WROX Certified Big Data Analyst Program by AnalytixLabs and Wiley

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1 WROX Certified Big Data Analyst Program by AnalytixLabs and Wiley Disclaimer: This material is protected under copyright act AnalytixLabs, Unauthorized use and/ or duplication of this material or any part of this material including data, in any form without explicit and written permission from AnalytixLabs is strictly prohibited. Any violation of this copyright will attract legal actions. Learn to Evolve

2 About AnalytixLabs AnalytixLabs is a capability building and training solutions firm led by McKinsey, IIM and IIT alumni with deep industry experience and a flair for coaching. We are focused at helping our clients develop skills in basic and advanced analytics to enable them to emerge as Industry Ready professionals and enhance their career opportunities. To know more about us or our faculty, please visit our website Bottom line Approach focus on practical & theory Content World class course structure Surpasses industry requirements Faculty Seasoned analytics professionals Together we have 30 + years of experience with prestigious firms, like Kinsey, KPMG, Deloitte and AOL Job-oriented training Lucrative job prospects in high growth domain Support for relevant certifications and diplomas Career counseling and planning Personal attention and Individual counselling Cater to Standard certifications Regular sessions by industry experts Value for money with high return on investment Industry best practices High quality course material and real life case studies 1

3 Candidates trained by us are working in leading companies across industries 2

4 Global Big Data Talent Skill Gap McKinsey Global Institute estimates a shortage of nearly 1.7 million big data talents by This includes a shortage of 140,000 to 190,000 workers with deep technical and analytical expertise, and a shortage of 1.5 million managers and analysts equipped to work with and use big data outputs 3

5 Program Objective WCBDA program aims to provide its students an international, wide-spectrum qualification for job-readiness and seamless absorption in Big Data job roles. The program will expose the students and professionals to the roles of Big Data Analysts who have: Ability to think analytically Understanding of storage, retrieval and mining of data Explanatory analysis & predictive modeling skills Possess Outcome-Oriented and Global Industry-Specific expertise in Critical Data Analytics and Data Management Skills Hands-on practical skills on Big Data tools R and Hadoop (MapReduce, Hbase, Hive, Pig, Oozie, Sqoop, Mahout, ZooKeeper and Flume) and Data visualization - Tableau Application of analytics in various domains, like Retail, Telecom, BFSI etc. Skills to leverage analytics to drive smart business decisions AnalytixLabs has collaborated extensively with industry experts to put together a program that is rigorous, effective and relevant. 4

6 WCBDA is a comprehensive program and encompasses the following modules, along with projects in the end Module 1 Module 2 Analytics using R and Tableau 42 hours + Practice Introduction to the R- environment Data Input & Output Data Manipulation Visualization Basic statistics Big Data Hadoop 30 hours + Practice Introduction to Big Data & Hadoop Hadoop Architecture MapReduce R-Hadoop Introduction to Flume & Sqoop Advanced Analytics Data Visualization using Tableau Machine Learning using R Social Media Analytics using R Applying overall Learning PIG HIVE Hbase Mahout ZooKeeper Misc Components Applying overall Learning Crafted by team of experts and maintains a balance between theoretical concepts and practical applications 5

7 Business Analytics using R & Tableau Duration: 42 hours + Practice sessions Introduction to R- environment The Workspace Input/ Output Useful Packages (Base & other packages) in R Graphic User Interfaces (R studio) Customizing Startup Batch Processing Reusing Results Data Input & Output (Importing & Exporting) Data Structure & Data Types (Vectors, Matrices, factors, Data frames, and Lists) Importing Data (Importing data from csv, txt, Excel and other files) Keyboard Input (Creating input by entering data) Database Input (Connecting to database and use the data) Exporting Data (Exporting files into different formats) Viewing Data (Viewing partial data and full data) Variable & Value Labels Date Values Missing Data Data Manipulation Creating New Variables (calculations & Binning) Operators (Using multiple operators) Built-in Functions & User Defined Function Control Structures(conditional statements, Loops) Sorting Data Merging and Appending Data Aggregating Data Reshaping Data Sub setting Data Data Type Conversions Visualization Creating Graphs Histograms & Density Plot Dot Plots Bar Plots Line Charts Pie Charts Boxplots Scatterplots Basic Statistics (Exploratory Analysis) Descriptive Statistics(central tendency/variance) Frequency Tables /Summarization Hypothesis Testing t-tests/z-test (1-sample, independent sample, paired sample) Analysis of Variance(ANOVA) Correlations/chi-square test 1/2 6

