A Detailed Guide to Data Analyst Course Syllabus

Data analysis is essential to making critical strategic decisions in today’s data-driven environment. Data analysis is therefore essential to the survival and development of every organization. In addition to helping organizations make thoughtful, well-informed decisions, data analysis has seen an increase in interest in India. Expert data analysts are in high demand today. A wide variety of courses are available to help you understand and study the detailed material in the data analyst course syllabus. 

A detailed guide to data analyst course syllabus

This article will guide you through how to choose the best data analyst course syllabus

Definition of Data Analytics

Using specialist hardware and software to analyze data collection is known as data analytics (DA). Data analytics is typically used to identify trends and make judgments about the facts contained in them. Data analytics tools and methodologies are widely used in the commercial sector to help companies make better business decisions.

Scientists and researchers use analytical tools to support and refute scientific hypotheses, models, and ideas. There are several ways in which data analytics strategies can be used to support firms in their efforts to improve customer service, enhance marketing campaigns, and generate revenue.

Companies can also use data analytics to react swiftly to changing market trends and gain an advantage over their competitors. In data analytics, the primary objective is to improve business performance, but depending on the specific application, evaluated data could include new data gathered for real-time analytics or archived documents.

Additionally, it may be derived from both internal and external sources. A variety of frameworks and tools are used during the analytical process to visualize the research findings. The type of data collected can be structured, semi-structured, or unstructured.

Data analysts are among the most exciting career options among the many employment types in the data analytics industry. They can assist businesses in transforming raw data into useful knowledge for corporate success. Getting a better understanding of what a data analyst is and discussing the data analyst course syllabus is the next step!

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Definition of Data Analyst

Businesses generate and collect data no matter what size they are. Data analysts are the ones who gather, prepare and analyze large datasets. Client reviews, financial records, logistics, market research, etc., are examples of this information.

The domains of data analysts are data management, data modeling, and reporting. In addition to enhancing the customer experience, pricing new goods, and reducing transportation costs, a data analyst uses this data to determine various actions.

As soon as you learn who a data analyst is, you must understand their functions and responsibilities. Data analysts work in the fields of reporting, data modeling, and data management.

Here is a guide to Data Analytics and Data Science

Data Analyst Course Syllabus

As part of the data analyst course syllabus, specialized software or systems are used to teach comprehensive methods for extracting, analyzing, and manipulating data. Studying topics such as Mathematics and Statistics, Data Structures, Stimulation, Collecting data, and comparable ones are intended to prepare the same individuals.

While a data analyst course syllabus may vary from program to program, some of the most used topics of data analytics are as under:


Data Structures and Algorithms

Data structures and algorithms are crucial topics in the data analyst course syllabus because they are essentially named places where data may be organized and stored. Data structures and algorithms are essentially a series of steps used to solve a specific problem.

The following are some of the concerns related to structures and algorithms:

  • Array, Iteration, and Invariants
  • List, recursion, stacks, and queues
  • Efficiency and complexities
  • Trees
  • Hash Tables
  • Binary search trees
  • Searching
  • Sorting

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Probability and Statistics

The whole point of probability and statistics is to deal with the relative frequency of events. Probability is concerned with estimating how similar future events will be, whereas statistics look at frequency.

The main branches of probability and statistics are as follows:

  • Business Statistics
  • Introduction to Statistical Analysis
  • Counting, Probability, and Probability Distributions
  • Sampling Distributions
  • Estimation and Hypothesis Testing
  • Scatter Diagram
  • ANOVA and Chi-square
  • Imputation Techniques
  • Data Cleaning
  • Correlation and Regression

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Introduction to Data Analytics

  • Data Analytics Overview
  • Importance of Data Analytics
  • Types of Data Analytics
  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics
  • Benefits of Data Analytics
  • Data Visualization for Decision Making
  • Data Types, Central Tendency Measures, and Dispersion Measures
  • Graphical Techniques, Skewness & Kurtosis, Box Plot
  • Descriptive Stats
  • Sampling Funnel
  • Sampling Variation
  • Central Limit Theorem
  • Confidence interval


Business Fundamentals

These are the core skills required to comprehend the numerous components of corporate management.

