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Data Science Course in San Francisco - Best Online Training & Certification

In this Data Science course in San Francisco helps you master Data Analytics, Business Analytics, Data Modeling, Machine Learning algorithms, K-Means Clustering, Naïve Bayes, etc. In this Data Science training you will learn R statistical computing, building recommendation engine for e-commerce, recommending movies and deploy market basket analysis in the retail sector. Get the best online data science course training in San Francisco from top data scientists

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Key Features

  • Instructor Led Training : 42 Hrs
  • Self-paced Videos : 28 Hrs
  • Exercises & Project Work : 56 Hrs
  • Certification and Job Assistance
  • Flexible Schedule
  • Lifetime free upgrade
  • 24 x 7 Lifetime Support & Access

About Data Science Course

Intellipaat Data Science academy offers the most in-depth and comprehensive Data Science training in the city of San Francisco. The entire course has been designed keeping in mind the industry requirements and includes the aspects of data mining, data analysis, discovery and transformation, working with Machine Learning algorithms, Deep Learning and gathering valuable business insights.

What will you learn in this Data Science training?

  1. Roles and responsibilities of a Data Scientist
  2. Working with Machine Learning algorithms
  3. Statistical computing
  4. R language integration with Hadoop
  5. Deploying recommender systems for real-world applications
  6. Impala installation and deploying it for massive parallel processing
  7. Predicting and analysis segmentation through clustering
  8. Linear and logistic regression
  9. Plotting techniques and sampling methods

Who should take up this Data Science Course online?

  • Big Data, BI and Analyst Professionals
  • Big Data Statisticians
  • Machine Learning Professionals
  • Predictive Analytics and Information Architects
  • Those looking for a Data Science career

What are the prerequisites for taking Data Science training?

Anybody can take up this course regardless of prior skills. Although, knowledge of statistics and mathematics is beneficial.

What are the Data Science job opportunities in San-Francisco?

The average salary for a Data Scientist in San Francisco stands at $140,000 – Indeed

The Silicon Valley has no shortage of tech companies that are ready to offer top salaries when it comes to hiring top-notch Data Scientists and Data Analysts. Due to this, when compared to other cities, San Francisco offers the best salary in the United States. You can grab these jobs by enrolling for a Data Science boot camp like the one offered by Intellipaat.

What is the Data Science market trend in San-Francisco?

Since it is the technology hub of not just the United States but the entire world, the domain of Data Science is really heating up in the city, thanks to the biggest IT companies and the next billion-dollar startup companies that are in a mad scramble to recruit the best talent in the domain of Data Science at top salaries.

Why should you take up the Data Scientist course in San-Francisco?

  • Data Scientist is the best job of the 21st century – Harvard Business Review
  • Global Big Data market to reach $122 billion in revenue in six years – Frost & Sullivan
  • The number of jobs for all the US Data Professionals will increase to 2.7 million per year – IBM

Taking Intellipaat Data Scientist training will ensure that you are in a position to land in your dream job since the entire training is in line with what the industry is expecting. At Intellipaat Data Science academy, the entire training focuses more on the practical aspects rather than mastering only the theoretical concepts. Thus, you will get hands-on experience in the field of Data Science making you qualified enough to work for the best companies as a Data Scientist.

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Data Science Course Content

Introduction to Data Science with R

What is Data Science, significance of Data Science in today’s digitally-driven world, applications of Data Science, lifecycle of Data Science, components of the Data Science lifecycle, introduction to big data and Hadoop, introduction to Machine Learning and Deep Learning, introduction to R programming and R Studio.

Hands-on Exercise – Installation of R Studio, implementing simple mathematical operations and logic using R operators, loops, if statements and switch cases.

Data Exploration

Introduction to data exploration, importing and exporting data to/from external sources, what is data exploratory analysis, data importing, dataframes, working with dataframes, accessing individual elements, vectors and factors, operators, in-built functions, conditional, looping statements and user-defined functions, matrix, list and array.

Hands-on Exercise – Accessing individual elements of customer churn data, modifying and extracting the results from the dataset using user-defined functions in R.

Data Manipulation

Need for Data Manipulation, Introduction to dplyr package, Selecting one or more columns with select() function, Filtering out records on the basis of a condition with filter() function, Adding new columns with the mutate() function, Sampling & Counting with sample_n(), sample_frac() & count() functions, Getting summarized results with the summarise() function, Combining different functions with the pipe operator, Implementing sql like operations with sqldf.

