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Intellipaat’s Data Science course in San Diego is curated by industry experts who will teach the concepts of R programming, Machine Learning algorithms, data acquisition, and data modeling, along with their practical applications in the domains of e-commerce, finance, and banking. By the end of our Data Science training, you will gain ample knowledge to become a certified Data Scientist.
This Data Science training in San Diego covers all the must-have modules of the domain, such as Machine Learning algorithms, Data Science life cycle, data mining, and data manipulation, and it makes the learners work on real-world datasets in projects.
There are no specific prerequisites. However, a little enthusiasm in learning mathematics and statistics can help you understand the concepts easily.
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Data Scientist | Hyderabad
The expert trainers here helped me acquire all the skills that I required to transfer my career from an Aircraft Maintenance Engineer (AME) to a certified Data Scientist. They taught all the necessary concepts and technologies, in...Read More
Aircraft Maintenance Engineer
Big Data Developer | Dallas
This is a great training program. The mentors and course instructors were interactive throughout the program. This course has helped me upskill myself in the numerous tools and technologies that work with Big Data. The experts hel...Read More
Computer Technical Specialist
Big Data Developer
Data Scientist | Bengaluru
My experience in learning from Intellipaat was amazing. The mentors were helpful throughout the course and helped me understand the concepts better by clearing all my doubts. Besides, the comprehensive course material was useful w...Read More
Manager Analytics & Data Science
Data Engineer | Pune
This e-learning institute has been extremely helpful for my career. The training program and interactive live lectures made it easy to master the concepts as per the industry demands. The curriculum was comprehensive and the train...Read More
Program Manager | Pune
Intellipaat served as a great platform for me to learn the latest upgrades and skills in the technology as per industry requirements. I was working as a Microsoft Dynamics Consultant but after taking the training from Intellipaat,...Read More
Microsoft Dynamics Consultant
Data Science Intern | Gurugram
This training helped me master the skills that are required to begin a career in the field of Data Science. The course curriculum allowed me to learn both basic and advanced level concepts in the field which helped me make a smoot...Read More
Data Science Intern
Self Paced Training
Online Classroom Preferred
Module 01 - Introduction to Data Science with RPreview
1.1 What is Data Science?
1.2 Significance of Data Science in today’s data-driven world, its applications of, , lifecycle, and its components
1.3 Introduction to R programming and RStudio
1. Installation of RStudio
2. Implementing simple mathematical operations and logic using R operators, loops, if statements, and switch cases
Module 02 - Data ExplorationPreview
2.1 Introduction to data exploration
2.2 Importing and exporting data to/from external sources
2.3 What are data exploratory analysis and data importing?
2.4 DataFrames, working with them, accessing individual elements, vectors, factors, operators, in-built functions, conditional and looping statements, user-defined functions, and data types
1. Accessing individual elements of customer churn data
2. Modifying and extracting results from the dataset using user-defined functions in R
Module 03 - Data ManipulationPreview
3.1 Need for data manipulation
3.2 Introduction to the dplyr package
3.3 Selecting one or more columns with select(), filtering records on the basis of a condition with filter(), adding new columns with mutate(), sampling, and counting
3.4 Combining different functions with the pipe operator and implementing SQL-like operations with sqldf
1. Implementing dplyr
2. Performing various operations for manipulating data and storing it
Module 04 - Data VisualizationPreview
4.1 Introduction to visualization
4.2 Different types of graphs, the grammar of graphics, the ggplot2 package, categorical distribution with geom_bar(), numerical distribution with geom_hist(), building frequency polygons with geom_freqpoly(), and making a scatterplot with geom_pont()
4.3 Multivariate analysis with geom_boxplot
4.4 Univariate analysis with a barplot, a histogram and a density plot, and multivariate distribution
4.5 Creating barplots for categorical variables using geom_bar(), and adding themes with the theme() layer
4.6 Visualization with plotly, frequency plots with geom_freqpoly(), multivariate distribution with scatter plots and smooth lines, continuous distribution vs categorical distribution with box-plots, and sub grouping plots
4.7 Working with co-ordinates and themes to make graphs more presentable, understanding plotly and various plots, and visualization with ggvis
