Online & Classroom

Be a Data Analyst

Learning by doing is what we believe. State-of-the-art labs to facilitate competent training.

Months Icon 3 Months
38 Modules
Get In Touch

Key Features

OSACAD is committed to bringing you the best learning experience with high-standard features including

Key Features

Learning by doing is what we believe. State-of-the-art labs to facilitate competent training.

Key Features
Physical & Virtual ONLINE CLASSROOMS

Providing the flexibility to learn from our classrooms or anywhere you wish considering these turbulent times.

Key Features
24/7 Support ON SLACK

Technical or Technological, we give you assistance for every challenge you face round-the-clock.

Key Features
JOB & INTERVIEW Assistance

Guiding in & out, until you get placed in your dream job.

Key Features
LIVE PROJECTS with Our Industry Partners

An inside look & feel at industry environments by handling real-time projects.

Key Features

Opportunity to prove your talent as an intern at our partner firms and rope for permanent jobs.

A career Path to be An Data Analyst

A career Path to be a data scientist

To be An Data Analyst you should

Step 1: Kick start with Intro To Data Analysis

What is Data Analysis Impact | Future Trends | Applications Begin the Data Analysis journey by learning the basics, applications and impact of Data Analysis in business.

A career Path to be a data scientist
A career Path to be a data scientist
Step 2: Then Mathematics

Understand and evaluate data by performing statistical analysis and data modeling

Step 3: Learn Programming for Data Preprocessing

Python I R
Become proficient in Data Analysis by mastering programming skills

A career Path to be a data scientist
A career Path to be a data scientist
Step 4: Learn Business Intelligence

Tableau | Qlikview | PowerBI
Get an in-depth understanding of the latest BI tools to present the analytics and insights gained from models created

A career Path to be a data scientist

You as a Data Scientist

Why Data Analyst ?

Why Data Science
Gain problem solving skills

At heart, analytics is all about solving problems.The ability to think analytically and approach problems in the right way is a skill that's always useful, not just in the professional world, but in everyday life as well

Why Data Science
High demand

Data analysts are valuable, and with a looming skills shortage on the horizon as more and more businesses and sectors start working with big data, this value is only going to increase

Why Data Science
A range of related skills

The great thing about being an analytics specialist is that the field encompasses so much more than simply knowing how to work with data and solve problems. Yes, those are undoubtedly crucial elements, but data analysts also need to know how to communicate complex information to those without expertise

Who is This program for

  • Data Scientists,Statisticians,Business Analysts,Project Managers
  • Business Intelligence & Testing Professionals
  • Data Analysts and Functional Experts
  • Anyone who wish to embark on a career in analytics domain
Who is this program


Best-in-class content by leading faculty and industry leaders in the form of videos,
cases and projects, assignments and live sessions.


