Big Data, Data Science Master Course

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1 About Intellipaat Intellipaat is a fast-growing professional training provider that is offering training in over 150 most sought-after tools and technologies. We have a learner base of 600,000 in over 32 countries and growing. For job assistance and placement we have direct tie-ups with 80+ MNCs. Key Features of Intellipaat Training: Instructor Led Training Self-Paced Training Exercise and project work Lifetime Access 294 Hrs of highly interactive instructor led training 365 Hrs of Self-Paced sessions with Lifetime access 528 Hrs of real-time projects after every module Lifetime access and free upgrade to latest version Support Lifetime 24*7 technical support and query resolution Get Certified Get global industry recognized certifications Job Assistance Job assistance through 80+ corporate tie-ups Flexi Scheduling Attend multiple batches for lifetime & stay updated. About the Course Intellipaat s Big Data, Data Science master s program will provide you in-depth knowledge on designing, developing and deploying data science and big data application in the real-world along with performance tuning of the application. This course will make you Big Data & Data Science Architect and by the end of the course you will have expertise on Hadoop Developer, Administration, testing and analysis Modules, working with real-time analytics, statistical computing, parsing machine-generated data, creating NoSQL applications and finally the domain of deep Learning in artificial intelligence. Instructor Led Duration 33 Hrs Weekend Batch 3 Hrs/Session Self Paced Duration 365 Hrs

2 Why take this Course? This Intellipaat training program has been created keeping in mind the needs of the industry. You will gain mastery in the complete domain of Data Science, Hadoop, Artificial Intelligence, and Machine Learning to take on various roles and responsibilities in today's. So you will be better prepared to take on challenging roles in the Big Data and Data Science domains at top-notch salaries. Course Curriculum Big Data Hadoop and Spark Module /Topic Hands-on exercises Installation of Hadoop and Hadoop Ecosystems Introduction to Big Data Hadoop. Understanding HDFS & MapReduce Working with HDFS, replicating the data Determining block size, familiarizing with Namenode and Datanode Deep Dive in MapReduce The detailed methodology for writing the Word

3 Count Program in MapReduce Writing custom partitioner MapReduce with Combiner, Local Job Runner Mode, Unit Test ToolRunner, MapSide Join, Reduce Side Join, Using Counters Joining two datasets using Map-Side Join &Reduce-Side Join Introduction to Hive Creating of Hive database, how to drop the database, changing the database, creating of Hive table, loading of data, dropping the table and altering it Writing hive queries to pull data using filter conditions, group by clauses, partitioning Hive tables Advance Hive & Impala Working with Hive queries, writing indexes, joining table, deploying external table Sequence table and storing data in another table Introduction to Pig Working with Pig in MapReduce and local mode, loading of data Limiting data to 4 rows, storing the data in a file Working with Group By, Filter By, Distinct, Cross, Split in Hive Flume, Sqoop & HBase Working with Flume on generating of Sequence Number and consuming it Using the Flume Agent to consume the Twitter data, using AVRO to create Hive Table AVRO with Pig, creating Table in HBase, deploying Disable, Scan and Enable Table Writing Spark Applications using Scala Writing Spark application using Scala

4 Understanding the robustness of Scala for Spark real-time analytics operation Spark framework RDD in Spark The Resilient Distributed Dataset in Spark and how it helps to speed up big data processing How to deploy RDD with HDFS, using the inmemory dataset, using the file for RDD How to define the base RDD from an external file Deploying RDD via transformation, using the Map and Reduce functions Working on word count and count log severity Data frames and Spark SQL Data querying and transformation using Data Frames Finding out the benefits of Data Frames over Spark SQL and Spark RDD Machine Learning using Spark (Mlib) Writing spark code using Mlib Spark Streaming Deploying Spark streaming for data in motion and checking the output is as per the requirement Hadoop Administration - Multi-Node Cluster Setup using Amazon ec2 The method to build a multi-node Hadoop cluster using an Amazon EC2 instance, working with the Cloudera Manager Hadoop Administration - Cluster Configuration The method to do performance tuning of MapReduce program Hadoop Administration - Maintenance, Monitoring and Troubleshooting How to go about ensuring the MapReduce Filesystem Recovery for various different scenarios, JMX monitoring of the Hadoop cluster? How to use the logs and stack traces for

