Best Online Data Analyst Course Training - Gurgaon
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Re-invent your career in data analytics by enrolling in Data Analyst Institutes in Gurgaon. Once you sign up for this Data Analytics Training in Gurgaon, you would get access to hybrid and online modes of classes and materials because the classroom facilities are available only in cities such as Bangalore, Pune, Chennai, and Hyderabad.
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Data Analytics Courses in Gurgaon helps students and working professionals to get introduced to the concepts of Artificial Intelligence and Machine Learning that is very relevant in today’s digitalized world. Pursuing our course opens a new forum of opportunities in this competitive world of corporate giants who look for talented professionals with relevant analytical skills. Thus, this data analytics course will help you to be well-versed with data visualization tools such as Power BI and Tableau along with analytical and programming tools such as NumPy, Python, SQL, NoSQL, PySpark, R Programming and so on, which will enhance your chances of landing a job in the analytical industry.
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SUNY invests $6 billion yearly in development and research and has long-standing expertise in data sciences and artificial intelligence.
The goal of 360DigiTMG's partnership with SUNY is to help introduce learners in order integrated blended educational experiences with the aid of our well designed, globally recognised curriculum.
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Certifications demonstrate your commitment to the profession and motivation to learn. Instill employer’s confidence in you and catch the attention of recruiters with these certificates.
Data Analyst Course fees in Gurgaon
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Upskilling
Employee Upskilling
- On site or virtual based sessions
- Customised Course
- Curriculum with industry relevant use cases
- Pre & Post assessment service
- Complimentary basic Courses
- Corporate based learning management system with team and individual dashboard and reports
Data Analytics Course Overview
360DigiTMG offers the best Data Analytics certification courses in Gurgaon. The training program equips you with an apt understanding of data processing tools like Excel, SQL/NoSQL, and Data Visualization tools like Tableau and Power BI. While SQL/NoSQL is used to work with the data stored in the Database Management software, Tableau and Power BI are used in analyzing it and presenting visual stories to end-users. Concepts such as Data Preparation, Data Cleansing, and Exploratory Data Analysis are explored in detail. Influential concepts like Data Mining of Structured (RDBMS) and Unstructured (Big Data) data, with the aid of real-life examples, are illustrated. Advanced Excel aids in data proficiency Concepts and it will help to reduce reduces working hours.
What is Power BI?
Power BI is the perfect tool to convert unrelated sources of data into coherent, visually immersive, and interactive insights. It uses a collection of software services, connectors, and apps to get the work done. Whether your data be an Excel spreadsheet or a collection of cloud-based and on-premises hybrid data warehouses. With Power BI it gets easy to connect to different data sources, visualize and understand what is vital, and share. Power BI has numerous elements that all work together, but the below three points are the basic ones.
- Power BI Desktop: As the name suggests is a Power BI application for Windows Desktop.
- Power BI Service: An online SaaS service
- Power BI Mobiles apps: This is an app special for Windows, iOS, and Android devices.
What is Tableau?
Tableau is a very famous and most used Data Visualization tool, and it helps in understanding the trends, insights, and patterns to other connections in Dataset. Its major task is to connect and extract the data from different stored places. Tableau specializes in pulling data from any platform or database. After the initial launch of Tableau, the ready data connectors will allow you to connect to any database. Data extracted by the above process will be connected to the data engine, Tableau desktop. Dashboards are created by Data Analysts or Data Engineer’s using the extracted data and are shared with users in the form of static files. With the help of the Tableau Server, the end-user can access files from all locations.
What is No SQL?
Not only SQL (No SQL) is a form of non-tabular databases that store data differently than traditional data storing databases. The databases change with respective data models. Key-value, wide-column, and graph are popular documents. It provides flexible schemas and scales easily with bigger datasets. No SQL database is used actively with
- Fast-paced agile development
- Storage of structured and semi-structured data
- Huge volumes of data
- Requirements of scale-out architecture
- Application paradigms like microservices and real-time streaming
What is SQL?
