data science & data analytics combo Training in Pune/ Online

Courses Included:-

SQL+ Python Scripting + Data Science With Machine Learning + PowerBI or Tableau

Duration of Training : 6 months

Batch type : Weekdays/Weekends

Mode of Training : Classroom/Online/Corporate Training

Why Radical Technologies

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Learn from Industry Experts, Hands-on labs

Flexible Options: online, instructor-led, self-paced

14+ Years of Industry Recognitions

1 Lakh+ Students Trained

50,000+ Students Placed

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ORACLE SQL

Training Highlights :

Training will be for experienced as well as for freshers also where everyone will get familiar with RDBMS concepts using Oracle and will be expert in SQL programming. Training will be user friendly and will be more focusing on real time programs as per industry standards.

Subject Overview :

By attending the training, you will become the expert in SQL programming. There will be complete subject covered by explaining each in & out of SQL. We shall be covering the following in training :

• Why to use SQL

• History of SQL

• Advantage of SQL

• Types of SQL

• Practical implementation of each SQL

• Writing simple and complex queries

• Constraints

• Different Types of Functions

• Group By with Having

Interview Preparation :

As per the topics covered, after each topics there will be interview questions preparation.

COURSE CONTENT :

For whom SQL is? :

IT or NON IT people can easily enter into leading Database Technology which is commonly used for all domain application software as back-end. There are so many reason to go ahead :

Oracle SQL is basic learning platform to get in IT, which is easy to learn.
On completion Oracle SQL training you can attempt certification exam.
Certified candidate can take benefit as IT document and can get good opportunity in IT world.
Oracle SQL is easy for any support project for initial stage which get into IT carrier. In case you want to grow for development environment than it’s entry for programming.
Now a days SQL is basic skill set for any IT/NON IT folks, It’s always helpful for any functional, support and technical consultant.

WEEK -01:

Oracle 12c SQL :

SQL Basic

[1] Introduction to Oracle DB 12c

Oracle Database 12c: Focus Areas, Fusion Middle-ware, Oracle Cloud, Services, Deployment Methods, RDBMS, Data Model, ER Diagram, Relation DB Terminology, SQL Statements and Development Environment, HR Schema Tables, DB Documentation.

[2] Retrieving Data using SQL SELECT statement

Basic Select Statement, Arithmetic Expression, Defining Null values, Concatenation Operator & Literal, Duplicate Rows, Displaying table structure. Practice Overview.

[3] Restricting & Sorting Data

Use of Where clause, Character Strings & Date Data, Comparison, Logical, Range, Pattern, & Other operators, Rules Precedence, Order By Clause, Substitution variables. Practice Overview .

WEEK-02 :

[4] Single Row Function & Customized Output

SQL function, Single Row Function, Character/Case Functions, Number Functions, Nesting Function, Date Functions etc. Practice Overview.

[5] Conversion & Conditional Expressions

Conversion Function, Implicit/Explicit Data Conversion, General Function with covering NVL, NVL2, NULLIF, COALESCE, Conditional Expression with Decode function and Case Expression. Practice Overview.

[6] Reporting Aggregated Data using Group Function

Usages of Group function, Nesting Group Function, Creating Group Data, Restricting group data using Having Clause. Practice Overview.

[7] Displaying Data from Multiple Tables using Join

Type of Joins, Explaining with Natural Join, Using Clause, On Clause, SQL 99 Syntax, Three-Way Join, Self Join, NonEqui Join, Inner Versus Outer Join, Cross Join etc. Practice overview.

WEEK-03 :

[8] Using Sub-queries to solve queries

Scenario to use Sub-query, Rules for Sub-queries, Type of Sub-queries, Single Row Sub-queries and Multi Row Sub-queries. Null value in Sub-query etc. Practice Overview.

SQL Advanced :

[9] Using Set Operator

Set Operator Rules, Covering Set Operator as UNION, UNION ALL, INTERSECT, MINUS etc. Practice Overview.

