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Data Science Certification Course using R

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Why should you take Data Science course ?

  • The number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings by 2020 - Forbes
  • The average salary for a Data Scientist is $120k as per Glassdoor
  • Businesses analysing data will see $430 billion in productivity benefits over their rivals not analysing data by 2020
  • 32K + satisfied learners. Reviews
About the Course

Edureka's Data Science Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR.

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Instructor-led Data Science live online classes

26 th  Nov
Mon - Fri (15 Days) Timings : 10:00 AM - 12:00 PM (EST)
Sold Out
14 th  Dec
Fri & Sat (5 Weeks) Weekend Batch Timings : 09:30 PM - 12:30 AM (EST)
Filling Fast
19 th  Jan
Sat & Sun (5 Weeks) Weekend Batch Timings : 10:00 AM - 01:00 PM (EST)

Course Price

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EMI starts at 5602 / month.

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Introduction to Data Science
Learning Objectives - Get an introduction to Data Science in this module and see how Data Science helps to analyze large and unstructured data with different tools. 

Topics:
  • What is Data Science?
  • What does Data Science involve?
  • Era of Data Science
  • Business Intelligence vs Data Science
  • Life cycle of Data Science
  • Tools of Data Science
  • Introduction to Big Data and Hadoop
  • Introduction to R
  • Introduction to Spark
  • Introduction to Machine Learning

Statistical Inference
Learning Objectives - In this module, you will learn about different statistical techniques and terminologies used in data analysis. 

Topics:
  • What is Statistical Inference?
  • Terminologies of Statistics
  • Measures of Centers
  • Measures of Spread
  • Probability
  • Normal Distribution
  • Binary Distribution

Data Extraction, Wrangling and Exploration
Learning Objectives - Discuss the different sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format. 

Topics:
  • Data Analysis Pipeline
  • What is Data Extraction
  • Types of Data
  • Raw and Processed Data
  • Data Wrangling
  • Exploratory Data Analysis
  • Visualization of Data

Hands-On/Demo:
  • Loading different types of dataset in R
  • Arranging the data
  • Plotting the graphs

Introduction to Machine Learning
Learning Objectives - Get an introduction to Machine Learning as part of this module. You will discuss the various categories of Machine Learning and implement Supervised Learning Algorithms. 

Topics:
  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Supervised Learning algorithm: Linear Regression and Logistic Regression

Hands-On/Demo:
  • Implementing Linear Regression model in R
  • Implementing Logistic Regression model in R

Classification Techniques

Learning Objectives - In this module, you should learn the Supervised Learning Techniques and the implementation of various techniques, such as Decision Trees, Random Forest Classifier, etc. 

Topics:
  • What are classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Perfect Decision Tree
  • Confusion Matrix
  • What is Random Forest?
  • What is Navies Bayes?
  • Support Vector Machine: Classification

Hands-On/Demo:
  • Implementing Decision Tree model in R
  • Implementing Linear Random Forest in R
  • Implementing Navies Bayes model in R
  • Implementing Support Vector Machine in R

Unsupervised Learning
Learning Objectives - Learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data. 

Topics:
  • What is Clustering & its use cases
  • What is K-means Clustering?
  • What is C-means Clustering?
  • What is Canopy Clustering?
  • What is Hierarchical Clustering?

Hands-On/Demo:
  • Implementing K-means Clustering in R
  • Implementing C-means Clustering in R
  • Implementing Hierarchical Clustering in R

Recommender Engines
Learning Objectives - In this module, you should learn about association rules and different types of Recommender Engines. 

Topics:
  • What is Association Rules & its use cases?
  • What is Recommendation Engine & it’s working?
  • Types of Recommendations
  • User-Based Recommendation
  • Item-Based Recommendation
  • Difference: User-Based and Item-Based Recommendation
  • Recommendation use cases

Hands-On/Demo:
  • Implementing Association Rules in R
  • Building a Recommendation Engine in R

Text Mining
Learning Objectives - Discuss Unsupervised Machine Learning Techniques and the implementation of different algorithms, for example, TF-IDF and Cosine Similarity in this Module. 

Topics:
  • The concepts of text-mining
  • Use cases
  • Text Mining Algorithms
  • Quantifying text
  • TF-IDF
  • Beyond TF-IDF

Hands-On/Demo:
  • Implementing Bag of Words approach in R
  • Implementing Sentiment Analysis on Twitter Data using R

Time Series

Learning Objectives - In this module, you should learn about Time Series data, different component of Time Series data, Time Series modeling - Exponential Smoothing models and ARIMA model for Time Series Forecasting. 