8 Business Analytics using R & Tableau Duration: 42 hours + Practice sessions Advanced Analytics (Advanced Statistics) Introduction to predictive modeling & applications Linear(Simple & Multiple) Regression Logistic Regression Introduction to segmentation Segmentation using cluster analysis Data Visualization using Tableau Introduction to Tableau & Environment Building basic views & sharing your work- overview Data importing & manipulation Maps/Tables/Calculated fields Parameters Data visualization with Charts maps Building & customizing Reports Building & customizing Dashboards Social Media Analytics using R Social Media Characteristics of Social Media Applications of Social Media Analytics Metrics(Measures Actions) in social media analytics Examples & Actionable Insights using Social Media Analytics Text Analytics Sentiment Analysis using R Text Analytics Word cloud analysis using R Projects (Applying overall Learning) Solve Business problems using R/Tableau 2/2 Machine Learning using R What is Machine Learning? Applications of Machine Learning Algorithms Classification & Regression Problems Training & Testing concepts Cost & optimization functions Artificial Neural Networks(ANN) Support Vector Machines(SVM) Decision Tress & Random Forest Baysian Network case 7

9 Big Data Hadoop Duration: 30 hours + Practice sessions Introduction to Big Data & Hadoop What is Big Data? Types of Data Characteristics of Big Data Need for understanding Big Data (Application of Big Data) Traditional Approaches and its limitations Introduction to Hadoop and eco-system Getting Started with Hadoop (software installation etc.) Hadoop Architecture Hadoop Commercial version vs Apache Hadoop Hadoop Cluster in commodity hardware Hadoop core components HDFS layer HDFS operation principle Basic Hadoop commands MapReduce Introduction to MapReduce Hadoop MapReduce example Hadoop MapReduce Characteristics Setting up your MapReduce Environment Building a MapReduce Program Input Formats in MapReduce Output Formats in MapReduce Basic MapReduce Programming using R R-Hadoop Introduction to RHdfs, Rmr and Rhbase Develop Map reduce code using R for Local & Hadoop env Exploratory analysis using R-Hadoop Predictive analytics using R- Hadoop Overview of Parallelization using R without Hadoop Introduction to Flume & Sqoop Introduction to Sqoop (Why, what, processing, under the hood) Exporting data from Hadoop using Sqoop Introduction to Flume Flume Use Cases Hands on Exercise using Flume and Sqoop PIG Introduction to PIG Components of PIG PIG Data Model Creating MapReduce programs using PIG Hands on Exercise using PIG 1/2 8

10 Big Data Hadoop Duration: 30 hours + Practice sessions HIVE Introduction to HIVE and its characteristics Components of HIVE HIVE Data Models Serialization/De-serialization HIVE file formats HIVE Query Language HIVE Functions Difference between HIVE and PIG Hands on Exercise using HIVE HBase HBase introduction and its Characteristics HBase Architecture Storage Model of HBase When to use HBase HBase Data Model HBase Families HBase Components Data Storage Hands on Exercise using Hbase ZooKeeper Introduction to ZooKeeper & its Features Features of ZooKeeper Challenges faced in distributed applications Coordination ZooKeeper: Goals and Uses ZooKeeper: Entities, Data Model, Services 2/2 Misc Components Overview of Apache Oozie Overview of Storm Overview of Apache Cassandra Overview of Apache Spark Overview of H2O Social Media Analytics(Text Analysis, Word cloud) Project (Applying overall Learning) Solve Business problems using all the components of Hadoop Mahout Mahout introduction and its Characteristics Mahout Architecture When to use Mahout What are the Machine Learning topics are covered in Mahout Hands on Exercise using Mahout 9

11 Time and investment Big Data Analytics: 72 hours + Practice, INR 32,000/ $700 (introductory price) Certification Cost: INR 2000/ $50 (only applicable for WCBDA students) Duration: 12 Weekend, 72 hrs live training - Saturday, Sunday (3 hours each) + Practice Training mode: Fully interactive online class (In addition to the above, you will also get access to the recordings for self study and practice) Components: Content Resources Print and e-format, Simulations and Videos, Virtual Lab with software and datasets, Industry- relevant project work Certification: Participants will be awarded an International certificate on successful completion of the stipulated requirements including an evaluation at the end Placements: AnalytixLabs has an extensive Industry Network to facilitate Placements for its students 10

12 AnalytixLabs

13 Free career counseling and job assistance Extensive Industry Network to facilitate Placements Identify available career options for an individual Resume writing and interview preparation Recommend additional skill set and training/course to enhance employability Structure and define a career path Estimate the economic goals and compensation trends Advice and design strategies to meet individual goals 12

14 We provide trainings in a fully interactive online Saves commuting time and resources in today s chaotic world Delivered lectures are recorded and can be replayed by individuals as per their needs Fully interactive live online class with personal attention Best use of time and resources One of strongest global trends in education, even in developing countries Access to quality training and 24x7 practice sessions available at the comfort of your place Studies prove that online education beats the conventional classroom 13

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