Comprehensive Data Analytics Syllabus: Courses And Curriculum

Data Analytics Syllabus: Understanding data analytics syllabus is essential to plan the studies after enrolling in a data analytics course. Examining the syllabus also gives you insights into the key concepts covered in a specific course. You can compare the syllabus of various data analytics courses before enrolling. In this article, we will discuss the common concepts covered in most data analytics courses.

Definition of Data Analytics

Data analytics is a process of collecting and interpreting data to recognize current market trends. When you analyze the data, you also get insights into customer behavioral patterns. The collected data is categorized and analyzed to obtain key insights. Data visualization helps you to present the data in a pictorial form using charts and graphs.

Also read: Data Analytics Meaning, Importance, Techniques, Examples

Data Analytics Syllabus: Key Topics and Components

We have provided the key topics and concepts covered in different sections of data analytics courses:

Business Statistics

Business Statistics is used to analyze business data for understanding, correlating, and forecasting future trends. You need to be well-versed in Mathematics to understand the concepts well. The entire business statistics syllabus has been provided below:

Chapter	Syllabus
Introduction to Statistical Analysis	Correlation and Regression Data Cleaning Imputation techniques Anova and Chi Square Scatter Diagram Estimation and Hypothesis Testing Sampling Distributions Counting, Probability, and Probability Distributions
Introduction to Data Analytics	Data Analytics: An Overview Importance of Data Analytics Types of Data Analytics Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis Benefits of Data Analytics Data Visualization for Decision Making Data Types, Measure of central tendency, Measures of dispersion Graphical techniques, box plot, skewness and kurtosis Descriptive Stats Sampling Funnel, Central Limit Theorem, Confidence Interval, Sampling Variation

Excel

Excel is a spreadsheet software that helps you sort, filter, and manage data. It is typically used to perform complex calculations. The syllabus for Microsoft Excel has been given below:

Chapter

Syllabus

Excel: Basic to Advanced

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
Changing the “Value if false” Condition to Text
Pivot Tables
Creating a Pivot Table
Specifying Pivot Table Data
Changing a PivotTables Calculation
Filtering and Sorting a Pivot Table
Creating a Pivot Chart
Grouping Items
Updating a Pivot Table
Formatting a Pivot Table
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 Own Sheet
Adding Data Labels
Exploding a Slice of a Pie Chart
Data Analysis Overview
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

Boost Your Productivity with Advanced Excel Tips

SQL

SQL (Structured Query Language) is a database management system. You can use it to store and manage data. Some of the key aspects of its syllabus have been mentioned below:

Chapter

Syllabus

Structured Query Language

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 Subqueries to solve queries
The SET Operators
Data Manipulation Statements
Use of DDL Statements to Create and Manage Tables
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 Subqueries
Regular Expression Support

Tableau

Tableau is a data visualization software which also helps in analyzing data. Some of the crucial components of its syllabus have been listed below:

Modules	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, and 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 or Side by Side Bars Chart 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	WMS Server Map as a Background Map Mapbox Maps as a Background Map Use Google Maps Symbol Map Basic Maps Reference Distributions Reference Bands Add Reference Lines Individual Axis Blended Axis Dual Axis Reports
Module 5: Learn Tableau Calculations and Filters	Extract Filters Data Source Filters Slicing Filters Context Filters Filters on Measures Top and Bottom Filters Conditional Filters Filters on Dimensions Quick Filters Filters Introduction Calculating Running Total Advanced Approach to Calculate Rank Basic Approach to Calculate Rank Calculated Fields
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

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Power BI

Power BI converts raw data into insights that are immersive and meaningful. You can feed data from spreadsheets, cloud, or data warehouse to this software. The syllabus related to Power BI have been mentioned in the below table:

Module	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 a sample Explore the Power BI portal Connect to Excel Data that can be Refreshed
Module 2: Viz and Tiles	Overview: Visualizations Using Visualizations Create a new report Create and arrange visualizations Format a Visualization Create Chart Visualizations Use text, map, and gauge visualizations and save a report Use a slicer to filter visualizations Sort, copy, and paste visualizations Download and use a custom visual from the gallery
Module 3: Reports and Dashboards	Modify and Print a Report Rename and delete report pages Print a report page Add a filter to a page or report Set visualization interactions Send a report to PowerPoint Create a Dashboard Create and manage dashboards Pin a report tile to a dashboard Pin a live report page to a dashboard Pin a tile from another dashboard Pin an Excel element to a dashboard Manage pinned elements in Excel Add a tile to 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 View reports and dashboards in the iPad app 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 Power BI Service Export a report from Power BI service to desktop
Module 6: DAX Functions	New Dax Functions Data and time functions Time intelligence functions Filter functions Information functions Logical functions Math and trig functions Parent and child functions Text functions

Python

Python is a programming language that is used for data visualization, data analysis, and web development. The important components of its syllabus have been provided below:

Chapter	Syllabus
Python Basics	The Print Statement Comments Operators in Python Shallow Copy Deep Copy Simple Output Formatting Simple Input and Output String Operations in Python Python Data Structures and Data Types

Also read: Analysis of Algorithm in Data Structure

These were some key details regarding the data analytics syllabus. If you want to pursue a career in data analytics, you must enroll in a competitive online course. PW Skills offers a wide range of data analytics courses. Apart from live classes from professionals, you can join the job assurance program to secure your job after course completion.

Want to pursue a dream career in data analytics? Enroll in online courses offered by PW Skills and acquire all the knowledge and skills to become a full-stack data analyst!

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FAQs

What is the curriculum for the data analytics course?

The data analytics syllabus includes topics such as data blending, regression analysis, analytics tools and techniques, Python/R programming, inferential statistics, data extraction, and hypothesis testing.

Which are the main subjects covered in data analytics?

The main subjects included in data analytics syllabus include decision making in context of business analytics, numerical analysis, statistical analysis, systems analysis and design.

What is the tenure of data analytics courses?

Most data analytics courses have a tenure of 1 year. Some courses even have a tenure of 1, 3, or 6 months.

Are data analytics courses difficult?

The difficulty level of the data analytics courses depends on their syllabus. Some individuals find it difficult because they have to master some technical skills. Also, as this field is evolving at a rapid rate, you will have to keep upgrading your skills constantly.

What does a data analyst do?

A data analyst tries to solve business problems using insights obtained from data. He needs to identify the genuine data sources and deploy data collection tools to obtain, clean, and organize data. Finally, the data is processed to extract meaningful insights.

What is the difference between data analytics and data science?

Both data science and data analytics involve the use of data to gain meaningful insights. However, data science is more related to creating data models whereas data analytics is more focussed on observing historic data before making business decisions. So, the way in which data is used and manipulated is different in both these concepts.

Which skills do data analysts need?

Some in-demand data analytics skills include knowledge in machine learning, SQL, statistical visualization, data management, and some other statistical and mathematical concepts.