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Gain Essential Python Skills: Excel in Data Analysis, Earn Certification, Advance Your Career!
BEST INSTITUTE 4.9 Ratings
Elysium Academy leads in Data Analysis, offering a comprehensive program to leverage, interpret, and utilize data innovatively. Our curriculum covers statistical analysis, data visualization, machine learning, and data-driven decision-making. With hands-on projects and real-world applications, students gain practical experience. Expert instructors provide personalized guidance, ensuring confidence in professional settings. Join us at our Madurai campus in Anna Nagar, the best computer center near you, to master data analysis.
2.2
Version
90 Hours
Duration
35 Hours
Theory
55 Hours
Practical
Our Highlights
- Industry Based Projects
- Personalized coordinator.
- Trainer feedback.
- Trainer availability post sessions.
- Get your staff certified.
- Certificate from governing bodies.
- Recognized worldwide
- Hands on assignment
What Will You Learn?
- Master Python essentials: variables, data types, loops, and functions.
- Explore advanced concepts like object-oriented programming (OOP) and error handling.
- Utilize powerful libraries such as NumPy and Pandas for data manipulation and analysis.
- Visualize data effectively using Tableau and Power BI.
- Implement machine learning models with Scikit-learn and R.
- Dive into deep learning techniques using TensorFlow.
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Course was selected for our collection of top-rated courses trusted by businesses worldwide.
Reasons to do
Data Analysist Course Certification Training
Course Description
Our Python for Data Analysts (R, Tableau & Power BI) program offers participants the opportunity to learn essential programming languages crucial for data analysis today. Dive into the captivating field of data analysis and master Python, R, Tableau, Power BI, and their applications with the guidance of experienced instructors. Graduates will emerge ready to tackle real-world data challenges.
Course Goals
This course aims to help you master Python programming concepts tailored for data analysis. Through this training, you will gain proficiency in Data Analysis, Data Visualization, Machine Learning, Web Scraping, and NLP. You’ll work with powerful tools like Python, R, Tableau, and Power BI to enhance your skills. Join us to unlock the full potential of data analysis and elevate your career to new heights.
Future Scope of this Course
- High demand for data analysts due to the exponential growth of data generation.
- Skills in R, Tableau, and Power BI are relevant across various industries, enhancing employability.
- Integration with AI, machine learning, and big data technologies is increasing in importance.
- Organizations are adopting data-driven decision-making processes, creating a continuous need for data analysts.
- Graduates can pursue diverse roles such as Data Analyst, Business Analyst, Data Scientist, and BI Developer.
Our Career Service
Data Analysist Course (R,Tableau & Power BI) Certificate
Our Training Program Benefits
- Live, interactive training by experts.
- Curriculum that focuses on the learner.
- Challenge-based, hands-on project.
- Opportunities for team building.
- Cost- saving training.
- Convenient for your employees.
- Completely tailor-made curriculum.
