Understanding about log analysis with Spark, first log analyzers in Spark, working with various buffers like array, compact and protocol buffer.
Apache Storm Course Content
Understanding Architecture of Storm
Big Data characteristics, understanding Hadoop distributed computing, the Bayesian Law, deploying Storm for real time analytics, the Apache Storm features, comparing Storm with Hadoop, Storm execution, learning about Tuple, Spout, Bolt.
Installation of Apache storm
Installing the Apache Storm, various types of run modes of Storm.
Introduction to Apache Storm
Understanding Apache Storm and the data model.
Apache Kafka Installation
Installation of Apache Kakfa and its configuration.
Apache Storm Advanced
Understanding of advanced Storm topics like Spouts, Bolts, Stream Groupings, Topology and its Life cycle, learning about Guaranteed Message Processing.
Various Grouping types in Storm, reliable and unreliable messages, Bolt structure and life cycle, understanding Trident topology for failure handling, process, Call Log Analysis Topology for analyzing call logs for calls made from one number to another.
Overview of Trident
Understanding of Trident Spouts and its different types, the various Trident Spout interface and components, familiarizing with Trident Filter, Aggregator and Functions, a practical and hands-on use case on solving call log problem using Storm Trident.
Storm Components & classes
Various components, classes and interfaces in storm like – Base Rich Bolt Class, i RichBolt Interface, i RichSpout Interface, Base Rich Spout class and the various methodology of working with them.
Understanding Cassandra, its core concepts, its strengths and deployment.
Twitter Boot Stripping, detailed understanding of Boot Stripping, concepts of Storm, Storm Development Environment.
Data Science Course Content
Introduction to Data Science and Statistical Analytics
Introduction to Data Science, Use cases, Need of Business Analytics, Data Science Life Cycle, Different tools available for Data Science
Introduction to R
Installing R and R-Studio, R packages, R Operators, if statements and loops (for, while, repeat, break, next), switch case
Data Exploration, Data Wrangling and R Data Structure
Importing and Exporting data from external source, Data exploratory analysis, R Data Structure (Vector, Scalar, Matrices, Array, Data frame, List), Functions, Apply Functions
Bar Graph (Simple, Grouped, Stacked), Histogram, Pi Chart, Line Chart, Box (Whisker) Plot, Scatter Plot, Correlogram
Introduction to Statistics
Terminologies of Statistics ,Measures of Centers, Measures of Spread, Probability, Normal Distribution, Binary Distribution, Hypothesis Testing, Chi Square Test, ANOVA
Predictive Modeling – 1 ( Linear Regression)
Supervised Learning – Linear Regression ,Bivariate Regression, Multiple Regression Analysis, Correlation( Positive, negative and neutral), Industrial Case Study, Machine Learning Use-Cases, Machine Learning Process Flow, Machine Learning Categories
Predictive Modeling – 2 ( Logistic Regression)
What is Classification and its use cases?, What is Decision Tree?, Algorithm for Decision Tree Induction, Creating a Perfect Decision Tree, Confusion Matrix
Random Forest, What is Naive Bayes?
What is Clustering & its Use Cases?, What is K-means Clustering?, What is Canopy Clustering?, What is Hierarchical Clustering?
Association Analysis and Recommendation engine
Market Basket Analysis (MBA), Association Rules, Apriori Algorithm for MBA, Introduction of Recommendation Engine, Types of Recommendation – User-Based and Item-Based, Recommendation Use-case
Introduction to Text Mining, Introduction to Sentiment, Setting up API bridge, between R and Tweeter Account, Extracting Tweet from Tweeter Acc, Scoring the tweet
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
R Programming Course Content
Introduction to R
R language for statistical programming, the various features of R, introduction to R Studio, the statistical packages, familiarity with different data types and functions, learning to deploy them in various scenarios, use SQL to apply ‘join’ function, components of R Studio like code editor, visualization and debugging tools, learn about R-bind.
R Functions, code compilation and data in well-defined format called R-Packages, learn about R-Package structure, Package metadata and testing, CRAN (Comprehensive R Archive Network), Vector creation and variables values assignment.
R functionality, Rep Function, generating Repeats, Sorting and generating Factor Levels, Transpose and Stack Function.
