Top 5 Data Analytics Courses in Agra With Placements

Agra is a city, located at the banks of the river Yamuna, which has witnessed various historical events that took place on its soil since ancient times, and stations one of the seven wonders of the world. However, the city has evolved with time providing various computational and technical courses such as data analytics courses. In this article, we will discuss the top 5 data analytics courses in Agra that can provide a successful career to an individual aspiring to become a data analyst, so let’s begin with it.

List of best data analytics courses in Agra

What is Data Analytics?

Data analytics is the technique or method used to collect raw data or information from various sources to organize and analyze collected data so that the obtained result can be interpreted and implemented for the betterment of an organization or a company.

This technique is used in various industries in the present times starting from financial industries to IT industries. Every industry needs to keep records of every data of the office so that it can be analyzed at the end using various data analytics software.

Various students in India are interested in data analytics courses, especially in the city of Agra. Hence, there are a large number of searches related to data analytics courses in Agra.

Most of the data analytics courses in Agra are in online mode learning with a reasonable course fee. The institutes that dispense data analytics courses in Agra are quite famous all over the country.

Must check the other best courses available in Agra



Skills Required for Data Analytics:

Various institutes are providing data analytics courses in Agra so that they can make their students skilled enough to build up a career in this field. Data Analytics is a discipline that requires computer-related skills as the work is related to organizing and analyzing data available in digital records.

A certain set of skills or expertise is required to develop a career in data analytics. One needs to have a proper degree in that subject from a reputed university and have the proper knowledge and expertise in programming languages such as Python and JavaScript.

The person who is aspiring to become a data analyst should have proper knowledge of data analysis and visualization tools which would help in understanding statistics and machine language.

These are some of the major skills that an aspiring data analyst should have to develop a career in data analysis. The institutes providing data analytics courses in Agra inculcate all the above-mentioned skills in the course structures.

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A Career in Data Analytics:

Data analytics can be a great career choice for any IT and computer science student who is interested in this field. Various multinational companies such as Wipro and TCS take up data analysts for their company to have a better understanding of the logistics and resources of the company.

There are various business industries as well as consultancy services that take up data analysts. The life of a data analyst involves dealing with various forms of data or information that they organize and interpret to give a proper solution or fruitful advice to the company or business organization to develop their administration or business structure.

The institutions that provide data analytics courses in Agra enlighten their students with all the above-mentioned knowledge and skills. Some of them even provide hands-on training as well.

You should check the courses in other rewarding cities


Top 5 Data Analytics Courses in Agra:


Rank# 1. Data Analysis Courses in Agra


When looking for the best data analytics courses in Agra, IIM SKILLS comes at the top considering its exceptional live training, hands-on assignments, and round-the-clock support.

The course is curated by industry experts who know the ins and outs of the subject, they put all their expertise into the course structure to make it easy to learn to the students, whether they are beginners or professionals.

Along with an exceptional training course, they also have a 2-month internship which makes the learning even practical and also helps to build a portfolio. During the course, students gain immense skills with the help of tools that are also taught.


They make sure the students know the in-depth of the tools as they are the core of the job of a data analyst. Talking about the syllabus covered, the course has a wide range of core topics, basic to advanced, making it comprehensive for beginners to learn with ease.

Each and every topic is covered in-depth with assignments so the student can have a deeper knowledge about it. Let’s now know about the modules covered in the course.

Also Read: IIM SKILLS Data Analytics Course Review


Data Analytics Course Syllabus

Data Analytics Using Advanced Excel (9 Hours)
Data Analytics Using VBA (15 Hours)
Data Analytics Using SQL (39 Hours)
Data Analytics Using Power BI (24 Hours)
Data Analytics Using Python-1(10 Hours)
Data Analytics Using Python-2 (10 Hours)
Data Analytics Using Tableau (15 Hours)
R For Data Science (20 Hours)
Data Analytics Using Alteryx (18 Hours)

Each subject mentioned above is subdivided into various sections and taught in a detailed manner by the trainers. Not only do they teach but also assist the students whenever they face challenges in understanding any topic.


If not cleared, special sessions are arranged for them to make sure they are well-cleared. So, without any doubt, IIM SKILLS is a must-go for anyone looking to advance in their career.

