Data Analytics Course with Placement
Build Your Data Analytics Career with Our 100% Job Assurance Course
For fresh graduates/Early career professionals.
Weekday 
Classroom & Live Online Training6 Months Program
5 Guaranteed Interviews
5+ Tools
25+ Projects
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Join The Best Data Analytics Course
Take your career to new heights with our data analytics course, meticulously designed to foster the skills required for the modern data analyst. Every step of this data analytics course is designed to help you land your dream job as a data analyst. This 100% Job Assurance program is ideal for recent graduates and professionals who want to develop a successful data analytics career.
100% Job Assurance
Our data analytics course comes with a job assurance that offers you 5 guaranteed interviews at over 500 top-tier partner organizations hiring professionals.
Job-specific Curriculum
Learn the practical applications of data analytics, Python, SQL, power BI, and tableau while gaining expertise in these subjects.
Real-world Projects
Implement what you’ve learned with over 25 real-world projects and case studies specially formulated by industry experts to make you job-ready.
Hands - on Learning Module
Our expert faculty delivers our robust curriculum using an interactive methodology and hands-on training methods to prepare you to work in various data analyst roles.
Curriculum
- Excel Basics
- Formatting & Managing your Worksheet & workbook
- Basic Formulas,
- Number Formats & Working
- Working with Menus Tabs
- Excel Advance Options
- Formats, conditional Formatting & Sorting
- Cell Formatting
- Data Bars
- Colour Scales
- Icon Sets
- Conditional formatting with formulas,
- Sorting basic & advance
- Filters Basic & Advance
- Data Management
- Data from other Sources
- Data Validation and dealing with Invalid Data
- Group and Outline Data,
- Data Consolidation
- Text to Column.
- Removing Duplicates
- Formulas & Functions
- Text Formulas: Exact, Find,
- Lookup Formulas: Vlookup, Hlookup, Lookup, Match etc.
- Logical Formulas: If, AND, OR, NOR etc.
- Date & Time Formulas: Today, Now, Years, etc.
- Financial, Math, Trig, Index, Formulas etc.
- How & when to apply Nested Functions
- Chart & Graphs
- Chart Basics: What, When & How
- Why to use Line Chart
- Most Commonly used: Column Chart
- Typical Spark lines
- Data Analysis
- Creating a PivotTable,
- Specifying the Data a PivotTable Analyses
- Changing a PivotTable’s Calculation
- Selecting What Appears in a PivotTable
- Grouping Dates in a PivotTable,
- Updating a PivotTable
- Formatting and Charting a PivotTable
- Scenario Modelling
- Goal Seek
- Introduction To Power BI Introduction to Power BI
- Power BI – Download and Install Power BI Desktop
- Enable Preview Features
- Get Data from Excel
- Simple Charts in Power BI
- Create Stacked Bar Chart
- Create Pie Chart
- Create Scroller
- Create Animated Bar Chart Race
- Create Decomposition Tree
- Introduction to Power Query – ETL Tool
- Power Query Introduction
- Text Functions
- Date Functions
- Number Function
- Append Queries in Power Query
- Append Excel Tables
- Append CSV Files
- Append Files from different data sources
- Append Excel Files from Folder
- Merge Queries in Power Query
- Merge Table from Excel
- Merge Queries from Different data sources
- Get Data in Power BI
- Get Data from PDF
- Get Data from MS Access
- Get Data from SQL
- Get Live data from Website
- Conditional Column in Power Query
- Conditional Column
- Column from Examples
- Miscellaneous Topics in Power Query
- Unpivot In Power Query
- Pivot In Power Query
- Fill Down
- Data Type
- Error Fixing
- Group By
- M Language in Power Query
- M Functions – Date
- Basics of M Language
- Decoding M Language
- Introduction to Power Pivot
- Power Pivot Introduction
- Power Query vs Power Pivot
- Connect different data in Power Pivot
- Connect to different Data Sources
- Connect tables with difference Column Names
- No VLOOKUP, No Merge Queries
- Introduction to DAX Functions in Power Pivot
- DAX – Date Functions
- DAX – Text Functions
- DAX – Number Functions
- Handle Large Data in Power Pivot Append 5 Million Records
- Measures Vs Columns in Power BI
- Time Intelligence Functions in DAX (Power Pivot)
- DAX – CALCULATE Single Criteria
- DAX – CALCULATE Multiple Criteria
- DAX – Time Intelligence Functions
- Table Functions in DAX (Power Pivot)
- DAX – FILTER
- DAX – DISTINCT
- DAX – ALL Function
- DAX – CALENDARAUTO
- Introduction to Power View
- Table Format
- Sort, Keep Only, Exclude
- Aggregation
- Conditional Formatting
- Matrix
- Matrix rows and columns hirearchy
- Create Hirearchy
- Basic Charts in Power BI
- Line Chart in Power BI