The main topics covered by business basics are:

  • Teamwork in Business
  • The Foundations of Business
  • Ethics and Social Responsibility
  • Structuring organizations
  • Motivating Employees
  • Managing Human resources
  • Economics of Business
  • Operations Management

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Text Analytics

This automated process converts huge amounts of unstructured text into quantitative data to spot trends, insights, and patterns.

The following are the main topics in text analytics:

  • Natural language basics
  • Processing and understanding text
  • Text Summarization
  • Text Similarity and Clustering
  • Text classification
  • Semantic and Sentiment Analysis

Data Collection

It is a technique for assessing and accumulating information about certain variables in an already-existing system, enabling the user to compute results.

The main topics under study for data collection are:

  • Survey Sampling
  • Observational result
  • Statistical Techniques
  • Analysis of Unstructured Data
  • Extracting and Presenting Statistics

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Data Visualization

  • Data visualization’s principal subjects include:
  • Java
  • CSS
  • Customized geographic map
  • Creation of Bar Chart
  • Scatter Plot


Skills Included

Candidates need to be aware of the skills necessary to become successful data analysts. Additionally, one needs to have strong numerical and analytical skills as well as a deep understanding of computer tools like Microsoft Succeed, Python, SQL, R, and the Statistical Language to excel at data analytics (R).

Skills That Are Included in the Data Analyst Course Syllabus Are:

Excel: Basics to Advanced

It is used to create grids with text, numbers, and formulas to manipulate data and track expenses, financial results, and other things.

The topics discussed in the Microsoft Excel course syllabus are as per the following list:

  • 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 and Clearing Formatting
  • Using Conditional Formatting to Hide Cells
  • Using the IF Function
  • The “Value if false” Condition is changed to Text
  • 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 to its Sheet
  • Adding Data Labels
  • Exploding a Slice of a Pie Chart
  • Data Analysis − Overview


Types of Data Analysis

  • Data Analysis Process
  • Working with a range of 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

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Python Basics

Python can be thought of as sophisticated object-oriented programming. It is the software that lends itself to developing the best because of its flexible binding, high-level built-in structures, and language. Support is provided for packages and modules that provide the modularity and programmability of programs.


The following is a list of the topics covered in the curriculum for the Python course:

  • 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 statements
  • 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

The R programming language enables statistical approaches and data visualization. It is used by specialists, data analysts, and other experts because it is made up of a collection of libraries.

The subjects covered in the data analyst course syllabus for the R programming course are listed below:

  • Background and Nuts & Bolts
  • Programming
  • Loop Functions and Debugging
  • Simulation and Profiling



A language known as SQL, or Structured Query Language, can be used to communicate with databases. Data extraction, manipulation, and management are made easier with the help of the Relational Database Management System (RDMS). Many various database systems, including Oracle, MySQL, and others, support this programming language.

The subjects covered by the course syllabus for the SQL course are listed below:

  • 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
  • Aggregate Data Using the Group Functions
  • Display Data from Multiple Tables Using Joins
  • Use Sub-Queries to Solve Queries
  • The SET Operators
  • Data Manipulation Statements
  • Table Management and Creation Using DDL Statements
  • Other Schema Objects
  • Control User Access
  • Management of Schema Objects
  • Manage Objects with Data Dictionary Views
  • Manipulate Large Data Sets
  • Data Management in Different Time Zones
  • Retrieve Data Using Sub-queries
  • Regular Expression Support



You can prepare, analyze, collaborate on, and integrate your big data results with the help of Tableau, a comprehensive data analytics platform. With Tableau, users can swiftly communicate their findings across the organization and pose fresh inquiries concerning controlled big data. Tableau is a Master of Visual analysis for self-service.

The subjects covered by the Tableau course syllabus are listed below:


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
  • Reporting Totals, Subtotals, and Grand Totals

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
  • Chart with Side by Side Bars or Grouped Bars
  • 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
  • Map box 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 the Dashboard
  • Dashboard Objects
  • Create a Story

Module 7: Server

  • Tableau online.
  • Overview of Tableau Server.
  • Publishing Tableau objects and scheduling/subscription.