If you are dealing with Relational Databases, then SQL is the standard language. It can be used to insert, update, delete and search database records. We can also optimize and maintain the databases with the help of SQL. There are five types of SQL queries that are widely used: Data Definition Language (DDL), Data Manipulation Language (DML), Data Control Language (DCL), Transaction Control Language (TCL), Data Query Language (DQL) With the help of SQL users can:
- Access data in RBDMS systems
- Describing data
- It gives you access to manipulate specific data in the database.
- Creating databases and tables
- You can monitor who can use, see and alter the tables.
What is Advanced Excel?
Knowledge in Excel means processing the ability to use spreadsheets, calculations, automation, and tables efficiently to process huge data for businesses. Advance Excel will make this task easier by solving complex things in Excel. For example, it can manage high amounts of data using advanced level functions and excel features like power query, advanced data filters, asap features, power map. Advance Excel aids in data proficiency and reduces working hours.
Learning Outcomes of Data Analytics Training in Gurgaon
The main aim of the course is to give you an outlook towards the various techniques used in handling huge data sets via Data Analytics. Learners get to assess the applications of these technologies that are used in storing and processing huge amounts of data. This module instructs the student on the various techniques used to analyze structured and unstructured data, building visual stories using Tableau and or Power BI capabilities. This is the ideal course for professionals who want to acquire in-depth knowledge of daily used Data frameworks. The three-month Data Analytics training in Gurgaon will cover the essential tools like SQL, NoSQL, Tableau, Power BI, and Advanced Excel concepts. Students will learn to store, retrieve, manipulate, and analyze large datasets stored in Database management systems like relational database management systems or document-based database systems. They will also be introduced to various concepts to represent the data on the serving layer to show results in easier and readily consumable visual formats. The course contains multiple applied case studies that enable the participants to solve complex business problems improving profitability in their companies.
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Who Should Sign Up?
- IT Engineers
- Data and Analytics Manager
- Business Analysts
- Data Engineers
- Banking and Finance Analysts
- Marketing Managers
- Supply Chain Professionals
- HR Managers
Data Analyst Course Syllabus in Gurgaon
- Introduction to Python Programming
- Installation of Python & Associated Packages
- Graphical User Interface
- Installation of Anaconda Python
- Setting Up Python Environment
- Data Types
- Operators in Python
- Arithmetic operators
- Relational operators
- Logical operators
- Assignment operators
- Bitwise operators
- Membership operators
- Identity operators
- Data structures
- Vectors
- Matrix
- Arrays
- Lists
- Tuple
- Sets
- String Representation
- Arithmetic Operators
- Boolean Values
- Dictionary
- Conditional Statements
- if statement
- if - else statement
- if - elif statement
- Nest if-else
- Multiple if
- Switch
- Loops
- While loop
- For loop
- Range()
- Iterator and generator Introduction
- For – else
- Break
- Functions
- Purpose of a function
- Defining a function
- Calling a function
- Function parameter passing
- Formal arguments
- Actual arguments
- Positional arguments
- Keyword arguments
- Variable arguments
- Variable keyword arguments
- Use-Case *args, **kwargs
- Function call stack
- Locals()
- Globals()
- Stackframe
- Modules
- Python Code Files
- Importing functions from another file
- __name__: Preventing unwanted code execution
- Importing from a folder
- Folders Vs Packages
- __init__.py
- Namespace
- __all__
- Import *
- Recursive imports
- File Handling
- Exception Handling
- Regular expressions
- Oops concepts
- Classes and Objects
- Inheritance and Polymorphism
- Multi-Threading
- List Comprehensions
- List comprehension
- Dictionary comprehension
- Enumerate
- Zip and unzip
- Generator Expressions
- Tuples – Nested, Names, Unpacking
- Splitting – Slicing Objects, Ellipsis
- Augmented Assignments with Sequences
- Build-in Sort Functions
- Ordered Sequences with Bisect
- Arrays, Memory Views, Deques
- Handling Missing Keys
- Set Theory, Variations, Operations
- Higher-Order Functions