5 monitoring and troubleshooting, using the Job Scheduler for scheduling jobs in the same cluster, getting the MapReduce job submission flow, FIFO schedule, getting to know the Fair Scheduler and its configuration ETL tool connectivity with Hadoop &Oozie Connecting to HDFS from ETL tool and moving data from Local system to HDFS Moving Data from DBMS to HDFS, Working with Hive with ETL Tool Creating MapReduce job in ETL tool Project Solution Discussion and Cloudera Certification Tips & Tricks The project of a real-world high-value Big Data Hadoop application and getting the right solution based on the criteria set by the Intellipaat team Apache Spark and Scala: Module /Topic Introduction of Scala Pattern Matching Executing the Scala Code Classes concept in Scala Case classes and pattern matching Concepts of traits with an example Scala Java interoperability Scala collections Mutable collections vs. Immutable collections Use case bobs rockets package Introduction to Spark Spark Basics Working with RDDs in Spark Aggregating Data with Pair RDDs Writing and Deploying Spark Application Parallel Processing Spark RDD Persistence

6 Spark Streaming &Mlib Improving Spark Performance Spark SQL and Data Frames Scheduling / Partitioning Data Science with R Module /Topic Hands-on exercises Introduction to Data Science and Statistical Analytics Introduction to R R studio & its environment Introduction Basic Operations Conditional Statements Control flow statements Data Exploration, Data Wrangling, and R Data Structure Data Types in R Data Structures in R (vector, list, complex, data frame etc) Importing files (text, csv, db, JSON) Functions in R (build-in & custom functions) Data Wrangling (subset, NA imputation, sampling data into test and train, Normalization) Data Visualization Visualization importance Graphs with the base package: Bar Graph, Line Graph, box plot, scatter plot ggplot2 package: Bar graph, line graph, box plot, scatter plot, color & shape importance Introduction to Statistics Hypothesis Testing (z-score, t-test, chisquare test) using R Understand Mean, Median, Mode, variance & standard deviation in R Learn probability, conditional probability, Confusion Matrix, Bayes Theorem

7 Predictive Modeling - 1 ( Linear Regression) Data preprocessing Build a Linear Regression model using R Understand the results and evaluation Metrics (RMSE, MAPE) Predictive Modeling - 2 ( Logistic Regression) Create glm model, Prediction, the importance of Threshold Evaluation metrics for logistic, Accuracy, Precision, ROC curve analysis, Kappa metric Decision Trees Entropy, Information Gain Rpart& C5.0 Decision Tree Algorithms Accuracy, Precision, Recall, F1 Score of the classifier Random Forest Understanding Random forest with an example Unsupervised learning Clustering(Agglomerative or divisive) K-means clustering and graphs Hierarchical clustering Association Analysis and Recommendation engine Building a recommendation engine using R Sentiment Analysis R code to get twites using Twitter API Sentiment Analysis of twites for either Positive sentiment or Negative sentiment Time Series Real-Time hands-on with stock price data of any organization. Understand the Components of Time Series (Trend, Randomness, Seasonality) Build an ARIMA Model to predict the future stock price

8 Python for Data Science: Module / Topic Introduction to Python Hands-on Exercises Install Anaconda Python distribution for your OS (Windows/Linux/Mac) Basic constructs of Python language Write your first Python program Write a Python Function (with and without parameters) Use Lambda expression Write a class, create a member function and a variable Create an object Write a for loop to print all odd numbers Writing Object-Oriented Program in Python and connecting with Database File Handling, Exception Handling in Python Open a text file and read the contents Write a new line in the opened file Use pickle to serialize a python object, desterilize the object Raise an exception and catch it Mathematical Computing with Python (NumPy) Import Numpy module Create an array using ND-array Calculate std deviation on an array of numbers Calculate correlation between two variables Scientific Computing with Python (SciPy) Import SciPy Apply Bayes theorem using SciPy on the given dataset Data Visualization (Matplotlib) Plot Line, Pie, Scatter, Histogram and other charts using Matplotlib

9 Data Analysis and Machine Learning (Pandas) / Data Manipulation with Python Import Pandas Use it to import data from a JSON file Select records by a group and apply a filter on top of that View the records Perform Linear Regression analysis Create a Time Series Machine Learning, Natural Language Processing (Scikit-Learn) Setup Jupyter Notebook environment Load a dataset in Jupyter Use algorithm in Scikit-Learn package to perform ML techniques Train a model Create a search grid Web Scraping for Data Science Install Beautifulsoup and lxml Python parser Make a Soup object using an input HTML file Navigate Py objects in the soup tree Search tree Print output Python on Hadoop Write a basic MapReduce Job in Python and connect with Hadoop Framework to perform the task Writing Spark code using Python Implement sandbox Run a python code in a sandbox Work with HDFS file system from the sandbox Tableau Desktop 10: Module / Topic Hands-on Exercises Introduction to Data Visualization and Power of Tableau Architecture of Tableau