  • Why Python Is Hot.
  • Choosing the Right Python.
  • Tools for Success.
  • An excellent, free learning environment.
  • Installing Anaconda and VS Code.
  • Writing Python in VS Code.
  • Choosing your Python interpreter
  • Writing some Python code.
  • Getting back to VS Code Python
  • Using Jupyter Notebook for Coding
  • Using Python Interactive Mode.
  • Opening Terminal.
  • Getting your Python version .
  • Going into the Python Interpreter .
  • Entering commands.
  • Using Python’s built-in help.
  • Exiting interactive help.
  • Searching for specific help topics online.
  • Lots of free cheat sheets .
  • Creating a Python Development Workspace.
  • Creating a Folder for your Python Code .
  • Typing, Editing, and Debugging Python Code.
  • Writing Python code.
  • Saving your code.
  • Running Python in VS Code.
  • Simple debugging.
  • The VS Code Python debugger .
  • Writing Code in a Jupyter Notebook.
  • Creating a folder for Jupyter Notebook.
  • Creating and saving a Jupyter notebook.
  • Typing and running code in a notebook .
  • Adding some Markdown text.
  • Saving and opening notebooks.
  • The Zen of Python.
  • Object-Oriented Programming.
  • Indentations Count, Big Time .
  • Using Python Modules.
  • Syntax for importing modules.
  • Using an alias with modules .
  • Open the Python App File .
  • Typing and Using Python Comments.
  • Understanding Python Data Types.
  • Numbers.
  • Words (strings).
  • True/false Booleans .
  • Doing Work with Python Operators.
  • Arithmetic operators
  • Comparison operators.
  • Boolean operators.
  • Creating and Using Variables.
  • Creating valid variable names.
  • Creating variables in code.
  • Manipulating variables.
  • Saving your work.
  • Running your Python app in VS Code.
  • What Syntax Is and Why It Matters.
  • Putting Code Together.
  • Calculating Numbers with Functions.
  • Still More Math Functions .
  • Formatting Numbers .
  • Formatting with f-strings .
  • Showing dollar amounts.
  • Formatting percent numbers .
  • Making multiline format strings .
  • Formatting width and alignment.
  • Grappling with Weirder Numbers.
  • Binary, octal, and hexadecimal numbers.
  • Complex numbers.
  • Manipulating Strings.
  • Concatenating strings.
  • Getting the length of a string.
  • Working with common string operators.
  • Manipulating strings with methods .
  • Uncovering Dates and Times.
  • Working with dates.
  • Working with times.
  • Calculating timespans.
  • Accounting for Time Zones .
  • Working with Time Zones.
  • Main Operators for Controlling the Action .
  • Making Decisions with if.
  • Adding else to your if login.
  • Handling multiple else’s with elif.
  • Ternary operations.
  • Repeating a Process with for.
  • Looping through numbers in a range .
  • Looping through a string.
  • Looping through a list.
  • Bailing out of a loop .
  • Looping with continue
  • Nesting loops.
  • Looping with while .
  • Starting while loops over with continue.
  • Breaking while loops with break.
  • Defining and Using Lists.
  • Referencing list items by position.
  • Looping through a list
  • Seeing whether a list contains an item. . . .
  • Getting the length of a list.
  • Adding an item to the end of a list.
  • Inserting an item into a list .
  • Changing an item in a list.
  • Combining list
  • Removing Removing list items.
  • Clearing out a list.
  • Counting how many times an item appears in a list .
  • Finding an list item’s index.
  • Alphabetizing and sorting lists. . . . . .
  • Reversing a list.
  • Copying a list .
  • What’s a Tuple and Who Cares? .
  • Working with Sets.
  • Creating a Data Dictionary.
  • Accessing dictionary data.
  • Getting the length of a dictionary.
  • Seeing whether a key exists in a dictionary
  • Getting dictionary data with get().
  • Changing the value of a key.
  • Adding or changing dictionary data.
  • Looping through a Dictionary.
  • Data Dictionary Methods.
  • Copying a Dictionary.
  • Deleting Dictionary Items.
  • Using pop() with Data Dictionaries.
  • Fun with Multi-Key Dictionaries.
  • Using the mysterious fromkeys and setdefault methods.
  • Nesting Dictionaries
  • Creating a Function.
  • Commenting a Function.
  • Passing Information to a Function.
  • Defining optional parameters with defaults.
  • Passing multiple values to a function.
  • Using keyword arguments (kwargs).
  • Passing multiple values in a list.
  • Passing in an arbitrary number of arguments .
  • Returning Values from Functions.
  • Unmasking Anonymous Functions.
  • Mastering Classes and Objects.
  • Creating a Class.
  • How a Class Creates an Instance .
  • Giving an Object Its Attributes.
  • Creating an instance from a class.
  • Changing the value of an attribute.
  • Defining attributes with default values .
  • Giving a Class Methods.
  • Passing parameters to methods.
  • Calling a class method by class name .
  • Using class variables.
  • Using class methods.
  • Using static methods .
  • Understanding Class Inheritance
  • Creating the base (main) class.
  • Defining a subclass.
  • Overriding a default value from a subclass.
  • Adding extra parameters from a subclass.
  • Calling a base class method.
  • Using the same name twice.
  • Understanding Exceptions
  • Handling Errors Gracefully.
  • Being Specific about Exceptions.
  • Keeping Your App from Crashing.
  • Adding an else to the Mix.
  • Using try . . . except . . . else . . . finally.
  • Raising Your Own Errors
  • Understanding Text and Binary Files.
  • Opening and Closing Files.
  • Reading a File’s Contents.
  • Looping through a File.
  • Looping with readlines().
  • Looping with readline().
  • Appending versus overwriting files.
  • Using tell() to determine the pointer location.
  • Moving the pointer with seek().
  • Reading and Copying a Binary File.
  • Conquering CSV Files .
  • Opening a CSV file.
  • Converting strings.
  • Converting to integers .
  • Converting to date.
  • Converting to Boolean
  • Converting to floats.
  • From CSV to Objects and Dictionaries.
  • Importing CSV to Python objects.
  • Importing CSV to Python dictionaries.
  • Organizing JSON Data.
  • Understanding Serialization .
  • Loading Data from JSON Files.
  • Converting an Excel date to a JSON date.
  • Looping through a keyed JSON file.
  • Converting firebase timestamps to Python dates .
  • Loading unkeyed JSON from a Python string .
  • Loading keyed JSON from a Python string.
  • Changing JSON data .
  • Removing data from a dictionary.
  • Dumping Python Data to JSON.
  • How the Web Works.
  • Understanding the mysterious URL.
  • Exposing the HTTP headers.
  • Opening a URL from Python .
  • Posting to the Web with Python.
  • Scraping the Web with Python .
  • Parsing part of a page.
  • Storing the parsed content
  • Saving scraped data to a JSON file .
  • Saving scraped data to a CSV file .
  • Understanding the Python Standard Library .
  • Using the dir() function.
  • Using the help() function .
  • Exploring built-in functions .
  • Exploring Python Packages .
  • Importing Python Modules .
  • Making Your Own Modules.