[10] Managing Tables using DML Statements

Data Manipulation Language, Covering INSERT, UPDATE, DELETE & DB Transaction control using COMMIT, ROLLBACK, SAVEPOINT. Use of For update clause in SELECT Statement.

[11] Introduction to Data Definition Language

Database Object, Naming Rules, Various Data Types, CREATE TABLE statement, Constraint guidelines, Defining constraints as NOT NULL, UNIQUE, PRIMARY KEY, FOREIGN KEY & CHECK etc. ALTER TABLE, DROP TABLE Statement. Create Table using Sub-query. Practice Overview.

WEEK-04 :

[12] Introduction to Data Dictionary Views

Data Dictionary Structure, How to use Data Dictionary, USER_OBJECTS, ALL_OBJECT, USER_CONSTRAININTS. TABLE Information, Column Information etc. Practice overview.

[13] Creating Sequence, Synonyms & Indexes

Importance of Sequence, Synonyms & Indexes. Defining Sequence, Synonyms & Indexes & DROP statement etc. Practice overview.

[14] Creating Views

What’s View? Advantage of Views, CREATE Simple & Complex views, Rules for performing DML operation on views. Modifying & Removing a view etc. Practice Overview.

[15] Retrieving Data by using Sub-Query

Multiple Column Sub-query, Column Comparison, Pair & Non Pair Sub-queries, Scalar Sub queries, Co-related Sub-queries, Use of WITH clause. Practice Overview.

PYTHON SCRIPTING

Highlights :

Python is a widely used general-purpose, high-level programming language. Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles. It features a dynamic type system and automatic memory management and has a large and comprehensive standard library.The official repository of third-party software for Python contains more than 38,000 packages covering a wide range of functionality.

Audience :

Application programmers, automation engineer, testers, system administrators,
web-crawlers and UNIX/NT power users.

Pre-requisite : Basic of UNIX or Windows

For whom Python is?

IT folks who want to excel or change their profile in a most demanding language which is in demand by almost all clients in all domains because of below mentioned reasons-

Python is open source (Cost saving)
Python has relatively few keywords, simple structure, and a clearly defined syntax. This allows the student to pick up the language in a relatively short period of time.
Python comes with a large collection of prebuilt and portable functionality known as the standard library. Python has more than 20 Thousand modules. Every new development comes very early in Python like Hadoop interface, Raspberry Pi and many more!
Python can run on a wide variety of hardware platforms and has the same interface on all platforms.
You can add low-level modules to the Python interpreter. These modules enable programmers to add to or customize their tools to be more efficient.

Django framework might be the most famous Python web framework, there is also a host of successful small and micro-frameworks.

Who use Python?

Google makes extensive use of Python in its web search system, and employs Python’s creator Guido van Rossum.
The YouTube video sharing service is largely written in Python.
Disney uses Python in many of their creative processes.
Mozilla uses Python to explore their extensive code base and releases tons of open source packages built in python.
Dropbox file hosting service is implemented using Python, Guido van Rossum now working here.
The popular Bit Torrent peer-to-peer file sharing system is a Python program.
Intel, Cisco, Hewlett-Packard, Seagate, Qualcomm, and IBM use Python for hardware testing.
JPMorgan Chase, UBS, Getco, and Citadel apply Python for financial market forecasting.
NASA, Los Alamos, JPL, use Python for scientific programming tasks.
iRobot uses Python to develop commercial robotic vacuum cleaners.
The NSA uses Python for cryptography and intelligence analysis.
And Many More

COURSE CONTENT :

PYTHON – CORE

Introduction
What is Python..?
A Brief history of Python
Why Should I learn Python..?
Installing Python
How to execute Python program
Write your first program
Overview on Jupyter Notebook
Overview on Command Line

Variables & Data Types
Variables
Numbers
String
Boolean
List
Set
Tuple
Dictionary

Conditional Statements & Loops
if…statement
if…else statement
elif…statement
The while…Loop
The for….Loop

Control Statements
continue statement
break statement
pass statement

Functions
Define function
Calling a function
Function arguments
Built-in functions

