Data Analytics

A Comprehensive Guide To Data Analyst Course Syllabus

Data analysis is a process where one checks, clears, transforms, and does modeling of data with an ambition to discover information that could be of use, come to a conclusion, and support decision-making in the end. The process of data analytics has multiple aspects and ways, encompassing diversified ways with various names, which are further used in several businesses, science, and social fields. Read the complete article if you are curious to know what is covered in the data analyst course syllabus. 


In today’s world of business, data analysis plays a major role in making more scientific decisions which help businesses to be operated more effectively and efficiently. The word Analysis refers to dividing a whole process in such a way that its components could be separated well for individual examination.

Data analysis is a procedure where users can choose it as a very helpful tool for converting the obtained raw data into such a type of information that could be used for decision making.


Types of Data to Be Studied in Data Analyst Course Syllabus-

Structured data – This type of data is usually scrutinized via geometric devices or machine learning algorithms.

Unstructured data – Text mining or natural language processing techniques can be utilized to analyze such data.

Spatial data- Can be analyzed using geographic material systems or Geographical Information Science techniques.

Temporal data- Can be analyzed using time series analysis or event-based modeling.


Difference Between Data Analysis and Statistics

Statistics and data analysis are two disciplines that focus on gathering, organizing, interpreting, and presenting data. Both investigations employ statistical and mathematical analysis techniques to conclude regarding a particular set of facts.

There are, however, a few significant distinctions between the two professions. Data analysis focuses primarily on obtaining specific information from a given set of data. Also, since a lot of data needs to be processed in this situation, computer-like equipment is utilized to handle the data properly.

Statisticians, on the other hand, tend to concentrate more on the academic aspects of data collection and analysis. The application of statistical methods to problems encountered in the actual world also reduces statistics.

One of the most significant differences between analysts and statisticians is that data analysts typically work with massive datasets that may be too comprehensive and complex for statistical procedures.

Data analysts may therefore utilize techniques like data mining or machine learning to identify distinct trends and patterns in data in order to resolve such problems. So, despite the fact that statistics and data analysis both focus on data analysis, there are several key distinctions between the two disciplines.

Larger and more complicated datasets are often the focus of data analysts, who may also employ a variety of analytical techniques. The theoretical underpinnings of data analysis and the application of statistical methods to practical issues are increasingly the emphases of statistics.


Steps in Data Analysis- Must Known Thing in Data Analyst Course Syllabus


Data Collection:

Gathering data is a crucial and fundamental phase that is necessary for data analysis. Many sources, including surveys, databases, etc., can be used to gather this information. These are only a few of the instruments that can be used to collect data: data entry forms, questionnaires, etc.


Data Cleaning:

After the necessary data has been found and gathered, it must be filtered before being analyzed further. A data cleaning method is required to remove any inaccurate or wrong data and prevent skewing of the analytical result. Some of the more well-known data cleaning programs include Excel, SPSS, SAS, and others.


Data analysis:

Once the data has been cleansed, it must be examined to draw out relevant information. Data analysts frequently use R, Python, SPSS, and Excel as data analysis tools.


Data Interpretation:

To fully comprehend the analysis’s findings, the data must be thoroughly scrutinized once it has been analyzed. Making the data useful and understandable is its main goal. It is possible to employ tools for this data interpretation, such as charts, graphs, tables, etc.


Data Reporting:

The next step after data interpretation is to present the findings in a form that others can understand. This step entails producing a variety of reports, including text reports, visual reports, etc., that can be used to present the data analysis findings.

One or more of the data reporting tools that can be utilized for this purpose includes Microsoft Word, PowerPoint, Excel, etc. In addition, Python and R programming have a large number of reporting packages that provide you access to a greater range of conception and broadcasting techniques.


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


Microsoft Excel Basic to Advanced

This is used to create text, number, and formula grids that aid in data manipulation so that costs, earnings, and other factors can be kept under control.