Python For Data Analysis

Data Analysis

  • Knowledge Domains of the Data Analyst
  • Computer Science
  • Mathematics and Statistics
  • Machine Learning and Artificial Intelligence
  • Professional Fields of Application
  • When the Data Become Information
  • When the Information Becomes Knowledge
  • Types of Data
  • Problem Definition
  • Data Extraction
  • Data Preparation
  • Data Exploration/Visualization
  • Predictive Modeling
  • Model Validation
  • Deployment
  • NumPy: A Little History
  • The NumPy Installation
  • Ndarray: The Heart of the Library
  • Create an Array
  • Types of Data
  • The dtype Option
  • Intrinsic Creation of an Array
  • Arithmetic Operators
  • The Matrix Product
  • Increment and Decrement Operators
  • Universal Functions (ufunc)
  • Aggregate Functions
  • Indexing
  • Slicing
  • Iterating an Array

Conditions and Boolean Arrays

Shape Manipulation

  • Vectorization
  • Broadcasting

Structured Arrays

  • Loading and Saving Data in Binary Files
  • Reading File with Tabular Data

The pandas Library—An Introduction

  • Understanding Exceptions
  • Installation from Anaconda
  • Installation from PyPI
  • Installation on Linux
  • Installation from Source
  • A Module Repository for Windows

Test Your pandas Installation

Getting Started with pandas


  • Flexible Arithmetic Methods
  • Operations between DataFrame and Series
  • Functions by Element
  • Functions by Row or Column
  • Statistics Functions

Sorting and Ranking

Correlation and Covariance

  • Assigning a NaN Value
  • Filtering Out NaN Values
  • Filling in NaN Occurrences
  • Reordering and Sorting Levels
  • Summary Statistic by Level

Pandas: Reading and Writing Data

I/O API Tools

CSV and Textual Files

  • Using RegExp for Parsing TXT Files
  • Reading TXT Files into Parts or Partially
  • Writing Data in CSV
  • Writing Data in HTML
  • Reading Data from an HTML File

Reading Data from XML

  • Serialize a Python Object with cPickle
  • Pickling with pandas
  • Loading and Writing Data with SQLite
  • Loading and Writing Data with PostgreSQL

Reading and Writing Data with a NoSQL Database: MongoDB

pandas in Depth: Data Manipulation

  • Built-in Methods for Manipulation of Strings
  • Regular Expressions
  • GroupBy
  • A Practical Example
  • Hierarchical Grouping
  • Chain of Transformations
  • Functions on Groups

Advanced Data Aggregation

Data Visualization with matplotlib

The matplotlib Library


IPython and IPython QtConsole

  • Backend Layer
  • Artist Layer
  • Scripting Layer (pyplot)
  • pylab and pyplot
  • A Simple Interactive Chart
  • Set the Properties of the Plot
  • matplotlib and NumPy
  • Saving the Code
  • Converting Your Session as an HTML File
  • Saving Your Chart Directly as an Image

Handling Date Values

Chart Typology

  • Horizontal Bar Chart
  • Multiserial Bar Chart
  • Multiseries Bar Chart with pandas DataFrame
  • Multiseries Stacked Bar Charts
  • Stacked Bar Charts with pandas DataFrame
  • Other Bar Chart Representations
  • 3D Surfaces
  • Scatter Plot in 3D
  • Bar Chart 3D
  • Display Subplots within Other Subplots
  • Grids of Subplots

R For Data Analysis

Getting the Hang of R

  • The R Website
  • Downloading and Installing R from CRAN
  • Installing R on Your Windows Computer
  • Installing R on Your Macintosh Computer
  • Installing R on Your Linux Computer

Running the R program

Finding Your Way with R

  • Getting Help via the CRAN Website and the Internet
  • The Help Command in R
  • Help for Windows Users
  • Help for Macintosh Users
  • Help for Linux Users
  • Help For All Users
  • Anatomy of a Help Item in R

Command Packages

  • Standard Command Packages
  • What Extra Packages Can Do for You
  • How to Get Extra Packages of R Commands
  • How to Install Extra Packages for Windows Users
  • How to Install Extra Packages for Macintosh Users
  • How to Install Extra Packages for Linux Users
  • Running and Manipulating Packages
  • Loading Packages
  • Windows-Specific Package Commands
  • Macintosh-Specific Package Commands
  • Removing or Unloading Packages

Some Simple Math

  • Use R Like a Calculator
  • Storing the Results of Calculations

Reading and Getting Data into R

  • Using the combine Command for Making Data
  • Entering Numerical Items as Data
  • Entering Text Items as Data
  • Using the scan Command for Making Data
  • Entering Text as Data
  • Using the Clipboard to Make Data
  • Reading a File of Data from a Disk
  • Reading Bigger Data Files
  • The read.csv() Command
  • Alternative Commands for Reading Data in R
  • Missing Values in Data Files

Viewing Named Objects

  • Viewing Previously Loaded Named-Objects
  • Viewing All Objects
  • Viewing Only Matching Names
  • Removing Objects from R