Hands-on Exercise – Implementing dplyr to perform various operations for abstracting over how data is manipulated and stored.

Data Visualization

Introduction to visualization, Different types of graphs, Introduction to grammar of graphics & ggplot2 package, Understanding categorical distribution with geom_bar() function, understanding numerical distribution with geom_hist() function, building frequency polygons with geom_freqpoly(), making a scatter-plot with geom_pont() function, multivariate analysis with geom_boxplot, univariate Analysis with Bar-plot, histogram and Density Plot, multivariate distribution, Bar-plots for categorical variables using geom_bar(), adding themes with the theme() layer, visualization with plotly package & building web applications with shinyR, frequency-plots with geom_freqpoly(), multivariate distribution with scatter-plots and smooth lines, continuous vs categorical with box-plots, subgrouping the plots, working with co-ordinates and themes to make the graphs more presentable, Intro to plotly & various plots, visualization with ggvis package, geographic visualization with ggmap(), building web applications with shinyR.

Hands-on Exercise – Creating data visualization to understand the customer churn ratio using charts using ggplot2, Plotly for importing and analyzing data into grids. You will visualize tenure, monthly charges, total charges and other individual columns by using the scatter plot.

Introduction to Statistics

Why do we need Statistics?, Categories of Statistics, Statistical Terminologies,Types of Data, Measures of Central Tendency, Measures of Spread, Correlation & Covariance,Standardization & Normalization,Probability & Types of Probability, Hypothesis Testing, Chi-Square testing, ANOVA, normal distribution, binary distribution.

Hands-on Exercise – Building a statistical analysis model that uses quantifications, representations, experimental data for gathering, reviewing, analyzing and drawing conclusions from data.

Machine Learning

Introduction to Machine Learning, introduction to Linear Regression, predictive modeling with Linear Regression, simple Linear and multiple Linear Regression, concepts and formulas, assumptions and residual diagnostics in Linear Regression, building simple linear model, predicting results and finding p-value, introduction to logistic regression, comparing linear regression and logistics regression, bivariate & multi-variate logistic regression, confusion matrix & accuracy of model, threshold evaluation with ROCR, Linear Regression concepts and detailed formulas, various assumptions of Linear Regression,residuals, qqnorm(), qqline(), understanding the fit of the model, building simple linear model, predicting results and finding p-value, understanding the summary results with Null Hypothesis, p-value & F-statistic, building linear models with multiple independent variables.

Hands-on Exercise – Modeling the relationship within the data using linear predictor functions. Implementing Linear & Logistics Regression in R by building model with ‘tenure’ as dependent variable and multiple independent variables.

Logistic Regression

Introduction to Logistic Regression, Logistic Regression Concepts, Linear vs Logistic regression, math behind Logistic Regression, detailed formulas, logit function and odds, Bi-variate logistic Regression, Poisson Regression, building simple “binomial” model and predicting result, confusion matrix and Accuracy, true positive rate, false positive rate, and confusion matrix for evaluating built model, threshold evaluation with ROCR, finding the right threshold by building the ROC plot, cross validation & multivariate logistic regression, building logistic models with multiple independent variables, real-life applications of Logistic Regression.

Hands-on Exercise – Implementing predictive analytics by describing the data and explaining the relationship between one dependent binary variable and one or more binary variables. You will use glm() to build a model and use ‘Churn’ as the dependent variable.

Decision Trees & Random Forest

What is classification and different classification techniques, introduction to Decision Tree, algorithm for decision tree induction, building a decision tree in R, creating a perfect Decision Tree, Confusion Matrix, Regression trees vs Classification trees, introduction to ensemble of trees and bagging, Random Forest concept, implementing Random Forest in R, what is Naive Bayes, Computing Probabilities, Impurity Function – Entropy, understand the concept of information gain for right split of node, Impurity Function – Information gain, understand the concept of Gini index for right split of node, Impurity Function – Gini index, understand the concept of Entropy for right split of node, overfitting & pruning, pre-pruning, post-pruning, cost-complexity pruning, pruning decision tree and predicting values, find the right no of trees and evaluate performance metrics.