The subjects covered in the Microsoft Excel course syllabus are listed below:

  • Excel tutorial: teaches fundamental and advanced Excel skills.
  • Text to Columns: Choose Fixed Width from the radio option under the Data tab, then click Next.
  • Concatenate- this function in Excel helps us to combine text from different cells into one cell, making work easier.
  • The function concatenate
  • Absolute cell references ensure that column and row coordinates are maintained even when a formula is copied from one cell to another.
  • Data validation: Before using or importing data, this step is performed to ensure its accuracy and quality.
  • Date and time computations
  • Conditional formatting: Using techniques like cell shading, icon placement, and backdrop color to create patterns and trends in data.
  • Trying with different font names, sizes, colors, alignments, and spacing.
  • Removing the formatting from chosen cells and their contents.
  • Cells Can Be Hidden Using Conditional Formatting
  • Using the IF Function, a function that aids in comparing a value to what is anticipated.
  • Pivot tables are used to quickly summarize vast amounts of data.
  • How to Build a Pivot Table?
  • How to Provide PivotTable Data?
  • Modifying a calculation in PivotTables
  • How to Filter and Sort a PivotTable
  • How to create a Pivot Chart?
  • By combining two or more rows or columns, the user is given the ability to minimize and maximize the data.
  • How to Update a Pivot Table?
  • Slicers are tools for filtering data in tables and can be used to format pivot tables.
  • Charts: A visual representation of data that facilitates comprehension.
  • Making a Basic Chart
  • Non-Adjacent Cells in a Chart
  • Using the Chart Wizard to Create a Chart
  • Changing Charts
  • An Embedding Chart Can Be Moved
  • Embedding Chart Sizing
  • The Chart Type Has Been Changed
  • Understanding the different types of charts, such as line, bar, area, stock, and surface charts, is important.
  • Modifying the Display of Data
  • The Legend Being Moved
  • Setting up Charts
  • Itemizing Charts
  • Formatting and Aligning Numbers in All Text
  • Formatting Data Markers and the Plot Area
  • Pie charts show data in a circular format with a single row or single column.
  • How to Make a Pie Chart?
  • The Pie Chart being moved
  • Labeling the Data
  • Exploding a Pie Chart Slice
  • Analyses of Data: Overview
  • Range Names Creation
  • Creating Constant Names
  • Controlling Names
  • The domain of a Name
  • Changing Names
  • Using names
  • Making a Formula Using Names
  • Ranges versus Tables: Differences
  • Table creation
  • Table name
  • Table Headers- Instead of Column Letters for Handling Names in a Table
  • Spreading a Formula in a Table Resizing the Table and Eliminating Duplicates
  • Adapt to Range
  • Optional Table Styles
  • Table Designs- changing the default table format.
  • Data Cleansing Using Text Functions
  • Text Functions for Data Formatting, Extracting Data Values from Text, and Eliminating Unnecessary Characters from Text
  • format dates- used to change date formats.
  • Formatting with conditions- helps in highlighting certain values and making certain cells easy to identify.
  • Sorting- rearranging the data to find values quickly.
  • Using Lookup Filtering Functions- this helps to find records in a table that satisfies a formula.
  • Pivoting- moving rows to column or column to rows



A general-purpose language that could be used to create a variety of different programs. One of the advanced object-oriented programs because of its flexible binding, high-level built-in structures, and language. It is one of the best in the market which best helps in development.