Types of Data Items

  • Number Data
  • Text Items
  • Converting Between Number and Text Data

The Structure of Data Items

  • Vector Items
  • Data Frames
  • Matrix Objects
  • List Objects

Examining Data Structure

Working with History Commands

  • Using History Files
  • Viewing the Previous Command History
  • Saving and Recalling Lists of Commands
  • Alternative History Commands in Macintosh OS
  • Editing History Files

Saving Your Work in R

  • Saving the Workspace on Exit
  • Saving Data Files to Disk
  • Save Named Objects
  • Save Everything
  • Reading Data Files from Disk
  • Saving Data to Disk as Text Files
  • Writing Vector Objects to Disk
  • Writing Matrix and Data Frame Objects to Disk
  • CONTENTS Writing List Objects to Disk
  • Converting List Objects to Data Frames

Manipulating Objects

  • Manipulating Vectors
  • Selecting and Displaying Parts of a Vector
  • Sorting and Rearranging a Vector
  • Returning Logical Values from a Vector
  • Manipulating Matrix and Data Frames
  • Selecting and Displaying Parts of a Matrix or Data Frame
  • Sorting and Rearranging a Matrix or Data Frame
  • Manipulating Lists

Viewing Objects within Objects

  • Looking Inside Complicated Data Objects
  • Opening Complicated Data Objects
  • Quick Looks at Complicated Data Objects
  • Viewing and Setting Names
  • Rotating Data Tables

Constructing Data Objects

  • Making Lists
  • Making Data Frames
  • Making Matrix Objects
  • Re-ordering Data Frames and Matrix Objects

Forms of Data Objects: Testing and Converting

  • Testing to See What Type of Object You Have
  • Converting from One Object Form to Another
  • Convert a Matrix to a Data Frame
  • Convert a Data Frame into a Matrix
  • Convert a Data Frame into a List
  • Convert a Matrix into a List
  • Convert a List to Something Else

Summary Commands

Summarizing Samples

  • Summary Statistics for Vectors
  • Summary Commands With Single Value Results
  • Summary Commands With Multiple Results
  • Cumulative Statistics
  • Simple Cumulative Commands
  • Complex Cumulative Commands
  • Summary Statistics for Data Frames
  • Generic Summary Commands for Data Frames
  • Special Row and Column Summary Commands
  • The apply() Command for Summaries on Rows or Columns
  • Summary Statistics for Matrix Objects
  • Summary Statistics for Lists

Summary Tables

  • Making Contingency Tables
  • Creating Contingency Tables from Vectors
  • Creating Contingency Tables from Complicated Data
  • Creating Custom Contingency Tables
  • Creating Contingency Tables from Matrix Objects
  • Selecting Parts of a Table Object
  • Converting an Object into a Table
  • Testing for Table Objects
  • Complex (Flat) Tables
  • Making “Flat” Contingency Tables
  • Making Selective “Flat” Contingency Tables
  • Testing “Flat” Table Objects
  • Summary Commands for Tables
  • Cross Tabulation
  • Testing Cross-Table (xtabs) Objects
  • A Better Class Test
  • Recreating Original Data from a Contingency Table

Summary Tables

Looking at the Distribution of Data

  • Stem and Leaf Plot
  • Histograms
  • Density Function
  • Using the Density Function to Draw a Graph
  • Adding Density Lines to Existing Graphs
  • Types of Data Distribution
  • The Normal Distribution
  • Other Distributions
  • Random Number Generation and Control
  • Random Numbers and Sampling
  • The Shapiro-Wilk Test for Normality
  • The Kolmogorov-Smirnov Test
  • Quantile-Quantile Plots
  • A Basic Normal Quantile-Quantile Plot
  • Adding a Straight Line to a QQ Plot
  • Plotting the Distribution of One Sample Against Another

Using the Student’s t-test

  • Two-Sample t-Test with Unequal Variance
  • Two-Sample t-Test with Equal Variance
  • One-Sample t-Testing 183 Using Directional Hypotheses
  • Formula Syntax and Subsetting Samples in the t-Test

The Wilcoxon U-Test (Mann-Whitney)

  • Two-Sample U-Test
  • One-Sample U-Test
  • Using Directional Hypotheses
  • Formula Syntax and Subsetting Samples in the U-test

Paired t- and U-Tests

Correlation and Covariance

  • Simple Correlation
  • Covariance
  • Significance Testing in Correlation Tests
  • Formula Syntax

Tests for Association

  • Multiple Categories: Chi-Squared Tests
  • Monte Carlo Simulation
  • Yates’ Correction for 2 n 2 Tables
  • Single Category: Goodness of Fit Tests

Box-whisker Plots

  • Basic Boxplots
  • Customizing Boxplots
  • Horizontal Boxplots

Scatter Plots

  • Basic Scatter Plots
  • Adding Axis Labels
  • Plotting Symbols
  • Setting Axis Limits
  • Using Formula Syntax
  • Adding Lines of Best-Fit to Scatter Plots

Pairs Plots (Multiple Correlation Plots)

Line Charts

  • Line Charts Using Numeric Data
  • Line Charts Using Categorical Data

Pie Charts

Cleveland Dot Charts

Bar Charts

  • Single-Category Bar Charts
  • Multiple Category Bar Charts
  • Stacked Bar Charts
  • Grouped Bar Charts
  • Horizontal Bars
  • Bar Charts from Summary Data