Hands-on Exercise – Implementing Random Forest for both regression and classification problems. You will build a tree, prune it by using ‘churn’ as the dependent variable and build a Random Forest with the right number of trees, using ROCR for performance metrics.

Unsupervised learning

What is Clustering & it’s Use Cases, what is K-means Clustering, what is Canopy Clustering, what is Hierarchical Clustering, introduction to Unsupervised Learning, feature extraction & clustering algorithms, k-means clustering algorithm, Theoretical aspects of k-means, and k-means process flow, K-means in R, implementing K-means on the data-set and finding the right no. of clusters using Scree-plot, hierarchical clustering & Dendogram, understand Hierarchical clustering, implement it in R and have a look at Dendograms, Principal Component Analysis, explanation of Principal Component Analysis in detail, PCA in R, implementing PCA in R.

Hands-on Exercise – Deploying unsupervised learning with R to achieve clustering and dimensionality reduction, K-means clustering for visualizing and interpreting results for the customer churn data.

Association Rule Mining & Recommendation Engine

Introduction to association rule Mining & Market Basket Analysis, measures of Association Rule Mining: Support, Confidence, Lift, Apriori algorithm & implementing it in R, Introduction to Recommendation Engine, user-based collaborative filtering & Item-Based Collaborative Filtering, implementing Recommendation Engine in R, user-Based and item-Based, Recommendation Use-cases.

Hands-on Exercise – Deploying association analysis as a rule-based machine learning method, identifying strong rules discovered in databases with measures based on interesting discoveries.

Self Paced

Introduction to Artificial Intelligence 

Introducing Artificial Intelligence and Deep Learning, what is an Artificial Neural Network, TensorFlow – computational framework for building AI models, fundamentals of building ANN using TensorFlow, working with TensorFlow in R.

Time Series Analysis

What is Time Series, techniques and applications, components of Time Series, moving average, smoothing techniques, exponential smoothing, univariate time series models, multivariate time series analysis, Arima model, Time Series in R, sentiment analysis in R (Twitter sentiment analysis), text analysis.

Hands-on Exercise – Analyzing time series data, sequence of measurements that follow a non-random order to identify the nature of phenomenon and to forecast the future values in the series.

Support Vector Machine - (SVM)

Introduction to Support Vector Machine (SVM), Data classification using SVM, SVM Algorithms using Separable and Inseparable cases, Linear SVM for identifying margin hyperplane.

Naïve Bayes

What is Bayes theorem, What is Naïve Bayes Classifier, Classification Workflow, How Naive Bayes classifier works, Classifier building in Scikit-learn, building a probabilistic classification model using Naïve Bayes, Zero Probability Problem.

Text Mining

Introduction to concepts of Text Mining, Text Mining use cases, understanding and manipulating text with ‘tm’ & ‘stringR’, Text Mining Algorithms, Quantification of Text, Term Frequency-Inverse Document Frequency (TF-IDF), After TF-IDF.

Case Study

The Market Basket Analysis (MBA) case study

This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.

Logistic Regression Case Study

In this case study you will get a detailed understanding of the advertisement spends of a company that will help to drive more sales. You will deploy logistic regression to forecast the future trends, detect patterns, uncover insights and more all through the power of R programming. Due to this the future advertisement spends can be decided and optimized for higher revenues.

Multiple Regression Case Study

You will understand how to compare the miles per gallon (MPG) of a car based on the various parameters. You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc. It includes the model building, model diagnostic, checking the ROC curve, among other things.

Receiver Operating Characteristic (ROC) case study

You will work with various data sets in R, deploy data exploration methodologies, build scalable models, predict the outcome with highest precision, diagnose the model that you have created with various real world data, check the ROC curve and more.

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Data Science Projects

What projects I will be working in this Data Science certification course?