- Map with Pie Chart in Power BI
- Filled Map in Power BI
- Line Chart Formatting
- TreeMap
- Funnel
- Line Chart Forecast
- Ribbon
- Waterfall Chart
- Map
- Card and KPI in Power BI Card (Number)
- Card (Text)
- Card (Date)
- Multi Row Card
- KPI In Power BI
- Slicers and Filters in Power BI
- Slicer (Text)
- Slicer (Date)
- Hirearchy Date Slicer
- Slicer (Number)
- Timeline Slicer
- Filter on Visual
- Filter on All Pages
- Drill through
- Fancy Charts in Power BI Word Cloud
- Sankey Chart
- Infographic
- Play Axis
- Walkers Animated Pictogram
- Scatter Plot
- Sunbrust
- Drill Down Count
- Guage Chart
- Builing blocks for Dashboard in Power BI Service
- Insert Image
- Insert Shape
- Bookmarks
- Introduction to Power BI Service
- Publish to Power BI Service
- Export to PPT and PDF
- Edit Report In Power BI Service
- Comments in Power BI Service
- Subscribe reports in Power BI Service
- Share Reports from Power BI Service
- Dashboards in Power BI Service
- Create Reports in Power BI Service
- Dashboard Limitations
- Add Video in Power BI Service
- Create Alerts in Power BI Service
Module 1: Introduction to Tableau and Data Visualization
- 1.1 Introduction to Tableau
- What is Tableau and why use it in Data Analytics?
- Tableau Desktop vs. Tableau Server vs. Tableau Online
- Installing Tableau and setting up the environment
- Tableau interface overview: Workbooks, Sheets, Dashboards, and Stories
- 1.2 Data Connection in Tableau
- Connecting to various data sources (Excel, CSV, SQL, Web Data Connectors)
- Data preparation: data cleaning and transformations within Tableau
- Data Blending vs. Data Joining
- 1.3 Tableau Data Types and Fields
- Dimensions and Measures: Understanding the difference
- Discrete vs. Continuous fields
- Date and Time fields handling
- Creating calculated fields
Module 2: Basic Data Visualizations
- 2.1 Creating Basic Charts
- Building Bar, Line, and Pie charts
- Creating Text Tables and Heat Maps
- Scatter Plots and Bubble Charts
- 2.2 Formatting and Customization
- Formatting the worksheet (font, colors, alignment)
- Customizing marks: shapes, size, color
- Using filters and sorting data
- 2.3 Interactive Dashboards
- Introduction to Dashboards in Tableau
- Combining multiple charts into a dashboard
- Adding interactivity: Filter actions, Highlight actions, URL actions
- Creating Storyboards for data storytelling
- 2.4 Aggregating Data
- Using aggregation functions in Tableau: Sum, Average, Count, etc.
- Creating Aggregated Views and drilling down into data
- Working with hierarchies and grouping data
Module 3: Advanced Tableau Techniques
- 3.1 Advanced Chart Types
- Dual-Axis charts and Combo Charts
- Waterfall charts, Bullet charts, and Gantt charts
- Tree Maps, Trellis, and Box Plots
- Histograms and Distribution Plots
- 3.2 Calculations and Expressions
- Introduction to calculated fields
- Aggregated and table calculations
- Using conditional logic in calculations (IF statements, CASE expressions)
- Date calculations and running totals
- 3.3 Parameters and Filters
- Creating and using parameters in Tableau
- Dynamic filtering: Top N, Relative filters, Context filters
- Parameterized control of visualizations
- 3.4 Level of Detail (LOD) Expressions
- Introduction to LOD expressions
- Fixed, Include, and Exclude LODs
- Use cases for LOD expressions in complex data analysis
Module 1: Introduction to Python for Data Analytics
- 1.1 Python Overview
- What is Python and why it is used in Data Analytics
- Installing Python and setting up the environment
- Python IDEs (Jupyter Notebook, PyCharm, VSCode)
- Basic syntax and variables in Python
- 1.2 Python Data Types
- Integers, Strings, Lists, Tuples, Dictionaries, Sets
- Working with Strings and String operations
- Data Type conversion
- 1.3 Control Structures
- Conditional statements: if, elif, else
- Loops: for, while
- List comprehensions
- 1.4 Functions and Modules
- Defining functions
- Lambda functions
- Importing libraries and modules
Module 2: Python Libraries for Data Analytics
- 2.1 Numpy for Data Manipulation
- Introduction to NumPy arrays
- Array operations: arithmetic, broadcasting, and slicing
- Matrix operations and linear algebra
- Statistical functions with NumPy
- 2.2 Pandas for Data Analysis
- Introduction to Pandas DataFrames and Series
- Importing and exporting data (CSV, Excel, SQL)
- Data cleaning: Handling missing data, duplicate values, and data transformation
- Indexing and selecting data in DataFrames
- Aggregating and grouping data
- Merging and concatenating DataFrames
- 2.3 Matplotlib for Data Visualization
- Introduction to data visualization
- Plotting basic graphs: line, bar, histogram, scatter plots