Matrices and Vectors
Introduction to matrix and vector in R, understanding the various functions like Merge, Strsplit, Matrix manipulation, rowSums, rowMeans, colMeans, colSums, sequencing, repetition, indexing and other functions.
Reading data from external files
Understanding subscripts in plots in R, how to obtain parts of vectors, using subscripts with arrays, as logical variables, with lists, understanding how to read data from external files.
Generate plot in R, Graphs, Bar Plots, Line Plots, Histogram, components of Pie Chart.
Analysis of Variance (ANOVA)
Understanding Analysis of Variance (ANOVA) statistical technique, working with Pie Charts, Histograms, deploying ANOVA with R, one way ANOVA, two way ANOVA.
K-Means Clustering for Cluster & Affinity Analysis, Cluster Algorithm, cohesive subset of items, solving clustering issues, working with large datasets, association rule mining affinity analysis for data mining and analysis and learning co-occurrence relationships.
Association Rule Mining
Introduction to Association Rule Mining, the various concepts of Association Rule Mining, various methods to predict relations between variables in large datasets, the algorithm and rules of Association Rule Mining, understanding single cardinality.
Regression in R
Understanding what is Simple Linear Regression, the various equations of Line, Slope, Y-Intercept Regression Line, deploying analysis using Regression, the least square criterion, interpreting the results, standard error to estimate and measure of variation.
Analyzing Relationship with Regression
Scatter Plots, Two variable Relationship, Simple Linear Regression analysis, Line of best fit
Deep understanding of the measure of variation, the concept of co-efficient of determination, F-Test, the test statistic with an F-distribution, advanced regression in R, prediction linear regression.
Logistic Regression Mean, Logistic Regression in R.
Advance Logistic Regression
Advanced logistic regression, understanding how to do prediction using logistic regression, ensuring the model is accurate, understanding sensitivity and specificity, confusion matrix, what is ROC, a graphical plot illustrating binary classifier system, ROC curve in R for determining sensitivity/specificity trade-offs for a binary classifier.
Receiver Operating Characteristic (ROC)
Detailed understanding of ROC, area under ROC Curve, converting the variable, data set partitioning, understanding how to check for multicollinearlity, how two or more variables are highly correlated, building of model, advanced data set partitioning, interpreting of the output, predicting the output, detailed confusion matrix, deploying the Hosmer-Lemeshow test for checking whether the observed event rates match the expected event rates.
Kolmogorov Smirnov Chart
Data analysis with R, understanding the WALD test, MC Fadden’s pseudo R-squared, the significance of the area under ROC Curve, Kolmogorov Smirnov Chart which is non-parametric test of one dimensional probability distribution.
Database connectivity with R
Connecting to various databases from the R environment, deploying the ODBC tables for reading the data, visualization of the performance of the algorithm using Confusion Matrix.
Integrating R with Hadoop
Creating an integrated environment for deploying R on Hadoop platform, working with R Hadoop, RMR package and R Hadoop Integrated Programming Environment, R programming for MapReduce jobs and Hadoop execution.
R Case Studies
Logistic Regression Case Study
In this case study you will get a detailed understanding of the advertisement spends of a company that will help to drive more sales. You will deploy logistic regression to forecast the future trends, detect patterns, uncover insights and more all through the power of R programming. Due to this the future advertisement spends can be decided and optimized for higher revenues.
Multiple Regression Case Study
You will understand how to compare the miles per gallon (MPG) of a car based on the various parameters. You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc. It includes the model building, model diagnostic, checking the ROC curve, among other things.
Receiver Operating Characteristic (ROC) case study
You will work with various data sets in R, deploy data exploration methodologies, build scalable models, predict the outcome with highest precision, diagnose the model that you have created with various real world data, check the ROC curve and more.
SAS Course Content
Introduction to SAS
Introduction to Base SAS, Installation of SAS tool, Getting started with SAS, various SAS Windows – Log, Explorer, Output, Search, Editor, etc. working with data sets, overview of SAS Functions, Library Types and programming files
SAS Enterprise Guide
Import/Export Raw Data files, reading and sub setting the data set, various statements like WHERE, SET, Merge
Hands-on Exercise – Import Excel file in workspace, Read data, Export the workspace to save data
SAS Operators & Functions
Various SAS Operators – Arithmetic, Logical, Comparison, various SAS Functions – NUMERIC, CHARACTER, IS NULL, CONTAINS, LIKE, Input/Put, Date/Time, Conditional Statements (Do While, Do Until, If, Else)
Hands-on Exercise – Apply logical, arithmetic operators and SAS functions to perform operations
Compilation & Execution
Understanding about Input Buffer, PDV (Backend), learning what is Missover
Defining and Using KEEP and DROP statements, apply these statements, Format and Labels in SAS.