Project 1: Market Basket Analysis

Domain: Inventory Management

Problem Statement: As a new manager in the company, you are assigned the task of increasing cross selling

Topics: Association Rule Mining, Data Extraction, Data Manipulation


  • Performing association rule mining
  • Understanding where to implement Apriori Algorithm
  • Setting association rules with respect to confidence

Project 2: Credit Card Fraud Detection

Domain: Banking

Problem Statement: Analysis of probability of being involved in a fraudulent operation

Topics: Algorithms, V17 Predictor, Data Visualization, R Language


  • Understanding working with the credit card dataset
  • Performing data analysis on various labels in the data
  • Making use of V17 as predictor and using V14 for analysis
  • Plotting score performance with respect to variables

Project 3: Data Cleaning using Census Dataset

Domain: Government

Problem Statement: Performing Data Cleansing operation on a raw dataset

Topics: Data Analysis, Data preprocessing, Cleaning Ops, Data Visualization, R Language


  • Understanding working with the census dataset
  • Changing around various with respect to a label to perform analysis
  • Creation of functions to eliminate values which are not required
  • Verifying the completion of data cleansing operation

Project 4: Loan Approval Prediction

Domain: Banking

Problem Statement: Prediction of approval rate of a loan by using multiple labels

Topics: Data Analysis, Data preprocessing, Cleaning Ops, Data Visualization, R Language


  • Performing Data Preprocessing
  • Building a model and applying PCA
  • Building a Naïve Bayes model on the training dataset
  • Prediction of values after performing analysis

Project 5: Book Recommendation System

Domain: E-Commerce

Problem Statement: Creating a model, which can recommend books, based on user interest

Topics: Data Cleaning, Data Visualization, User Based Collaborative Filtering


  • Finding the most popular books using various techniques
  • Creating a Book Recommender model using User Based Collaborative Filtering

Project 6: Netflix Recommendation System

Domain: E-Commerce

Problem Statement: Simulating the Netflix Recommendation System

Topics: Data Cleaning, Data Visualization, Distribution, Recommender Lab


  1. Working with raw data
  2. Using the Recommender Lab library in R
  3. Making use of real data from Netflix 

Project 7: Creating a Pokemon Game using Machine Learning

Domain: Gaming

Problem Statement: Creating a game engine for Pokemon using Machine Learning

Topics: Decision Tress, Regression, Data Cleaning, Data Visualization


  1. Predicting which Pokemon will win based Attack vs Defense
  2. Finding whether a Pokemon is legendary using Decision Trees
  3. Understanding the dynamics of decision making in Machine Learning

Case Study 1:  Introduction to R Programming

Problem Statement: Working with various operators in R

Topics: Arithmetic Operators, Relational Operators, Logical Operators


  1. Working with Arithmetic Operators
  2. Working with Relational Operators
  3. Working with Logical Operators

Case Study 2: Solving Customer Churn using Data Exploration

Problem Statement: Understanding what to do to reduce customer churn using Data Exploration

Topics: Data Exploration


  1. Extracting Individual columns
  2. Creating and applying filters to manipulate data
  3. Using loops for redundant operations

Case Study 3: Creating Data Structures in R

Problem Statement: Implementing various Data Structures in R for various scenarios

Topics: Vectors, list, Matrix, Array


  1. Creating and Implementing Vectors
  2. Understanding Lists
  3. Using Arrays to store Matrices
  4. Creating and implementing Matrices

Case Study 4: Implementing SVD in R

Problem Statement: Understanding the use Single Value Decomposition in R by making use of the MovieLense Dataset

Topics: 5-fold cross validation, Real Rating Matrix


  1. Creating a custom  recommended movie set for each user
  2. Creating User Based Collaborative Filtering Model
  3. Creating RealRatingMatrix for Movie recommendation

Case Study 5: Time Series Analysis

Problem Statement: Performing TSA and understanding concepts of ARIMA for a given scenario

Topics: Time Series Analysis, R Language, Data Visualization, ARIMA model


  • Understand how to fit an ARIMA model
  • Plotting PACF charts and finding optimal parameters
  • Building the ARIMA model
  • Prediction of values after performing analysis
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Sample Data Science Video Tutorials

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Data Science Certification

The entire Data Scientist course content is designed by industry professionals to get the best jobs in top MNCs.  As part of Data Science online courses, you will be working on real-time projects and assignments that have immense implications in the real-world industry scenarios, thus helping you fast-track your career effortlessly.

At the end of this Data Science training program, there will be quizzes that perfectly reflect the type of questions asked in the respective certification exams and help you score better.

Intellipaat Course Completion Certification will be awarded upon the completion of the project work (after expert review) and upon scoring at least 60% marks in the quiz. Intellipaat certification is well recognized in the top 80+ MNCs and our alumni work in organizations like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.

