Data Analyst Course in Uppal, Hyderabad
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Transform your career with our Data Analytics course in Uppal, Hyderabad. Benefit from hands-on projects and dedicated placement support.
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Data Analyst Course fees in Uppal, Hyderabad
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- 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 Uppal. 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 Uppal
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 Uppal 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 Uppal
- 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 Uppal
The rapid scale of digital penetration over the last 10 years has changed the landscape of our universe. The size of the digital universe was roughly 140 billion gigabytes in 1995. In 2020 this number will swell to 50 trillion gigabytes. In this scenario, millions of new workers are needed to manage this digital world and companies will vie with each other for hundreds of thousands of workers. India is currently among the top 10 countries in Big Data Analytics and has around 600 firms. The Big Data Analytics market in India is worth $ 2 Billion and is tipped to reach $ 16 Billion by 2025.
In 2025, India will have a 32 percent share of global markets. India contributes to over 6% of the data analytics job openings worldwide. Currently, there are approximately 97000 jobs open in India 24% of which are clustered around Bengaluru. Uppal and Mumbai are also the emerging hotspots for job seekers in the data analytics landscape in India. A Senior Data Analyst in Uppal normally possesses 4 years plus experience. The minimum salary of a senior data analyst is around Rs. 6.5 lacs and the maximum Rs.11.8 lacs. A Junior Data Analyst (727 openings) garners a minimum of Rs. 4.0 lacs and a maximum of Rs.10 lacs.
Some of the top sightseeing places to see in Hyderabad, Uppal, Ramoji Film City, Hyderabad, B.M. Birla Science Museum, Mecca Masjid, NTR Gardens, Chilkur Balaji Temple, Taj Falaknuma Palace Tombs and many others.
Why Choose 360DigiTMG for Data Analyst Training Institute in Uppal, Hyderabad?
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"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
360DigiTMG provides a good Certification Program on Life Sciences and Healthcare Analytics meant for medical professionals. The course is devoted to Clinical Healthcare Data Analysis. Medical professionals will learn to interpret Electronic Health Record (EHR) data types and structures and apply predictive modelling on the same. In addition to this, they will learn to apply machine learning techniques to healthcare data.
Students must possess a Bachelor's degree in Mathematics / Statistics / Computer Science / Data Science or a Bachelor's degree in Engineering (any discipline) from a recognized institute.
The duration of this course is Four months. It comprises of 132 plus hours of classroom or online sessions, 80 plus hours of assignments and e-learning and 80 plus hours of live project work.
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 approached 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
The curriculum of this course includes the following subjects:
- Data Preparation
- Data Cleansing
- Exploratory Data Analysis
- Feature Engineering
- Feature Extraction
- Feature Selection
- Hypthesis 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
The most obvious benefit of pursuing a this course is that one can apply to a plethora of job opportunities available in the data science market. The demand for data science professionals in India it has increased by 400% and the supply has increased by 19% only. This is the most sought after qualification. It is also the most lucrative career option with salaries hitting the ceiling.
Another benefit would be the range of analytical and problem-solving skills that a student acquires from a data analytics training. These skills can be used to analyse big data and draw meaningful insights from the same.
The third benefit is that you exhibit better business decision-making skills in the workplace.
Data Analytics is useful for Chartered Accountants. Chartered accountants can use big data analytics and machine learning to re-engineer the audit process. Network Analytics and Graph Data are used to identify fraudulent practices. The tools of data analytics can be used to detect business risks as well.
360DigiTMG offers a Data Science Course for Internal Auditors and a Certification Program in Financial Analytics. Chartered Accountants can pursue either one of these courses to develop cutting-edge analytical skills.
Data Analytics is widely used in the Financial Services industry today. Finance professionals can benefit from this course. They will understand how data analytics is employed in Stock Market Investments, Banking, Financial Advisory and Management, EPS etc. The application of Artificial Intelligence in Algorithmic Stock Trading, Automated Robo-Advisors and Fraud Detection Systems are also elaborated in-depth in a financial analytics course.
360DigiTMG offers a very comprehensive Certification Program in HR Analytics for HR professionals. The module includes
- Enabling Workforce Analytics
- Predictive Modelling for Ethnic Diversity
- Machine Learning to predict Employee Turnover
- NLP techniques to screen and recruit candidates
- Predicting Employee Performance
- Predictive Modelling of sickness/ absence
- Deep Learning for Emotion Mining in Workforce Analytics
The course material can be downloaded from our online Learning Management System (AISPRY).