Copy Graphics to Other Applications

  • Use Copy/Paste to Copy Graphs
  • Save a Graphic to Disk
  • Windows
  • Macintosh
  • Linux

Examples of Using Formula Syntax for Basic Tests

Formula Notation in Graphics

Analysis of Variance (ANOVA)

  • One-Way ANOVA
  • Stacking the Data before Running Analysis of Variance
  • Running aov() Commands
  • Simple Post-hoc Testing
  • Extracting Means from aov() Models
  • Two-Way ANOVA
  • More about Post-hoc Testing
  • Graphical Summary of ANOVA
  • Graphical Summary of Post-hoc Testing
  • Extracting Means and Summary Statistics
  • Model Tables
  • Table Commands
  • Interaction Plots
  • More Complex ANOVA Models
  • Other Options for aov()
  • Replications and Balance

Creating Data for Complex Analysis

  • Data Frames
  • Matrix Objects
  • Creating and Setting Factor Data
  • Making Replicate Treatment Factors
  • Adding Rows or Columns

Summarizing Data

  • Simple Column and Row Summaries
  • Complex Summary Functions
  • The rowsum() Command
  • The apply() Command
  • Using tapply() to Summarize Using a Grouping Variable
  • The aggregate() Command

Simple Linear Regression

  • Linear Model Results Objects
  • Coefficients
  • Fitted Values
  • Residuals
  • Formula
  • Best-Fit Line
  • Similarity between lm() and aov()
  • >

Multiple Regression

  • Formulae and Linear Models
  • Model Building
  • Adding Terms with Forward Stepwise Regression
  • Removing Terms with Backwards Deletion
  • Comparing Models

Curvilinear Regression

  • Logarithmic Regression
  • Polynomial Regression

Plotting Linear Models and Curve Fitting

  • Best-Fit Lines
  • Adding Line of Best-Fit with abline()
  • Calculating Lines with fitted()
  • Producing Smooth Curves using spline()
  • Confidence Intervals on Fitted Lines

Summarizing Regression Models

  • Diagnostic Plots
  • Summary of Fit

Adding Elements to Existing Plots

  • Linear Model Results Objects
  • Error Bars 364 Using the segments() Command for Error Bars
  • Using the arrows() Command to Add Error Bars
  • Adding Legends to Graphs
  • Color Palettes
  • Placing a Legend on an Existing Plot
  • Adding Text to Graphs
  • Making Superscript and Subscript Axis Titles
  • Orienting the Axis Labels
  • Making Extra Space in the Margin for Labels
  • Setting Text and Label Sizes
  • Adding Text to the Plot Area
  • Adding Text in the Plot Margins
  • Creating Mathematical Expressions
  • Adding Points to an Existing Graph
  • Adding Various Sorts of Lines to Graphs
  • Adding Straight Lines as Gridlines or Best-Fit Lines
  • Making Curved Lines to Add to Graphs
  • Plotting Mathematical Expressions
  • Adding Short Segments of Lines to an Existing Plot
  • Adding Arrows to an Existing Graph

Matrix Plots (Multiple Series on One Graph)

Multiple Plots in One Window

  • Splitting the Plot Window into Equal Sections
  • Splitting the Plot Window into Unequal Sections

Exporting Graphs

  • Using Copy and Paste to Move a Graph
  • Saving a Graph to a File
  • Windows
  • Macintosh
  • Linux
  • Using the Device Driver to Save a Graph to Disk
  • PNG Device Driver
  • PDF Device Driver
  • Copying a Graph from Screen to Disk File
  • Making a New Graph Directly to a Disk File

Copy and Paste Scripts

  • Make Your Own Help File as Plaintext
  • Using Annotations with the # Character

Creating Simple Functions

  • One-Line Functions
  • Using Default Values in Functions
  • Simple Customized Functions with Multiple Lines
  • Storing Customized Functions

Making Source Code

  • Displaying the Results of Customized Functions and Scripts
  • Displaying Messages as Part of Script Output
  • Simple Screen Text
  • Display a Message and Wait for User Intervention

Case study 1 - wildfire activity in the western united states

  • Exercise 1 Have the number of wildfires increased or decreased in the past few decades?
  • Exercise 2 Has the acreage burned increased over time?
  • Exercise 3 is the size of individual wildfire increased over time?
  • Exercise 4 has the length of the fire season increased over time?
  • Exercise 5 does the average wildfire size differ by federal organization?

