Data Science course helps you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, Naive Bayes. You’ll perform Big Data Analytics with R Programming, Hadoop and solve real life case studies on Finance, E-Comm, Social Media.

Why this course ?

Businesses Will Need One Million Data Scientists by 2018 – KDnuggets

Roles like chief data & chief analytics officers have emerged to ensure that analytical insights drive business strategies – Forbes

The average salary for a Data Scientist is $113k (Glassdoor)

About The Course :
 
This Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on ‘R’ capabilities.
 
 
Course Objectives:
 
After the completion of the Data Science course, you should be able to:
1. Gain insight into the ‘Roles’ played by a Data Scientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
 
Who should go for this course?
 
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a ‘Data Scientist’
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. ‘R’ professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
 
Pre-requisites
 
There is no specific pre-requisite for the course however exposure to statistics and mathematical aptitude will be beneficial. Edureka will provide you complementary self paced courses covering essentials of Hadoop, Statistics, R and Mahout to brush up the fundamentals required for the course.
 
Why Learn Data Science?
 
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
The following blogs will help you understand the significance of Data Science training:
www.edureka.co/blog/advantages-of-data-science-training/
www.edureka.co/blog/enroll-for-a-data-science-course/
 
Which Case-Studies will be a part of the Course?
 
Towards the end of the Course, you will be working on a live project. Here are the few Industry-wise case studies e.g. Finance, Retail, Media, Aviation, Sports etc. which you can take up as your project work:
 
Project#1: Flight Delay Prediction
Industry : Aviation
Description : The goal of this project is to predict the Arrival Time of a flight given the parameters like:”UniqueCarrier”, “DepDelay”, “AirTime”, “Distance”, “ArrDelay”, etc. Whether these attributes affect the arrival delay and if yes, to which extent? Construct a model and predict the arrival delay.
Compute the (Source Airport – Destination Airport) mean scheduled time, actual and inflight time with the help of MapReduce in R and visualize the results using R.
 
 
Project #2: Stock Market Prediction
Industry : Finance
Description : This problem is about making predictions on the stock market data.The dataset contains the daily quotes of the SP500 stock index from 1970-01-02 to 2009-09-15 (10,000+ daily sessions). For each day information is given on the Open, High, Low and Close prices, and also for the Volume and adjusted close price.
 
 
Project #3: Twitter Analytics
Industry : Social Media
Description : This problem is about social media analytics. This 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. This problem is mainly about how to use twitter analytics to find meaningful data by performing Sentiment analysis of the tweets obtained and visualizing the conclusions.
 
 
Project #4: Recommendation System
Industry : e-commerce
Description : The problem of creating recommendations given a large data set from directly elicited ratings is a widely potential area which was lately boosted by players like Amazon, Netflix, Google to name a few. In this project, you are given a collection of real world data from the different users involving the products they like, rating assigned to the product, etc. and you have to create and come up with recommendations for the users.
 
 
Project #5: NFL Data Analysis
Industry : Sports
Description : The dataset is a set of tweets by fans from a NFL game. This project is about analyzing the tweets posted by football fans all over the world on the NFL tournament semi-finals and find out insights like: top 10 most popular topics being discussed, most talked about team etc.
 
 


Course Curriculum

Introduction to Data Science
Goal – Get an introduction to Data Science in this Module and see how Data Science helps to analyze large and unstructured data with different tools. 00:00:00
Objectives - At the end of this Module, you should be able to:

Define Data Science

Discuss the era of Data Science

Describe the Role of a Data Scientist

Illustrate the Life cycle of Data Science

List the Tools used in Data Science

State what role Big Data and Hadoop, R, Spark and Machine Learning play in Data Science

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
Goal – In this Module, you should learn about different statistical techniques and terminologies used in data analysis. 00:00:00
Objectives - At the end of this Module, you should be able to: • Define Statistical Inference • List the Terminologies of Statistics • Illustrate the measures of Center and Spread • Explain the concept of Probability • State Probability Distributions Topics: • What is Statistical Inference? • Terminologies of Statistics • Measures of Centers • Measures of Spread • Probability • Normal Distribution • Binary Distribution
Data Extraction, Wrangling and Exploration
Goal – 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. 00:00:00
Objectives - At the end of this Module, you should be able to: • Discuss Data Acquisition techniques • List the different types of Data • Evaluate Input Data • Explain the Data Wrangling techniques • Discuss Data Exploration 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
Goal – 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. 00:00:00
Objectives - At the end of this module, you should be able to:

Define Machine Learning

Discuss Machine Learning Use cases

List the categories of Machine Learning

Illustrate Supervised Learning Algorithms

Topics:

What is Machine Learning?

Machine Learning Use-Cases

Machine Learning Process Flow

Machine Learning Categories

Supervised Learning

    o     Linear Regression

    o     Logistic Regression

Hands-On/Demo:

Implementing Linear Regression model in R

Implementing Logistic Regression model in R 

Classification
Goal – In this module, you should learn the Supervised Learning Techniques and the implementation of various Techniques, for example, Decision Trees, Random Forest Classifier etc. 00:00:00
Objectives - At the end of this module, you should be able to:

Define Classification

Explain different Types of Classifiers such as,

   o   Decision Tree

   o   Random Forest

   o   Naïve Bayes Classifier

   o   Support Vector Machine

Topics:

What is 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
Goal – Learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data. 00:00:00
Objectives - At the end of this module, you should be able to:

Define Unsupervised Learning

Discuss the following Cluster Analysis

    o     K - means Clustering

    o     C - means Clustering

    o     Hierarchical Clustering

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
Goal – In this module, you should learn about association rules and different types of Recommender Engines. 00:00:00
Objectives - At the end of this module, you should be able to:

Define Association Rules

Define Recommendation Engine

Discuss types of Recommendation Engines

    o     Collaborative Filtering

    o     Content-Based Filtering

Illustrate steps to build a Recommendation Engine

Topics:

What is Association Rules & its use cases?