Must Read:


Course Fees: Rs 49,900 + GST

Benefits of the Course:

  • 6 months of Live training
  • Tool driven training
  • Assignments and live projects
  • 2 months of internship
  • Master certification
  • Lifetime access to the course material
  • 24/7 support

Contact Details:

Customer Care No: +91 9580 740 740

Email: [email protected]

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Rank# 2. Data Analysis Courses in Agra

2. NIIT Centre Agra

The National Institute of Informational Technology is a national-level institute that provides various courses on Information Technology all over the country including the data analytics course at its center in Agra. NIIT is one of the best institutes that provides data analytics courses in Agra.

The course in data analytics at NIIT provides a dual qualification for college. The program is led in online mode along with job assistance and placements. The skills that an individual is going to learn from this course are data analysis, data visualization, relational database management systems, skills in query language, python programming, and tableau.

This entire program is going to prepare an individual for an entry-level data analyst job. This program at NIIT can be pursued by final-year students of engineering and computer science.

Course Structure:

Level 2

Part 1:

Course 1: Data Analytics using Excel

  • Perform basic Data Analysis using Excel.
  • Measure Central Tendencies using Statistical Techniques.
  • Data Visualization using Excel.
  • Measuring the spreadsheet and Correlation of Data.

Course 2: Analytics Using Skills Query Language

  • Creating database objects and Populating data.
  • Query data using Skills Query Language.
  • Retrieve data from multiple tables.

Course 3: Introduction to Programming using Python Language

  • Working with the Python interpreter.
  • Decision-making with conditional statements.
  • Interactive statements and list operations.
  • Looping over data structures.
  • Writing modular programs in Python.

Course 4: Python for Data Science

  • Creating and manipulating arrays (1D & 2D) using NumPy.
  • Creating and manipulating series and data frames.
  • Manipulating data frames.
  • Advanced data frame manipulation operations.

Part 2:

Course 5: Statistics & Data Visualization Using Python

  • Descriptive analysis using Python.
  • Prediction using probability.
  • Sampling techniques and distribution.
  • Discrete probability distribution.
  • Continuous probability distribution.
  • Hypothesis testing using the Z test.
  • Hypothesis testing using T-test.

Course 6: Exploratory Data Analysis

  • EDA for bank customer churn analysis.
  • EDA for IBM employee attrition.
  • EDA case study 1.

Course 7: EDA Case Study 1

  • Exploring Tableau.
  • Creating Visuals.
  • Exploring time series data.
  • Visualization of spatial and relational data.
  • Visualize data distribution and create a dashboard.


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Course 8: Utilizing Tableau 2 to Explore Data Analysis

  • Design an advanced dashboard.
  • Exploratory data analysis through visualization.

Level 2

Course 9: Project

  • Perform EDA.
  • Visualize to present insights.

Course Fee:

The Data Analytics Courses in Agra are not very expensive. The price of the course is very reasonable. The fees of the course in NIIT Agra are around 75000/- excluding 18% GST for level 1 of the Data Analytics course which needs to be paid seven days before the commencement of the class.

The discount that is being provided on the program fee is rupees 5000/-. One can also pay the down-payment fees of rupees 8050.85 excluding GST in eight installments before which the seat at the branch can be booked at rupees 12711.86/- excluding GST.

To Get in Touch With Them:

Mobile No: 80030006448/0562-404115, 9267777755.

Email id: [email protected]



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Rank# 3. Data Analysis Courses in Agra

3. is an IT company which provides training in various software to students as well as working people who are interested in information technology. It also provides a data analytics course under the program of Data Science for students aspiring to become data analysts. is a company that has been providing data analytics courses in Agra since its commencement. The company provides training in a way that can produce the best IT professionals who are aware of project management.

It has got the best teaching staff with members having expertise in various domains such as website development and digital marketing as well.

The company makes use of the best technology and software available in the market for teaching its students. The students are prepared for an industrial environment with exposure to real-time projects.

Course Structure:

Introduction to Data Science:

  • Need for a data scientist.
  • The foundation of data science.
  • What is business intelligence?
  • What are data analysis, data mining, and machine learning?
  • Analytics vs Data Science.
  • Value Chain.
  • Types of analytics.
  • Lifecycle Probability.
  • Analytics Project Lifecycle.


  • Basis of data categorization.
  • Types of data.
  • Data collection types.
  • Forms of data and resources.
  • Data quality,
  • Changes and Data quality issues,
  • Quality story.
  • What is Data Architecture?
  • Components of Data Architecture.
  • OLTP vs OLAP.
  • How is Data stored?

