Data Analyst Course Syllabus 2026: Complete Course Syllabus Outline
August 1, 2026|
Vanthana Baburao|
Data Analytics|
Businesses and organizations rely on data analysis to make critical strategic choices in today’s data-driven environment. Therefore, data analysis is crucial to the survival and development of every firm. Organizations may make thoughtful, well-informed decisions with the aid of data analysis. India also sees a rise in interest in data analysis. Expert data analysts are in high demand right now. Many different course options may help you study and grasp the detailed material in the data analyst course syllabus, but with so many choices available, it helps to know Which Data Analytics Certification Is the Best before you commit to one.
What is Data Analytics?
Data analytics is the process of examining data to find trends and draw conclusions. Businesses usually use specific software and tools for this, which helps them make smarter and quicker decisions. Companies in every industry depend on data analytics to shape their strategy and remain competitive. Analytics tools are also used by scientists and researchers to support or refute scientific models, ideas, and hypotheses.
Data analytics strategies may support firms’ attempts to boost customer service, enhance marketing campaigns, and generate revenue. Analytics also provides companies the ability to react swiftly to changing market trends and acquire an advantage over competing companies. The fundamental goal of data analytics is to enhance business performance, though.
Depending upon the specific application, the data that is evaluated may be made up of new data that has been collected for real-time analytics or archival documents. Additionally, it could originate from a combination of both internal systems and outside data sources.
The type of data gathered may be organized, semi-structured, or unstructured. Visual representations of the end results as graphs and charts that show the research’ precise findings are available. A variety of frameworks and tools are used during the analytical process.
Professionals who can assist businesses in transforming raw data into knowledge that is useful for corporate success are in great demand. Data analysts have some of the most exciting career options among the many employment types in the data analytics industry. The next step is to comprehend what a data analyst is. Also, discuss the data analyst course syllabus!
What is the role of a Data Analyst?
Data analysts are responsible for collecting, cleaning, and reviewing large datasets. Every business, no matter its size, generates data whether that's customer reviews, financial records, logistics data, or market research.
A data analyst is a specialist who uses this data to determine various actions, including, to name a few, how to enhance customer experience, price new goods, and lower transportation costs. Data management, data modeling, and reporting are the domains of data analysts.
It is crucial to understand a data analyst’s functions and responsibilities after learning who they are.
Once you are aware of who is a data analyst. To comprehend the data analyst course syllabus, let’s move on to the following part.
With the assistance of various software or specialized systems, the data analyst course syllabus is created to offer comprehensive instruction in data extraction, analysis, and manipulation. Through the study of topics like Mathematics and Statistics, Data Structures, Simulation, Collection of data, and comparable ones, it intends to educate the same.
Although the data analyst course syllabus may vary from program to program or curriculum to curriculum, the following are some frequent data analytics topics:
Data Structures and Algorithms
The difference between a data structure, which is essentially a named place that may be used to organize and store data, and an algorithm, which is a series of steps used to solve a certain issue. Data structures and algorithms are a very crucial topic in data analyst courses syllabus. Data structure and algorithmic concerns include:
Array, Iteration, and Invariants
List, recursion, stacks, and queues
Efficiency and complexities
Trees
Hash Tables
Binary search trees
Searching
Sorting
Probability and Statistics
This is the entire purpose of probability and statistics to handle the relative occurrence of events. While statistics examine the frequency, the probability is concerned with predicting how similar future events will be. These are the primary areas of probability and statistics:
Introduction to Statistical Analysis
Counting, Probability, and Probability Distributions
These are the fundamental abilities needed to comprehend the many parts of managing a corporation. Business basics’ primary subjects include:
Teamwork in Business
The foundations of Business
Ethics and Social responsibility
Structuring organizations
Motivating Employees
Managing Human resources
Economics of Business
Operations Management
Text Analytics
To identify trends, insights, and patterns, this automated procedure transforms massive amounts of unstructured text into quantitative data. The primary subjects in text analytics are:
Natural language basics
Processing and understanding text
Text Summarization
Text similarity and Clustering
Text classification
Semantic and Sentiment analysis
Data Collection
It is a method of gauging and gathering data on certain variables in an already-existing system, allowing the user to compute outcomes. The primary subject areas for data collection are:
Candidates need to understand the abilities that will enable them to land a successful job as data analysts. Additionally, to excel at data analytics, one must possess strong numerical and analytical abilities as well as a thorough grasp of computer programs like Microsoft Excel, Python, SQL, R, and the Statistical Language (R).