10 Play with the tableau desktop, interface to learn its user interface Share an existing work, Export an existing work Working with Metadata& Data Blending Creation of sets Connect to an excel sheet and import data Use metadata and extracts Handle NULL values Clean up the data before the actual use Perform various join techniques Perform data blending from more than one sources Create and edit sets using Marks, Highlight desired items, Make groups, Applying sorting on the result, Make Hierarchies in the created set Working with Filters Add Filter on the dataset by date/dimensions/measures Use the interactive filter to views Remove some filters to see the result Organizing Data and Visual Analytics Apply labels, annotations, tooltips to graphs, Edit the attributes of axes Set a reference line, Do k-means cluster analysis on a dataset Working with Mapping Plot latitude and longitude on geo map Edit locations on the map, Create custom geo-coding Use images of a map and plot points on it, find coordinates in the image Create a polygon map, Use WMS Working with Calculations & Expressions

11 Working with Parameters Create new parameters to apply on a filter Pass parameters to filters to select columns Pass parameters to filters to select charts Charts and Graphs Plot a histogram, heat map, tree map, funnel chart and others using the same data set Do market basket analysis on a given dataset Dashboards and Stories Create a dashboard view Include objects, legends, and filters Make the dashboard interactive Create and edit a story with visual effects, annotation, description Integration of Tableau with R and Hadoop Deploy R on the tableau Create a line graph using R interface Connect tableau with Hadoop and extract data Data Science with SAS: Module / Topic Introduction to SAS SAS Enterprise Guide SAS Operators & Functions Compilation & Execution Using Variables Creation and Compilation of SAS Datasets Hands-on Exercises Working with multiple data sets Import Excel file in the workspace Read data Export the workspace to save data Apply logical, arithmetic operators and SAS functions to perform operations Use KEEP and DROP statements Use delimiter rules on raw data files

12 SAS Procedures Module / Topic Hands-on Exercises Use SORT, FREQ, SUMMARY, EXPORT and other procedures Input statement and formatted input Read standard and non-standard numeric inputs with Formatted inputs Control while a record loads Control a Line pointer Write Multiple IN and OUT statements SAS FORMAT SAS Graphs Interactive Data Processing Data Transformation Function Output Delivery System (ODS) SAS MACROS PROC SQL Advanced Base SAS Summarization Reports Format a variable, deploy format rule on PROC DATA set, Use ATTRIB statement Plot graphs using PROC GPLOT Display charts using PROC GCHART Working with interactive dashboards Use Functions in data transformation Optimize data, generate RTF, pdf, HTML and doc files Write a macro Use positional parameters Create SQL query to select and add a condition Use a CASE in select query Use web UI to do statistical operations Use PROC SORT to sort the results, List ODS, Find mean using PROC Means, print using PROC PRINT Splunk Developer and Admin: Module / Topic Splunk Development concepts Basic Searching Using Fields in Searches Hands-on Exercises Write a basic search query Use Fields in Search, Use Fields Sidebar,

13 Saving and Scheduling Searches Creating Alerts Scheduled Reports Tags and Event Types Creating and Using Macros Workflow Splunk Search Commands Transforming Commands Reporting Commands Mapping and Single Value Commands Splunk Reports & visualizations Analyzing, Calculating and Formatting Results Correlating Events Enriching Data with Lookups Creating Reports and Dashboards Getting started with Parsing Using Pivot Use Field Extractor (FX), delimit field Extraction using FX Schedule a search, Save a search result, Share and export a search result Create an alert, view fired alerts Deploy tags for Splunk search, generate and implement event types in Search Define a Macro with arguments and use variables in it Create GET, POST, and Search workflow Create search pipeline, specify indexes in search, highlight syntax, use autocomplete feature, use search commands like tables, fields, sort, multi-kv, rename, rex&erex Use Top, Rare, Stats Commands Create reports using following commands and their functions: add coltotals, and totals Track IP using ip location, get geo data using geostats Create time charts, omit null values and format results Calculate and analyze results, perform conversion on a data value, round off numbers, use eval command, write conditional statements, apply filters on calculated search results Generate Report on Transactions, Group events using fields and time Define and configure automatic lookup, deploy lookup in reports and searches Create search charts, reports and dashboards, Edit reports and Dashboard, Add reports to dashboard Extract useful data from raw data, perform the transformation, parse different values and preview Select a data model object, create a pivot report, create an instant pivot from a