Modules & Packages
Modules
How to import a module…?
Built-in Module – OS
Built-in Module – sys
Built-in Module – argparse
Built-in Module – statistics
Built-in Module – math

Packages
How to create packages

Classes & Objects
Introduction about classes & objects
Creating a class & object
Inheritance
Methods Overriding
Data hiding

Files & Exception Handling
Writing data to a file
Reading data from a file
Read and Write data from csv file
try…except
try…except…else
finally

Project Work – PYTHON : DATA ANALYSIS

Getting started with Python Libraries
what is data analysis ?
why python for data analysis ?
Essential Python Libraries
Installation and setup
Ipython
2.7 VS 3.5

NumPy Arrays
Creating multidimensional array
NumPy-Data types
Array attributes
Indexing and Slicing
Creating array views and copies
Manipulating array shapes
I/O with NumPy

Working with Pandas
Installing pandas
Pandas dataframes
Pandas Series
Data aggregation with Pandas DataFrames
Concatenating and appending DataFrames
Joining DataFrames
Handling missing data

Data Loading,Storage and file format
Writing CSV files with numpy and pandas
HDF5 format
Reading and Writing to Excel with pandas
JSON data
Parsing HTML with Beautiful Soup
PyTables

Python Regular Expressions
What are regular expressions?
The match Function
The search Function
Matching vs searching
Search and Replace
Extended Regular Expressions
Wildcard

Python Oracle Database Access
Install the cx_Oracle and other Packages
Create Database Connection
CREATE, INSERT, READ, UPDATE and DELETE Operation
DML and DDL Oepration with Databases
Performing Transactions
Handling Database Errors
Disconnecting Database

PROJECT WORK :

Random password generator	Mini
CLI based scientific calculator	Mini
Instagram bot	Mini
Expense Tracker	Mini
Site connectivity checker	Mini
Lawn Tennis Match Highlight (Can be extended to any sport)	Major
NLP library	Major

DATASCIENCE & MACHINE LEARNING WITH PYTHON

Data Science & ML With Python Training & Certification in Pune

Highly Experienced Certified Trainer with 10+ yrs Exp. in Industry

Realtime Projects, Scenarios & Assignments

Learn Data Science, Deep Learning & Machine Learning with Python

Live Machine Learning & Deep Learning Projects

WANT TO BE A FUTURE DATA SCIENTIST ?

Introduction :

This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median mode etc. and eventually covers all aspects of an analytics (or) data science career from analyzing and preparing raw data to visualizing your findings. If you’re a programmer or a fresh graduate looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic to Advance techniques used by real-world industry data scientists.

Data Science, Statistics with Python :

This course Start with introduction to Data Science and Statistics using Python. It covers both the aspects of Statistical concepts and the practical implementation using Python. If you’re new to Python, don’t worry – the course starts with a crash course to teach you all basic programming concepts. If you’ve done some programming before or you are new in Programming, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC’s; the sample code will also run on MacOS or Linux desktop systems.

Analytics :

Using Spark and Scala you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Data frames to manipulate data with ease.

Machine Learning and Data Science :

Spark’s core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We’ll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets.

Real Life examples :

Every concept is explained with the help of examples, case studies and source code wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant. finance context.

Target Audience :

Engineering/Management Graduate or Post-graduate Fresher Students who want to make their career in the Data Science Industry or want to be future Data Scientists.
Engineers who want to use a distributed computing engine for batch or stream processing or both
Analysts who want to leverage Spark for analyzing interesting datasets
Data Scientists who want a single engine for analyzing and modelling data
MBA Graduates or business professionals who are looking to move to a heavily quantitative role.
Engineering Graduate/Professionals who want to understand basic statistics and lay a foundation for a career in Data Science
Working Professional or Fresh Graduate who have mostly worked in Descriptive analytics or not work anywhere and want to make the shift to being data scientists
Professionals who’ve worked mostly with tools like Excel and want to learn how to use Python for statistical analysis.