Big Data:

  • What is Big Data?
  • 5 vs of Big Data.
  • Technologies and Challenges of Big Data Architecture.
  • Big Data distributed computing and complexity.
  • Map reduce framework.
  • Hadoop ecosystem.

Data Science Deep Drive:

  • What is data science?
  • Why are data scientists in demand?
  • What is a data product?
  • The growing need for data science.
  • Large-scale Analysis Cost vs Storage.
  • Data science skills.
  • Data Science use cases and data science project life cycle and stages.
  • Map-reduce framework.
  • Hadoop ecosystem.
  • Data acquisition.
  • Where to source data.
  • Evaluating input data.
  • Data formats, Quantity, and Data Quality.
  • Resolution Techniques.
  • Data transformation.
  • File format conversions.

Introduction to R Programming:

  • Introduction to R.
  • Business Analytics.
  • Analytics concepts.
  • The importance of R in analytics.
  • R language community and eco-system.
  • Usage of R in industry.
  • Installing R and other packages.
  • Use the command line to perform basic R operations.
  • Usage of various GUI and IDE R studios.


R programming Concepts:

  • The data types in R and their uses.
  • Built-in functions in R.
  • Subsetting methods.
  • Summarize data using functions.
  • Use of functions like head(), and tail(), for inspecting data.
  • Use cases for problem-solving using R.

Data Manipulation in R:

  • Various phases of data cleaning.
  • Functions used in inspection.
  • Data cleaning techniques.
  • Uses of functions involved.
  • Use cases for data cleaning using R.
  • Data import techniques in R.
  • Importing data from statistical formats.
  • Packages installation for database import.
  • Connecting to relational database management system from R using ODBC and basic skills query language queries in R.
  • Web scraping.
  • Other concepts on data import techniques.

Exploratory Data Analysis (EDA) Using R:

  • What is EDA?
  • Why do we need EDA?
  • Goals of EDA.
  • Types of EDA.
  • Implementation of EDA.
  • Boxplots, cor() in R.
  • EDA functions.
  • Multiple packages in R
  • Some fancy plots.
  • Use cases for EDA using R.

Data Visualization in R:

  • Storytelling with Data.
  • Principle tenets.
  • Elements of Data Visualization.
  • Infographics vs Data Visualization.
  • Data Visualization & Graphical Functions in R.
  • Plotting Graph.
  • Customizing Graphical parameters to improvise the plots.
  • Various GUIs
  • Spatial analysis.
  • Other visualization concepts.


Big Data and Hadoop Introduction:

  • What are Big Data and Hadoop?
  • Challenges of Big Data.
  • Traditional approach Vs Hadoop
  • Hadoop Architecture.
  • Distributed Model.
  • Block structure file system.
  • Technologies supporting big data.
  • Fault Tolerance.
  • Why Hadoop?
  • Hadoop Eco-system.
  • Use-cases of Hadoop.
  • Fundamental design principles of Hadoop’.
  • Comparison of Hadoop vs relational database management system.

Understand Hadoop Cluster Architecture:

  • Hadoop cluster and Architecture.
  • 5 daemons.
  • Hands-on exercise.
  • Typical Workflow.
  • Writing files to HDFS.
  • Reading files from HDFS.
  • Lack of awareness.
  • Before map reduction.

Map Reduce Concepts:

  • Map reduce concepts.
  • What is map reduction?
  • Why is the map reduced?
  • Map-reduce flow and map-reduce in the real world.
  • Whaareis mapper, reducer, and Shuffling?
  • Word Count problem.
  • Hands-on exercise.
  • Distributed word count flow and solution.
  • Log processing and map-reduce.

Hadoop 2.0 and YARN:

  • Hadoop 1.0 Challenges.
  • NN Scalability, SPOF, and HA.
  • Job Tracker Challenges.
  • Hadoop 2.0 New Features.
  • Hadoop 2.0 Cluster Architecture & Federation.
  • Hadoop 2.0 HA.
  • Yarn and Hadoop Ecosystem.
  • Yarn MR Application flow.


To Get in Touch With Them:

Mobile No: 8445022510

Email id: [email protected]

Address: 8/195, Engineer’s Colony,

Opp. Omaxe SRK Mall,

Bye Pass Road (NH-2), Agra.