Yes, We do teach data visualization with Tableau as part of this course.
As soon as a student joins a course he is assigned a mentor. If the institute feels that a particular student requires additional assistance then they will assign some more mentors for a single student.
We host several free webinars on data analytics on youtube. They can be accessed from the link given below
https://www.youtube.com/channel/UCNGIDQ466bNY87eEeKeQuzANo. The cost of the certificate is absorbed in the course fee.
All classroom sessions are video recorded and lodged in our Learning Management System AISPRY. If you miss a data analytics classroom session you can access the recorded session from the Learning Management System.
Once a student completes his course and receives the Course Completion Certificate, he is eligible for an internship. We offer an internship with INNODATATICS ltd. The students gets involved in a live project with INNODATATICS. At the end of his internship, he will receive an Internship Certificate in recognition of his efforts.
A fresh graduate will greatly benefit from the internship opportunity with INNODATATICS that our institute offers. He will work on a live project and get hands-on experience of implementing a data analytics solution. This will improve his employability immensely. Most employers value live project experience only.
You can apply for the following jobs after completing the course:
- Data Analyst
- Data Scientist
- Data Engineer
- Data Architect
- Business Analyst
A Data Analyst deals with Data Cleansing, Exploratory Data Analysis and Data Visualisation, among other functions. The analyst's job is to sift through historical data to understand the present state of the business.
A Data Scientist builds algorithms to solve business problems using statistical tools such as Python, R, SAS, STATA, Matlab, Minitab, KNIME, Weka etc. He also performs predictive modelling to facilitate proactive decision-making. Machine learning algorithms are used to build predictive models using Regression Analysis and a Data Scientist must develop expertise in Neural Networks and Feature Engineering.
A Data Engineer is essentially a programmer in Spark, Python and R and complements the role of a data scientist.
A Data Architect is entrusted with the task of establishing hardware and software infrastructure needed to perform Data Analysis. They have to select Hard Disk, Network Architecture, Databases, GPUs etc.
We offer end to end placement assistance to our students. We assist them in resume preparation and conduct several rounds of mock interviews. We circulate their resumes to reputed placement consultants with whom we have a long-standing agreement. Once placed we offer technical assistance for the first project on the job.
Business Analytics is the emerging field in data science. It is definitely worth pursuing a data analytics after your MBA. You can specialize in Financial Analytics, HR Analytics or Supply Chain Analytics. Once you finish your data analytics you can apply for the position of Business Analyst.
Field of Data Analyst Jobs in Uppal, Hyderabad
There are around 97000 vacant positions in data analytics in India. The opportunities for fresher account for 21 percent of analytics jobs in India. Employers are companies like Tech Mahindra, TCS, Genpact, Wipro, and HCL Infosystems.
Salaries for Data Analyst in Uppal, Hyderabad
The average salary of a Data Analyst in India is Rs.5 lacs per annum. A fresher can earn anywhere between Rs.1.62 to Rs.3.23 lacs, a junior analyst gets around Rs.4,51000 per annum while a Senior Data analyst earns Rs.7,74000 per annum.
Projects for Data Analytics in Uppal, Hyderabad
The Indian government has initiated several data analytics projects in the fields of Agriculture, Electricity, Water, HealthCare, Education, Road Traffic Safety and Air Pollution.
Role Of Open Source Tools In Analytics
Python is easy to learn and maintain and therefore a Godsend to developers in Data Science. Its extended library makes it possible to stretch the applications of Python from Big Data Analytics to Machine Learning. R is the preferred tool of statisticians that enables effective data storage.
Modes of Training for Data Analytics
The course in Uppal, Hyderabad is designed to suit the needs of students as well as working professionals. We at 360DigiTMG give our students the option of both classroom and online learning. We also support e-learning as part of our curriculum.
Industry Applications of Data Analytics
Data Analytics is used for securities fraud early warning, card fraud detection systems, demand enterprise risk management, analysis of healthcare information, seismic interpretation, reservoir characterization, energy exploration, traffic control and route planning.
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360DigiTMG – Data Analytics, Data Science Course Training Hyderabad
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