4.8 Geographic visualization with ggmap() and building web applications with shinyR
1. Creating data visualization to understand the customer churn ratio using ggplot2 charts
2. Using plotly for importing and analyzing data
3. Visualizing tenure, monthly charges, total charges, and other individual columns using a scatter plot
Module 05 - Introduction to StatisticsPreview
5.1 Why do we need statistics?
5.2 Categories of statistics, statistical terminology, types of data, measures of central tendency, and measures of spread
5.3 Correlation and covariance, standardization and normalization, probability and the types, hypothesis testing, chi-square testing, ANOVA, normal distribution, and binary distribution
1. Building a statistical analysis model that uses quantification, representations, and experimental data
2. Reviewing, analyzing, and drawing conclusions from the data
Module 06 - Machine LearningPreview
6.1 Introduction to Machine Learning
6.2 Introduction to linear regression, predictive modeling, simple linear regression vs multiple linear regression, concepts, formulas, assumptions, and residuals in Linear Regression, and building a simple linear model
6.3 Predicting results and finding the p-value and an introduction to logistic regression
6.4 Comparing linear regression with logistics regression and bivariate logistic regression with multivariate logistic regression
6.5 Confusion matrix the accuracy of a model, understanding the fit of the model, threshold evaluation with ROCR, and using qqnorm() and qqline()
6.6 Understanding the summary results with null hypothesis, F-statistic, and
building linear models with multiple independent variables
1. Modeling the relationship within data using linear predictor functions
2. Implementing linear and logistics regression in R by building a model with ‘tenure’ as the dependent variable
Module 07 - Logistic RegressionPreview
7.1 Introduction to logistic regression
7.2 Logistic regression concepts, linear vs logistic regression, and math behind logistic regression
7.3 Detailed formulas, logit function and odds, bivariate logistic regression, and Poisson regression
7.4 Building a simple binomial model and predicting the result, making a confusion matrix for evaluating the accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR
7.5 Finding out the right threshold by building the ROC plot, cross validation, multivariate logistic regression, and building logistic models with multiple independent variables
7.6 Real-life applications of logistic regression
1. Implementing predictive analytics by describing data
2. Explaining the relationship between one dependent binary variable and one or more binary variables
3. Using glm() to build a model, with ‘Churn’ as the dependent variable
Module 08 - Decision Trees and Random ForestPreview
8.1 What is classification? Different classification techniques
8.2 Introduction to decision trees
8.3 Algorithm for decision tree induction and building a decision tree in R
8.4 Confusion matrix and regression trees vs classification trees
8.5 Introduction to bagging
8.6 Random forest and implementing it in R
8.7 What is Naive Bayes? Computing probabilities
8.8 Understanding the concepts of Impurity function, Entropy, Gini index, and Information gain for the right split of node
8.9 Overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning, pruning a decision tree and predicting values, finding out the right number of trees, and evaluating performance metrics
1. Implementing random forest for both regression and classification problems
2. Building a tree, pruning it using ‘churn’ as the dependent variable, and building a random forest with the right number of trees
3. Using ROCR for performance metrics
Module 09 - Unsupervised LearningPreview
9.1 What is Clustering? Its use cases
9.2 what is k-means clustering? What is canopy clustering?