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Data Science Training Review

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  1. Profile photo of swetha pandit Swetha Pandit 

    Valuable material for learning. Worth spending!

    Their Data Science courses are well structured and taught by recognized professionals which helps one to learn Data Science fast. I have found the videos to be of excellent quality. Thanks.

  2. Profile photo of Giri Karnal Giri Karnal 

    Excellent training

    I had taken the Data Science masters’ program which is a combo of SAS, R and Apache Mahout. Since there are so many technologies involved in Data Science online courses, getting your query resolved at the right time becomes the most important aspect. But with Intellipaat, there was no such problem as all my queries were resolved in less than 24 hours.

  3. Profile photo of Nitesh Kumar Dash Nitesh Kumar Dash 

    Learner-friendly training

    The Intellipaat Data Science certification training videos really made me excited about studying Data Science. They were so elaborate and so professionally created that I could learn Data Science from the comfort of my home, thanks to those learner-friendly videos. I am grateful to Intellipaat.

  4. Profile photo of DATTATREYA R Vikrant Singh 

    Good work

    It was a wonderful experience and learning from Data Science Online Courses and Intellipaat trainers. The trainers were hands-on and provided real-time scenarios. According to me, for learning cutting-edge and latest technologies Intellipaat is the right place.

  5. Profile photo of muppallabhanubigdatahadoopadm Bhanukumar Muppalla 

    Comprehensive learning experience

    Data Science training includes a lot of constituent components, and the Intellipaat Data Science courses provide the most comprehensive and in-depth learning experience. I really liked the projects in Data Science which were real-world projects that helped me take on a Data Science role in the real world much easier.

  6. Profile photo of Bharat Rathore Bharat Rathore 

    Quality Content

    I really appreciate the quality of the material and the content of this Data Science certification course!! Thanks to all Intellipaat team!

  7. Profile photo of ghosh.sudipto Sudipto 

    The best platform to learn data science.

    Intellipaat data science course is outstanding. Trainer is an experienced data scientist who has a good hold on the subject. Now I’m an expert in data science and can confidently make a career in it.

  8. Varsha Tyagi 

    Good Learning Experience

    I was searching for Data science courses on the internet, then I landed upon intellipaat. That's really good in terms of content. Their sample video is also awesome which impressed me a lot to take the data science course. Trainers command of the particular technology is great. The support team is also good. Really appreciate.

  9. Profile photo of ksharat.1234 Sharath Reddy Yellapati 

    Well-organized classes, and highly intellectual instructors.

    The Data Scientist course material was very well organized. The trainer explained the basics of each module to me. All my queries were addressed very clearly. The trainer also made me realize how important Data Science courses are for beginners in the IT stream. I suggest this as the best Data Science courses available online.

  10. Profile photo of shrey.9129 Shreyash Limbhetwala 

    Highest Quality Training

    I want to talk about the rich LMS that Intellipaat data science training offered. The extensive set of PPTs, PDFs, and other related Data Science online courses material were of the highest quality and due to this my learning with Intellipaat was excellent and I could clear the Cloudera Data Scientist certification in the first attempt.

  11. Profile photo of KevinKWada Kevin K Wada 


    Thank you very much for your top class service provided. A special mention should be made regarding your patience in listening to my query and giving me a solution which was exactly what I was looking for in the first place. I am giving a 10 out of 10!

  12. Profile photo of mandepudisri5 Ramyasri Mandepudi 

    You make a difference

    My issue was resolved thanks to the deep domain expertise of the trainer. I am greatly indebted to you for assigning such knowledgeable and experienced trainers for Data Science certification course. It really makes a difference to the learner.

  13. Profile photo of Prasil das Prasil das 

    Fully Satisfied

    "Awesome response time to query resolution. Thanking you for resolving all my issues and helping me realize the tough concepts through the highly insightful videos "

  14. Profile photo of Sulekha Roy Sulekha Roy 


    I think Data Science online courses are a very good way of starting to learn Data Science and make a career in it. The instructors are reasonably good. The projects were also very interesting and relevant to current industry trends.

  15. Profile photo of Kavita Mehra Kavita Mehra 

    Perfect Data Science Training!

    The data science classes were highly interactive and also practical oriented. The office staff was cordial and co-operative. Every teaching session was recorded each day and was put on-line by the institute which was really helpful. The trainer was very patient and able to solve or give some hints to solve all the questions posed to him.