Chapter-1 R Introduction
1. R Introduction
- Introduction to R Studio
- R installation
- R Advantage & Disadvantage
- R Hadoop Integration
- R Packages
- List of R Packages
- Basic Syntax
- Comments
- Data Types
- Data Structures
- Variables
- Keywords
- Operators
- Input/Output
- R variables
- Scope of Variables
- Dynamic Scoping
- Lexical Scoping
Chapter-2 Control Statements
1. Control Statements
- R if Statement
- If-else Statement
- Else if Statement
- R Switch Statement
- R Next Statement
- R Break Statement
- R For Loop
- R Repeat Loop
- R While Loop
- Create Functions
- Function Arguments
- Types of Function
- Recursive Function
- Conversion Functions
- Local and global variable
Chapter-3 Data Structures
1. Data Structures
- Strings
- Lists
- Arrays
- Matrix
- Data Frame
- Classes
- Objects
- Encapsulation
- Polymorphism
- Inheritance
- Abstraction
- Looping Over Objects
- S3 class
- Explicit
- R Debugging
- Error Handling
- Reading Files
- Writing Files
- Working with Binary Files
- R CSV File
- R Excel File
- R Binary File
- R JSON File
- R XML File
- R Database
- R Data Visualization
- R Pie Charts
- R Boxplot
- R Histogram
- R Line Graphics
- R Scatterplots
Chapter-4 Manipulating Data
1. Manipulating Data
- Selecting rows/observations
- Selecting columns/fields
- Merging data
- Relabeling the column names
- Converting variable types
- Data sorting
- Data aggregation
- Linear Regression
- Multiple Regression
- Logistic Regression
- Poisson Regression
- Normal Distribution
- Binomial Distribution
- R Classification
- Time Series Analysis
- R Random Forest
- T-Test in R Chi-Square Test
Chapter-5 Introduction to Tableau
1. Introduction to Tableau
- What is Tableau
- Architecture of Tableau
- Features of Tableau
- Installation of Tableau Desktop/Public
- Navigation
- Design Flow
- File System
- Data Types
- Data Source Connection
- Import Excel File
- Data Cleaning
- Join Database
- Data Blending
- Split the Text to Columns
- Displaying data in worksheet
- Adding Renaming and Duplicating
- Pivot Table and Heat Map
- Highlight Table
- Bar Chart
- Line Chart
- Area Chart
- Pie Chart
- Scatter Plot
- Word Cloud
- Tree Map
- Blended Axis
- Dual Axis
Chapter-6 Advance Data Visualization/Graph
1. Introduction to Tableau
- What is Tableau
- Architecture of Tableau
- Features of Tableau
- Installation of Tableau Desktop/Public
- Navigation
- Design Flow
- File System
- Data Types
- Data Source Connection
- Import Excel File
- Data Cleaning
- Join Database
- Data Blending
- Split the Text to Columns
- Displaying Data in Worksheet
- Adding, Renaming, and Duplicating
- Pivot Table and Heat Map
- Highlight Table
- Bar Chart
- Line Chart
- Area Chart
- Pie Chart
- Scatter Plot
- Word Cloud
- Tree Map
- Blended Axis
- Dual Axis
- Bar Chart
- Stacked Bar Chart
- Bar in Bar Chart
- Combo Chart
- Line Chart
- Single Axis
- Dual Axis
- Blended Axis
- Dual Axis Chart
- Line
- Bar
- Lollipop Chart
- Donut
- Bullet Graph
- Histogram Chart
- Animated Graph
Chapter-7 Building View Advance Map Option
1. Building View Advance Map Option
- Explain latitude and longitude
- Default location/Edit locations
- Symbol Map & Filled Map
- Map Layer
- Image in Map
- Map Option
- Connecting to Different Data Source
- Excel
- CSV
- SQL Server
- Live vs Extract Connection
- Creating Extract
- Refreshing Extract
- Increment Extract
- Refreshing Live
- Data Source Editor
- Pivoting and Splitting
- Data Interpreter: Clean Dirty Data
- TWB vs TWBX
- How to Create a Packaged Workbook
- Difference Between .tde and .hyper File
Chapter-8 Advanced Data Preparation
1. Advanced Data Preparation
- Joins
- Inner
- Left
- Right
- Outer
- Complex Join
- Referential Integrity
- Union
- Data Blending and When Required
- Cross DB Join
- Why Visualization Came into the Picture?