Case study 2- single family residential home and rental value

  • Exercise 1 what is the trend for home value in the Austin metro area
  • Exercise 2 what is the trend for rental rates in the Austin metro area?
  • Exercise 3 determining the price-rent ratio for the Austin metropolitan area
  • Exercise 4 comparing residential home values in Austin to other texas and u.s. Metropolitan areas


  • Tableau Desktop: Personal 8 Tableau Desktop: Professional
  • Tableau Reader
  • Tableau Public
  • Tableau Online
  • Tableau Server
  • Tableau Terminology
  • View the Underlying Data
  • View the Number of Records
  • How to Make a Line Graph in Tableau
  • Independent Axes in Tableau
  • Date Hierarchies in Tableau
  • An Explanation of Level of Detail
  • An Introduction to Encoding
  • Label and Tooltip Marks Cards
  • Intro
  • Dimension Filters in Tableau
  • Measure Filters in Tableau
  • More Options with Filters
  • Macro Filters
  • Why Use Calculated Fields?
  • More on Aggregating Calculated Fields

Power BI

  • The Microsoft Self-Service Business Intelligence Solution
  • The Power BI Universe
  • Installing Power BI Desktop
  • A First Power BI Desktop Dashboard
  • The Data Load Process
  • The Power BI Desktop Window
  • Your First Visualizations
  • Interactive Dashboards
  • Formatting Reports
  • Creating and Modifying Reports
  • The Power BI Desktop Query Editor
  • Data Sources
  • Loading Data
  • Loading Multiple Files from a Directory
  • Loading the Contents of a Folder
  • The Navigator Dialog
  • Adding Your Own Data
  • SQL Server
  • Oracle Databases
  • Other Relational Databases
  • Microsoft SQL Server Analysis Services Data Sources
  • SSAS Tabular Data Warehouses
  • Import or Connect Live
  • ODBC Sources
  • OLE DB Data Sources
  • Modifying Connections
  • Changing Permissions
  • Refreshing Data from Databases and Data Warehouses
  • DirectQueryandConnectLive
  • Microsoft SQL Server Data
  • SQL Server Analysis Services Dimensional Data
  • Microsoft SQL Server Analysis Services Tabular Data Sources
  • Direct Query with Non-Microsoft Databases
  • Direct Queryand In-MemoryTables
  • Direct Query and Refreshing the Data
  • Web and CloudServices
  • Web Pages
  • Salesforce
  • Microsoft Dynamics365
  • Azure SQL Database
  • AzureSQLDataWarehouse
  • Connecting to SQL Server on an Azure Virtual Machine
  • Azure Blob Storage
  • Azure Security
  • PowerBIDesktopQueries
  • Query or Load?
  • The Power BI Desktop Query Editor
  • DatasetShaping
  • Removing Records
  • Sorting Data
  • FilteringData
  • Grouping Records
  • Saving Changes the Query Editor
  • Exiting the Query Editor
  • Viewing Full Record
  • Power BI Desktop Query Editor Context Menus
  • Using the First Row As Headers
  • ChangingDataType
  • Replacing Values
  • TransformingColumnContents
  • FillingDownEmptyCells
  • ExtractingPartofaColumn’sContents
  • Duplicating Columns
  • Splitting Columns
  • Merging Columns
  • Custom Columns
  • Creating Columns from Examples
  • Adding Conditional Columns
  • Index Column
  • The Power BI Desktop Query Editor View Ribbon
  • Merging Data Appending Data
  • Changing the Data Structure
  • Parsing JSON Files
  • The List Tools Transform Ribbon
  • Convert a Column to a List
  • Parsing XML Data from a Column
  • Parsing JSON Data from a Column
  • Managing theTransformation Process
  • Modifying the Code for a Step
  • Modifying Data Source Settings in the Query Editor
  • Managing Queries
  • Pending Changes
  • Reusing Data Sources
  • Parameterizing Queries
  • Power BI Templates
  • Copying Data from Power BI Desktop Query Editor
  • Data Modeling in the Power BI Desktop Environment
  • Data Modelor Query?
  • The Power BI Desktop Data View Ribbons
  • Managing Power BI Desktop Data
  • Power BI Desktop DataTypes
  • Formatting Power BI Desktop Data
  • Preparing Data for Dashboards
  • Sorting Data in Power BI Desktop Tables
  • Adding Hierarchies
  • Creating and Modifying Groups
  • Deleting a Group
  • Designing a Power BI Desktop DataModel
  • Creating Relationships
  • Advanced RelationshipOptions
  • Managing Relationships BetweenTables
  • TypesofCalculations
  • Adding New Columns
  • Concatenating Column Contents
  • TweakingText
  • Simple Calculations
  • Calculating AcrossTables
  • Cascading Column Calculations
  • UsingFunctionsinNewColumns
  • Simple Logic: The IF( ) Function
  • Making Good Use of the Formula Bar
  • A First Measure: Number of Cars Sold
  • Basic Aggregations Measures
  • Using Multiple Measures
  • Cross-TableMeasures
  • More Advanced Aggregations
  • FilterContext
  • Filtering Data in Measures
  • Simple Filters
  • More Complex Filters
  • Calculating Percentages of Totals
  • Filtering on Measures
  • Displaying Rank
  • A Few Comments and Notes on Using Measures
  • Calculation Options
  • Simple Date Calculations
  • Adding Time Intelligence to a DataModel
  • Creating and Applying a DateTable
  • Calculating the Difference Between Two Dates
  • Applying Time Intelligence
  • Comparisons with Previous Time Periods
  • ComparisonwithaParallelPeriodinTime
  • Rolling Aggregations over a Period of Time
  • Power BI Desktop Dashboards
  • Working with Tables
  • Changing the Table Size and Position
  • Changing Column Order
  • Renaming Fields
  • Removing Columns from aTable
  • Table Granularity
  • Enhancing Tables
  • FormattingTables
  • TableStyle
  • Adding and Formatting Titles
  • ModifyingtheTableBackground
  • Table Borders
  • RowFormatting
  • Table Grid
  • Column Headers
  • Column Formatting
  • FormattingTotals
  • Conditional Formatting
  • Creating Matrix
  • Expanding and Drilling Down and Up
  • Visualize Source Data
  • Viewing Records
  • Including and Excluding Matrix Elements
  • Displaying Multiple Values As Rows
  • Formatting Matrix
  • SortingDatainMatrices
  • Cards
  • Multirow Cards
  • Switching Between Table Types
  • A First Chart
  • Basic Chart Modification
  • Essential Chart Adjustments
  • Donut Charts
  • Funnel Charts
  • Multiple Data Values in Charts
  • 100% Stacked Column and Bar Charts
  • Scatter Charts
  • BubbleCharts
  • WaterfallCharts
  • RibbonCharts
  • Dual-AxisCharts
  • Data Details
  • Drilling into and Expanding Chart Data Hierarchies
  • Including and Excluding Data Points
  • Multiple Chart Formatting
  • Chart Legends
  • Tooltips
  • Specific Chart Formatting
  • Bubble Chart Play Axis
  • Chart Analytics
  • Scatter Chart Symmetry Shading and Ratio Line
  • TreeMaps
  • Gauges
  • KPIs
  • RVisuals
  • Additional Visuals
  • Loading Custom Visuals
  • ARapidOverviewofaSelectionofCustomVisuals
  • Custom Slicers
  • Working with Bing Maps
  • Creating Maps in Power BI Desktop
  • Using Geographical Data
  • Drilling Down in Maps
  • Adjusting the Map Display in Power BI Desktop
  • Filled Maps
  • Shape Maps
  • Formatting Maps
  • ARCGis Maps
  • Filters
  • Visual-LevelFilters
  • Filtering Different Data Types
  • Advanced Text Filters
  • Specific Visualization-Level Filters
  • Multiple Filters
  • Page-Level Filters
  • Report-LevelFilters
  • Removing Filters
  • Filter Field Reuse
  • Using The Filter Hierarchy
  • Filtering Tips
  • Slicers
  • Date Slicers
  • Formatting Slicers
  • Using Charts As Slicers
  • Charts As Complex Slicers
  • Specifying Visual Interactions
  • What-If Slicers
  • Custom Visuals As Slicers
  • Choosing the Correct Approach to Interactive Data Selection
  • Formatting Ribbons
  • Formatting the Page
  • Aligningand Distributing Visuals
  • Adding Text Boxes to Annotate a Report
  • ModifyingthePageBackgroundColor
  • Images


