About Data Analytics Course
The Data Analytics course focuses on analyzing, interpreting, and visualizing data to support business decision-making.
Data Analytics Course Objectives
The Data Analytics course focuses on analyzing, interpreting, and visualizing data to support business decision-making.
Pre-Requisites To Learn Data Analytics
Beginner-friendly course.
Basic mathematics knowledge is helpful.
Interest in data and analysis is required.
Basic mathematics knowledge is helpful.
Interest in data and analysis is required.
Top Career Roles after Data Analytics Course:
• Data Analyst
• Business Analyst
• Reporting Analyst
• Business Analyst
• Reporting Analyst
Course Outline
-
Module 1: Excel for Data Analysis
View Details - • MS Office Versions (similarities and differences)
- • Excel Interface (latest available version)
- • Rows and Columns
- • Keyboard Shortcuts for Navigation
- • Data Entry (Fill Series)
- • Find and Select, Clear Options, Ctrl+Enter
- • Formatting Options (Font, Alignment, Clipboard)
- • Referencing, Named Ranges, Arithmetic Functions
- • Logical Functions: IF, AND, OR, Nested IFS, IFERROR
- • Lookup Functions: LOOKUP, VLOOKUP, Nested VLOOKUP, HLOOKUP, INDEX, INDEX-MATCH, INDIRECT, OFFSET
- • Data Validation (Dependent Drop-down)
- • Date Functions: DATE, DAY, MONTH, YEAR, YEARFRAC, DATEDIFF, EOMONTH
- • Text Functions: TEXT, UPPER, LOWER, PROPER, LEFT, RIGHT, SEARCH, FIND, MID, TTC, Flash Fill
- • Data Cleaning and Handling: Remove Duplicates, Number Formatting, SORT, FILTER, Advanced Filter
- • Data Visualization: Conditional Formatting, Charts (Bar, Column, Line, Scatter, Combo, Gantt, Waterfall, Pie)
- • Data Summarization: Pivot Tables and Pivot Charts, Slicers, Calculated Fields and Items
- • Dashboard Creation: Design and Placement, Formatting, Colors, Shapes
- • Connecting to Data: Power Query, Power Pivot, Data Cleaning, Merging, Appending, Loading Data from Databases, Workbooks, Webpages
-
Module 2: SQL Server and SQL Integration
View Details - • Introduction to SQL and Database Concepts (DBMS, RDBMS)
- • SQL Query Execution and MySQL Setup
- • Basic SQL Keywords: SELECT, LIMIT/OFFSET, ORDER BY, DISTINCT, WHERE, Comparison Operators, NULL, Logical Operators, Aggregate Functions (COUNT, MAX, MIN, AVG, SUM), GROUP BY, HAVING, Wildcards
- • Joins: Inner Join, Outer Join, Left Join, Right Join, Self Join
- • Subqueries/Nested Queries/Inner Queries
- • Triggers, Stored Procedures
- • DML: INSERT, UPDATE, DELETE
- • DDL: CREATE TABLE, ALTER (Add, Drop, Modify), DROP TABLE, TRUNCATE
- • DCL: GRANT, REVOKE
- • Advanced SQL: Nested Queries, Window Functions, Data Import/Export, Performance Tuning and Optimization
-
Module 3: Power BI for Data Visualization
View Details - • Introduction to Data Warehousing and Tools
- • Power BI Overview and Flow of Activity
- • Power BI Primary Tools: Power Pivot, Power Query, Power View, Power Map, Power Q&A, Power BI Desktop
- • Power BI Desktop Installation and Data Connections
- • Query Editor: Data Transformation and Cleaning
- • Modeling Data: Relationships, Calculated Columns, Data Optimization
- • Power BI Services: Licensing, Workspaces, Data Gateways, Content Packs, Publishing Reports
- • Dashboard Creation: Pinning Visuals, Applying RLS, Adding Tiles
- • Power BI Datasets and Sharing
- • Power BI Q&A, Quick Insights, Power BI Publisher for Excel
-
Module 3 (Alternative): Tableau for Data Visualization
View Details - • Data Visualization and Analytics
- • Tableau Dashboard Elements
- • Tableau Desktop and Server Overview
- • Charts: Line, Bar, Area, Stacked Bar, Multi-line, Pie, Donut, Tree Map, Histogram, Filled Map, Symbol Map
- • Connecting to Excel and Other Data Sources
- • Table Joins and Multi-Table Joins
- • Metadata and Table Handling
- • Calculated Fields: String and Date Operations
- • Hierarchies, Groups, Static and Dynamic Group Creation
- • Sorting, Dual Axis Graphs, Multi-line Charts, Box Plots, Histograms, Pareto Chart, Motion Chart, Funnel Chart, Waterfall Chart
-
Module 4: Data Visualization using Python
View Details - • Python Introduction: History, Installation (Anaconda, Jupyter Notebook), First Program
- • Python Basics: Identifiers, Keywords, Indentation, Comments, Variables, Data Types, Inbuilt Functions
- • Control Flow: if-else, elif, while, for, range, break, assert, pass, return
- • Functions: Defining and Calling, User-Defined, Lambda, Map, Filter, Format, Split, Strip
- • Object Oriented Programming: Classes, Objects, Self, Constructors, Global and Local Variables, Static and Dynamic Variables, Abstraction, Inheritance, Encapsulation, Polymorphism
- • Exception Handling: Compile-Time and Runtime Errors, try-except-else, try-finally, Raising Exceptions, User-Defined Exceptions
- • GUI Development: Tkinter, Button, Label, Text Widget
- • Data Analysis Libraries: Pandas, NumPy, Matplotlib, Seaborn
- • Project Discussion: Defining Business Problem, Constraints, Flow Diagram, Libraries Used, Results, Future Scope, References
Reviews
Course Content
Outstanding
Puncuality
Outstanding
Trainers
Outstanding
Rohit Verma
The real-time projects at ITKUL made a huge difference. By the time I attended interviews, I already had hands-on experience with Java full-stack development.
Anjali Mehta
I joined ITKUL with zero experience in data analytics. The structured modules and real-time projects boosted my confidence, and now I’m working in a data-driven role.
Smit Mahajan
The ethical hacking and network security labs at ITKUL were amazing. The trainers guided us through real-world scenarios, and I successfully cleared my first job interview