- Importance of Visualizing Data
- Poor Visualization vs Perfect Visualization
- vPrinciples of Visualization
- Goal of Data Visualization
- Filter
- Types of Filter
- Quick Filter
- Global Filter
- Normal Filter
- Relevant Filter
- Dimension Filter
- Measure Filter
- Condition Based Filter
- Advanced Filter Using Wild Card
- Right Click Filtering
- Top & Bottom N Filter
- Filtering Order of Operation
- Extract Filter
- Data Source Filter
- Context Filter
- Action Filter
- Filter
- Highlight
- Go to URL
- Go to Sheet
- Set Action
- Parameter Action
- Action Jumps
- Viz in Tool Tip
Chapter-9 Basic Calculation
1. Basic Calculation
- Sorting
- Calculation - String, Basic, Date and Logical
- Continuous and Discrete data
- Working with Dates
- Creating calculated Field
- Logical Function
- Case in Function
- ZN Function
- Else if Function
- Ad-hoc Function
- Manipulating Text - Left and Right Function
- Table Calculation
- Running Total
- Percent
- Percent Total
- Year Over Year Growth
- LOD
- Include
- Exclude
- Fixed
Chapter-10 Grouping Data/Dynamic Representation
1. Grouping Data/Dynamic Representation
- Groups
- Sets
- In/Out Sets
- Combined Sets
- Top and Bottom In Single View Parameters
- Dynamic Measure
- Dynamic Dimension
- Hierarchies
- Bins
- Combined Field
- Trend Line
- Forecasting
- C luster
- Reference Line
- Box Plot (Understanding Outliers In Data)
- Distribution Band
- Reference Band
- Size
- Updating Axis
- Colors
- Borders
- Transparency
- Chart Line
- Reference Time
- Mark Label
- Annotation
- Responsive Tool Tip
- Canvas Selection
- Tiled Object
- Floating Object
- Pixel Perfect Alignment
- Summary Box
- Chart Titles and Captions
- Adding Image and Text
- Adding Shading
- Adding Separator Lines
- Dynamic Chart Title
- Information Icons
- Creating a Story
- Publishing to PDF
- Exporting to Pivot Table and Images
- Exporting Packaged Workbooks
- Tableau Reader
- Tableau Online
- Tableau Server
- Tableau Public
- Version Control
Chapter-11 Introduction To Power Bi
Introduction To Power BI
- Introduction to Power BI: Need, Importance
- Power BI - Advantages and Scalable Options
- History - Power View, Power Query, Power Pivot
- Power BI Data Source Library and DW Files
- Cloud Collaboration and Usage Scope
- Business Analyst Tools, MS Cloud Tools
- Power BI Installation and Cloud Account
- Power BI Cloud and Power BI Service
- Power BI Architecture and Data Access
- On-Premise Data Access and Microsoft OneDrive
- Power BI Desktop - Installation, Usage
- Sample Reports and Visualization Controls
- Power BI Cloud Account Configuration
- Understanding Desktop & Mobile Edition
- Report Rendering Options and End User Access
- Power View and Power Map, Power BI Licenses
- Report Design with Legacy & DAT Files
- Report Design With Database Tables
- Understanding Power BI Report Designer
- Report Canvas; Report Pages: Creation, Renames
- Report Visuals, Fields and UI Options
- Experimenting Visual Interactions: Advantages
- Reports with Multiple Pages and Advantages
- Pages with Multiple Visualizations Data Access
- PUBLISH Options and Report Verification in Cloud
- GET DATA Options and Report Field
- Report View Options: Full, Fit to Page, Width Scale
- Report: Design using Databases & Queries
- Query Settings and Data Preloads
- Navigation Options and Report Refresh
- Stacked Bar Chart, Stacked Column Chart
- Clustered Bar Chart, Clustered Column Chart
- Adding Report Titles, Report Format Options
- Focus Mode, Explore and Export Settings
Chapter-12 Report Visualization and Properties
1. REPORT VISUALIZATIONS and PROPERTIES
- Power BI Design: Canvas, Visualizations, and Fields
- Import Data Options with Power BI Model, Advantages
- Direct Query Options and Real-time (LIVE) Data Access
- Drag Fields and Filters with Visualizations
- Visualization Filters, Page Filters, Report Filters
- Conditional Filters and Clearing Testing Sets
- Creating Customized Tables With Power BI
- General Properties: Sizing, Dimensions, and Positions
- Alternate Text and Tiles (Header, Column, Row) Properties
- Grid Properties (Vertical, Horizontal) and Styles
- Table Styles: Alternate Row Colors - Static, Dynamic