9.3 What is hierarchical clustering?
9.4 Introduction to unsupervised learning
9.5 Feature extraction, clustering algorithms, and the k-means clustering algorithm
9.6 Theoretical aspects of k-means, k-means process flow, k-means in R, implementing k-means, and finding out the right number of clusters using a scree plot
9.7 Dendograms, understanding hierarchical clustering, and implementing it in R
9.8 Explanation of Principal Component Analysis (PCA) in detail and implementing PCA in R
1. Deploying unsupervised learning with R to achieve clustering and dimensionality reduction
2. K-means clustering for visualizing and interpreting results for the customer churn data
Module 10 - Association Rule Mining and Recommendation EnginesPreview
10.1 Introduction to association rule mining and MBA
10.2 Measures of association rule mining: Support, confidence, lift, and apriori algorithm, and implementing them in R
10.3 Introduction to recommendation engines
10.4 User-based collaborative filtering and item-based collaborative filtering, and implementing a recommendation engine in R
10.5 Recommendation engine use cases
1. Deploying association analysis as a rule-based Machine Learning method
2. Identifying strong rules discovered in databases with measures based on interesting discoveries
Module 11 - Introduction to Artificial IntelligencePreview
Module 12 - Time Series AnalysisPreview
12.1 What is a time series? The techniques, applications, and components of time series
12.2 Moving average, smoothing techniques, and exponential smoothing
12.3 Univariate time series models and multivariate time series analysis
12.4 ARIMA model
12.5 Time series in R, sentiment analysis in R (Twitter sentiment analysis), and text analysis
1. Analyzing time series data
2. Analyzing the sequence of measurements that follow a non-random order to identify the nature of phenomenon and forecast the future values in the series
Module 13 - Support Vector Machine (SVM)Preview
Module 14 - Naïve BayesPreview
14.1 What is the Bayes theorem?
14.2 What is Naïve Bayes Classifier?
14.3 Classification Workflow
14.4 How Naive Bayes classifier works and classifier building in Scikit-Learn
14.5 Building a probabilistic classification model using Naïve Bayes and the zero probability problem
Module 15 - Text MiningPreview
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Practice Essential Tools
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Market Basket Analysis
This is an inventory management project where you will find the trends in the data that will help the company to increase sales. In this project, you will be implementing association rule mining, data extraction, and data manipulation for the Market Basket Analysis.
Credit Card Fraud Detection
The project consists of data analysis for various parameters of banking dataset. You will be using a V7 predictor, V4 predictor for analysis, and data visualization for finding the probability of occurrence of fraudulent activities.
Loan Approval Prediction
In this project, you will use the banking dataset for data analysis, data cleaning, data preprocessing, and data visualization. You will implement algorithms such as Principal Component Analysis and Naive Bayes after data analysis to ...Read More
Netflix Recommendation System
Implement exploratory data analysis, data manipulation, and visualization to understand and find the trends in the Netflix dataset. You will use various Machine Learning algorithms such as association rule mining, classification algor...Read More
Case Study 1: Introduction to R Programming
In this project, you need to work with several operators involved in R programming including relational operators, arithmetic operators, and logical operators for various organizational needs.
Case Study 2: Solving Customer Churn Using Data Exploration
Use data exploration in order to understand what needs to be done to make reductions in customer churn. In this project, you will be required to extract individual columns, use loops to work on repetitive operations, and create and implement filters for data manipulation.
Case Study 3: Creating Data Structures in R
Implement numerous data structures for numerous possible scenarios. This project requires you to create and use vectors. Further, you need to build and use metrics, utilize arrays for storing those metrics, and have knowledge of lists.
Case Study 4: Implementing SVD in R
Utilize the dataset of MovieLens to analyze and understand single value decomposition and its use in R programming. Further, in this project, you must build custom recommended movie sets for all users, develop a collaborative filterin...Read More
Case Study 5: Time Series Analysis
This project required you to perform TSA and understand ARIMA and its concepts with respect to a given scenario. Here, you will use the R programming language, ARIMA model, time series analysis, and data visualization. So, you must un...Read More
Via Intellipaat PeerChat, you can interact with your peers across all classes and batches and even our alumni. Collaborate on projects, share job referrals & interview experiences, compete with the best, make new friends – the possibilities are endless and our community has something for everyone!
You can unlock your certificate in three simple steps:
The Data Science certification you receive from Intellipaat is valid for your entire lifetime and is recognized by top organizations across the world.
On successfully completing the Data Science course and passing the Data Science training certification exam, you will receive Intellipaat’s Data Science certificate via our Learning Management System. You can download or share your certificate from this through either email or LinkedIn.
