Data Analytics Course Training in Delhi

SASVBA (The Best Educational Hub)




Data Analytics Training Institute in Delhi

SASVBA Provides the Data Analytics institute in Delhi with Latest Development Environment and Structures. We keep Our Programs Up to Date with the Latest modern trends. SASVBA Is One of the best education Data Analytics Courses in Delhi  Which Helps Learners Crack Interviews in Tech Giants. We train university learners as well as school students.

Data Analytics Course:

SlotsWeek DaysWeek Ends
Course Duration2 Months3.5 Months
Class Duration2 Hours3 Hours
Training ModeClassroom/OnlineClassroom/Online

SASVBA Institute Has a very good and supportive environment with high-performance Computers having Up-to-Date IDE’s. We Also Provide Online Classes to ensure the comfort of Our Students So that they can easily Study Where Ever They Want, When Ever They Want. SASVBA Institute Data Analytics Faculty Is Highly Experienced and Heard Thousands of Success Story of Our Students.

  • Courses Can Be Customized as per the requirement of Students.

What is Data Analytics?

Data analyst refers to a quantitative and qualitative approach and method which is used to improve productivity and business profitability. It is a technique of extracting, acknowledging, and analyzing information such as behavioral data, business patterns, and systems that are dynamic and necessary for business. Every business structure needs to perform big data analytics courses in delhi which can provide various benefits such as improved customer satisfaction, enhancing the productivity and performance of the organization, and can also accommodate the companies with the most significant growth chances. 

Data analysis is also held as an internal function of any business organization which deals with characters and figures. Intercourse deep knowledge of recording and analyzing forward with dissecting information and presenting the findings to make better decisions going for the management. However, to become a Data analytics courses in Delhi professional, one should also have experience in various data analytics courses in Delhi tools such as Python, R-Programming, MS Excel and Access, Visual Basic for Application and Macros(VBA/Macros), SQL, Tableau, and Business Intelligence devices. 

Data analytics courses in Delhi ncr is commonly described as a process of examining data sets to draw conclusions based on information available in them with the help of various software or specialized systems. Analytics courses in Delhi has become a very crucial part of commercial applications over the last few years as it allows more-informed business choices based on scientific data and research.

Data analysis courses in Delhi tools help experts and researchers in verifying or reject scientific models, theories, and hypotheses, thereby improving the company improve operational performance and customer assistance, gain the competitive edge and increase profits. Big data courses in Delhi tools use actual data or the data being treated in real-time.Data analytics course in Delhi methodologies involve exploratory data analysis (EDA) and confirming data analysis (CDA). The former plans to find models and relationships in the given data; the latter measures whether data about a data set are true or false.

There are two forms of data analysis: quantitative data report, which involves an analysis of numerical data that can be statistically analyzed; and qualitative data analysis which is also interpretive and forms outcomes from non-numerical data.

Other types of data science courses in Delhi include data miningpredictive analytics, and machine learning. After completing a big data course in Delhi course, one can get jobs like data engineer, data scientist, data architect, database administration, and big data training in Delhi. The starting salary of a data scientist course in Delhi as per is Rs 4 lakh and upwards.

The various secure way to get valuable skills and masteries Business Intelligence and data science course in Noida tools is by following position data science training in Delhi, Noida & Gurgaon provided by SASVBA Institute. The data scientist course in Delhi is designed by industry experts that give great and comprehensive expertise in different data science training in Delhi methods and tools, enabling the participant to become an expert quickly. The data scientist course in Delhi, Tableau & R-Programming Training Program will help the learners acquire expertise in divining customer Trends and behavior, analysis, interpreting and delivering data in meaningful ways, driving effective decision making forward with enhancing the business productivity. 

Anyone with a graduation standard is eligible to attend a High-quality data science training in Training Course in Delhi NCR, which is specifically targeted towards both freshers also working experts who want to enter into the field of hadoop training in delhi ncr or grow fluent in Data Analysis techniques. The data analytics courses in Noida Certification Training is conducted by highly certified topic matter experts with the word 10 to 15 years of experience in the relevant field.

Big data courses in Delhi is a field of computer science where data is data is converted into meaningful information. hadoop jobs in Delhi NCR are concerned with computer science.

SASVBA Is a Highly Commended Institute in Delhi NCR. We provide 100% Placement Support So that Seniors Would not have any Burden of Placements.

SASVBA Is a Highly Recommended Institute in Delhi NCR. We provide 100% Placement Support So that Students Would not have any Burden of Placements.

We have Collaborations with TCS, Wipro, Infosys, JIO, Airtel, Tech Mahindra, HCL, IBM, etc. These Companies help us to Place students in appropriate Companies. According to their Skills. SASVBA Institute is trending because We Successfully Placed Almost Thousands of Students in Appropriate Companies that’s why SASVBA is the Best Data Analytics training institute in Delhi/NCR.