- Sparse, Flashy Rows, Condensed Table Reports: Focus Mode
- Totals Computations, Background, Borders Properties
- Column Headers, Column Formatting, Value Properties
- Conditional Formatting Options: Color Scale
- Page Level Filters and Report Level Filters
- Visual-Level Filters and Format Options
- Report Fields, Formats, and Analytics
- Page-Level Filters and Column Formatting, Filters
- Background Properties, Borders, and Lock Aspect
- Chart report types and properties
- Stacked bar chart, stacked column chart
- Clustered bar chart, clustered column chart
- 100% stacked bar chart, 100% stacked column chart
- Line charts, area charts, stacked area charts
- Line and stacked row charts
- Line and stacked column charts
- Waterfall chart, scatter chart, pie chart
- Field Properties: Axis, legend, value, tooltip
- Field Properties: Color saturation, filter types
- Formats: Legend, axis, data labels, plot area
- Data Labels: Visibility, color, and display units
- Data Labels: Precision, position, text options
- Analytics: Constant line, position, labels
- Working with waterfall charts and default values
- Modifying legends and visual filters - options
- Map Reports: Working with map reports
- Hierarchies: Grouping multiple report fields
- Hierarchy levels and usages in visualization
- Preordered attribute collection - advantages
- Using field hierarchies with chart reports
- Advanced query mode connection settings options
- Direct import and in-memory loads, advantages
- Hierarchies and Drilldown Options
- Hierarchy Levels and Drill Modes - Usage
- Drill thru Options with Tree Map and Pie Chart
- Higher Levels and Next Level Navigation Options
- Aggregates with Bottom/Up Navigations
- Multi Field Aggregations and Hierarchies in Power BI
- DRILLDOWN, SHOWNEXTLEVEL, EXPANDTONEXTLEVEL
- SEE DATA and SEE RECORDS Options Differences
- Toggle Options with Tabular Data, Filters
- Drilldown Buttons and Mouse Hover Options @ Visuals
- Dependant Aggregations, Independent Aggregations
- Automated Records Selection with Tabular Data
- Report Parameters: Creation and Data Type
- Available Values and Default Values, Member Values
- Parameters for Column Data and Table Query Filters
- Parameters Creation - Query Mode Option
- Linking Parameters to Query Columns - Options
- Edit Query Options and Parameter Manage Entries
- Connection Parameters and Dynamic Data Sources
- Synonyms - Creation and Usage Options
Chapter-13 Power Query and M Language
POWER QUERY & M LANGUAGE
- Understanding Power Query Editor - Options
- Power BI Interface and Query/Dataset
- Working with Empty Tables and Load/IIRS Edits
- Empty Table Names and Header Row Promotions
- Undo Headers Options, Blank Columns Detection
- Data Imports and Query Marking in Query Editor
- JSON Files & Binary Formats with Power Query
- JavaScript Object Notation - Usage with M Language
- Applied Steps and Usage Options, Revert Options
- Creating Query Groups and Query References: Usage
- Query Rename, Load Enable, and Data Refresh Options
- Combine Queries - Merge Join and Anti Join Options
- Combine Queries - Union and Union All as New Dataset
- M Language - Nested Join and Joinkind Functions
- REPLACE, REMOVE ROWS, REMOVE COL BLANK M Language
- Column Splits and Filled Up / Filled Down Options
- Query Hide and Change Type Options, Code Generation
- Invoke Function and Freezing Columns
- Creating Reference Tables and Queries
- Detection and Removal of Query Datasets
- Custom Columns within Power Query
- Power Query Expressions and Usage
- Blank Queries and Enumeration Value Generation
- M Language Semantics and Syntax, Transform Types
- IF ELSE Conditions, Transform Column() Types
- RemoveColumns(), SplitColumns(), ReplaceValue()
- Table Distinct Options and GROUP BY Options
- Table Group(), Table Sort() with Type Conversions
- PIVOT Operation and Table Pivot(), List Functions
- Using Parameters with M Language (Power Query Editor)
- Advanced Query Editor and Parameter Scripts
- List Generation and Table Conversion Options
- Aggregations using Power Query & Usage in Reports
- Report Generation using Web Pages & HTML Tables
- Reports from Page Collection with Power Query