Topics:
  • What is Time Series data?
  • Time Series variables
  • Different components of Time Series data
  • Visualize the data to identify Time Series Components
  • Implement ARIMA model for forecasting
  • Exponential smoothing models
  • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • Implement respective ETS model for forecasting

Hands-On/Demo:
  • Visualizing and formatting Time Series data
  • Plotting decomposed Time Series data plot
  • Applying ARIMA and ETS model for Time Series Forecasting
  • Forecasting for given Time period

Deep Learning
Learning Objectives - Get introduced to the concepts of Reinforcement learning and Deep learning in this module. These concepts are explained with the help of Use cases. You will get to discuss Artificial Neural Network, the building blocks for Artificial Neural Networks, and few Artificial Neural Network terminologies. 

Topics:
  • Reinforced Learning
  • Reinforcement learning Process Flow
  • Reinforced Learning Use cases
  • Deep Learning
  • Biological Neural Networks
  • Understand Artificial Neural Networks
  • Building an Artificial Neural Network
  • How ANN works
  • Important Terminologies of ANN’s
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About Data Science Certification Course

Data science is a "concept to unify statistics, data analysis and their related methods" to "understand and analyse actual phenomena" with data. Data Science Training employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science from the sub-domains of machine learning, classification, cluster analysis, data mining, databases, and visualization. The Data Science Certification Course enables you to gain knowledge of the entire life cycle of Data Science, analyse and visualise different data sets, different Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes.

What are the objectives of our Data Science Online Course?
Data Science Certification Training is designed by industry experts to make you a Certified Data Scientist. The Data Science course offers:
  • In-depth knowledge of Data Science Life Cycle and Machine Learning Algorithms
  • Comprehensive knowledge of various tools and techniques for Data Transformation
  • The capability to perform Text Mining and Sentimental analyses on text data and gain an insight into Data Visualization and Optimization techniques
  • The exposure to many real-life industry-based projects which will be executed in RStudio
  • Projects which are diverse in nature covering media, healthcare, social media, aviation and HR
  • Rigorous involvement of an SME throughout the Data Science Training to learn industry standards and best practices

Why should you go for Data Science Training?
Data science is an evolutionary step in interdisciplinary fields like the business analysis that incorporate computer science, modelling, statistics and analytics. To take complete benefit of these opportunities, you need a structured training with an updated curriculum as per current industry requirements and best practices.
Besides strong theoretical understanding, you need to work on various real-life projects using different tools from multiple disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes.
Additionally, you need the advice of an expert who is currently working in the industry tackling real-life data-related challenges.

What are the skills that you will be learning with our Data Science Training?
Data Science Training will help you become a Data Science Expert. It will hone your skills by helping you to understand and analyze actual phenomena with data and provide the required hands-on experience for solving real-time industry-based projects.

During this Data Science course, you will be trained by our expert instructors to:
  • Gain insight into the 'Roles' played by a Data Scientist
  • Analyze several types of data using R
  • Describe the Data Science Life Cycle
  • Work with different data formats like XML, CSV, etc.
  • Learn tools and techniques for Data Transformation
  • Discuss Data Mining techniques and their implementation
  • Analyze data using Machine Learning algorithms in R
  • Explain Time Series and it’s related concepts
  • Perform Text Mining and Sentimental analyses on text data
  • Gain insight into Data Visualization and Optimization techniques
  • Understand the concepts of Deep Learning

Who should go for this Data Science Course?
The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data. It is best suited for:
  • Developers aspiring to be a 'Data Scientist'
  • Analytics Managers who are leading a team of analysts
  • Business Analysts who want to understand Machine Learning (ML) Techniques
  • Information Architects who want to gain expertise in Predictive Analytics
  • 'R' professionals who wish to work Big Data
  • Analysts wanting to understand Data Science methodologies

What are the pre-requisites for Edureka's Data Science Course?

There is no specific pre-requisite for Data Science Training. However, a basic understanding of R can be beneficial. Edureka offers you a complimentary self-paced course, i.e. "R Essentials" when you enroll in Data Science Training.

Edureka’s Data Scientist with proficiency in R Certificate Holders work at 1000s of companies like

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80% Course completion rate
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What are the system requirements for this Data Science Training?
If you have a Windows system, you should have:
  • Microsoft Windows 7 or newer (32-bit and 64-bit)
  • Microsoft Server 2008 R2 or newer
  • Intel Pentium 4 or AMD Opteron processor or newer
  • 2 GB memory
  • 1.5 GB minimum free disk space
  • 1366 x 768 screen resolution or higher

If you have a MAC system, you should have:
  • iMac/MacBook computers 2009 or newer
  • OSX 10.10 or newer
  • 5 GB minimum free disk space
  • 1366 x 768 screen resolution or higher

How will I execute the practicals in Data Science Training course?
For executing the practicals, you will set-up R programming IDE on your machine, you can:
  • Download RStudio Desktop Open Source License from the Rstudio Official Website for free
  • Or, purchase the licensed Full- version of RStudio Desktop Commercial License

The detailed step by step installation guides will be present in your LMS which will help you to install and set-up the required environment. In case you come across any doubt, the 24*7 support team will promptly assist you.