- Function Annotations
- Functional Programming Packages
- Procedural vs Functional
- Pure functions
- Map()
- Reduce()
- Filter()
- Lambdas
- Loop vs Comprehension vs Map
- Identify, Equality & References
- MySQL db Module
- INSERT, READ, DELETE, UPDATE, COMMIT, ROLLBACK operations on SQL using Python
- Python Packages
- Pandas – Series, Dataframes
- Numpy – Arrays, Memory, Matrices, Broadcasting, Masked Arrays
- Scipy
- Matplotlib
- Seaborn
- Sklearn (Scikit Learn)
- Statsmodels
- Jupyter Notebooks, IPython Notebooks
- Data Collection using CSV, JSON, XML, HTML & Scrapping
- Data Wrangling
- Understanding
- Filtering
- Typecasting
- Transformations & Normalization
- Imputation
- Handling Duplicates & Categorical Data
- Data Summarization
- Data Visualizations using Python Packages
- Line Chart
- Bar Chart
- Histogram
- Pie Charts
- Box Plots
- Scatter Plots
- Figures & Subplots
- Additional Visualization Packages – bokeh, ggplot, plotly
- Python XML and JSON parsers
- Basic Images Processing using Python OpenCV
- Dates and Times
- Binary Data
- Pythonic Programming
- Exception Handling
- Purpose of Exception Handling
- Try block
- Except block
- Else block
- Finally block
- Built-in exceptions
- Order of ‘except’ statements
- Exception - mother of all exceptions
- Writing Custom exceptions
- Stack Unwinding
- Enhancing Classes
- Metaprogramming
- Developer Tools
- Unit Testing with PyTest
- Multi-Threading
- Program Memory Layout
- Concurrency
- Parallelism
- Process
- Thread of execution
- Creating a thread
- Joining a thread
- Critical section
- Locks
- PyQt
- Network Programming
- Scripting for System Administration
- Serializing
- Advanced-Data Handling
- Implementing Concurrency
- Asynchronous programming
- The asyncio framework
- Reactive programming
- Parallel Processing
- Introduction to parallel programming
- Using multiple processes
- Parallel Cython with OpenMP
- Automatic parallelism
- Introduction to Concurrent and Parallel Programming
- Technical requirements
- What is concurrency?
- Not everything should be made concurrent
- The history, present, and future of concurrency
- A brief overview of mastering concurrency in Python
- Setting up your Python environment
- Django with REST Webservices
- Client-Server architecture
- Web Application
- Web framework
- Installing Django modules
- Creating first basic Django
- Creating Model classes
- Django Template tags and template programming
- Django rest framework
- Understanding REST Architecture
- HTTP GET, POST
- JSON serialization
- Writing REST API
- Web Extraction
- Beautiful Soup
- Selenium
- Serialization pickling, XML & JSON
- Introduction to Serialization
- Structure and Container
- Pickle Module
- pickling built-in data structures
- byte strings
- binary
- xml parsing and construction - xml
- json parsing and construction - json, simplejson
- Logging
- Purpose of logging
- Logging levels
- Types of logging
- Logging format
- Logging Handlers
- Disadvantages of excessive logging
- Custom loggers
- Eye for Detail - (Tableau Crosstabs), Highlight tables
- Comparative Analysis - Bar Graphs, Side-By-Side Bars, Circle Views, Heat Map, Bubble Chart
- Composition Analysis - Pie Chart, Donut Chart, Stacked Bar Graph
- Trend Analysis - Line Graphs and Area Graphs (Discrete and Continuous)
- Hierarchial Data Representation - Tree Map
- Correlation Analysis - Scatter Plot
- Distribution Analysis - Tableau Histogram, Box and Whisker Plot
- GeoSpatial Data Representation - Filled Maps, Symbol Maps, Combination Maps, Polygon Maps
- Relative comparison of 2 Measures - Bullet Graph, Dual Axis Chart, Dual Combination Chart, Blended Axis Chart, Bar in a Bar Chart
- Pareto Analysis - Pareto Chart
- Statistical Control Chart
- Tableau Gantt Chart
- Tableau Desktop Specialist
- Tableau Desktop Certified Associate
- Power BI Tips and Tricks & ChatGPT Prompts
- Overview of Power BI
- Architecture of Power BI
- Power BI and Plans
- Installation and introduction to Power BI
- Importing data
- Changing Database
- Data Types in Power BI
- Basic Transformations
- Managing Query Groups
- Splitting Columns
- Changing Data Types
- Working with Dates
- Removing and Reordering Columns
- Conditional Columns
- Custom columns
- Connecting to Files in a Folder
- Merge Queries
- Query Dependency View
- Transforming Less Structured Data
- Query Parameters
- Column profiling
- Query Performance Analytics
- M-Language
- Managing Data Relationships
- Data Cardinality
- Creating and Managing Hierarchies Using Calculated Tables
- Introduction to Visualization
- What is Dax?