Hands-on Exercise – Use KEEP and DROP statements
Creation and Compilation of SAS Data sets
Understanding Delimiter, dataline rules, DLM, Delimiter DSD, raw data files and execution, list input for standard data.
Hands-on Exercise – Use delimiter rules on raw data files
The various SAS standard Procedures built-in for popular programs – PROC SORT, PROC FREQ, PROC SUMMARY, PROC RANK, PROC EXPORT, PROC DATASET, PROC TRANSPOSE, , PROC CORR etc.
Hands-on Exercise – Use SORT, FREQ, SUMMARY, EXPORT and other procedures
Input statement and formatted input
Reading standard and non-standard numeric inputs with Formatted inputs, Column Pointer Controls, Controlling while a record loads, Line pointer control / Absolute line pointer control, Single Trailing , Multiple IN and OUT statements, DATA LINES statement and rules, List Input Method, comparing Single Trailing and Double Trailing.
Hands-on Exercise – 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 statements – standard and user-written, associating a format with a variable, working with SAS FORMAT, deploying it on PROC Data sets, comparing ATTRIB and FORMAT statements.
Hands-on Exercise – Format a variable, deploy format rule on PROC DATA set, Use ATTRIB statement
Understanding PROC GCHART, various Graphs, Bar Charts – Pie, Bar, 3D, plotting variables with PROC GPLOT.
Hands-on Exercise – Plot graphs using PROC GPLOT Display charts using PROC GCHART
Interactive Data Processing
SAS advanced data discovery and visualization, point-and-click analytics capabilities, powerful reporting tools.
Data Transformation Function
Character Functions, Numeric Functions, Converting Variable Type.
Hands-on Exercise – Use Functions in data transformation
Output Delivery System (ODS)
Introduction to ODS, Data Optimization, How to generate files (rtf, pdf, html, doc) using SAS
Hands-on Exercise – Optimize data, generate rtf, pdf, html and doc files
Macro Syntax, Macro Variables, Positional Parameters in a Macro, Macro Step
Hands-on Exercise – Write a macro, Use positional parameters
SQL Statements in SAS, SELECT, CASE, JOIN, UNION, Sorting Data
Hands-on Exercise – Create sql query to select and add a condition
Use a CASE in select query
Advanced Base SAS
Base SAS web-based interface and ready-to-use programs, advanced data manipulation, storage and retrieval, descriptive statistics.
Hands-on Exercise – Use web UI to do statistical operations
Report Enhancement, Global Statements, User-defined Formats, PROC SORT, ODS Destinations, ODS Listing, PROC FREQ, PROC Means, PROC UNIVARIATE, PROC REPORT, PROC PRINT
Hands-on Exercise – Use PROC SORT to sort the results, List ODS, Find mean using PROC Means, print using PROC PRINT
Splunk Developer Topics
Splunk Development concepts
Introduction to Splunk, Splunk developer roles and responsibilities
Writing Splunk query for search, Autocomplete to build a search, time range, refine search, work with events, identify the contents of search, control a search jobHands-on Exercise – Write a basic search query
Using Fields in Searches
Understand Fields, Use Fields in Search, Use Fields Sidebar, regex field extraction using Field Extractor (FX), delimiter field Extraction using FXHands-on Exercise – Use Fields in Search, Use Fields Sidebar, Use Field Extractor (FX), delimit field Extraction using FX
Saving and Scheduling Searches
Writing Splunk query for search, sharing, saving, scheduling and exporting search resultsHands-on Exercise – Schedule a search, Save a search result, Share and export a search result
Creation of alert, explaining alerts and viewing fired alertsHands-on Exercise – Create an alert, view fired alerts
Describe and Configure Scheduled Reports
Tags and Event Types
Introduction to Tags in Splunk, deploying Tags for Splunk search, understanding event types and utility, generating and implementing event types in SearchHands-on Exercise – Deploy tags for Splunk search, generate and implement event types in Search
Creating and Using Macros
Define Macros, Arguments and Variables in a MacroHands-on Exercise – Define a Macro with arguments and use variables in it
GET, POST, and Search workflow actionsHands-on Exercise – Create GET, POST, and Search workflow
Splunk Search Commands
Search Command study, search practices in general, search pipeline, specify indexes in search, syntax highlighting, autocomplete, search commands like tables, fields, sort, multikv, rename, rex & erexHands-on Exercise – Create search pipeline, specify indexes in search, highlight syntax, use autocomplete feature, use search commands like tables, fields, sort, multikv, rename, rex & erex