Skills that are included in the Data Analyst Course Syllabus are:
Microsoft Excel Basic to Advanced
It is utilized to build text, number, and formula grids that aid in data manipulation to track expenditures, financial outcomes, and other things.
The following list includes the topics discussed in the Microsoft Excel course syllabus:
Excel tutorial
Text to Columns
Concatenate
The Concatenate Function
The Right Function with Concatenation
Absolute Cell References
Data Validation
Time and Date Calculations
Conditional Formatting
Exploring Styles
Clearing Formatting
Using Conditional Formatting to Hide Cells
Using the IF Function
Pivot Tables
Creating a Pivot Table
Specifying PivotTable Data
Changing a PivotTables Calculation
Filtering and Sorting a PivotTable
Creating a PivotChart
Grouping Items
Updating a PivotTable
Formatting a PivotTable
Using Slicers
Charts
Creating a Simple Chart
Charting Non-Adjacent Cells
Creating a Chart Using the Chart Wizard
Modifying Charts
Moving an Embedded Chart
Sizing an Embedded Chart
Changing the Chart Type
Chart Types
Changing the Way Data is Displayed
Moving the Legend
Formatting Charts
Adding Chart Items
Formatting All Text
Formatting and Aligning Numbers
Formatting the Plot Area
Formatting Data Markers
Pie Charts
Creating a Pie Chart
Moving the Pie Chart
Adding Data Labels
Exploding a Slice of a Pie Chart
Data Analysis − Overview
types of Data Analysis
Data Analysis Process
Working with Range Names
Copying Name using Formula Autocomplete
Range Name Syntax Rules
Creating Range Names
Creating Names for Constants
Managing Names
Scope of a Name
Editing Names
Applying Names
Using Names in a Formula
Viewing Names in a Workbook
Copying Formulas with Names
Difference between Tables and Ranges
Create Table
Table Name
Managing Names in a Table
Table Headers replacing Column Letters
Propagation of a Formula in a Table
Resize Table
Remove Duplicates
Convert to Range
Table Style Options
Table Styles
Cleaning Data with Text Functions
Removing Unwanted Characters from Text
Extracting Data Values from Text
Formatting Data with Text Functions
Date Formats
Conditional Formatting
Sorting
Filtering
Lookup Functions
Pivoting
Python
One may categorize Python as advanced object-oriented programming. With its flexible binding, high-level built-in structures, and language, it is the program that lends itself to developing the best. Packages and modules that guarantee program modularity and programmability are supported.
The Subjects Covered by the Python Course Curriculum Are Listed Below:
File operations using Python
Looping in Python
Python Syntax
Functions
Function Arguments, and Control Flow
Working with Lists
Python Modules
Decorators and generators
Using Dictionaries
Errors and Exception Handling
Comparisons and Operators
The print statement
Comments
Python Data Structures & Data Types
String Operations in Python
Simple Input & Output
Simple Output Formatting
Deep copy
Shallow copy
Operators in python
R Programming
Data visualization and statistical methods are made possible with the R programming language. Specialists, data analysts, and other professionals utilize it because it comprises a compilation of libraries.
The Following List Includes the Subjects Taught in the R Programming Course Syllabus:
Background and Nuts & Bolts
Programming
Loop Functions and Debugging
Simulation and Profiling
SQL
Databases may be contacted using a language called SQL, or Structured Query Language. Utilizing the Relational Database Management System, aids in the extraction, manipulation, and management of data (RDMS). This programming language is supported by several different database systems, including Oracle, MySQL, and others.
The Following List Includes the Subjects Taught in the SQL Course Syllabus:
Introduction to Oracle Database
Retrieve Data using the SQL SELECT Statement
Learn to Restrict and Sort Data
Usage of Single-Row Functions to Customize Output
Invoke Conversion Functions and Conditional Expressions
A complete data analytics tool called Tableau enables you to prepare, analyze, work together, and integrate your big data findings. With Tableau, users can quickly share their findings throughout the enterprise and ask new questions about managed large data. Tableau excels at self-service visual analysis.