Rank# 4. Data Analysis Courses in Agra

4. Syphon Analytics

Syphon Analytics is a company that provides services and educational training to various employees of different industries. This institute provides three different courses on Data Analytics starting from Data Analysis with powerful business intelligence, Machine Learning with Python, and DAX masterclass for powerful business intelligence and Excel power users.

Syphon Analytics is the only institute that provides three different programs on data analytics courses in Agra. This institute provides a satisfactory course structure for all three courses on Data Analysis. It gives project and case-study-based training. The live training is provided by experts.

There are always regular updates as per industry requirements. The company also provides mock interviews to prepare one for the challenges ahead. It provides a certificate for the successful completion of the courses. The duration of the first course is 50 hours, the duration of the second course is 60 hours and the third course is for 20 hours.

Structure of Data Analysis Courses in Agra:

Power Business Intelligence Complete Introduction;

  • Power Query Overview.
  • Power Pivot Overview.
  • Power view Overview.
  • Power Business Intelligence Service Overview.
  • Power business intelligence Desktop user interface.
  • Building blocks of powerful business intelligence.

Power Query:

  • Introduction to Power Query and Power Query Editor using the interface.
  • Methods in Data Processing
  • Various Data types
  • Power Query filters
  • Inbuilt Column Transformations.
  • In-built Row transformation.
  • Combine Queries.
  • Merge queries.
  • Query options.
  • Home tab options.
  • Transform tab options.
  • Add column tab options.
  • View tab, Tools tab, and Help tab options.

Power Pivot:

  • Power Pivot Software Overview.
  • In-memory database vs relational database.
  • Data were housing Concepts.
  • Power business intelligence data modeling.
  • Enhancing the data model – DAX.
  • Evaluation context or initial context.
  • Calculated column, measures, and Calculated Table.
  • Row and filter Context.
  • Content Transition.
  • DAX- Functions – Categories.
  • DAX Test Functions.
  • DAX logical functions.
  • DAX Date and Time Functions.
  • DAX Filter and functions.
  • DAX Math and Statistical Functions.
  • DAX time intelligence functions.
  • Quick measure.

Power View:

  • Report View/ Power View.
  • Visuals interactions.
  • Filters in power view.
  • Hierarchies and drill-down Reports.
  • Power business intelligence visualizations.
  • Visuals for filtering.
  • Visualizing Categorical Data.
  • Visualizing Trend Data.
  • Visualizing KPI data.
  • Visualizing tabular data.
  • Visualizing Geographical Data.
  • Grouping, Binning, and Sorting.
  • Bookmarks, Selection pane, and Buttons.


Power Business Intelligence:

  • Power Business Intelligence Service Introduction.
  • Adding data to power business intelligence service and creating multiple new reports.
  • Dashboard management.
  • Building Blocks of Power Business Intelligence.
  • Data Connectivity Modes in Power Business Intelligence.
  • Installing and configuring the data gateways.
  • Understanding sub folder in Workspace.
  • Data flow
  • Dataset Actions.
  • Excel Workbooks.
  • Report Actions.
  • Dashboard Actions.
  • Team Collaboration in Power business intelligence using workspaces.
  • Sharing power business intelligence content using basic sharing, content packs, and apps.
  • Power business intelligence with low-level security.
  • Syncing with low-level published power business report files.
  • Using One Drive.
  • Deployment Pipelines.

Machine Learning with Python:

Module 1: Python for data science /machine learning crash course.

  • Data types.
  • Strings, List, Tuple, Dictionary, Sets.
  • If, else-if, and else statements.
  • For loop.
  • While loop.
  • Lambda Expressions.
  • List and dictionary.
  • Map and filter.
  • Exception Handling.
  • OOPs, Concepts.

Module 2: Data Analysis NumPy and Pandas.

  • Introduction to NumPy
  • NumPy Arrays.
  • NumPy Array Indexing.
  • NumPy operations.
  • Introduction to Pandas.
  • Data frames.
  • Missing values and treatment.
  • Merging, Joining, and Concatenation.
  • Data Input and Output.
  • Project on Data Analysis using pandas.

Module 3: Exploratory Data Analysis.

  • What is EDA? Why is it a very important step?
  • Introduction to data visualization.
  • Hands-on training.
  • Hands-on training.
  • Various types of visualizations with their typical use cases.
  • Project on Exploratory Data Analysis to derive insights from data.