Using Top, Rare, Stats CommandsHands-on Exercise – Use Top, Rare, Stats Commands
Using following commands and their functions: addcoltotals, addtotals,top, rare,statsHands-on Exercise – Create reports using following commands and their functions: addcoltotals, addtotals
Mapping and Single Value Commands
iplocation, geostats, geom, addtotals commandsHands-on Exercise – Track ip using iplocation, get geo data using geostats
Splunk Reports & visualizations
Explore the available visualizations, create charts and time charts, omit null values and format resultsHands-on Exercise – Create time charts, omit null values and format results
Analyzing, Calculating and Formatting Results
Calculating and analyzing results, value conversion, roundoff and format values, using eval command, conditional statements, filtering calculated search resultsHands-on Exercise – Calculate and analyze results, perform coversion on a data value, roundoff a numbers, use eval command, write conditional statements,apply filters on calculated search results
Search with Transactions, Report on Transactions, Group events using fields and time, Transaction vs StatsHands-on Exercise – Generate Report on Transactions, Group events using fields and time
Enriching Data with Lookups
Learn about data lookups, example, lookup table, defining and configuring automatic lookup, deploying lookup in reports and searchesHands-on Exercise – Define and configure automatic lookup, deploy lookup in reports and searches
Creating Reports and Dashboards
Creating search charts, reports and dashboards, Editing reports and Dashboard, Adding reports to dashboardHands-on Exercise – Create search charts, reports and dashboards, Edit reports and Dashboard, Add reports to dashboard
Getting started with Parsing
Working with raw data for data extraction, transformation, parsing and previewHands-on Exercise – Extract useful data from raw data, perform transformation, parse different values and preview
Describe Pivot, Relationship between data model and pivot, select a data model object, create a pivot report, instant pivot from a search, add a pivot report to dashboardHands-on Exercise – Select a data model object, create a pivot report, create instant pivot from a search, add a pivot report to dashboard
Common Information Model (CIM) Add-On
What is Splunk CIM, Using the CIM Add-On to normalize dataHands-on Exercise – Use the CIM Add-On to normalize data
Splunk Administration Course Content
Overview of Splunk
Introduction to the Splunk 3 tier architecture, understanding the Server settings, control, preferences and licensing, the most important components of Splunk tool, the hardware requirements, conditions for installation of Splunk.
Understanding how to install and configure Splunk, index creation, input configuration in standalone server, the search preferences, installing Splunk in the Linux environment.
Splunk Installation in Linux
Installing Splunk in the Linux environment, the various prerequisites, configuration of Splunk in Linux.
Distributed Management Console
Introduction to the Splunk Distributed Management Console, index clustering, forwarder management and distributed search in Splunk environment, providing the right authentication to users, access control.
Introduction to Splunk App
Introducing the Splunk app, managing the Splunk app, the various add-ons in Splunk app, deleting and installing apps from SplunkBase, deploying the various app permissions, deploying the Splunk app, apps on forwarder.
Splunk indexes and users
Understanding the index time configuration file and search time configuration file.
Splunk configuration files
Learning about the index time and search time configuration files in Splunk, installing the forwarders, configuring the output and inputs.conf, managing the Universal Forwarders.
Splunk Deployment Management
Deploying the Splunk tool, the Splunk deployment Server, setting up the Splunk deployment environment, deploying the clients grouping in Splunk.
Understanding the Splunk Indexes, the default Splunk Indexes, segregating the Splunk Indexes, learning about Splunk Buckets and Bucket Classification, estimating index storage, creating new index.
User roles and authentication
Understanding the concept of role inheritance, Splunk authentications, native authentications, LDAP authentications.