The Subjects Covered by the Python Course Curriculum Are Listed Below:

  • File operations using Python
  • Looping in Python- meaning repeating something till a particular result is derived.
  • Python Syntax- this is the set of rules that declares how this program will be written and explained.
  • Functions
  • Function Arguments, and Control Flow
  • Working with Lists
  • Python Modules- there could be around 200 modules that can define functions, classes, and variables.
  • Decorators and generators
  • Using Dictionaries
  • Errors and Exception Handling
  • Comparisons and Operators
  • The print statement
  • Comments
  • Python Data Structures & Data Types- containers used to organize data.
  • String Operations in Python- presenting different types of operations that can be employed on the program’s string type of variables.
  • Simple Input & Output
  • Simple Output Formatting
  • Deep copy- a copy of an object independent of the original object.
  • Shallow copy- building a new collection object,
  • Operators in python


R Programming

R programming is a language that aids in statistical approaches and data visualization. Specialists, data analysts, and other professionals use it in their industries because of its integrated feature of libraries.


The Following List Includes the Subjects Taught in the R Programming Data Analyst Course Syllabus:

  • Background and Nuts & Bolts
  • Programming
  • Loop Functions and Debugging- entails dissecting the issue into tiny components. Small enough for you to write one to three lines of code at a time. You run the program every time you compose one of these brief passages. In most cases, it fails, so you try again.
  • Simulation and Profiling


Structured Query Language

Using the (RDMS) Relational database management system helps in taking information out, manipulating, and managing the data. Another database system that helps in supporting this language is Oracle, MySQL, and others.


The Following List Includes the Subjects Taught in the SQL Data Analyst Course Syllabus:

  • Introduction to Oracle Database- One RDBMS is Oracle Database. An object-relational database management system (ORDBMS) provides object-oriented features such as user-defined types, inheritance, and polymorphism (ORDBMS).
  • Retrieve Data using the SQL SELECT Statement- A database table’s records are retrieved using a SQL SELECT statement in accordance with clauses (such as FROM and WHERE) that define criteria.
  • 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- The results of two component searches are combined using set operators into a single result.
  • Data Manipulation Statements- The component of the SQL language used for data retrieval and manipulation is called a DML (Data Manipulation Language) statement. You can carry out operations like adding new rows, updating and deleting existing rows, combining tables, and more using these statements.
  • Creating and Managing Tables Using DDL Statements
  • Other Schema Objects- Logical data storage structures are schema objects.
  • Control User Access- You can prevent unauthorized access to crucial apps and network components by managing 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- Simple characters, like /ABC/, or a mixture of simple and special characters, like /ab*c/ or /Chapter (d+).d*/, make up a regular expression pattern.



This is a data analysis tool that helps us to prepare, check, work together, and integrate all our enlarged data findings. With this users can easily and quickly share their information and question further about managing large data. This is best for self-service visual analysis.


Module 1: Tableau Course Material

Module 2: Learn Tableau Basic Reports

Module 3: Learn Tableau Charts

Module 4: Learn Tableau Advanced Reports

Module 5: Learn Tableau Calculations & Filters

Module 6: Learn Tableau Dashboards

Module 7: Server


Data Visualization-

  • Java- The majority of back-end development projects, particularly those requiring big data and Android development, use Java, one of the most popular programming languages, as the server-side language. Java is also frequently used for desktop, mobile, gaming, and computationally intensive applications.
  • CSS- Cascading Style Sheets, or CSS, is an abbreviation. A computer language called CSS is used to lay out and organize web pages (HTML or XML). This language is made up of “cascading style sheets,” which are also known as CSS files, and incorporates coding elements.
  • Customized geographic map- Google Maps allows you to add a variety of different components to a custom map. You can draw directions, marker points, shapes, and lines straight into the map.
  • Creation of bar chart, and scatter plot.