COURSE CONTENT :

Introduction to Data Science with Python

What is analytics & Data Science?
Common Terms in Analytics
Analytics vs. Data warehousing, OLAP, MIS Reporting
Relevance in industry and need of the hour
Types of problems and business objectives in various industries
How leading companies are harnessing the power of analytics?
Critical success drivers
Overview of analytics tools & their popularity
Analytics Methodology & problem solving framework
List of steps in Analytics projects
Identify the most appropriate solution design for the given problem statement
Project plan for Analytics project & key milestones based on effort estimates
Build Resource plan for analytics project

Python Essentials

Why Python for data science?
Overview of Python- Starting with Python
Introduction to installation of Python
Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
Understand Jupyter notebook & Customize Settings
Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
Installing & loading Packages & Name Spaces
Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
List and Dictionary Comprehensions
Variable & Value Labels – Date & Time Values
Basic Operations – Mathematical – string – date
Reading and writing data
Simple plotting
Control flow & conditional statements
Debugging & Code profiling
How to create class and modules and how to call them?

Scientific Distributions Used In Python For Data Science

NumPy, pandas, scikit-learn, stat models, nltk

Accessing/Importing And Exporting Data Using Python Modules

Importing Data from various sources (Csv, txt, excel, access etc)
Database Input (Connecting to database)
Viewing Data objects – subsetting Data, methods
Exporting Data to various formats
Important python modules : Pandas, beautiful soup

Data Manipulation – Cleansing – Munging using python modules

Cleansing Data with Python
Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
Python Built-in Functions (Text, numeric, date, utility functions)
Python User Defined Functions
Stripping out extraneous information
Normalizing data
Formatting data
Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc.)

Data Analysis – Visualization Using Python

Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
Creating Graphs – Bar/pie/line chart/histogram/boxplot/scatter/density etc.)
Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas and SciPy. Stats etc.)

Introduction to Statistics

Basic Statistics – Measures of Central Tendencies and Variance
Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
Inferential Statistics -Sampling – Concept of Hypothesis Testing Statistical Methods – Z/t-tests (One sample, independent, paired), Analysis of variance, Correlations and Chi-square
Important modules for statistical methods : NumPy, SciPy, Pandas

Introduction to Predictive Modelling

Concept of model in analytics and how it is used?
Common terminology used in analytics & Modelling process
Popular modelling algorithms
Types of Business problems – Mapping of Techniques
Different Phases of Predictive Modelling

Data Exploration For Modelling

Need for structured exploratory data
EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
Identify missing data
Identify outliers data
Visualize the data trends and patterns

Data Preparation

Need of Data preparation
Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
Variable Reduction Techniques – Factor & PCA Analysis

Segmentation : Solving Segmentation Problems

Introduction to Segmentation
Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
Behavioural Segmentation Techniques (K-Means Cluster Analysis)
Cluster evaluation and profiling – Identify cluster characteristics
Interpretation of results – Implementation on new data

Linear Regression : Solving Regression Problems

Introduction – Applications
Assumptions of Linear Regression
Building Linear Regression Model
Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
Assess the overall effectiveness of the model
Validation of Models (Re running Vs. Scoring)
Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
Interpretation of Results – Business Validation – Implementation on new data

Logistic Regression : Solving Classification Problems

Introduction – Applications
Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
Building Logistic Regression Model (Binary Logistic Model)
Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
Validation of Logistic Regression Models (Re running Vs. Scoring)
Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
Interpretation of Results – Business Validation – Implementation on new data

Time Series Forecasting : Solving Forecasting Problems

Introduction – Applications
Time Series Components (Trend, Seasonality, Cyclicity and Level) and Decomposition
Classification of Techniques (Pattern based – Pattern less)
Basic Techniques – Averages, Smoothening, etc
Advanced Techniques – AR Models, ARIMA, etc
Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