What is Recommendation Engine & it’s working?

Types of Recommendation Types

User-Based Recommendation

Item-Based Recommendation

Difference: User-Based and Item-Based Recommendation

Recommendation Use-case

Hands-On/Demo:

Implementing Association Rules in R

Building a Recommendation Engine in R

Text Mining
Goal – Discuss Unsupervised Machine Learning Techniques and the implementation of different algorithms, for example, TF-IDF and Cosine Similarity in this Module. 00:00:00
Objectives - At the end of this module, you should be able to:

Define Text Mining

Discuss Text Mining Algorithms

    o     Bag of Words Approach

    o     Sentiment Analysis

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
Goal – In this module, you should learn about Time Series data, different component of Time Series data, Time Series modelling – Exponential Smoothing models and ARIMA model for Time Series forecasting. 00:00:00
Objectives - At the end of this module, you should be able to:

Describe Time Series data

Format your Time Series data

List the different components of Time Series data

Discuss different kind of Time Series scenarios 

Choose the model according to the Time series scenario

Implement the model for forecasting

Explain working and implementation of ARIMA model

Illustrate the working and implementation of different ETS models

Forecast the data using the respective model

 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
Goal – 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. 00:00:00
Objectives - At the end of this module, you should be able to:

Define Reinforced Learning

Discuss Reinforced Learning Use cases

Define Deep Learning

Understand Artificial Neural Network

Discuss basic Building Blocks of Artificial Neural Network

List the important Terminologies of ANN’s

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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FAQs

Yes, One can always use Windows to work on assignments. The initial 5 modules require R set-up which can be easily installed on your windows system. To work on Hadoop on your Windows, the simplest way is to install VMware Player on your Windows Machine. VMPlayer is a software using which you can run another operating system on your Windows. So, by using a VMPlayer and run an Ubuntu/Cloudera OS you can work on the R+Hadoop or Mahout environment and run the assignments. System Requirements to run VMplayer: RAM: 3 to 4 GB. Processor: i3 or above. A 64-bit system is recommended to run the Mahout libraries. To set-up the same, detailed step-wise installation guides are provided in the LMS. Our 24*7 team support will guide you to get the set-up ready.

We will help you to setup the required environment for practicals. The set-up will comprise: – R programming IDE set-up – Setting up Hadoop – Performing R+Hadoop Integration – Installing Mahout The detailed step-wise installation guides are provided in the LMS for you. In case you come across any doubt, the 24*7 support team will promptly assist you.

You will never lose any lecture. You can choose either of the two options: 1. View the class presentation and recordings that are available for online viewing through the LMS. 2. You can attend the missed session, in any other live batch. Please note, access to the course material will be available for lifetime once you have enrolled into the course.

All our instructors are working professionals from the Industry and have at least 10-12 yrs of relevant experience in various domains. They are subject matter experts and are trained by Edureka for providing online training so that participants get a great learning experience.

Edureka is the largest online education company and lots of recruitment firms contacts us for our students profiles from time to time. Since there is a big demand for this skill, we help our certified students get connected to prospective employers. We also help our customers prepare their resumes, work on real life projects and provide assistance for interview preparation. Having said that, please understand that we don’t guarantee any placements however if you go through the course diligently and complete the project you will have a very good hands on experience to work on a Live project.

Yes, this can be done. Moreover, this ensures that when you will start with your batch, the concepts explained during the classes will not be totally new to you.

Requesting for a support session is a very simple process. As soon as you join the course, the contact number and email-id of the support team will be available in your LMS. Just a phone call or email will solve the purpose.

You can go through the sample class recording and it would give you a clear insight about how the classes are conducted, quality of instructors and interactiveness in a class.

You can pay by Credit Card, Debit Card or NetBanking from all the leading banks. We use a CCAvenue Payment Gateway. For USD payment, you can pay by Paypal. We also have EMI options available.

You can give us a CALL at +91 9599586895 OR email at support@eduqfa.com

Certificates

Reviews

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I would like to recommend any one who wants to be a Data Scientist just one place: Edureka. Explanations are clean, clear, easy to understand. Their support team works very well such any time you have an issue they reply and help you solving the issue. I took the Data Science course and I'm going to take Machine Learning with Mahout and then Big Data and Hadoop and after that since I'm still hungry I will take the Python class and so on because for me Edureka is the place to learn, people are really kind, every question receives the right answer. Thank you Edureka to make me a Data Scientist.

Eric Arnaud
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Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best.

Gnana Sekhar Vangara
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It was a great experience to undergo and get certified in the Data Science course from Edureka. As a working professional, it has not only given me an exposure to the domain, but also helped me learn cross technologies and develop an inclination towards it. The trainer had a great hold on the domain, who came with a handy industry experience. Quality of the training materials, assignments, project, support and other infrastructures are a top notch. Definitely a course to undergo if you are a Big Data enthusiast. Thanks Edureka and team! Pleasure being an associate.

Balasubramanya SP
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I took kafka and datascience classes with EDUREKA and its overall nice.After through scanning of available online courses, I decided to go with edureka and am quite satisfied with it. To start with the Sales and support team- they were fantastic- really fast and responsive. There was never any technical issue like audio/video/connectivity during the course which is good. The classes were very smooth.The instructors were really good and deliverd the course content very well. They had very good theoretical and practical knowledge of the respective courses. Great Job! Thanks for the learning experience! Keep it up!!!

Janardhan Singamaneni

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