- How to write DAX
- Types of Function in DAX
- Creating Calculated Measures
- Types of Application of DAX
- Introduction
- Pie and Doughnut charts
- Treemap
- Bar Chart with Line (Combo Chart)
- Filter (Including TopN)
- Slicer
- Focus Mode and See Data
- Table and Matrix
- Gauge, Card, and KPI
- Coloring Charts
- Shapes, Textboxes, and Images
- Gridlines and Snap to Grid
- Custom Power BI visuals
- Tooltips and Drilldown
- Page Layout and Formatting
- Visual Relationship
- Maps
- Python and R . Visual Integration
- Analytics Pane
- Bookmarks and Navigation
- Selection pane
- Overview of Dashboards and Service
- Uploading to Power BI Service
- Quick Insights
- Dashboard Settings
- Natural Language Queries
- Featured Questions
- Sharing a Dashboard
- In-Focus Mode
- Notifications and Alerts in the Power BI Service
- Personal Gateway Publishing to Web Admin Portal
- Introduction
- Creating a Content Pack
- Using a Content Pack
- Row Level Security
- Summary
- Accessing Looker Studio
- Connectors
- Creating a Report
- Controlling Data Access
- Editing Data Source Schema
- Other Common Data Source Operations
- Creating and Publishing Report
- Sharing a Report
- Creating Explorer
- Exporting from Explorer
- Using Explorer in analyst WorkFlow
- Understanding Dimensions and metrics
- Adding Dimensions
- Adding Metrics
- Sorting data in the chart
- Tables and Pivot tables
- Bar Charts
- Time series, Line and Area Charts
- Scatter Charts
- Pie and Donut Charts
- Score Cards
- Geographical Charts
- Configuring other chart types
- Where to use filters - Reports, Pages, Groups, Filter controls, charts
- Understanding editor filters
- Adding editor filter
- Interactive filter controls
- Limitations of Filters
- Adding Graphic elements
- Background and Border
- Text styles
- Common chart style properties
- Configuring style properties in Report Themes
- Adding Design Components
- Embedding external content
- Operations you can do with Calculated Fields
- Data Source vs Chart specific Calculated Fields
- Manipulating Data with Functions
- Using Branching Logic in Calculated Fields
- Creating New Parameters
- Understanding Blends
- Data Source vs Blends
- Join Operators - Inner, Left, Right, Full Outer, Cross
- Join Conditions
- Build a Customer Churn Analysis Report
- Build a ECommerce Revenue Analysis Report
- Monitoring Usage Looker Studio Report
- Optimising Reports for Performance
- Viewing Data from Google my Business
- Using Google search console for Audience Insights
- Web Data Visualizations
- Data Workloads
- Data Analytics
- Relational Data Workloads
- Relational Data Management
- Provisioning & Configuring Relational Data Services
- Azure SQL Querying Techniques
- Non-relational Data Workloads
- Non-relational Data Services
- Azure Cosmos DB
- Non-relational Data Management
- Azure Analytics Workloads
- Modern Data Warehousing
- Azure Data Ingestion & Processing
- Azure Data Visualization
- Getting Started with Azure SQL
- Using Transact-SQL for Queries & Transactions
- Advanced Topics in Azure SQL Databases
- Certificate Course in Data Analytics by SUNY
Alumni Speak
"The training was organised properly, and our instructor was extremely conceptually sound. I enjoyed the interview preparation, and 360DigiTMG is to credit for my successful placement.”