  • SQL as a declarative language
  • Data definition language (DDL)
  • Data manipulation language (DML)
  • Data control language (DCL)
  • Transaction control language (TCL)
  • Relational database essentials
  • Databases vs spreadsheets
  • Database terminology
  • Relational schemas - Primary key
  • Relational schemas - Foreign key
  • Relational schemas - Unique key and null values
  • Relational Schemas – Relationships
  • Installing MySQL
  • Additional note – Installing – Visual C
  • Installing MySQL on macOS and Unix systems
  • The Client-Server Model
  • Setting up a connection
  • New Authentication Plugin - Creating a New User
  • Introduction to the MySQL interface
  • Creating a Database
  • Introduction to data types
  • String data types
  • Integers
  • Fixed and floating-point data types
  • Other useful data types
  • Creating a table
  • Updating a table
  • Deleting a table
  • Updating columns in a table
  • Retrieving a Table
  • Using databases and tables
  • Additional notes on using tables
  • PRIMARY KEY Constraint
  • FOREIGN KEY constraint
  • UNIQUE Constraint
  • DEFAULT Constraint
  • NOT NULL Constraint
  • SQL Comparison Operators
  • SQL Logical Operators
  • SQL IN
  • SQL OR
  • SQL Aggregate Functions
  • SQL Joins
  • SQL Outer Joins
  • SQL Joins Using WHERE or ON
  • SQL Joins with Comparison Operators
  • SQL Joins on Multiple Keys
  • SQL Self Joins
  • SQL Date Format
  • Data Wrangling with SQL
  • Using SQL String Functions to Clean Data
  • Writing Subqueries in SQL
  • SQL Window Functions
  • Performance Tuning SQL Queries
  • Pivoting Data in SQL
  • Stored Procedures in SQLs
  • Analytical Functions in SQL
Hours of Content
Case Study & Projects
Live Sessions
Coding Assignments
Capstone Projects to Choose From
Tools, Languages & Libraries

Languages and Tools covered

Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered

Hands On Projects

A Broader Analysis on Education System Across Globe

Building an Analytical Dashboard on Education System with all the features available in Tableau

NewZealand Regional Tourism Expenditure Analysis and Forecast

Building an Regional Tourism Dashboard to analyse different KPI's and performing Forecasting mechanism.