Machine Learning : Predictive Modelling

Introduction to Machine Learning & Predictive Modelling
Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation)
Overfitting (Bias-Variance Trade off) & Performance Metrics
Feature engineering & dimension reduction
Concept of optimization & cost function
Overview of gradient descent algorithm
Overview of Cross validation(Bootstrapping, K-Fold validation etc)
Model performance metrics (R-square, Adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics)

Unsupervised Learning : Segmentation

What is segmentation & Role of ML in Segmentation?
Concept of Distance and related math background
K-Means Clustering
Expectation Maximization
Hierarchical Clustering
Spectral Clustering (DBSCAN)
Principle component Analysis (PCA)

Supervised Learning : Decision Trees

Decision Trees – Introduction – Applications
Types of Decision Tree Algorithms
Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
Decision Trees – Validation
Overfitting – Best Practices to avoid

Supervised Learning : Ensemble Learning

Concept of Ensembling
Manual Ensembling Vs. Automated Ensembling
Methods of Ensembling (Stacking, Mixture of Experts)
Bagging (Logic, Practical Applications)
Random forest (Logic, Practical Applications)
Boosting (Logic, Practical Applications)
Ada Boost
Gradient Boosting Machines (GBM)
XGBoost

Supervised Learning : Artificial Neural Network – ANN

Motivation for Neural Networks and Its Applications
Perceptron and Single Layer Neural Network, and Hand Calculations
Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
Neural Networks for Regression
Neural Networks for Classification
Interpretation of Outputs and Fine tune the models with hyper parameters
Validating ANN models

Supervised Learning : Support Vector Machines

Motivation for Support Vector Machine & Applications
Support Vector Regression
Support vector classifier (Linear & Non-Linear)
Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
Interpretation of Outputs and Fine tune the models with hyper parameters
Validating SVM models

Supervised Learning : KNN

What is KNN & Applications?
KNN for missing treatment
KNN For solving regression problems
KNN for solving classification problems
Validating KNN model
Model fine tuning with hyper parameters

Supervised Learning : Naive Bayes

Concept of Conditional Probability
Bayes Theorem and Its Applications
Naïve Bayes for classification
Applications of Naïve Bayes in Classifications

Text Mining And Analytics

Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD); Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
Finding patterns in text: text mining, text as a graph
Natural Language processing (NLP)
Text Analytics – Sentiment Analysis using Python
Text Analytics – Word cloud analysis using Python
Text Analytics – Segmentation using K-Means/Hierarchical Clustering
Text Analytics – Classification (Spam/Not spam)
Applications of Social Media Analytics
Metrics(Measures Actions) in social media analytics
Examples & Actionable Insights using Social Media Analytics
Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)
Fine tuning the models using Hyper parameters, grid search, piping etc.

POWER BI

COURSE CONTENT

Data Transformations :

Introduction to Power BI Desktop
Changing Locale
Connecting to a Database
Basic Transformations
Managing Query Groups
Splitting Columns, Changing Data Types, Working with Dates
Removing and Reordering Columns
Conditional Columns
Merge Queries
Query Dependency View
Transforming Less Structured Data
Query Parameters

Data Modelling :

Managing Data Relationships
Creating Calculated Columns
Optimizing Models for Reporting
Creating Calculated Measures
Creating and Managing Hierarchies
Using Calculated Tables
Time Intelligence
Include and Exclude features
Grouping and Binning

Visualizing your Data :

Introduction to charts: Pie, Tree map, Combo charts, Map Visualizations, Scatter plot, Table, Matrix, Gauge, Card, Shapes, Textboxes, Images and KPI
Filter (Including TopN), Date Slicer
Coloring Charts
Page Layout, Positioning, Aligning, Sorting Visuals and Formatting
Visual Relationship
Custom Hierarchies

Working with PBI Service :

Overview of Dashboards and Service
Uploading to Power BI Service
Configuring a Dashboard
Dashboard Settings
In-Focus Mode
Pinning a Live Page
Custom URL and Title
Export to CSV and Excel
Power BI Notifications
Publishing to Web

Working with Excel :

Importing Excel Data using Simple Table
Connecting to Excel Workbook on OneDrive for Business
Pinning Excel Tables or Visuals