Pavan Satya
Senior Software Engineer
"Although data sciences is a complex field, the course made it seem quite straightforward to me. This course's readings and tests were fantastic. This teacher was really beneficial. This university offers a wealth of information."
Chetan Reddy
Data Scientist
"The course's material and infrastructure are reliable. The majority of the time, they keep an eye on us. They actually assisted me in getting a job. I appreciated their help with placement. Excellent institution.”
Santosh Kumar
Business Intelligence Analyst
"Numerous advantages of the course. Thank you especially to my mentors. It feels wonderful to finally get to work.”
Kadar Nagole
Data Scientist
"Excellent team and a good atmosphere. They truly did lead the way for me right away. My mentors are wonderful. The training materials are top-notch.”
Gowtham R
Data Engineer
"The instructors improved the sessions' interactivity and communicated well. The course has been fantastic.”
Wan Muhamad Taufik
Associate Data Scientist
"The instructors went above and beyond to allay our fears. They assigned us an enormous amount of work, including one very difficult live project. great location for studying.”
Venu Panjarla
AVP Technology
Why Data Analyst Course in Gurgaon
Gurgaon is the new name of a tech hub in India. It has emerged as a leading and a really significant center for education and careers for data analytics. Over 1,000 startups and numerous multinational corporations that make strong demands for data analysts show the growth at a CAGR of 28% during 2021-2026.
Besides, the average salary in Gurgaon for data analysts is around INR 6-12 lakhs per year, as well as experience and expertise. And of course, this is much higher than the national average - naturally, it's a very lucrative profession. In such cities, leading educational institutions are found teaching specialized courses in data analysis. In the past two years alone, such courses have gained 40% more students due to keen interest in the topic.
What's more, the companies of Gurgaon are actively looking for data analysts with strong analytical minds; in fact, as per the vacancy postings, the recruitments for data analysts have increased by more than 50% in the past year alone. Hence, the local courses curriculum also includes SQL, Python, and Tableau tools that will directly make the students job-ready right away.
Gurgaon has a very vibrant ecosystem, and the opportunity for networking with the workshops and meetups available is great. This increases students' chances of finding their dream job. It is not only acquiring the skills that one needs to become a successful data analyst but also entering the market at a very high time when it would be ripe with opportunities for growth and innovation.
Why Choose 360DigiTMG for Data Analyst Training Institute in Gurgaon?
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Our Alumni Work At
"AI to contribute $16.1 trillion to the global economy by 2030. With 133 million more engaging, less repetitive jobs AI to change the workforce." - (Source). Data Science with Artificial Intelligence (AI) is a revolution in the business industry.. AI is potentially being adopted in automating many jobs leading to higher productivity, less cost, and extensible solutions. It is reported by PWC in a publication that about 50% of human jobs will be taken away by the AI in the next 5 years.
There is already a huge demand for AI specialists and this demand will be exponentially growing in the future. In the past few years, careers in AI have boosted concerning the demands of industries that are digitally transformed. The report of 2018 states that the requirements for AI skills have drastically doubled in the last three years, with job openings in the domain up to 119%.
FAQs
A data analytics course, in which the study of the interpretation of data, application of statistical methods, use of tools for deriving insights, and finally, making informed decisions in various industries is taught.
Aspiring data analysts, professionals on the lookout for career growth, and those looking for data-driven decision-making will be at a core benefit from taking our data analytics course.
Gurgaon is a hub for a great deal of technological work, providing exposure to industry experts and offering opportunities for networking and possible collaborations, making it an excellent place to learn and grow a career in data analytics.
Yes, there are a number of data analyst institutes in Gurgaon that offer our online data analytics courses, which afford professionals and students the opportunity to learn at their own pace and convenience.
In general, I would say prior knowledge of statistics, acquaintance with MS Excel, and basic programming knowledge is helpful, but most of our courses also accept newcomers who receive foundational preparation.
Usually, instructors hold higher degrees in data science, statistics, or other related fields and industry experiences so students receive education from professionals that are knowledgeable about the topic.