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Data Analytics Course

  • Introduction to Types of Analytics
  • Project life cycle

Introduction to R and Python Basic Statistics

  • Balanced vs Imbalanced datasets
  • handling balanced vs imbalanced datasets
  •  handling imbalanced data
  • Sampling Funnel,
    • its application
    • its components
    • Population
    • Sampling frame
    • Simple random sampling
    • Sample
  • The measure of central tendency
    • Mean / Average
    • Median
    • Mode
  • Measure of Dispersion
    • Variance
    • Standard Deviation
    • Range
  • The expected value of probability distribution
  • Measure of Skewness
  • Measure of Kurtosis
  • Various graphical techniques to understand data
    • Bar plot
    • Histogram
    • Box plot
    • Scatter plot
  • Introduction to R and RStudio
  • Installation of Python IDE
  • Anaconda and Spyder
  • Python and Rt
  • Normal Distribution
  • Standard Normal Distribution / Z distribution
  • Z scores and Z table
  • QQ Plot / Quantile-Quantile plot
  • Sampling Variation
  • Central Limit Theorem
  • Sample size calculator
  • T-distribution / Student’s-t distribution
  • Confidence interval
    • Population parameter – Standard deviation known
    • Population parameter – Standard deviation unknown
  • Parametric vs Non-parametric tests
  • Formulating a Hypothesis
  • Choosing Null and Alternative hypothesis
  • Type I and Type II errors
  • Hypothesis testing
  • 2 sample t-test
  • 1 sample t-test
  • 1 sample z test
  • 2 Proportion test
  • Chi-Square test
  • Non-Parametric test
  • Non-Parametric test continued
  • Hypothesis testing using Python and R

Linear Regression

  • Scatter Diagram
  • Correlation Analysis
  • Principles of Regression
  • Introduction to Simple Linear Regression
  • R shiny and Python Flask
    • Introduction to R shiny &Python Flask
  • Multiple Linear Regression
  • Scatter diagram
    • Correlation Analysis
    • Correlation coefficient
  • Ordinary least squares
  • Principles of regression
  • Splitting the data into
    • training,
    • validation
    • testing datasets
  • Understanding Overfitting (Variance) vs Underfitting (Bias)
  • Generalization error and Regularization techniques
  • Introduction to Simple Linear Regression
  • Heteroscedasticity / Equal Variance
  • LINE assumption
    • Collinearity (Variance Inflation Factor)
    • Linearity
    • Normality
  • Multiple Linear Regression
  • Model Quality metrics
  • Deletion diagnostics

Logistic Regression

  • Principles of Logistic Regression
  • Types of Logistic Regression
  • Assumption and Steps in Logistic Regression
  • Analysis of Simple Logistic Regression result
  • Multiple Logistic Regression
  • Confusion matrix
    • False Positive, False Negative
    • True Positive, True Negative
    • Sensitivity, Recall, Specificity, F1
  • Receiver operating characteristics curve (ROC curve)
  • Lift charts and Gain charts 
  • Lasso and Ridge Regressions
  • Logit and Log-Likelihood
  • Category Baselining
  • Modeling Nominal categorical data
  • ALasso / Ridge regression
  • Supervised vs Unsupervised learning
  • Data Mining Process
  • Measure of distance
    • Numeric – Euclidean, Manhattan, Mahalanobis
    • Categorical –
      •  Binary Euclidean,
      • Simple Matching Coefficient,
      • Jacquard’s Coefficient
    • Mixed – Gower’s General Dissimilarity Coefficient
  • Types of Linkages
    • Single Linkage / Nearest Neighbour
    • Complete Linkage / Farthest Neighbour
    • Average Linkage
    • Centroid Linkage
  • Hierarchical Clustering / Agglomerative Clustering


  • K-Means Clustering
  • Measurement metrics of clustering –
    • Within the Sum of Squares,
    • Between the Sum of Squares,
    • Total Sum of Squares
  • Scree plot / Elbow Curve
  • K-Medians,
  • K-Medoids,
  • K-Modes,
  • Clustering Large Applications (CLARA),
  • Partitioning Around Medoids (PAM),
  • Noise (DBSCAN)
  • Why dimension reduction
  • Advantages of PCA
  • Calculation of PCA weights
  • 2D Visualization using Principal components
  • Basics of Matrix algebra
  • SVD – Decomposition of matrix data
  • Definition of a network
  • Node strength in a Network
    • Degree centrality
    • Closeness centrality
    • Eigenvector centrality
    • Adjacency matrix
    • Betweenness centrality
    • Cluster coefficient
  • Introduction to Google Page Ranking
  • What is Market Basket
  • Affinity Analysis
  • Measure of association
    • Support
    • Confidence
    • Lift Ratio
  • Apriori Algorithm
  • Sequential Pattern Mining’

Data Mining Unsupervised – Recommender system

  • User-based collaborative filtering
  • Measure of distance
  • similarity between users
  • Driver for recommendation
  • Computation reduction techniques
  • Search based methods
  • Item to item collaborative filtering
  • SVD in recommendation
  • The vulnerability of recommender systems