- Customizing plots (labels, titles, legends, colors)
- Working with multiple subplots
- Saving visualizations to files
- 2.4 Seaborn for Statistical Plots
- Introduction to Seaborn library
- Creating statistical visualizations: box plots, pair plots, heatmaps
- Customizing Seaborn plots
- Building the Database Schema
- Creating tables and columns
- Building tables with CREATE TABLE
- Modifying table structure with ALTER TABLE
- Adding columns to an existing table
- Removing tables with DROP TABLE
- Protecting data integrity with constraints
- Guaranteeing uniqueness with primary key constraints
- Enforcing integrity with foreign key constraints
- Imposing business rules with check constraints
- Improving performance with indexes
- Expediting data retrieval with indexes
- Recommending guidelines for index creation
- Manipulating Data
- Modifying table contents
- Adding table rows with INSERT
- Changing row content with UPDATE
- Removing rows with DELETE
- Applying transactions
- Controlling transactions with COMMIT and ROLLBACK
- Deploying BEGIN TRANSACTION in SQL Server
- Working with the SELECT Statement
- Writing Single Table queries
- Retrieving data with SELECT
- Specifying column expressions
- Sorting the result with ORDER BY
- Handling NULL values in expressions
- Restricting rows with the WHERE filter
- Testing for equality or inequality
- Applying wildcard characters
- Avoiding NULL value pitfalls
- Querying Multiple Tables
- Applying the ANSI/ISO standard join syntax
- Matching related rows with INNER JOIN
- Including nonmatched rows with OUTER JOIN
- Creating a Cartesian product with CROSS JOIN
- Combining results with set operators
- Stacking results with UNION
- Identifying matching rows with INTERSECT
- Utilizing EXCEPT to find nonmatching rows
- Employing Functions in Data Retrieval
- Processing data with row functions
- Conditional formatting with the CASE expression
- Utilizing the CASE expression to simulate IF tests
- Dealing with NULL values
- Performing analysis with aggregate functions
- Summarizing data using SUM, AVG and COUNT
- Finding the highest/lowest values with MAX and MIN
- Defining the summary level with GROUP BY
- Applying filter conditions with HAVING
- Constructing Nested Queries
- Applying subqueries in filter conditions
- Correlated vs. noncorrelated subqueries
- Testing the existence of rows
- Including subqueries in expressions
- Placing subqueries in the column list
- Creating complex expressions containing subqueries
- Handling subqueries that return no rows
- Developing In-Line and Stored Views
- Breaking down complex problems
- Selecting data from a query result set
- Subqueries in the FROM clause
- Creating views in a database
- Building reusable code
- Updateable vs. non-updateable views
- Breaking down complex problems
- Applying subqueries in filter conditions
- Processing data with row functions
- Applying the ANSI/ISO standard join syntax
- Writing Single Table queries
- Modifying table contents
- Creating tables and columns
What our Students are saying about data analytics course in jaipur
Python course
Great mentorship, python training , best learning experience at Seldom India jaipur.
Python course
Great mentorship, python training , best learning experience at Seldom India jaipur.
Python course
Great mentorship, python training , best learning experience at Seldom India jaipur.
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Frequently Asked Questions about Data Analytics Course
On this very page you will find a form titled ‘Get more information’. Fill in your details and we will get in touch.
Data Analytics involves examining data to discover useful information. Taking a Data Analytics course can teach you how to make sense of data for better decision-making.
While some math is helpful, our course is designed for various skill levels. We’ll guide you through the necessary concepts with clear explanations.
The course is hands-on. You’ll work on real projects, using tools and techniques used by Data Analysts, to gain practical experience.
You can explore roles like Data Analyst or Business Analyst in different industries such as finance, healthcare, and marketing.
No specific prerequisites are required. The course caters to beginners and those with some background in related fields.
Yes, we regularly update the curriculum to include the latest trends and technologies in the field of Data Analytics.
Yes, you’ll receive a certificate of completion, showcasing your skills to potential employers.
It equips you with skills valued in today’s data-driven workplaces, enhancing your career prospects.
Yes, the course is designed to be accessible to individuals without a technical background.
