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Data Science Course

5 ( 591 ) Ratings

This Data Science course in collaboration with IBM offers you multiple Data Science modules to let you master skills in Data Analytics, R programming, statistical computing, Machine Learning algorithms, k-means clustering, and more. It includes multiple hands-on exercises and project work in the domains of banking, finance, entertainment, etc. Our online Data Science training certification is well-recognized across 500+ employers and helps you land your dream job.

Ranked #1 Data Science Program by India TV

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MS Excel Self-paced Course

Key Highlights

42 Hrs Instructor Led Training
28 Hrs Self-paced Videos
56 Hrs Project & Exercises
Certification and Job Assistance
Flexible Schedule
Lifetime Free Upgrade
24 x 7 Lifetime Support & Access
Mentor Support
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Data Science CourseOverview

This best Data Scientist course online provides detailed learning through self-paced videos and live instructor-led sessions that help you gain skills in the shortest possible time. Data Scientists are among the highest-paid and most in-demand professionals. Our in-depth Data Science programs cover ‘What is Data Science?,’ statistical methods, data acquisition and analysis, Machine Learning algorithms, predict...

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Why should you take up this Data Science course?

The average annual salary of Data Scientists as per Indeed is US$122,801 in the United States.

  • Data Scientist is the best job in the 21st century – Harvard Business Review
  • The number of jobs for all data professionals in the United States will increase to 2.7 million – IBM
  • Global Big Data market achieves US$122 billion in sales in 6 years – Frost & Sullivan

The demand for Data Scientists far exceeds the supply. This is a serious problem in the data-driven world that we are living in today. As a result, most organizations are willing to pay high salaries for professionals with appropriate Data Science skills.

Data Science training online will help you become proficient in Data Science, R programming language, Data Analysis, Big Data, and more. Thus, you can easily accelerate your career in this evolving domain and take it to the next level.

Advantages of Data Science

In this Data Science online course, you will learn about:

  1. Introduction to Data Science and its importance
  2. Data Science life cycle and data acquisition
  3. Experimentation, evaluation, and project deployment tools
  4. Various Machine Learning algorithms
  5. Predictive analytics and segmentation using clustering
  6. Fundamentals of Big Data Hadoop
  7. Roles and responsibilities of a Data Scientist
  8. Using real-world datasets to deploy recommender systems
  9. Working on data mining and data manipulation

This Data Science course online can be signed up by:

  • Information Architects and Statisticians
  • Developers looking to master Machine Learning and Predictive Analytics
  • Big Data, Business Analysis, Business Intelligence, and Software Engineering Professionals
  • Aspirants looking to work as Machine Learning Experts, Data Scientists, etc.

There are no prerequisites for taking up this course. If you like mathematics, you can accelerate your learning through these Data Scientist online courses.

The main objective of this Data Science course is to help you gain proficiency in all basic and advanced level concepts in this field, such as Python, statistical computing, R programming, etc., and make you a successful Data Scientist.

Yes, Intellipaat offers a master’s course on Data Science. In which you will learn real-time analytics, statistical computation, SQL, parsing machine-generated data, and finally the domain of Deep Learning. You will also get an insight into how to use Big Data Analytics with Spark for Data Science as well. Additionally, you will have exclusive access to IBM Watson Cloud Lab for Chatbots. This course curriculum is designed by industry experts, and it includes 10 courses and 53 industry-based projects with 1 CAPSTONE project. The following courses will be covered in this course:

Online Instructor-led Courses:

  • Course 1: Data Science with R
  • Course 2: Python for Data Science
  • Course 3: Machine Learning
  • Course 4: Artificial Intelligence and Deep Learning with TensorFlow
  • Course 5: Big Data Hadoop & Spark
  • Course 6: Tableau Desktop 10
  • Course 7: Data Science with SAS

Self-paced Courses:

  • Course 8: Advanced Excel
  • Course 9: MongoDB
  • Course 10: MS-SQL

In the United States, the average salary of a Data Scientist is US$112,957. The average salary of Data Scientists in India is ₹853,191.

Many top companies hire Data Scientists. A few of them are Amazon, Google, IBM, Facebook, Microsoft, Walmart, Target, Visa, Bank of America, Accenture, Fractal Analytics, etc.

There are several ways to become a Data Scientist. The Data Scientists use a large number of Data Science tools/technologies, such as R and Python programming languages, and analysis tools, like SAS.

As a budding Data Scientist, you should be familiar with data analysis, statistical software packages, data visualization, and handling large datasets. Data Scientists’ major time spent in data exploration and data wrangling.

Criteria Data Analyst Business Analyst Data Scientist
Skill set Analyzing business needs Analyzing historical data Making data-driven decisions
Who is eligible? Anybody can learn Anybody can learn Anybody can learn
What do they do? Full life cycle analysis, including business needs, activities, and designing Implementing technology solutions and analyzing and reporting business capabilities Statistical analysis and the development of Machine Learning systems
Average salaries US$68,465 US$75,218 US$112,957

This Data Scientist training online course includes industry-based projects, which will help you in gaining hands-on experience and prepare you for challenging Data Science roles.

Project Name Industry Objective
Cold Start Problem in Data Science Entertainment Building a recommender system without historical data
Designing a Movie Recommendation Engine Entertainment Building a movie recommendation engine based on user interests
Making Sense of Customer Buying Patterns Ecommerce Deploying target selling to customers
Fraud Detection in the Banking System BFSI Deploying Data Science to detect fraudulent activities and taking remedial actions
  1. Understand the Problem

Data Scientists should be aware of the business pain points and ask the right questions.