Data Science Online Course Advisor

Suresh Paritala

A Senior Software Architect at NextGen Healthcare who has previously worked with IBM Corporation, Suresh Paritala has worked on Big Data, Data Science, Advanced Analytics, Internet of Things and Azure, along with AI domains like Machine Learning and Deep Learning. He has successfully implemented high-impact projects in major corporations around the world.

Samanth Reddy

A renowned Data Scientist who has worked with Google and is currently working at ASCAP, Samanth Reddy has a proven ability to develop Data Science strategies that have a high impact on the revenues of various organizations. He comes with strong Data Science expertise and has created decisive Data Science strategies for Fortune 500 corporations.

David Callaghan

An experienced Blockchain Professional who has been bringing integrated Blockchain, particularly Hyperledger and Ethereum, and Big Data solutions to the cloud, David Callaghan has previously worked on Hadoop, AWS Cloud, Big Data and Pentaho projects that have had major impact on revenues of marquee brands around the world.

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Frequently Asked Questions on Data Science

Why should I learn Data Science from Intellipaat?

Intellipaat offers exclusive Data Science Online Courses for professionals who want to expand their knowledge base and start a career in this exciting field. Many reasons for choosing Intellipaat include:

  • Personal mentor to track your progress at each stage of this Data Science courses
  • Immersive online instructor-led sessions conducted by SMEs
  • Extensive LMS, allowing you to view recorded sessions within 3 hours
  • Real-time exercises and assignments; and real-world projects
  • 24/7 learning support
  • Large community of like-minded learners
  • Industry recognized Intellipaat badge
  • Personalized job support
What are the different modes of training that Intellipaat provides?
At Intellipaat you can enroll either for the instructor-led online training or self-paced training. Apart from this Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience and they have been actively working as consultants in the same domain making them subject matter experts. Go through the sample videos to check the quality of the trainers.
Can I request for a support session if I need to better understand the topics?
Intellipaat is offering the 24/7 query resolution and you can raise a ticket with the dedicated support team anytime. You can avail the email support for all your queries. In the event of your query not getting resolved through email we can also arrange one-to-one sessions with the trainers. You would be glad to know that you can contact Intellipaat support even after completion of the training. We also do not put a limit on the number of tickets you can raise when it comes to query resolution and doubt clearance.
Can you explain the benefits of the Intellipaat self-paced training?
Intellipaat offers the self-paced training to those who want to learn at their own pace. This training also affords you the benefit of query resolution through email, one-on-one sessions with trainers, round the clock support and access to the learning modules or LMS for lifetime. Also you get the latest version of the course material at no added cost. The Intellipaat self-paced training is 75% lesser priced compared to the online instructor-led training. If you face any problems while learning we can always arrange a virtual live class with the trainers as well.
What kind of projects are included as part of the training?
Intellipaat is offering you the most updated, relevant and high value real-world projects as part of the training program. This way you can implement the learning that you have acquired in a real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning and practical knowledge thus making you completely industry-ready. You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. Upon successful completion of the projects your skills will be considered equal to six months of rigorous industry experience.
Does Intellipaat offer job assistance?
Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this we are exclusively tied-up with over 80 top MNCs from around the world. This way you can be placed in outstanding organizations like Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation part as well.
Is it possible to switch from self-paced training to instructor-led training?
You can definitely make the switch from self-paced to online instructor-led training by simply paying the extra amount and joining the next batch of the training which shall be notified to you specifically.
How are Intellipaat verified certificates awarded?
Once you complete the Intellipaat training program along with all the real-world projects, quizzes and assignments and upon scoring at least 60% marks in the qualifying exam; you will be awarded the Intellipaat verified certification. This certificate is very well recognized in Intellipaat affiliate organizations which include over 80 top MNCs from around the world which are also part of the Fortune 500 list of companies.
Will The Job Assistance Program Guarantee Me A Job?
In our Job Assistance program we will be helping you land in your dream job by sharing your resume to potential recruiters and assisting you with resume building, preparing you for interview questions. Intellipaat training should not be regarded either as a job placement service or as a guarantee for employment as the entire employment process will take part between the learner and the recruiter companies directly and the final selection is always dependent on the recruiter.
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