- Aggregate and Evaluate Options with M Language
- Creating High-Density Reports, ArcGIS Maps, ESRI Files
- Generating QR Codes for Reports
- Table Bars and Drill Thru Filters
Chapter-14 Dax
DAX
- Purpose of Data Analysis Expressions (DAX)
- Scope of Usage with DAX, Usability Options
- DAX Context: Row Context and Filter Context
- DAX Entities: Calculated Columns and Measures
- DAX Data Types: Numeric, Boolean, Variant, Currency
- Date Time Data Type with DAX, Comparison with Excel
- DAX Operators & Symbols: Usage, Operator Priority
- Parenthesis, Comparison, Arithmetic
- DAX Functions and Types: Table Valued Functions
- Filter, Aggregation, and Time Intelligence Functions
- Information Functions, Logical, Parent-Child Functions
- Statistical and Text Functions: Formulas and Queries
- Syntax Requirements with DAX, Differences with Excel
- Naming Conventions and DAX Format Representation
- Working with Special Characters in Table Names
- YTD, QTD, MTD Calculations with DAX
- DAX Calculations and Measures
- Using TOPN, RANKX, RANK EQ
- Computations using STDEV & VAR
- SAMPLE Function, COUNTALL, ISERROR
- ISTEXT, DATEFORMAT, TIMEFORMAT
- Time Intelligence Functions with DAX
- Data Analysis Expressions and Functions
- DAX: DATESYTD, DATESQTD, DATESMTD
- ENDOFYEAR, ENDOFQUARTER, ENDOFMONTH
- FIRSTDATE, LASTDATE, DATESBETWEEN
- CLOSINGBALANCEYEAR, CLOSINGBALANCEQTR
- SAMEPERIOD and PREVIOUSMONTH, QUARTER
- KRIS with DAX, Vertipaq Queries in DAX
- IF ELSE Conditions with DAX
- Slicing and Dicing Options with Columns/Measures
- DAX for Query Extraction, Data Mashup Operations
- Calculated Columns and Calculated Measures with DAX
Chapter-15 PowerBi Deployment & Cloud
Power BI Deployment & Cloud
- Power BI Report Validation and Publish
- Understanding Power BI Cloud Architecture
- Power BI Cloud Account and Workspace
- Reports and Dataset Items Validation
- Dashboards and Pins - Real-time Usage
- Dynamic Data Sources and Encryptions
- Personal and Organizational Content Packs
- Gateways, Subscriptions, Mobile Reports
- Data Refresh with Power BI Architecture
- PBIX and PBI Files with Power BI - Usage
- Visual Data Imports and Visual Slicers
- Cloud and On-Premise Data Sources
- How Power BI Supports Data Model?
- Relation between Dashboards to Reports
- Relation between Datasets to Reports
- Relation between Datasets to Dashboards
- Page to Report Mapping Options
- Publish Options and Data Import Options
- Need for Pins @ Visuals and Pins @ Report
- Need for Data Streams and Cloud
- Report Publish Options and Verifications
- Working with Power BI Cloud Interface & Options
- Navigation Paths with "My Workspace" Screen
- BILE View, Edit Reports, Access, Drilldown
- Saving Reports into PDF, PPTX, etc. Report Embed
- Report Rendering and Edit, Save, Print Options
- Report Pin and Individual Visual Pin Options
- Create and Use Dashboards, Menu Options
- Goto Dashboard and Goto Live Page Options
- Operations on Pinned Reports and Value
- Title, Media, Usage Metrics & Favorites
- Subscription Option and Reports with Mobile View
- Options with Report Page Print and Subscribe
- Report Actions: Usage Metrics, Analyze in Excel
- Report Actions: Related Items, Rename, Delete
- Dashboard Actions: Metrics, Related Items
- Dashboard Actions: Settings for Q&A, Delete
- Pin Actions: Metrics, Share, Related Items
- Pin Actions: Settings for Q&A, Delete
- Edit Dashboard (Cloud): On The Fly Reports
- Dataset Actions: Create Report, Refresh
- Scheduled Refresh & Related Items
- Dashboard Integration with Apps in Power BI
- Publish Power BI Report Templates
- Import and Export Options with Power BI
- Dataset Navigations and Report Navigations
- Quick Navigation Options with "My Workspace"
- Dashboards, Workbooks, Reports, Datasets
- Working with My Workspace Group
- Installing the Power BI Personal Gateway
- Automatic Refresh - Possible Issues
- Adding Images in the Dashboard
- Reading & Editing Power BI Views
- Power BI Templates (pbit) - Creation, Usage
- Managing Report in Power BI Services
- Power BI Gateway - Download and Installation
- Personal and Enterprise Gateway Features
- Power BI Settings Dataset Gateway Integration
