is given to every student. Personal mentorship is also provided to the students throughout their learning process. Students are given assignments and will be given the opportunity to handle real-time projects. After completion of the course, assistance will be given in developing resumes and mock interviews will be conducted by industry experts to prepare students ahead to face interviews.

What are the Tools covered as part of Data Science Training?

Python and R are considered to be the fundamental and most eminent tools for learning Data Science. Along with this, aspirants should learn tools like Tableau, Python Libraries such as Keras, Nympy, Scipy, Pandas, Tensor flow, etc.

About Instructor

data science course in Yelahanka

Mr. Bharani Kumar, CEO and Managing Director of this company has exceptional experience in professional training for more than15 years. He is an alumnus from IIT and ISB. He has trained more than 2500 students and his students are placed successfully across the globe. He uses innovative techniques and explains all the concepts with industry use cases to make the learning process easier and more efficient.

Career Options in Data Science

You can work as a Data Scientist, or Data Engineer, Data Analyst, or Python developer, Machine Learning Engineer in top-notch companies. The salaries for Data Science professionals are higher when compared to other software professionals. The Data Science course is formulated and tailored in such a way that it is specific and suits to students and working professionals.

Become Highly Demanding Data Science Professional

Companies are Hiring

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
    • 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

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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 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

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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

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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 Yelahanka

Yelahanka 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 Yelahanka.

Job role As per Glassdoor As per Payscale in Yelahanka
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.

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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.

Data Science Yelahanka FAQs

1. How long is the duration of the course?

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

2. What is the mode of training?

We provide training in both online and classroom sessions.

3. If I miss the class then how?

No need to worry. We provide LMS access to every student so that they can see the recorded version of the class they missed.

4. Will there be any guidance provided?

Personal guidance is provided throughout your learning journey.

5. What are the certifications provided?

Certifications from IBM and UTM Malaysia are provided.

6. Is this course job guaranteed?

We provide 100% Job assistance and provide you a career coach who guides you in preparing interviews and in resume building.

7. Is attendance compulsory?

Minimum 80% of attendance is compulsory if you want to excel.

8. What are the tools that are covered in Data Science?

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

9. What is the eligibility?

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

10. Will I get a free demo class before paying the fees?

Yes, anyone can attend a free demo class and you can attend the first 3 sessions of the training program for free.

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360DigiTMG - Data Science, Data Scientist Course Training in Bangalore

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