Splunk Administration Environment
Splunk installation, configuration, data inputs, app management, Splunk important concepts, parsing machine-generated data, search indexer and forwarder.
Basic Production Environment
Introduction to Splunk Configuration Files, Universal Forwarder, Forwarder Management, data management, troubleshooting and monitoring.
Splunk Search Engine
Converting machine-generated data into operational intelligence, setting up Dashboard, Reports and Charts, integrating Search Head Clustering & Indexer Clustering.
Various Splunk Input Methods
Understanding the input methods, deploying scripted, Windows, network and agentless input types, fine-tuning it all.
Splunk User & Index Management
Splunk User authentication and Job Role assignment, learning to manage, monitor and optimize Splunk Indexes.
Machine Data Parsing
Understanding parsing of machine-generated data, manipulation of raw data, previewing and parsing, data field extraction.
Search Scaling and Monitoring
Distributed search concepts, improving search performance, large scale deployment and overcoming execution hurdles, working with Splunk Distributed Management Console for monitoring the entire operation.
Deep Learning Course Content
Introduction to Machine Learning
The domain of machine learning and its implications to the artificial intelligence sector, the advantages of machine learning over other conventional methodologies.
Deep Learning Techniques
Introduction to Deep Learning within machine learning, how it differs from all others methods of machine learning, training the system with training data, supervised and unsupervised learning, classification and regression supervised learning, clustering and association unsupervised learning, the algorithms used in these types of learning.
TensorFlow for Training Deep Learning Model
Introduction to TensorFlowopen source software library for designing, building and training Deep Learning models, Python Library behind TensorFlow, Tensor Processing Unit (TPU) programmable AI accelerator by Google.
Introduction to Neural Networks
Mapping the human mind with Deep Neural Networks, the various building block of Artificial Neural Networks, the architecture of DNN, its building blocks, the concept of reinforcement learning in DNN, the various parameters, layers, activation functions and optimization algorithms in DNN.
Using GPUs to train Deep Learning networks
Introduction to GPUs and how they differ from CPUs, the importance of GPUs in training Deep Learning Networks, the forward pass and backward pass training technique, the GPU constituent with simpler core and concurrent hardware.
Python Course Content
Introduction to Python
What is Python Language and features, Why Python and why it is different from other languages, Installation of Python, Anaconda Python distribution for Windows, Mac, Linux. Run a sample python script, working with Pyhton IDE’s. Running basic python commands – Data types, Variables,Keywords,etcHands-on Exercise – Install Anaconda Python distribution for your OS (Windows/Linux/Mac)
Basic constructs of Python language
Indentation(Tabs and Spaces) and Code Comments (Pound # character); Variables and Names; Built-in Data Types in Python – Numeric: int, float, complex – Containers: list, tuple, set, dict – Text Sequence: Str (String) – Others: Modules, Classes, Instances, Exceptions, Null Object, Ellipsis Object – Constants: False, True, None, NotImplemented, Ellipsis, __debug__; Basic Operators: Arithmetic, Comparison, Assignment, Logical, Bitwise, Membership, Indentity; Slicing and The Slice Operator [n:m]; Control and Loop Statements: if, for, while, range(), break, continue, else;Hands-on Exercise – 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
Wrting Object Oriented Program in Python and connecting with Database
Classes – classes and objects, access modifiers, instance and class members OOPS paradigm – Inheritance, Polymorphism and Encapsulation in Python. Functions: Parameters and Return Types; Lambda Expressions, Making connection with Database for pulling data.