Text Mining

  • Sources of data
  • Bag of words
  • Pre-processing,
  • corpus Document-Term Matrix (DTM) and TDM
  • Word Clouds
  • Corpus level word clouds
    • Sentiment Analysis
    • Positive Word clouds
    • Negative word clouds
    • Unigram, Bigram, Trigram
  • Semantic network
  • Clustering
  • Extract Tweets from Twitter
  • Extracting the reviews of the users from Amazon, Snapdeal and TripAdvisor
  • Install Libraries from Shell
  • Extraction and text analytics in Python

Natural Language Processing

  • LDA
  • Topic Modeling
  • Sentiment Extraction
  • Lexicons and Emotion Mining
  • Deciding the K value
  • Creating a KNN model by splitting the data
  • generalization and regulation techniques for avoiding overfitting and underfitting

Classifier – Naive Bayes

  • Probability – Recap
  • Bayes Rule
  • Naive Bayes Classifier
  • Text Classification using Naive Bayes

Decision Tree and Random Forest

  • Elements of Classification Tree –
    • Root node,
    • Child Node,
    • Leaf Node, etc.
  • Greedy algorithm
  • Measure of Entropy
  • Attribute selection using Information Gain
  • Ensemble techniques
  • Decision Tree C5.0 and various arguments
  • Random Forest and understanding various arguments

Bagging and Boosting

  • Boosting / Bootstrap Aggregating
  • AdaBoost / Adaptive Boosting
  • Stacking
  • Gradient Boosting
  • Extreme Gradient Boosting (XGB)

Black Box Methods

  • Artificial Neural Network
  • Biological Neuron vs Artificial Neuron
  • ANN structure
  • Activation function
  • Network Topology
  • Support Vector Machines
  • Classification Hyperplanes
  • Best fit “boundary”
  • Kernel Trick
  • The concept with a business case
  • Introduction to time series data
  • Steps of forecasting
  • Components of time series data
  • Scatter plot and Time Plot
  • Lag Plot
  • ACF – Auto-Correlation Function / Correlogram
  • Visualization principles
  • Naive forecast methods
  • Errors in the forecast and its metrics
  • Model-Based approaches
    • Linear Model
    • Exponential Model
    • Quadratic Model
    • Additive Seasonality
    • Multiplicative Seasonality
  • Model-Based approaches
  • AR (Auto-Regressive) model for errors
  • Random walk
  • Auto-Regressive Moving Average,
    • Order p and q
  • Auto-Regressive Integrated Moving Average,
    • Order p, d, and q
  • A data-driven approach to forecasting
  • Smoothing techniques
    • Moving Average
    • Exponential Smoothing
    • Holts / Double Exponential Smoothing
    • Winters / HoltWinters
  • De-seasoning and de-trending
  • Econometric Models
  • Forecasting Best Practices
  • Forecasting using Python
  • Forecasting using R
  • Basics of Hadoop and Spark
  • Basics of R
  • Basics of Python
  • Basics of MYSQL
  • Tableau

Module 10:

  • Assignments
  • Projects
  • Predicting the flight delays– Aviation
    • Used in predicting flight delays
  • Predict impurity in ore – Manufacturing
    • Used in predicting impurity in Ore
  • Predicting the oil price – Oil and Gas
    • Used in Predicting Future Oil Price
  • Electric Motor Temperature – Automotive
    • Used in predicting Motor Temperature
  • SASVBA is One of the most famous institutes in Delhi NCR and had trained thousands of students in this field
  • SASVBA provides an opportunity for students to work on Live projects
  • SASVBA Converts a Normal Student to an I.T. Professional
  • SASVBA Provides Recorded Classes
  • SASVBA Offers Online IDE’S, Live Classes, Online Debug Sessions
  • SASVBA Provides Study Material: E-Books, Books, Notes
  • SASVBA Placements team arrange interview Calls to students After 70% percent, of the Course, has been Completed.
  • To Increase Experience of Student Our Team and Students Collaborate to Work on Live Projects
  • SASVBA also Provides Aptitude Training for students to crack interview rounds.
  • We Also Help Students to Write a Job Specific Resume

Today, there is a very high demand for Data Analytics and it is very popular. Data Analytics is everywhere. It is concerned with data science. Therefore, there is a high demand for Data Analytics.

Google, Facebook, Instagram, Stack overflow, GitHub, YouTube, Space X, Tesla, Microsoft, etc.

One Can Easily Customize their Course According to their requirements also provide custom training programs so that we can fulfill the Expectations of the students.

Data Analytics is a field of computer science where data is data is converted into meaningful information. Data Analytics is concerned with computer science.

Today, there is a very high demand for Data Analytics and it is very popular. Data Analytics is everywhere. It is concerned with data science. Therefore, there is a high demand for Data Analytics.

Recorded lectures Are provided if you miss any class you can see them and continue with us.

Our Students are working with companies like:

  • Genpact
  • Infosys
  • TCS
  • Airtel
  • Goldman Sachs
  • Idea
  • Reliance
  • HCL
  • And Many More……