- Configuring Dataset for Manual Refresh of Data
- Configuring Automatic Refresh and Schedules
- Workbooks and Alerts with Power BI
- Dataset Actions and Refresh Settings with Gateway
Chapter-16 Insights And Subscriptions
Insights And Subscriptions
- Data Navigation Paths and Data Splits
- Getting data from existing systems
- Data Refresh and LIVE Connections
- pbit and pbix: differences, Usage Options
- Quick insights for Power BI Reports
- Quick insights for Power BI Dashboards
- Generating insights with Cloud Datasets
- Generating Reports with Cloud Datasets
- Using relational databases on-premises
- Using relational databases in the cloud
- Consuming a service content pack
- Creating a custom data set from a service
- Creating a content pack for your organization
- Consuming an organizational content pack
- Updating an organizational content pack
- Adding Tiles: Images, Videos, Data Streams
- Creating New Reports from Cortana, Advantages
What is the Data Analyst Course (R, Tableau & Power BI) offered by Elysium Academy?
The Data Analyst Course at Elysium Academy covers data analysis using R, Tableau, and Power BI, teaching you how to collect, process, and visualize data for actionable insights. This course is ideal for those looking to start a career in data analysis.
What makes Elysium Academy the best Data Analyst training center near me?
Elysium Academy excels in Data Analyst training due to its detailed curriculum, practical exercises, and expert instructors who provide hands-on experience with the latest tools and techniques in data analysis.
What kind of projects will I work on during the course?
You will work on real-world projects involving data cleaning, analysis, visualization, and the implementation of machine learning models using Python, R, Tableau, and Power BI.
Will I receive a certificate upon completion of the course?
Yes, you will receive a certificate of completion that verifies your proficiency in Python for Data Analysis with R, Tableau, and Power BI.
Are there opportunities for hands-on practice and assignments?
Yes, the course includes hands-on assignments and projects designed to apply your learning to practical data analysis and visualization tasks.
Is this course suitable for beginners in R, Tableau, and Power BI?
Yes, this course is designed for beginners. It covers the fundamentals of R, Tableau, and Power BI along with Python, tailored for data analysis purposes.
Who are the instructors for this course?
The course is taught by experienced instructors who have expertise in Python, R, Tableau, and Power BI, with practical experience in data analysis.
What tools and libraries will I learn as part of the course?
You will learn Python for data manipulation and analysis, R for statistical analysis, and Tableau/Power BI for data visualization and dashboard creation.
Will I have access to course materials after completion?
Yes, you will have access to the course materials, including videos, slides, and code examples, even after completing the course.
Can I get support from instructors after completing the course?
Yes, you will have access to post-session support from instructors to help with any questions or clarifications related to Python, R, Tableau, or Power BI concepts.
- Data Analysist Course (R,Tableau & Power BI)
- Duration: 90 Hours
- Level: Beginner
- Days: 45 Days
- Chapters: 16
- Language: English
- Certifications: Yes
- Code: EAPL/PROF/PRTC15
- Course Code: EAPDA
- Sub Category: Data Science And Analytics Training Course
Data Analysist
Data Analysist Features
Master Python, R, Tableau, and Power BI for comprehensive data analysis and visualization.
Gain practical experience through hands-on projects using real-world datasets.
Learn advanced data manipulation techniques using Python and R for effective data analysis.
Visualize data and create interactive dashboards using Tableau and Power BI.
Explore the realm of statistical analysis and data modeling through the use of R programming.
Earn a certification upon completion and receive career support to advance in data analysis roles.
What Will You Learn?
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