Top Books for Data Analyst Course Syllabus

  • The Elements of Data Mining, Statistical Learning, Inference, and Prediction by Robert Tibshirani, Trevor Hastie, and Jerome Friedman is a top data analytics book.
  • John M. Chambers, “Data Analysis Software: Programming with R (Statistics and Computing)”
  • Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, and Keying Ye, Probability & Statistics for Engineers & Scientists
  • Data Mining and Analysis by Wagner Meira and Mohamed J. Zaki


Find here the best data analytics courses:


Data Analyst Course Syllabus


Data Structures and AlgorithmsSupply Chain Analytics
Probability and StatisticsCustomer Analytics
Relational Database Management SystemsRetail Analytics
Business FundamentalsSocial Network Analysis
Text AnalyticsPricing Analytics
Data CollectionMarketing Analytics
Data VisualizationOptimization
Statistical AnalysisMachine Learning
Forecasting AnalyticsSimulation


Top Tools and Software Used in Data Analyst Course Syllabus

Many tools have helped with several functions with the demand for data analytics in the market. Irrespective of whether it is user-friendly or open source, following are the some of the tools in data analytics.


  • Tableau:  Corporate Data Warehouse, Excel, etc. are a few of the data sources that require a free connection which is made easier with this software. It then creates maps, visualizations, and dashboards with real-time updates on the web


  • Qlik View: It offers in-memory data processing quickly with the results delivered to the end-users.


  • Python: It is an open-source, straightforward to understand, create, and maintain object-oriented programming language. It provides a number of machine learning and visualization libraries, including Tensor Flow, Matplotlib, Scikit-learn, Pandas, and Keras. Any platform, such as MongoDB database, SQL server, or JSON, can be used to build this tool.


  • Rapid Miner: 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. Predictive analytics, namely text analytics, data mining, and machine learning, are utilized with it.


  • Open Refine- Often known as Google Refine, is a tool for data cleansing that will assist you in preparing data for analysis. For the transformation and parsing of data from websites, it is used to clean up messy data.


  • SAS: This tool can analyze data from a variety of sources and is a programming language and environment for data manipulation and analytics.


Factors on Which Data Analyst Salary Depends in India

The compensation of a data analysis specialist in India can vary depending on a number of factors. Here are a few examples:


  1. The company’s size: Generally speaking, larger businesses tend to provide higher compensation than smaller ones. They can do this because they have the funds and resources to do so.


  1. The company’s location: Businesses in major cities and metro areas typically offer higher compensation. This is due to the greater cost of living in metro areas and major cities.


  1. The company’s nature: Businesses that operate in industries like IT, BPO, KPO, etc. typically provide higher compensation than other businesses. This is so that they can handle the massive volumes of skilled people that these industries demand.


  1. The Data Analysis Specialist’s experience: Normally, people with more experience make more money than newcomers or those with less experience. This is due to the fact that they are capable of handling difficult duties and have greater expertise and knowledge in the pitch.


  1. The data analysis specialist’s education: Individuals with higher qualifications such as a master’s degree or a Ph.D. typically make more money than people who merely have a bachelor’s degree. This is due to the fact that they are more knowledgeable and qualified in the pitch.


Data Analyst Course Syllabus- FAQS

  • Can I pursue the data analyst course syllabus online after class 10th

Answer- No, one can pursue only after the 12th.


  • Which types of data analytics tools are there?

Answer- The tools of Data Analytics are Microsoft Excel, Tableau, Python, SQL, R, and so on.


  • Does the data analyst course syllabus have a good career?

Answer- As per the report of the World Economic Forum, most companies will be hiring Data Analysts from next year onwards. So on that note, candidates must be aware of the skills that will help them get a good position as a Data Analyst.


  • What is the data analyst course syllabus?

Answer- Students have to learn a wide range of data analyst course syllabus. The course includes subjects like Data Collection, Data Visualization, Probability and Statistics, Data Structures and Algorithms, and many more.


  • Is learning the data analyst course syllabus difficult?

Answer- The best answer would be it depends. The data analysis courses require the learner to have a good understanding of the different programming languages and analytical software.


  • What is data analytics salary?

Answer- In India, data analysts can expect a salary of INR 420,000.


  • How long does it take to become a data analyst?

Answer- The UG course in Data analytics takes about 4 years to complete. Certificate and diploma courses in Data Analytics can range from a few weeks to 12 months.

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