Indian COVID-19 Tracker

Build the interactive Dashboard for tracking the covid-19 cases in India via Regionwise, Statewise, Citywise, Districtwise using the live COVID-19 API

Blood Donation Database

This database would store interrelated data on patients, blood donors, and blood banks

Cooking Recipe Portal

You will model a web portal where a stored procedure will display your cooking recipes under different categories

Hospital Management System

In this project all patients and doctors will have a unique and will be related in the database depending on the ongoing treatments. Also, there will be separate modules for hospital admission, patients’ discharge summary, duties of nurses and ward boys, medical stores, etc.

Event Data Analysis using AWS ELK Stack

This Elasticsearch example deploys the AWS ELK stack to analyse streaming event data. Tools used include Nifi, PySpark, Elasticsearch, Logstash and Kibana for visualisation

Insurance Pricing Forecast Using Regression Analysis

In this project, we are going to talk about insurance forecast by using regression techniques

Sentiment Analysis of Twitter Data using PySpark and Live Graphs

In this project, Sentiment Analysis Application is developed using Pyspark which is combination of Apache Spark and Python. This application fetches Twitter data in live stream and classifies tweets into positive and negative categories


Our training is based on latest cutting-edge infrastructure technology which makes you ready for the industry.Osacad will Present this certificate to students or employee trainees upon successful completion of the course which will encourage and add to trainee’s resume to explore a lot of opportunities beyond position

Enroll Now
Learn From Home

First-Ever Hybrid Learning System

Enjoy the flexibility of selecting online or offline classes with Osacad first-ever hybrid learning model.
Get the fruitful chance of choosing between the privilege of learning from home or the
advantage of one-on-one knowledge gaining - all in one place.

Learn From Home

Learn from Home

Why leave the comfort and safety of your home when you can learn the eminent non-technical courses right at your fingertips? Gig up to upskill yourself from home with Osacad online courses.

Learn From Home

Learn from Classroom

Exploit the high-tech face-to-face learning experience with esteemed professional educators at Osacad. Our well-equipped, safe, and secure classrooms are waiting to get you on board!

Our Alumina Works at
Our Alumina Works Our Alumina Works Our Alumina Works Our Alumina Works Our Alumina Works Our Alumina Works Our Alumina Works Our Alumina Works Our Alumina Works



Artificial Intelligence which is a global company with headquarters in Chicago, USA. Artificial Intelligence has partnered with GamaSec, a leading Cyber Security product company. Artificial Intelligence is focusing on building Cyber Security awareness and skills in India as it has a good demand in consulting and product support areas. The demand for which is predicted to grow exponentially in the next 3 years. The Artificial Intelligence training programs are conducted by individuals who have in depth domain experience. These training sessions will equip you with the fundamentalknowledge and skills required to be a professional cyber security consultant.

All graduates of commerce, law, science and engineering who want to build a career in cyber security can take this training.

There are a number of courses, which are either 3 months or 6 months long. To become a cyber security consultant we recommend at least 6 to 9 months of training followed by 6 months of actual project work.During project work you will be working under a mentor and experiencing real life customer scenarios.

You can get started by enrolling yourself. The enrollment can be initiated from this website by clicking on "ENROLL NOW". If you are having questions or difficulties regarding this, you can talk to our counselors and they can help you with the same.

Once you enroll with us you will receive access to our Learning Center. All online classrooms, recordings, assignments, etc. can be accessed here.

Get in touch with us

What do you benefit from this programs
  • Comprehensive Hands-on with MySQL, Pandas, Numpy, Scipy
  • Expertise Knowledge Level on Statistics and Probability
  • Gain Knowledge on Exploratory Data Analysis
  • Perform Pro level Data Visualizations using Tableau and PowerBI

I’m Interested

Related Courses


Python is a powerful general-purpose programming language. It is used in web development, data science, creating software... Read More

R For Data Analysis

R is a programming language that is designed and used mainly in the statistics, data science, and scientific communities. R has... Read More

Python For Data Analysis

Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in.... Read More


Tableau is a powerful and fastest growing data visualization tool used in the Business Intelligence Industry. It helps in simplifying... Read More

Success Stories

4th floor, Khajaguda Main Road, next to Andhra Bank, near DPS, Khajaguda, Gachibowli, Hyderabad, Telangana 500008

Success Stories
Madhapur ( Headquarters, Hyderabad)

Plot No. 430, Sri Ayyappa Society, Khanamet, Madhapur, Hyderabad-500081

Success Stories

Uptown Cyberabad Building, Block-C, 1st Floor Plot – 532 & 533, 100 Feet Road Sri Swamy Ayyappa Housing Society, Madhapur, Hyderabad, Telangana 500081

Success Stories

5999 S New Wilke Rd, Bldg 3, #308 Rolling Meadows, IL 60008

Call Us