  1. Collect Data

They need to collect enough data to understand the problem at hand and to better solve it in terms of time, money, and resources.

  1. Process the Raw Data

Data is rarely used in its original form. It must be processed, and there are several ways to convert it into a usable format.

  1. Explore the Data

Once the data has been processed and converted into a usable form, Data Scientists must examine it to determine the characteristics and find out obvious trends, correlations, and more.

  1. Analyze the Data

To understand the data, they use a variety of tool libraries, such as Machine Learning, statistics and probability, linear and logistic regression, time series analysis, and more.

  1. Communicate Results

At last, results must be communicated to the right stakeholders, laying the groundwork for all identified issues.

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With data collection, ’the sooner the better’ is the best answer - The CEO of Yahoo
Everything is going to be connected with data and mediated by software - The CEO of Microsoft
The world is now awash in data, and we can see consumers in a cleaner way - The Co-founder of PayPal

Career Transition

55% Average Salary Hike

$1,20,000 Highest Salary

12000+ Career Transitions

300+ Hiring Partners

Career Transition Handbook

Skills Covered

  • R Programming
  • Exploratory Data Analysis
  • Data Manipulation
  • Data Visualization
  • Statistics 
  • Machine Learning Algorithms
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Course Fees

Self Paced Training

  • 28 Hrs e-learning videos
  • Lifetime Free Upgrade
  • 24 x 7 Lifetime Support & Access

$264

Online Classroom Preferred

  • Everything in self-paced, plus
  • 42 Hrs of Instructor-led Training
  • 1:1 Doubt Resolution Sessions
  • Attend as many batches for Lifetime
  • Flexible Schedule
07 Aug

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

10 Aug

TUE - FRI

07:00 AM TO 09:00 AM IST (GMT +5:30)

14 Aug

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

22 Aug

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

$ 499 $399 10% OFF Expires in

Corporate Training

  • Customized Learning
  • Enterprise grade learning management system (LMS)
  • 24x7 Support
  • Enterprise grade reporting

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Data Science Course Content

Live Course

Module 01 - Introduction to Data Science with R

Preview

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

Module 02 - Data Exploration

Preview

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

Self-paced Course Content

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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Data Science Projects Covered

Peer Learning

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!

Data Science Certification

What should I do to unlock my Data Science certification?

You can unlock your certificate issued by us in three simple steps:

  1. Complete the Data Science online course along with the given assignments
  2. Work on several industry-based projects and execute the same successfully
  3. Pass the certification exam

The certification you receive from us is valid for your entire lifetime and is recognized by top organizations across the world.

On completing this Data Science online course and passing the exam, you will receive our Data Science certificate online via our Learning Management System. You can download or share your certificate from this through either email or LinkedIn.

Yes. The certification issued by us is industry-recognized. Besides, due to our affiliation with IBM, you will also receive a Data Science course completion certificate from IBM.

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Data Science Course FAQs

Why should I enroll in Intellipaat’s Data Science certification course?

We offer the best Data Science courses online for professionals who want to expand their knowledge base and start a career in this field. There are many reasons for choosing Intellipaat:

  • A personal mentor to track your progress
  • Immersive online instructor-led sessions conducted by SMEs
  • Extensive LMS, allowing you to view recorded sessions within 3 hours
  • Real-time exercises, assignments, and projects
  • 24/7 learning support
  • A large community of like-minded learners
  • Industry-recognized Intellipaat badge
  • Personalized job support

Intellipaat offers courses on Data Science, Machine Learning, Artificial intelligence, Python, Python for Data Science, Data Analytics, Business Analytics.

If you are looking for free resources on Data Science, then read our blogs on Data Science Tutorials and Data Science Interview Questions.

Data Science is a branch of computer science that deals with a wide range of algorithms, tools, scientific methods, and Machine Learning techniques to identify hidden trends and patterns from structured and unstructured data.

Data Scientists are experts in the field of Data Science who are responsible for collecting and analyzing huge chunks of structured and unstructured data from a range of data sources. These professionals combine their knowledge and skills in areas like mathematics, statistics, and computer science to enable organizations to analyze and process business data, and interpret this data to help the company make improved business decisions.

Python is the most popular and preferred language used in Data Science. This is because Python is an easy-to-use and easy-to-learn, open-source programming language. Moreover, it is a dynamic language that supports multiple paradigms. Apart from this, some of the other languages used in Data Science, include R and SQL.

There are numerous job opportunities available for Data Scientists, some of which are mentioned below:

  • Business Analyst
  • Data Analyst
  • Big Data Engineer
  • Data Scientist
  • Statistician
  • Data Engineer
  • Machine Learning Engineer
  • Data Architect

It does not take too long to become a Data Scientist. Once you complete the Data Science training with us, execute all the projects successfully, and meet all the requirements, you will receive an industry-recognized Data Science course completion certificate from us. Further, with the help of our placement team who will prepare your resume and conduct mock interviews before your job interviews, you will be able to crack your interview and land a high-paying job as a Data Scientist.

This Data Science course is designed for both beginners who are new to the field of Data Science and experienced professionals who wish to upskills themselves in this domain.

You will gain access to our job portal once you complete the entire training program and execute the assignments and projects that are part of the program.

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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