Yes. The Data Science certification issued by Intellipaat is industry-recognized. Besides, due to our affiliation with IBM, you will also receive a Data Science course completion certificate from IBM.
Excellent course, I found Intellipaat's coaching team to be talented in their respective domain. My learning was very good as it helped me in upgrading my skills, which proved to be a turning point in...Read More
Thank you very much for your top-class service. A special mention should be made for your patience in listening to my queries and giving me a solution, which was exactly what I was looking for. I am giving you a 10 on 10!
I had taken the Data Science master’s program, which is a combo of SAS, R programming language, and Apache Mahout. Since there are so many technologies involved in courses, getting our query resolve...Read More
It was a wonderful experience learning Data Science from Intellipaat. The trainers were hands-on and provided real-time scenarios. According to me, for learning cutting-edge technologies, Intellipaat is the right place.
I signed up for Intellipaat's Data science course online certification when i learned that it is a great place for learning new technologies. The course material offered by them is extremely useful. I...Read More
Intellipaat’s Data Science classes’ videos really made me excited about studying Data Science. They were so elaborate and so professionally created. I could learn Data Science from the comfort of ...Read More
Intellipaat’s online Data scientist training in San Diego was well-structured and taught by seasoned professionals. They helped me learn Data Science fast. I have found their videos to be of excellent quality. Thanks a lot!
The classes were highly interactive and also practical oriented. The office staff was cordial. Every teaching session was recorded each day and was put online. The trainer was very patient and giving ...Read More
Awesome response to queries! Thanking you for resolving all my issues and helping me learn tough concepts through highly insightful videos.
My issue was resolved, thanks to the deep domain expertise of the trainer. I am greatly indebted to Intellipaat for assigning such knowledgeable and experienced trainers for this Data Science certific...Read More
I want to talk about the rich LMS that Intellipaat’s Data Science programs offered. The extensive set of PPTs, PDFs, and other related material were of the highest quality, and due to this, my learn...Read More
Intellipaat’s Data Scientist training is outstanding. The trainer is an experienced Data Scientist who has a good hold on the subject. Now, I’m an expert in Data Science, and I am already placed in a reputed firm.
Best training program, My decision to learn from Intellipaat was the best to upgrade my career. This course gave thorough understanding of the subject. I recently completed the course and experienced ...Read More
I had the best learning experience at Intellipaat. The projects, assignments, and course content were awesome. I would like to enroll in other courses that are offered by Intellipaat.
Great course! I had a really good experience with this platform as they trained me well. I completed my training with this best Data Science training institute in San Diego where they provided extreme...Read More
Great teaching team. All trainers and support team were very helpful and easily reachable. The course content of this program covers all the topics, from basic to advanced modules.
Extremely grateful to Intellipaat! The explanation of concepts and topics were simple and comprehensive. Moreover, the training material was relevant, up to date, and easily understandable. They have a great support team.
Genuine platform for learning. I finished my course recently from Intellipaat. The trainers were excellent in teaching. Further, the course was well-structured and the lectures are really flexible. I ...Read More
Intellipaat offers an exclusive Data Science course in San Diego for professionals who want to expand their knowledge base and start a career in this field. There are many reasons for choosing Intellipaat:
Intellipaat offers one of the leading Data Science courses in San Diego that will help you to learn about practical implementations on real-time live projects. The courses like Artificial Intelligence, Data Analytics, Machine Learning, R Certification, Python for Data Science, Python, Business Analytics and others are comprehensive and tailor-made to suit you as per the industry’s needs.
At Intellipaat, you can enroll in either 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, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.
Intellipaat is offering the 24/7 query resolution, and you can raise a ticket with the dedicated support team at anytime. You can avail of the email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our trainers.
You would be glad to know that you can contact Intellipaat support even after the completion of the training. We also do not put a limit on the number of tickets you can raise for query resolution and doubt clearance.
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 real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, 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. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.
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 such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.
You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.
Once you complete Intellipaat’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500companies.
Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.