Module 4: Machine Learning.

  • Introduction to Machine Learning.
  • Supervised, Unsupervised, and semi-supervised Machine Learning.
  • Data Pre-processing.
  • Feature Engineering.
  • Regression vs Classification.
  • Build your first machine learning model: K Nearest Neighbour (KNN).
  • Build your second machine learning model: Naïve Bayes.
  • Build your third machine learning model: Logistics Regression.
  • Build your fourth machine learning model: Decision Tree.
  • Build your fifth machine learning model: Support Vector Machine (SVM).
  • Evaluating classification models: Performance metrics.
  • Regression ML Models.
  • Linear regression and other regression models.
  • Evaluating regression models: Performance metrics.
  • Unsupervised learning.
  • K means clustering.
  • Hierarchical Clustering.
  • Dimensionality reduction.
  • Principal Component Analysis (PCA).
  • Concept of Overfitting, Underfitting.
  • Bias Valance Trade-off.
  • Ensemble Learning.
  • Model Deployment.
  • Introduction to Deep Learning.


DAX Masterclass for Power Business Intelligence and Excel Power Users:

Module 1: Basics of Data Modeling

  • Introduction to Power Pivot.
  • What is data modeling?
  • Fact and dimensions tables.
  • Schemas in data modeling.
  • Cardinalities in data modeling.
  • Creating a data model.
  • How to enhance the data model.

Module 2: Familiarity With DAX 

  • What is DAX and why it is even required?
  • Naming conventions.
  • Formula Syntax.
  • Formatting DAX.
  • Requirements for DAX to work properly.

Module 3: DAX Foundations 

  • What is a calculated column and when it is required?
  • What is a measure and when it is required?
  • What is the calculated table and when it is required?
  • Familiarity with the DAX calculation engine.
  • Evaluation Context.
  • Filter Context.
  • Row context.
  • Understanding how roto w context works in calculated columns.
  • Understanding how to filter context works in measures.

Module 4: Getting Started With DAX Functions 

  • Types of DAX functions.
  • Aggregation functions – SUM, AVERAGE, COUNT, MIN, MAX.
  • Iterating Functions – SUMX, AVERAGEX, MINX, MAXX.
  • Error handling with DAX – BLANK, ISBLANK, IFERROR.
  • Conditional execution using logical functions – IF, SWITCH.
  • Text Functions.
  • Information Functions.
  • Conversion, Boolean, Date-Time.

Module 5: Basic Table Functions

  • What is a table function?
  • Filter function.
  • Values function.
  • Distinct Function.
  • Selected Value.
  • Has one Value
  • All function-to-functions filters.
  • All except.
  • All selected.

Module 6: Diving Deeper into Context

  • How to filter and work with relationships.
  • Comparative study of working on one table versus many tables.
  • Is a Filtered function.
  • Is Cross-Filtered Function.
  • Related function.
  • Earlier Function.

Module 7: Calculate the Game Changer

  • What is the calculate function and why it is so important?
  • The power of calculating function Transition.
  • User relationship function and its use with calculate.
  • What can the filter function do inside to calculate?

Module 8: Time Intelligence Function:

  • What are time intelligence functions?
  • Importance of date table.
  • Prime requirements for the data table.
  • Creating a date table using calendar and calendar auto function.
  • YTD aggregating function.
  • QT aggregating function.
  • MTD aggregating function.
  • Same period as last year.
  • Date add function.
  • Previous month.
  • Parallel Period.
  • Dates between.
  • Dates in the period.

Module 9: Variables and Parameters.

  • What are variables?
  • How are variables evaluated?
  • The proper way to use variables.
  • What-if parameter.
  • Parameter Table.

Module 10: Parent-Child Functions in DAX.

  • Parent-Child functions in DAX.

Module 11: Virtual Relationships.

  • Virtual vs Physical Relation Treats
  • Treat as
  • Use Relationship.
  • Cross Treats

Module 12: Advanced Table Functions.

  • Manipulate Virtual Tables using table functions.
  • Add Columns.
  • Cross Join.
  • Calculate table.
  • Summarize Columns.
  • Group by.

Module 13: Ranking Functions

  • Examples with ranking function.

Course Fee:

The course fee for the program is not mentioned on the company’s website.

To Get in Touch With Them:

Mobile No:

  • Call
  • Whatsapp at 8700286048.