The majority of our courses include practical projects, case studies, and workshops. The aim of such lectures is to apply theories in real-world scenarios by cultivating analytical abilities.
Evaluations mostly in the form of quizzes, projects, presentations, and exams on data analytics concepts helps understand and apply those concepts into the real-world situations and case-studies in the industry.
Most of the data analytics courses offer recognized certification when completed. You also are considered highly employable once you have completed these data analytics courses with certifications.
It varies really in that some of these analytics courses take a few weeks in terms of an intensive boot camp, while others may cover several months by nature for a comprehensive program and vary according to personal preference and schedule.
Topics of this general type include data visualization and an emphasis on statistical analysis, machine learning, data mining, and programming languages like Python or R.
Yes, indeed, the curriculum for several of our courses focuses on the application of real-world case studies in finance, healthcare, and e-commerce; these scenarios are meant to prepare students for challenges and problems in specific industries.
The curriculum of this course includes the following subjects:
No. The cost of the certificate is absorbed in the course fee.
- Data Preparation
- Data Cleansing
- Exploratory Data Analysis
- Feature Engineering
- Feature Extraction
- Feature Selection
- Hypothesis Testing
- Regression Analysis
- Predictive Modelling
- Data Mining - Supervised
- Data Mining - Unsupervised
- Text Mining
- Natural Language Processing
- Machine Learning
- Black Box Techniques - Neural Networks, SVM
- Time Series Analysis / Forecasting
- Project Management
Yes. Programming skills are integral to our data analytics courses, with an emphasis on a set of languages - Python and R, in particular - that are essential for data manipulation, analysis, and visualization.
Institutions usually maintain placement cells which consist of resume workshops, interview preparation, and job listings that connect students to employers.
The key objectives of this course are:
- Become proficient with different data generation sources
- Master Text Mining to generate Customer Sentiment Analysis
- Analyze and transform Structured and Unstructured data using different tools and techniques
- Learn the techniques of Descriptive and Predictive Analytics
- Apply Machine Learning approaches for business decisions
- Build prediction models for day-to-day applicability
- Perform forecasting to take proactive business decisions
- Represent business results using data visualization techniques
Many tech majors, startup companies, and consultancy firms recurrently recruit data analytics graduates in Gurgaon for a variety of data analysis, business intelligence, and data engineering roles.
Entry-level data analysts in Gurgaon receive between INR 4 and 8 lakhs per year as average gross salary, based on the skills, experience, and hiring company, with scope for quick hikes.
In this context, preparing would comprise common interview questions, a strong portfolio of projects, and necessary technical skills on relevant data analytics tools and languages.
Field of Data Analyst Jobs in Gurgaon
The field of data analyst jobs in Gurgaon is thriving, driven by tech companies, startups, and multinational firms seeking professionals to extract insights, drive decisions, and enhance business strategies through data-driven solutions.
Salaries for Data Analyst in Gurgaon
Salaries for data analysts in Gurgaon typically range from INR 4 to 10 lakhs per annum, influenced by experience, skills, and the hiring organization, with opportunities for rapid advancement in this growing field.
Projects for Data Analytics in Gurgaon
Data analytics projects in Gurgaon span diverse industries, including finance, healthcare, and e-commerce. Analysts work on predictive modeling, customer segmentation, and data visualization, driving insights that enhance business decision-making and performance.
Role Of Open Source Tools In Analytics
Open-source tools play a vital role in analytics by providing accessible, customizable solutions for data manipulation, visualization, and statistical analysis. They foster collaboration, innovation, and cost-effectiveness, empowering analysts worldwide to enhance insights.
Modes of Training for Data Analytics
Modes of training for data analytics include in-person classes, online courses, hybrid models, and boot camps. These formats cater to diverse learning preferences, ensuring flexible, comprehensive education tailored to individual needs and schedules.
Industry Applications of Data Analytics
Data analytics is widely applied across industries such as finance, healthcare, marketing, and e-commerce. It enhances decision-making, optimizes operations, improves customer experiences, and drives innovation through actionable insights derived from data.
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