Power BI

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 constantly updating 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
  • Put text, a map, and gauge visualizations together to make a report
  • Use a slicer to filter visualizations
  • Sort, copy, and paste visualizations
  • Use a custom image you downloaded from the gallery

Module 3: Reports and Dashboards

  • Modify and Print a Report
  • Rename and delete report pages
  • A page or report should have a filter
  • Set visualization interactions
  • Print a report page
  • Send a report to PowerPoint
  • Create a Dashboard
  • Create and manage dashboards
  • Pin a report tile to a dashboard
  • Pin a dashboard’s live report page
  • Pin a tile from another dashboard
  • Pin an Excel element 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 to share a tile
  • Embed a report in SharePoint Online

Module 5: Other Power BI Components and Table Relationship

  • Use Power BI Mobile Apps
  • Get Power BI for mobile
  • In the iPad app, you may view reports and dashboards
  • 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
  • Get Power BI Desktop data with the Power BI service
  • Export a report from the Power BI service to the Desktop

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

Machine Learning

Machine learning is an application of artificial intelligence that allows computers to automatically learn and develop without having been explicitly created. It highlights the development of computer programs that can get data and use it for independent research.

To find patterns in the data and enhance decisions, this course begins with data observations, such as first-hand experience or advice.

The topics covered in the syllabus for the machine learning course are listed below:

  • Introduction to different Learning methods
  • Decision Tree
  • Database and SQL
  • Data Pre-processing and Data Mining
  • Linear Regression
  • Exploratory Data Analysis
  • SVM
  • Logistic Regression
  • CNN
  • Naive Bayes


The Data Analyst Course Syllabus Software and Tools

Data analytics are becoming more and more necessary, and as a result, numerous solutions with different functionalities have been developed. The top data analytics tools, whether they are open-source or user-friendly, are listed below.


Any data source, including Excel, Corporate Data Warehouse, and others, is freely accessible using this program, which subsequently creates maps, visualizations, and web-based interfaces with real-time updates.


It offers quick in-memory data processing, with the end user receiving the results. Along with data association, data visualization, and data compression are all included.


An open-source object-oriented programming language is easy to comprehend, develop, and maintain. TensorFlow, Matplotlib, Scikit-learn, Pandas, and Keras are just a few of the machine learning and visualization modules it offers. This tool can be developed on any platform, including a SQL server, MongoDB database, or JSON.


Any type of data source, such as Tera data, Oracle, or SQL Server, can be used with this tool because it is an effective integrated space.


Frequently Asked Questions About Data Analyst Course Syllabus

Below are the FAQs about the data analyst course syllabus

Q1. What is Data Analytics?

  • Data analytics is the process of gathering, purifying, and analyzing unstructured data to find significant trends, patterns, and insights.
  • Data insights can be utilized to uncover solutions to even the most difficult situations and answers to a wide range of inquiries.
  • Data analytics enables us to comprehend the impacts of our current actions, potential future actions, and any likely results of a different course of action.
  • Data analytics essentially tells us what to do next. The goal is to use objective data to support well-informed judgments that aid a project in achieving its objectives.

Q2. How difficult is it to learn Data Analytics?

The job of a data analyst is not as intellectual as the title might imply, believe it or not. You don’t have to be an expert programmer or mathematician to succeed in this field. Being a successful data analyst requires a wide range of abilities, some of which are highly technical. Many of the most complex abilities used by a data analyst can be picked up on the job, but it’s crucial, to begin with, a strong foundation of the fundamental tools and methods. Anyone interested in entering the industry should think about obtaining a formal certification as the likelihood of learning things alone online is remote.

Q3. Would you recommend a career in data analytics?

Since there is a high demand for their skills and a small pool of skilled individuals, data analysts make good career alternatives. Additionally offered are generous benefits packages and high salary rates.

Q4: I don’t have any prior experience. Can I still work as a data analyst?

Without any past professional experience, it is feasible to become a data analyst. The best part of being a data analyst is getting to do that. The data analyst course syllabus covers education levels from beginner to advanced.

Q5: What is the difference between data analysis and data analytics?

Analytics differ from analysis in that they are scalable. Data analysis falls under the generic phrase of “data analytics.” Data analysis is the process of looking at data. Data analysis includes data gathering, organization, storage, analytical methods, and tools.


We have finally completed the guide on the Data Analyst course syllabus. You were given an explanation of data analytics and the topics included in the Data Analyst course syllabus. Data analytics is one of the industries with the highest demand and the possibility for the most lucrative career advancement. From the role of Data Analyst, you can go up to Senior Analyst, Data Scientist, Chief Technology Officer, and Analyst II. Be proficient in the data analyst course syllabus.