Email id: [email protected]

Address: 170 MMIG, Shaheed Nagar, Tajganj, Agra, Uttar Pradesh 282001.



Rank# 5. Data Analysis Courses in Agra

5. SVR Technologies

SVR Technologies is an IT company providing training to students and working employees of IT sectors and computer science respectively on various courses such as cyber security, Hadoop, CISCO, etc. Data Science or Analytics is one of the courses being provided by them as well as the course duration is 40 hours.

SVR Technologies is the only institute that provides practical-based Data Analytics Courses in Agra. The training is available in self-learning and online mode. The company delivers the course by experts in the subjects who have worked in real-time scenarios and have good experience in teaching students.

This training along with the theoretical part of the course also provides practical and hands-on training. The course also provides data Science along with knowledge of Artificial Intelligence.

Structure of Data Analytics Courses in Agra:

Introduction to Data Science Deep Learning and Artificial Intelligence:

Introduction to Deep Learning and Artificial Intelligence.

Deep Learning: A revolution in Artificial Intelligence.

  • Limitations of Machine Learning.

What is Deep Learning?

  • Need for data scientists.
  • Foundation of Data Science.
  • What is Business Intelligence?
  • What is Data Analysis?
  • What is Data Mining?

What is Machine Learning?

Analytics vs Data Science

  • Value Chain.
  • Types of Analytics.
  • Lifecycle Probability.
  • Analytics Project Lifecycle.
  • Deep Learning advantage over Machine Learning.
  • Reasons for Deep Learning.
  • Real-life use cases of Deep Learning.
  • Review of Machine Learning.


  • Basis of Data Categorization.
  • Types of Data.
  • Data Collection Types.
  • Forms of Data and Sources.
  • Data Quality and Changes.
  • Data Quality Issues.
  • Data Quality Story.
  • What is Data Architecture?
  • Components of Data Architecture.
  • OLTP vs OLAP.
  • How is Data stored?


  • Operators and Keywords for Sequences.
  • NumPy and Pandas.
  • Deep Dive Functions and Classes.


  • What is Statistics?
  • Descriptive Statistics.
  • Central Tendency Measures.
  • The story of average.
  • Dispersion measures.
  • Data distributions.
  • Central Limit theorem.
  • What is sampling?
  • Why sampling?
  • Sampling Methods.
  • Inferential statistics.
  • What is hypothesis testing?
  • Confidence Level.
  • Degrees of freedom.
  • What is the p-value?
  • Chi-Square Square test.
  • What is Anova?
  • Correlation vs Regression.
  • Uses of Correlation and regression.


Machine learning, Deep Learning, and Artificial Intelligence using Python:

  • Implementing Association rule mining.
  • Random Forest Classifier.
  • Naïve Bayes Classifier.
  • Project Work.

Course Fee:

The course fee for the program is not mentioned on the website.

To Get in Touch With Them:

Mobile No: 9885022027

Email id: [email protected]

Address: SVR Technologies Seethampet, Near Dwaraka Nagar, Agra – 530016



Frequently Asked Questions:

1. What is the minimum salary of a data analyst?

In India, the entry-level salary of a data analyst starts from 41K and it ranges from 1.9 lakhs to 11.5 lakhs annually. It is a great job position for a good career with a five-figure salary. The Data Analytics Courses in Agra assures its students a job with a good amount of salary.

2. What are the eligibility requirements of a data analyst?

In India, the eligibility requirement for a data analyst is to be very skilled with the data analysis tool to become a good data analyst. Besides, one should have a proper degree or diploma in data analytics from a recognized institute with marks above 50% and expert-level knowledge of programming languages such as Python and JavaScript. The institutes that dispense Data Analytics Courses in Agra have designed their program structure in a way that the students will get good hands-on experience in data analysis.

3. How to get assistance with job placements and resumes?

The institutes that are providing Data Analytics Courses in Agra are assisting with jobs and the development of the resumes of their students. There are a lot of students from these institutes who are getting placed in large multinational companies such as Wipro, TCS, and Cognizant.

4. What is the process of becoming a data analyst?

Data Analytics Courses in Agra provide an individual with all the required details, skills, and knowledge to become a successful data analyst. These courses dispense skills in all available data analysis tools and software such as Excel, Tableau, Apache Spark, and KNIME. One needs to do all the research on the discussed field and take up a degree course on data analytics and later a professional course from the institutes mentioned above.