14 Common Information Model (CIM) Add-On search, add a pivot report to a dashboard Use the CIM Add-On to normalize data Overview of Splunk: Topics Splunk Installation Splunk Installation in Linux Distributed Management Console Introduction to Splunk App Splunk indexes and users Splunk configuration files Splunk Deployment Management Splunk Indexes User roles and authentication Splunk Administration Environment Basic Production Environment Splunk Search Engine Various Splunk Input Methods Splunk User & Index Management Machine Data Parsing Search Scaling and Monitoring Splunk Cluster implementation Splunk Installation AI and Deep Learning: Topics Introduction to Neural Networks Multi-layered Neural Networks Regularization techniques (L1, l2) CNN: Convolutional Neural Networks LSTM: Long Short-Term Memory Hidden Markov Chatbots

15 MongoDB Developer & Admin: Module / Topic Introduction to NoSQL and MongoDB MongoDB Installation Importance of NoSQL CRUD Operations Data Modeling & Schema Design Data Management & Administration Data Indexing and Aggregation MongoDB Security Working with Unstructured Data Hands-on Exercises Install MongoDB, Install MongoChef (MongoGUI) Write a JSON document Use Insert query to Create a data entry, Use find query to Read data, Use update and replace queries to Update, Use delete query operations on a DB file Write a data model tree structure for a family hierarchy Use shard key and hashed shard keys, Perform backup and recovery of a dummy dataset, Import data from a CSV file, Export data to a CSV file Do aggregation using the pipeline, sort, skip and limit, Create an index on data using a single key, using multikey MongoDB integration with Java and Robomongo. Work with a variety of unstructured data like images, videos, log data, and others Apache Storm: Module / Topic Understanding Architecture of Storm Installation of Apache Storm Introduction to Apache Storm Apache Kafka Installation Apache Storm Advanced Apache Storm Topology Overviews of Trident Storm Components & Classes Cassandra Introduction Boot Stripping

16 Self-Paced Courses Hbase Our HBase course lets you master the powerful NoSQL distributed database.his HBase training provides a detailed understanding of HBase and no-sql concepts as well you will learn HBase architecture, data analytics using HBase, integration with Hive, monitoring cluster using ZooKeeper, advanced operations in HBase like integration and working with Hadoop eco-system. Cassandra This Cassandra course will provide you with extensive knowledge of Cassandra concepts, high-scalable data models and the Cassandra architecture which will enable you to build applications for big data. You will learn Cassandra configuration, installation, architecture, data modeling, Hadoop integration. Work on real-life industry projects. Couchbase Our Couchbase course provides hands-on experience to master the multi-model NoSQL file-oriented database. You will learn Couchbase distributed architecture, Couchbase Server, searching, querying, indexing data, Couchbase flexible data model, get hands-on experience in working with Couchbase Server that can store credentials and key-values. Mahout This Apache Mahout course covers Introduction to Mahout, Machine-learning, Recommendations using Mahout, Classifiers and Recommenders, a Collaborative Filtering process, Clustering process, Document Clustering, Classification Data, Pattern Mining, Pearson Correlation and Frequent Pattern Growth. Apache Solr Our Apache Lucene Solr training course lets you master Solr. Some of the major topics covered in this Apache Solr training are an introduction to Apache Lucene, Solr Installation, Solr Search, Sorting, Indexing and Updating schema Linux Our Linux training course lets you master the Linux OS. This is an in-depth Training Course that gives you all the skills needed for working as a Linux Administrator. You will learn about the Red Hat System, installation, managing the boot processes, performing various operations, understanding Linux Kernel, testing and debugging.

17 Java This comprehensive course lets you master Java programming language. We provide the best online training classes to help you learn OOP concepts, J2EE, core and advanced Java, JDBC, Objects & Classes. You will work on real-world industry projects. Apache Kafka Our Apache Kafka training course will give you hands-on experience to master the real-time stream processing platform. Some of the topics included in this online training course are the Kafka API, creating Kafka clusters, integration of Kafka with the Big Data Hadoop ecosystem along with Spark, Storm and Maven integration. Learning Path