The Following List Includes the Subjects Taught in the Tableau Course Syllabus:
Module 1: Tableau Course Material
Start Page
Show Me
Connecting to Excel Files
Connecting to Text Files
Connect to Microsoft SQL Server
Connecting to Microsoft Analysis Services
Creating and Removing Hierarchies
Bins
Joining Tables
Data Blending
Module 2: Learn Tableau Basic Reports
Parameters
Grouping Example 1
Grouping Example 2
Edit Groups
Set
Combined Sets
Creating a First Report
Data Labels
Create Folders
Sorting Data
Add Totals
Sub Totals
Grand Totals to Report
Module 3: Learn Tableau Charts
Area Chart
Bar Chart
Box Plot
Bubble Chart
Bump Chart
Bullet Graph
Circle Views
Dual Combination Chart
Dual Lines Chart
Funnel Chart
Traditional Funnel Charts
Gantt Chart
Grouped Bar
Heatmap
Highlight Table
Histogram
Cumulative Histogram
Line Chart
Lollipop Chart
Pareto Chart
Pie Chart
Scatter Plot
Stacked Bar Chart
Text Label
Tree Map
Word Cloud
Waterfall Chart
Module 4: Learn Tableau Advanced Reports
Dual Axis Reports
Blended Axis
Individual Axis
Add Reference Lines
Reference Bands
Reference Distributions
Basic Maps
Symbol Map
Use Google Maps
Mapbox Maps as a Background Map
WMS Server Map as a Background Map
Module 5: Learn Tableau Calculations & Filters
Calculated Fields
Basic Approach to Calculate Rank
Advanced Approach to Calculate Ra
Calculating Running Total
Filters Introduction
Quick Filters
Filters on Dimensions
Conditional Filters
Top and Bottom Filters
Filters on Measures
Context Filters
Slicing Filters
Data Source Filters
Extract Filters
Module 6: Learn Tableau Dashboards
Create a Dashboard
Format Dashboard Layout
Create a Device Preview of a Dashboard
Create Filters on Dashboard
Dashboard Objects
Create a Story
Module 7: Server
Tableau online.
Overview of Tableau Server.
Publishing Tableau objects and scheduling/subscription.
Power BI
Data extraction and visualization are both made possible by the cloud-based analysis tool Power BI. To provide you with a complete picture of the information assets in your firm, Power BI combines data from several sources.
The Following List Includes the Subjects Taught in the Power Bi Course Syllabus:
Module 1: Introduction to Power BI
Get Started with Power BI
Overview: Power BI concepts
Sign up for Power BI
Overview: Power BI data sources
Connect to a SaaS solution
Upload a local CSV file
Connect to Excel data
Connect to a sample
Create a Report with Visualizations
Explore the Power BI portal
Module 2: Viz and Tiles
Overview: Visualizations
Using visualizations
Create a new report
Create and arrange visualizations
Format a visualization
Create chart visualizations
Create a report using text, a map, and gauge visualizations.
Use a slicer to filter visualizations
Sort, copy, and paste visualizations
From the collection, save and utilize a unique image.
Module 3: Reports and Dashboards
Modify and Print a Report
Rename and delete report pages
Set visualization interactions
Print a report page
Send a report to PowerPoint
Create a Dashboard
Create and manage dashboards
Attach a report tile to a dashboard.
Pinning a dashboard’s live report page
An additional dashboard tile is pinned.
Attach an Excel component to a dashboard.
Manage pinned elements in Excel
Add a tile to a dashboard
Build a dashboard with Quick Insights
Set a Featured (default) dashboard
Ask Questions about Your Data
Ask a question with Power BI Q&A
Tweak your dataset for Q&A
Enable Cortana for Power BI
Module 4: Publishing Workbooks and Workspace
Share Data with Colleagues and Others
Publish a report to the web
Manage published reports
Share a dashboard
Create an app workspace and add users
Use an app workspace
Publish an app
Create a QR code
Embed a report in SharePoint Online
Module 5: Related Tables and Some other Power BI Components
Use Power BI Mobile Apps
Get Power BI for mobile
Use workspaces in the mobile app
Sharing from Power BI Mobile
Use Power BI Desktop
Install and launch Power BI Desktop
Get data
Reduce data
Transform data
Relate tables
Utilize the Power BI service to obtain Power BI Desktop data.
