A Data Science and Data Analytics combo represents a powerful skill set for individuals looking to work with data in a variety of roles.a Data Science and Data Analytics combo creates a holistic approach to working with data. It enables professionals to not only analyze historical data for insights but also apply predictive modeling and machine learning to drive future decision-making, making them invaluable in today’s data-driven business landscape.

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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

100% Placement Guarantee for the Right Candidate

10+ Years Real Time Experienced Trainers

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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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.


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.



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 



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

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

Control Statements
continue statement
break statement
pass statement

Define function
Calling a function
Function arguments
Built-in functions

Modules & Packages
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

How to create packages

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

Files & Exception Handling
Writing data to a file
Reading data from a file
Read and Write data from csv file


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

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

Python Oracle Database Access
Install the cx_Oracle and other Packages
Create Database Connection
DML and DDL Oepration with Databases
Performing Transactions
Handling Database Errors
Disconnecting Database


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









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 


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.


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.




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



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.



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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