Intellipaat Data Science training course in USA designed for beginning or enhancing a career in Data Analytics, Business Analytics, Machine Learning, and Business Intelligence. The course also provides hands-on experience on projects, including market basket analysis, credit card fraud detection, and loan approval prediction, among others. So, enroll for the best Data Science training in USA and upskill yourself.
Intellipaat’s Data Scientist training in USA gives extensive coverage on a range of topics relevant such as data exploration, data manipulation, data visualization, logistic regression, supervised and unsupervised learning, etc.
Intellipaat is a premier online training institute which helps you master concepts like
Intellipaat’s online training course is exclusively designed by industry experts for
There are no special prerequisites for this online training course. If you like mathematics, then it will help you.
Top companies that hire Data Scientists are:
The demand for Data Scientists far exceeds the supply. This is a serious problem in a data-driven world that we are living in today. As a result, most organizations are willing to pay high salaries for professionals with appropriate skills.
Data scientist training online will help you become proficient in Data Science, R programming, Data Analysis, Big Data, and more. Thus, you can easily accelerate your career in this evolving domain and take it to the next level.
As per Indeed, on average, a Data Scientist working in the United States earns US$122,801 yearly.
In this course, we have included real-world industry-based projects, which help you gain hands-on experience in the field and prepare you for challenging roles.
|BFSI||Fraud Detection in Banking System||Deploying Data Science to detect fraudulent activities and take remedial actions|
|Entertainment||Movie Recommendation Engine||Building a movie recommendation engine, based on user interests|
|E-commerce||Making Sense of Customer Buying Patterns||Deploying target selling to customers|
USA is having a very robust economy that revolves around high technology, manufacturing, business services, transportation and automobile, among others. Most of these businesses are looking to expand and scale through the deployment of Data Science domain. Thus, there are huge number of jobs in Data Science.
1.1 What is Data Science?
1.2 Significance of Data Science in today’s data-driven world, applications of Data Science, lifecycle of Data Science, and its components
1.3 Introduction to Big Data Hadoop, Machine Learning, and Deep Learning
1.4 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
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
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
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
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
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
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
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
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
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
11.1 Introducing Artificial Intelligence and Deep Learning
11.2 What is an artificial neural network? TensorFlow: The computational framework for building AI models
11.3 Fundamentals of building ANN using TensorFlow and working with TensorFlow in R
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
13.1 Introduction to Support Vector Machine (SVM)
13.2 Data classification using SVM
13.3 SVM algorithms using separable and inseparable cases
13.4 Linear SVM for identifying margin hyperplane
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
15.1 Introduction to the concepts of text mining
15.2 Text mining use cases and understanding and manipulating the text with ‘tm’ and ‘stringR’
15.3 Text mining algorithms and the quantification of the text
15.4 TF-IDF and after TF-IDF
Case Study 01: Market Basket Analysis (MBA)
1.1 This case study is associated with the modeling technique of Market Basket Analysis, where you will learn about loading data, plotting items, and running algorithms.
1.2 It includes finding out the items that go hand in hand and can be clubbed together.
1.3 This is used for various real-world scenarios like a supermarket shopping cart and so on.
Case Study 02: Logistic Regression
2.1 In this case study, you will get a detailed understanding of the advertisement spends of a company that will help drive more sales.
2.2 You will deploy logistic regression to forecast future trends.
2.3 You will detect patterns and uncover insight using the power of R programming.
2.4 Due to this, the future advertisement spends can be decided and optimized for higher revenues.
Case Study 03: Multiple Regression
3.1 You will understand how to compare the miles per gallon (MPG) of a car based on various parameters.
3.2 You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc.
3.3 The case study includes model building, model diagnostic, and checking the ROC curve, among other things.
Case Study 04: Receiver Operating Characteristic (ROC)
4.1 In this case study, you will work with various datasets in R.
4.2 You will deploy data exploration methodologies.
4.3 You will also build scalable models.
4.4 Besides, you will predict the outcome with highest precision, diagnose the model that you have created with real-world data, and check the ROC curve.
Free Career Counselling
The entire Data Science course content is designed by industry professionals for you to get the best jobs in top MNCs. As part of Data Science online courses, you will be working on various projects and assignments that have immense implications in real-world scenarios. They will help you fast-track your career effortlessly.
At the end of this Data Science online training program, there will be quizzes that perfectly reflect the type of questions asked in the respective certification exams.They will help you score better.
Intellipaat’s course completion certificate will be awarded to you when you complete the project work and score at least 60 percent marks in the quiz. This certification is well recognized in the top 80+ MNCs,such as Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Standard Chartered, TCS, Genpact, etc.
Intellipaat offers exclusive Data Science course in USA for professionals who want to expand their knowledge base and start a career in this field. There are many reasons for choosing Intellipaat:
Intellipaat is one of the most affordable e-learning providers today. It offers both online training and self-paced training, and you can avail them at their respective costs. Our self-paced training in United States costs about $264, while our online instructor-led training for the same costs $399.
This online training is curated by top Data Scientists from India and the United States. These SMEs have designed the course in such a manner that even if you are from a non-technical background and have almost zero knowledge of this domain, you can still learn and adapt all concepts easily. Also, we provide practical experience through real-time projects that allow even freshers to easily grasp the concepts.
Intellipaat does not directly forward resumes to any companies or recruiters. However, we do have a placement team that will conduct a number of mock interviews and will assist you in updating your resume to prepare you for job interviews. The team thus helps you land a lucrative job in the domain.
In the career mentoring session at Intellipaat, our experts offer solutions to all your queries that are based on career opportunities and the growth available in this domain.
In this online training, in collaboration with IBM, you can expect several benefits, including the following:
If you wish to enroll in our Data Scientist training in USA, then you need to first make a choice between online instructor-led training and self-paced training. Once you do that, you can make the payment using any major credit card, debit card, or EMI options.
Intellipaat provides various group offers and discounts for its online training as per the size and type of the particular group. If you wish to avail the discount, you need to get in touch with our course advisors who will explain to you all the details regarding it.
Since it involves various aspects of advanced technologies, such as Machine Learning, Deep Learning, and Artificial Intelligence, among others, it is comparatively difficult to learn. However, Intellipaat’s online training is offered by experts in this domain who have a lot of experience in the field. They make all concepts easier to understand as they explain each concept with the help of several real-life examples.
Intellipaat selects subject matter experts from top MNCs, who have at least 8 to 12 years of experience in the domain, as instructors. They are qualified instructors and are selected after going through our rigorous selection process and proving their capabilities.
Intellipaat’s teaching assistants are SMEs whose main aim is to make you a certified professional in the respective domain. The trainers conduct interactive video lectures to teach the latest technologies and enrich your experience with various industry-based projects. The teaching assistance provided by Intellipaat is only available during regular hours.
Intellipaat online course comprises all the topics that are required and significant to learn so that you can master this technology. Intellipaat’s training course comprises both basic and advanced-level concepts involved in this technology so that you can learn them and master the skills to pursue a career in this domain. Moreover, the trainers of this course are experts in the domain who spend time and effort to teach you all the concepts in detail.
Data Scientists, Business Analysts, and Data Engineers are among the most sought-after job positions in the United States. Due to the increase in the growth of Big Data and the surge in the awareness of aspirants, the number of skilled professionals coming into the market is on the rise. However, the demand-supply gap remains the same, or even more grave in terms of skilled professionals. Hence, this is the right time to get certified in Data Science and kick-start your career.
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.
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