Organization Packs, Security and Groups :

Creating a Group
Creating, Using and Editing a Content Pack
Row Level Security
Data Classification
Creating and Using Custom Visuals

TABLEAU

Learning Objectives :

At the end of this class, the student will be able to :

Build advanced chart types and visualizations
Build complex calculations to manipulate your data
Work with statistics and statistical techniques
Work with parameters and input controls
Implement advanced geographic mapping techniques and use custom images to build spatial visualizations of non-geographic data
Implement all options in working with data: Joining multiple tables, data blending, performance considerations and working with the Data Engine, and understand when to implement which connection method.
Build better dashboards using techniques for guided analytics, interactive dashboard design and visual best practices
Implement many efficiency tips and tricks
Understand the basics of Tableau Server and other options for sharing your results

Is SQL required to learn Tableau?

You must have to have a basic knowledge of SQL, and this would help in writing custom queries which is not possible through to frag and drop functionality to achieve complex data set building.

COURSE CONTENT :

1. Introduction and Getting Started

Why Tableau? Why Visualization?
The Tableau Product Line
Level Setting – Terminology
Getting Started – creating some powerful visualizations quickly
Review of some Key Fundamental Concepts
What is Tableau?
What does the Tableau product suite comprise of? How Does Tableau Work?
Tableau Architecture
Connecting to Data & Introduction to data source concepts

Understanding the Tableau workspace
Dimensions and Measures
Data Types & Default Properties
Tour of Shelves & Marks Card
Using Show Me
Saving and Sharing your work-overview

2. Filtering, Sorting & Grouping

Filtering, Sorting and Grouping are fundamental concepts
when working with and analyzing data. We will briefly review these topics as they apply to Tableau

Advanced options for filtering and hiding
Understanding your many options for ordering and grouping your data: Sort, Groups, Bins, Sets
Understanding how all of these options inter-relate

3. Working with Data

In the Advanced class, we will understand the difference between joining and blending data, and when we should do each. We will also consider the implications of working with large data sets, and consider options for when and how to work with extracts and the data engine. We will also investigate best practices in “sharing” data sources for Tableau Server users.

Data Types and Roles
Dimension versus Measures
Data Types
Discrete versus Continuous
The meaning of pill colors
Database Joins
Data Blending
Working with the Data Engine / Extracts and scheduling extract updates
Working with Custom SQL
Adding to Context
Switching to Direct Connection

4. Working with Calculated Data and Statistics

In the Fundamentals Class, we were introduced to some basic calculations: basic string and arithmetic calculations and ratios and quick table calculations. In the Advanced class, we will extend those concepts to understand the intricacies of manipulating data within Tableau

5. A Quick Review of Basic Calculations

Arithmetic Calculations

String Manipulation

Date Calculations

Quick Table Calculations

Custom Aggregations

Custom Calculated Fields

Logic and Conditional Calculations

Conditional Filters

6. Advanced Table Calculations

Understanding Scope and Direction

Calculate on Results of Table Calculations

Complex Calculations

Difference From Average

Discrete Aggregations

Index to Ratios

7. Working with Parameters

In the Fundamentals class, we were introduced to parameters – How to create a parameter and use it in a calculation. In the Advanced class, we will go into more details on how we can use parameters to modify our title, create What-If analysis, etc

Parameter Basics

Data types of parameters

Using parameters in calculated fields

Inputting parameter values and parameter control options

Advanced Usage of Parameters

Using parameters for titles, field selections, logic statements, Top X

8. Building Advanced Chart Types and Visualizations/Tips & Tricks

This topic covers how to create some of the chart types and visualizations that may be less obvious in Tableau. It also covers some of the more common tips & tricks/techniques that we use to assist customers in solving some of their more complex problems.