File Handling, Exception Handling in Python
Open a File, Read from a File, Write into a File; Resetting the current position in a File; The Pickle (Serialize and Deserialize Python Objects); The Shelve (Overcome the limitation of Pickle); What is an Exception; Raising an Exception; Catching an Exception;Hands-on Exercise – Open a text file and read the contents, Write a new line in the opened file, Use pickle to serialize a python object, deserialize the object, Raise an exception and catch it
Mathematical Computing with Python (NumPy)
Arrays and Matrices, ND-array object, Array indexing, Datatypes, Array math Broadcasting, Std Deviation, Conditional Prob, Covariance and Correlation.Hands-on Exercise – 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)
Builds on top of NumPy, SciPy and its characteristics, subpackages: cluster, fftpack, linalg, signal, integrate, optimize, stats; Bayes Theorem using SciPyHands-on Exercise – Import SciPy, Apply Bayes theorem using SciPy on the given dataset
Data Visualization (Matplotlib)
Plotting Grapsh and Charts (Line, Pie, Bar, Scatter, Histogram, 3-D); Subplots; The Matplotlib APIHands-on Exercise – Plot Line, Pie, Scatter, Histogram and other charts using Matplotlib
Data Analysis and Machine Learning (Pandas) OR Data Manipulation with Python
Dataframes, NumPy array to a dataframe; Import Data (csv, json, excel, sql database); Data operations: View, Select, Filter, Sort, Groupby, Cleaning, Join/Combine, Handling Missing Values; Introduction to Machine Learning(ML); Linear Regression; Time SeriesHands-on Exercise – Import Pandas, Use it to import data from a json file,,Select records by a group and apply filter on top of that, View the records, Perform Linear Regression analysis, Create a Time Series
Natural Language Processing, Machine Learning (Scikit-Learn)
Introduction to Natural Language Processing (NLP); NLP approach for Text Data; Environment Setup (Jupyter Notebook); Sentence Analysis; ML Algorithms in Scikit-Learn; What is Bag of Words Model; Feature Extraction from Text; Model Training; Search Grid; Multiple Parameters; Build a PipelineHands-on Exercise – 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
What is Web Scraping; Web Scraping Libraries (Beautifulsoup, Scrapy); Installation of Beautifulsoup; Install lxml Python Parser; Making a Soup Object using an input html; Navigating Py Objects in the Soup Tree; Searching the Tree; Output Print; Parsing Full or PartialHands-on Exercise – 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
Understanding Hadoop and its various components; Hadoop ecosystem and Hadoop common; HDFS and MapReduce Architecture; Python scripting for MapReduce Jobs on Hadoop frameworkHands-on Exercise – Write a basic MapReduce Job in Python and connect with Hadoop Framework to perform the task
Writing Spark code using Python
What is Spark,understanding RDDs, Spark Libs, writing Spark code using python,Spark Machine Libraries Mlib, Regression, Classification and Clustering using Spark MLlibHands-on Exercise – Implement sandbox, Run a python code in sandbox, Work with HDFS file system from sandbox
Tableau Course Content
Introduction to Data Visualization and Power of Tableau
What is data visualization, Comparision and benefits against reading raw numbers, Real usage examples from various business domains, Some quick powerful examples using Tableau without going into the technical details of Tableau
Architecture of Tableau
Installation of Tableau Desktop, Architecture of Tableau, Interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane etc), How to start with Tableau, Ways to share and exporting the work done in TableauHands-on Exercise – Play with the tableau desktop, interface to learn its user interface, Share an existing work, Export an existing work
Working with Metadata & Data Blending
Connection to Excels, PDFs and Cubes, Managing Metadata and Extracts, Data Preparation and dealing with NULL values, Data Joins (Inner, Left, Right, Outer) and Union, Cross Database joining, Data BlendingHands-on Exercise – 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
Creation of sets
Marks, Highlighting, Sort and Group, Working with Sets (Creation of sets, Editing sets, IN/OUT, Sets in Hierarchies)Hands-on Exercise – Create and edit sets using Marks, Highlight desired items, Make groups, Applying sorting on result, Make hierachies in the created set
Working with Filters
Filters (Addition and Removal), Filtering continuous dates, dimensions, measures, Interactive FiltersHands-on Exercise – Add Filter on data set by date/dimensions/measures, Use interactive filter to views, Remove some filters to see the result
Organizing Data and Visual Analytics
Formatting Data (Labels, Annotations, Tooltips, Edit axes), Formatting Pane (Menu, Settings, Font, Alignment, Copy-Paste), Trend and Reference Lines, Forecasting, k-means Cluster Analysis in TableauHands-on Exercise – 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
Coordinate points, Plotting Longitude and Latitude, Editing Unrecognized Locations, Custom Geocoding, Polygon Maps, WMS: Web Mapping Services, Background Image (Add Image, Plot Points on Image, Generate coordinates from Image)Hands-on Exercise –
Plot latitude and longitude on geo map, Edit locations on the map, Create custom geocoding, Use images of a map and plot points on it, find coordinates in the image, Create a polygon map, Use WMS