Publish a report to your desktop using the Power BI service.
Module 6: DAX functions
New Dax functions
Date and time functions
Time intelligence functions
Filter functions
Information functions
Logical functions
Math & trig functions
Parent and child functions
Text functions
If you're looking to enrol in our GEN AI Data Analytics Course in India, then check out our courses where we're providing:
An application of artificial intelligence called machine learning enables computers to learn and improve automatically without even being explicitly designed. It draws attention to the advancements in computer programs that can obtain data and utilize it to conduct independent research. This course starts with data observations, such as first-hand experience or guidance to look for patterns in the data and improve decisions.
The Following List Includes the Subjects Covered in the Syllabus for the Machine Learning Course:
Introduction to different Learning methods
Decision Tree
Database and SQL
Data Preprocessing and Data Mining
Linear Regression
Exploratory Data Analysis
SVM
Logistic Regression
CNN
Naive Bayes
Software and Tools Used in Data Analyst Course Syllabus
Due to the growing market need for data analytics, many solutions have emerged with various functions for this goal. The following are a few of the best data analytics tools, whether they are open-source or user-friendly.
Tableau
This program allows for free access to any data source, including Excel, Corporate Data Warehouse, and others, and then generates maps, visualizations, and interfaces with real-time updates for the web.
QlikView
It provides rapid in-memory data processing with results supplied to the end customer. It includes data compression together with data association and data visualization.
Python
It is simple to understand, create, and maintain an open-source object-oriented programming language. It provides several machine learning and visualization modules, including TensorFlow, Matplotlib, Scikit-learn, Pandas, and Keras. Any platform, such as a SQL server, MongoDB database, or JSON, may be used to create this tool.
RapidMiner
This tool is a potent integrated space that can be used with any form of data source, including Tera data, Oracle, Sybase, Microsoft SQL, Excel, Access, and others. The majority of its applications are predictive analytics, including text analytics, data mining, and machine learning.
OpenRefine
This program, commonly referred to as Google Refine, is a data cleaning tool that can assist you in cleaning up data for analysis. When parsing and transforming website data, it is employed to clean up messy data.
SAS
This technology, which can quickly be accessed and can analyze data from many sources, is a language of programming and framework for manipulation of data and analytics.
Once you've covered the syllabus and tools above, it's worth preparing with some common Data Analyst Interview Questions to see how these topics are actually tested by recruiters.
FAQs
Q1. What is the Data Analyst course syllabus?
The data analyst course syllabus includes a broad variety of topics that students must understand. Data collection, visualization techniques, statistics and probability algorithms and algorithms, and many other topics are covered in the course.
Q2. What does data analytics entail?
Graduates in data analytics are sought in almost every sector. Students who want to make a significant career change and earn a lot of money may choose a career in data analytics. Aspirants that have a solid grasp of the industry may also research alternative career paths.
Q3. Is a career in data analytics desirable?
Data analysts are excellent career choices since there is a strong demand for their services and a small pool of qualified candidates. High pay packages and fantastic benefits are also provided.
Q4. Can I work as a data analyst without any prior experience?
Yes, it is possible to become a data analyst without having prior work experience. That is the finest aspect of working as a data analyst. Beginner to advanced levels of education is covered in the data analyst course syllabus.
Q5. Is data analytics a good career choice in India?
Yes, Data analysts are in strong demand across every major industry, with limited supply of trained professionals, making it one of the most favorable entry-level tech careers in India right now.
Conclusion on Data Analyst Course Syllabus
Finally, we have reached the end of the data analytics course syllabus. Data analytics and the concepts covered in the Data Analyst course syllabus were explained to you.
One of the professions with the highest demand and the potential for the most profitable career advancement is data analytics. You can advance from the position of Data Analyst to that of Analyst II, Senior Analyst, Data Scientist, or even Chief Technology Officer.