18 Project Work Course Industry/Domain Project Big Data Hadoop &Spark Data Science With R Data Science with Python Tableau 10 Desktop Splunk Data Science with SAS Media Banking Banking Social Network Internet Services Social Network Sports Entertainment Social Network Internet Services Entertainment E-commerce Banking Software Banking Banking Software Sales Crime Healthcare Retail Healthcare Sales Banking Working with MapReduce, Hive, Sqoop Work on MovieLens data for finding top movies YARN project - End to End PoC Table Partitioning in Hive Connecting Pentaho with Hadoop Multi-Node Cluster setup Hadoop Testing using MRUnit Hadoop Weblog Analytics Hadoop Maintenance Twitter Sentimental Analysis Analysing IPL T20 Cricket Movie Recommendation Twitter API integration for tweet Analysis Data Exploration using Spark SQL - Wikipedia Understanding Cold Start Problem in Data Science Recommendation for Movie, Summary Making sense of Customer Buying Pattern Fraud Detection in Banking System Python web Scraping for Data Science Create a password generator Impact of pre-paid plans on the preferences of investors Prediction of Stock prices Server logs/ Firewall logs Tableau Interactive Dashboards Segmenting types of crimes and their frequency The visual mapping between Vaccination rate and Measles outbreak Analysing market performance Creating dashboards and report - unstructured employee data Field Extraction Categorization of patients based on a count of drugs for their therapy Build Revenue projections reports Impact of pre-paid plans on the preferences of investors

19 Analytics k-means Cluster analysis on iris dataset Deep Learning with TensorFlow Internet Services E-commerce Ecommerce Image recognition with Tensorflow Handwriting recognition with Neural Networks Building an AI-based Chatbot Ecommerce product recommendation MongoDB Working with MongoDB java driver Apache Storm Internet Services Call log analysis using Trident Twitter Data Analysis using Trident US Presidential Election Result Analysis using Trident DRPC Query Also, cover 7 additional projects as part of self-paced courses. Intellipaat Job Assistance Program Intellipaat is offering comprehensive job assistance to all the learners who have successfully completed the training. A learner will be considered to have successfully completed the training if he/she finishes all the exercises, case studies, projects and gets a minimum of 60% marks in the Intellipaat qualifying exam. Intellipaat has exclusive tie-ups with over 80 MNCs for placement. All the resumes of eligible candidates will be forwarded to the Intellipaat job assistance partners. Once there is a relevant opening in any of the companies, you will get a call directly for the job interview from that particular company. Frequently Asked Questions: Q 1. What is the criterion for availing the Intellipaat job assistance program? Ans. All Intellipaat learners who have successfully completed the training post April 2017 are directly eligible for the Intellipaat job assistance program. Q 2. Which are the companies that I can get placed in? Ans. We have exclusive tie-ups with MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, and more. So you have the opportunity to get placed in these top global companies.

20 Q 3. Does Intellipaat help learners to crack the job interviews? Ans. Intellipaat has an exclusive section which includes the top interview questions asked in top MNCs for most of the technologies and tools for which we provide training. Other than that our support and technical team can also help you in this regard. Q 4. Do I need to have prior industry experience for getting an interview call? Ans. There is no need to have any prior industry experience for getting an interview call. In fact, the successful completion of the Intellipaat certification training is equivalent to six months of industry experience. This is definitely an added advantage when you are attending an interview. Q 5. What is the job location that I will get? Ans. Intellipaat will try to get you a job in your same location provided such a vacancy exists in that location. Q 6. Which is the domain that I will get placed in? Ans. Depending on the Intellipaat certification training you have successfully completed, you will be placed in the same domain. Q 7. Is there any fee for the Intellipaat placement assistance? Ans. Intellipaat does not charge any fees as part of the placement assistance program. Q 8. If I don t get a job in the first attempt, can I get another chance? Ans. Definitely, yes. Your resume will be in our database and we will circulate it to our MNC partners until you get a job. So there is no upper limit to the number of job interviews you can attend. Q 9. Does Intellipaat guarantee a job through its job assistance program? Ans. Intellipaat does not guarantee any job through the job assistance program. However, we will definitely offer you full assistance by circulating your resume among our affiliate partners. Q 10. What is the salary that I will be getting once I get the job? Ans. Your salary will be directly commensurate with your abilities and the prevailing industry standards.

21 What makes us who we are? I am completely satisfied with the Intellipaat big data hadoop training. The trainer came with over a decade of industry experience. The entire big data online course was segmented into modules that were created with care so that the learning is complete and as per the industry needs. -Bhuvana Full marks for the Intellipaat support team for providing excellent support services. Since Hadoop was new to me and I used to have many queries but the support team was very qualified and very patient in listening to my queries and resolve it to my highest expectations. The entire big data course was completely oriented towards the practical aspects. - Bharati Jha