Which projects are included in this Data Science Training?
Edureka's Data Science Training includes real-time industry-based projects, which will hone your skills as per current industry standards and prepare you for the upcoming Data Scientist roles. 

Project#1: Movies Collection 
Industry: Entertainment Industry 
Description: The goal of this Use-Case is to explore the movie dataset, given the parameters like: "duration", "movie title", "gross collection", "budget", "title year", etc. You will explore the following:
  • Know top ten movies with the highest profits.
  • Know top rated movies in the list and average IMDB score.
  • Plot a graphical representation to show the number of movies released each year.
  • Group the movies into clusters based on the Facebook likes.
  • Group the directors based on movie collection and budget.

Project #2: Real Estate Price Prediction 
Industry: Business Intelligence and Analytics 
Description: The goal of this Use-case is to make predictions using Real Estate market data. The dataset contains the of the price of apartments in Boston. This data contains values such as "crime rate", "age", "accessibility", "population" etc. Based on this data, decide on the price of new apartments. 

Project #3: Diabetes Prediction 
Industry: Healthcare 
Description: The Use-Case focuses on making predictions based on the patient’s characteristic data set, the dataset contains attributes such as "glucose level", "blood pressure", "age" etc. At last, the goal is to make a high accuracy machine learning model to predict, whether a patient is Diabetic or not. 

Project #4: Recommendation System for Grocery Store 
Industry: Food Retail Industry 
Description: The Use-Case scenario is to create recommendations for customers of a grocery store based upon historical transaction data, which could recommend preferable articles. 

Project #5: Twitter Analytics 
Industry: Social Media Analytics 
Description: This Use-Case focuses on social media analytics. The problem can be defined as Measuring, Analyzing, and Interpreting interactions and associations between people, topics and ideas. The dataset to be analyzed is captured by Live Twitter Streaming. You have to do the following:
  • Perform Sentiment analysis on the tweets obtained and visualize the conclusions.
  • Compare two football clubs, based on the tweets they are receiving from their fans.

Project #6: Air Passengers Forecasting 
Industry: Commercial Aviation 
Description: This Use-Case is about analyzing the data and applying time series model to forecast the number of bookings an Airline firm can expect each month. The dataset we will analyze contains monthly totals of international airline passengers between 1949 to 1960.You have to make informed decisions on staffing, hospitality and pricing for tickets.

Instructor-led Sessions

30 Hours of Online Live Instructor-led Classes. Weekend class: 10 sessions of 3 hours each. Weekday class: 15 sessions of 2 hours each.

Real-life Case Studies

Live project based on any of the selected use cases, involving the implementation of Data Science.

Assignments

Each class has practical assignments which shall be finished before the next class and helps you to apply the concepts taught during the class.

Lifetime Access

You get lifetime access to the Learning Management System (LMS). Class recordings and presentations can be viewed online from the LMS.

24 x 7 Expert Support

We have a 24x7 online support team available to help you with any technical queries you may have during the course.

Certification

Towards the end of the course, you will be working on a project. Edureka certifies you as a 'Data Science  Expert' based on the project.

Forum

We have a community forum for all our customers wherein you can enrich their learning through peer interaction and knowledge sharing.

What if I miss a class?
You will never lose any lecture. You can choose either of the two options:
  • View the recorded session of the class available in your LMS.
  • You can attend the missed session, in any other live batch.

Will I Get Placement Assistance?
To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.

Can I attend a Demo Session before Enrolment?
We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrolment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in the class.

Who are the instructors at Edureka?
All the instructors at Edureka are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by Edureka for providing an awesome learning experience.

What if I have more queries?
You can Call us at +91 98702 76457 /1844 230 6362 ( US Tollfree ) OR Email us at sales@edureka.co . We shall be glad to assist you. 

How do I avail EMI option as a method of payment?

You no longer need a credit history or a credit card to purchase this course. Using ZestMoney, we allow you to complete your payment with a EMI plan that best suits you. It's a simple 3 step procedure:
  • Fill your profile: Complete your profile with Aadhaar, PAN and employment details.
  • Verify your account: Get your account verified using netbanking, ekyc or uploading documents
  • Activate your loan: Setup automatic repayment using NACH to activate your loan
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Checkout our Data Science Masters Program

Data Science Masters Program makes you proficient in tools and systems used by Data Science Professionals. It includes training on Statistics, Data Science, Python, Apache Spark & Scala, Tensorflow and Tableau. The curriculum has been determined by extensive research on 5000+ job descriptions across the globe.

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