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In this Data Science course in Fremont, Intellipaat’s SMEs will make you proficient in the core Data Science concepts, such as Predictive Analytics, data manipulation, R programming, etc., along with their applications on real-world datasets. Intellipaat excels by providing lifetime access to the Data Science training course content to help you become a certified Data Scientist and job assistance to land your dream job.
Intellipaat’s knowledgeable trainers will make you an expert in implementing data analysis, data modeling, statistical computing, k-means clustering, and Machine Learning algorithms, along with a focus on the real-time capstone project.
Though there is no requirement for specific skill sets, a little interest in exploring the mathematical and statistical concepts can benefit the learners in grasping the concepts quickly.
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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
Hands-on Exercise:
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
Hands-on Exercise:
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
Hands-on Exercise:
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
Hands-on Exercise:
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
Hands-on Exercise:
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
Hands-on Exercise:
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
Hands-on Exercise:
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
Hands-on Exercise:
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
Hands-on Exercise:
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
Hands-on Exercise:
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
Hands-on Exercise:
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
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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 predict the approval rate of a loan using various parameters.
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 algorithms, and many more to create movie recommendation systems for viewers using Netflix dataset.
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 filtering model based on the users, and for a movie recommendation, you must create realRatingMatrix.
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 understand how to build an ARIMA model and fit it, find optimal parameters by plotting PACF charts, and perform various analyses to predict values.
You can unlock your Intellipaat 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 given by Intellipaat is industry-recognized. Besides, due to our affiliation with IBM, you will also receive a Data Science course completion certificate from IBM.
Data Science course in Fremont includes a lot of constituent components, and Intellipaat’s course provided the most comprehensive and in-depth learning experience. I really liked the real-world projects in Data Science, which helped me take on a Data Science role in a reputed company much easier.
Excellent course, I found Intellipaat's training team to be talented in their respective domain. My training was very good as it helped me in upgrading my skills, which proved to be a turning point in my career. Intellipaat's mentor was well-experienced and his teaching method was really great. This online course helped me to get deep understanding of the technology. Moreover, the exercise and projects provided as a part of the Data Scientist course in Fremont made me to get hands-on experience. Besides, the online support team was very helpful and offered their assistance as and when I needed via calls and emails.
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 training courses, getting our 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.
I was searching for various programs of Data Science, and I landed on Intellipaat’s course page. It was really good in terms of content. The sample video provided was also awesome, which impressed me a lot while deciding to take up the one among various courses. Moreover, the trainer’s command over the technology was great. The support team was really good. Really appreciable!
Intellipaat’s Data Science course in Fremont 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 my home, thanks to those learner-friendly videos. I am grateful to Intellipaat!
Bharat Rathore
I really appreciate the quality of the material and the content of this Data Science certification course!! Thanks to all Intellipaat team!
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 my career. Intellipaat's mentor was well-experienced and his teaching method was really great. This, one of the best Data Science programs helped me to get deep understanding of the technology. Moreover, the exercise and projects provided as a part of the course made me to get hands-on experience. Besides, the online support team was very helpful and offered their assistance as and when I needed via calls and emails.
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 my home, thanks to those learner-friendly videos. I am grateful to Intellipaat!
Swetha Pandit
Intellipaat’s Data scientist courses online are well structured and taught by recognized professionals. They help one learn Data Science fast. I have found their videos to be of excellent quality. Thanks a lot!
Kavita Mehra
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 hints to solve all the questions posed to him.
Sulekha Roy
I think this Data Science training course is a good way to start learning Data Science and make a career in it. Instructors are reasonably good. Also, projects were interesting and relevant to the current industry trends.
I want to talk about the rich LMS that Intellipaat’s Data Science program offered. The extensive set of PPTs, PDFs, and other related material were of the highest quality, and due to this, my learning with Intellipaat was excellent. I could clear the Cloudera Data Scientist certification exam in the first attempt.
I was searching for Data Science courses online, and I landed on Intellipaat’s. It was really good in terms of content. The sample video provided was also awesome, which impressed me a lot while deciding to take up the course. Moreover, the trainer’s command over the technology was great. The support team was really good. Really appreciable!
I signed up for Intellipaat's Data science course when i learned that it is a great place for learning new technologies. The training material offered by them is extremely useful. I chose to opt for the weekend classes as per my convenience . The trainer of this course was really good and helped me learn the subject well. Besides, the online support team helped me to resolve any technical issue I had.
Great course, I had a really good experience from this Data Science course in Fremont, they trained me well. I completed my Python training with this institute where they provided extremely good training and good support too. Further, the course instructors were really well-educated who explained all the topics with great enthu.
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 am currently working and I still get the time to complete the course within the given time and it is mainly possible because of the 24*7 support system and the clarity of their teaching. Besides, they make us do hands-on exercises and project, making us gain in-depth knowledge of the concepts. I strongly recommend others to take this course as well.
Great Data Science training program. It was an amazing experience learning from Intellipaat. During this covid 19, it was extremely fruitful to learn and spend ample amount of time as a part of this institute. Intellipaat has also provided great support during the course. Besides, the instructor was very supportive in the course, guidance was also given to complete assignment and project which was very helpful.
Intellipaat offers exclusive Data Science course in Fremont for professionals who want to expand their knowledge base and start a career in this field. There are many reasons for choosing Intellipaat:
Intellipaat, one of the leading E-learning institute in Fremont provides best courses in R programming language, Artificial Intelligence, Machine Learning, Data Analytics, and others to help to increase your understanding of your field of work and to validate your skills.
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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