Bar in Bar
Box Plot
Bullet Chart
Custom Shapes
Gantt Chart
Heat Map
Pareto Chart
Spark Line
KPI Chart

9. Best Practices in Formatting and Visualizing

Formatting Tips
Drag to Legend
Edit Legend
Highlighting
Labeling
Legends
Working with Nulls
Table Options
Annotations and Display Options
Introduction to Visualization Best Practices

10. Data Analysis And Reporting

Data Analysis using Pivot Tables – use of row and column shelf, values and filters
Difference between data layering and cross tabulation, summary reports, advantages and limitations
Change aggregation types and summarisation
Creating groups and bins in pivot data
Concept of calculated fields, usage and limitations
Changing report layouts – Outline, compact and tabular forms
Show and hide grand totals and subtotals
Creating summary reports using pivot tables

11. SQL : Data Manipulation (DML Commands)

Insert, Update & Delete statements
Select statement – Subsetting, Filters, Sorting. Removing Duplicates, grouping and aggregations etc
Where, Group By, Order by & Having clauses
SQL Functions – Number, Text, Date, etc
SQL Keywords – Top, Distinct, Null, etc
SQL Operators – Relational (single valued and multi valued), Logical (and, or, not), Use of wildcard operators and wildcard characters, etc

12. SQL : Accessing Data From Multiple Tables Using SELECT

Append and JoinsUnion and Union All – Use & constraints
Intersect and Except statements
Table Joins – inner join, left join, right join, full join
Cross joins/cartesian products, self joins, natural joins etc
Inline views and sub-queries
Optimizing your work

13. Tableau : Data Handling & Summaries

Date Aggregations and Date parts
Cross tab & Tabular charts
Totals & Subtotals
Bar Charts & Stacked Bars
Line Graphs with Date & Without Date
Tree maps
Scatter Plots
Individual Axes, Blended Axes, Dual Axes & Combination chart
Parts of Views
Sorting
Trend lines/ Forecasting
Reference Lines
Filters/Context filters
Sets
In/Out Sets
Combined Sets
Grouping
Bins/Histograms
Drilling up/down – drill through
Hierarchies
View data
Actions (across sheets)

14. Tableau : Building Advanced Reports/ Maps

Explain latitude and longitude
Default location/Edit locations
Building geographical maps
Using Map layers

15. Tableau : Calculated Fields

Working with aggregate versus disaggregate data
Explain – #Number of Rows
Basic Functions (String, Date, Numbers etc)
Usage of Logical conditions

16. Tableau : Table Calculations

Explain scope and direction
Percent of Total, Running / Cumulative calculations
Introduction to LOD (Level of Detail) Expressions
User applications of Table calculations

17. Tableau : Parameters

Using Parameters in
Calculated fieldsBins
Reference Lines
Filters/Sets
Display Options (Dynamic Dimension/Measure Selection)
Create What-If/Scenario analysis

18. Tableau : Building Interactive Dashboards

Combining multiple visualizations into a dashboard (overview)
Making your worksheet interactive by using actions
Filter
URL
Highlight
Complete Interactive Dashboard for Sales & Services

19. Tableau : Formatting

Options in Formatting your Visualization
Working with Labels and Annotations
Effective Use of Titles and Captions

20. Tableau : Working With Data

Multiple Table Joins
Data Blending
Difference between joining and blending data, and when we should do each
Toggle between to Direct Connection and Extracts

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13. Tableau : Data Handling & Summaries

14. Tableau : Building Advanced Reports/ Maps

15. Tableau : Calculated Fields

16. Tableau : Table Calculations

17. Tableau : Parameters

18. Tableau : Building Interactive Dashboards

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20. Tableau : Working With Data

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Is SQL required to learn Tableau?

10. Data Analysis And Reporting

11. SQL : Data Manipulation (DML Commands)

12. SQL : Accessing Data From Multiple Tables Using SELECT

13. Tableau : Data Handling & Summaries

14. Tableau : Building Advanced Reports/ Maps

15. Tableau : Calculated Fields

16. Tableau : Table Calculations

17. Tableau : Parameters

18. Tableau : Building Interactive Dashboards

19. Tableau : Formatting

20. Tableau : Working With Data

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