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Professional Course in Data Analytics

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Our Data Analytics course equips you with essential skills and insights to excel in today's evolving landscape. Demonstrate your expertise in Data Analytics and position yourself as a top candidate for leading employers.

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Program Satisfaction

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Professional Data Analytics Masterclass by 20+ Years of Industry Experts

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Professional Data Analytics Certificate from Industry Leaders

SUNY data science certificate in USA

In terms of providing cognitive approaches and consulting services, SUNY is a pioneer.

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.

360DigiTMG data analytics certificate in USA

Specialist trainers – highly experienced industry experts and professors from premier engineering and B-schools.

Reputed institute – carries a legacy of training 20,000+ professionals and 10,000+ students from across the globe.

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.

Program Fee details

Virtual Instructor-led Training (VILT)

  • 10+ hours of live online doubt clarification sessions
  • Free access to USD 500 worth study materials - mindmaps, digital book on Data Science & many more
  • Blockchain security enabled tamper-proof certificate(s)
  • Free Learning Management System Access
  • Real-life industry-based projects with AiSPRY

USD 839

Employee
Upskilling

  • Free access to USD 500 worth study materials - mindmaps, digital book on Data Science & many more
  • Blockchain security enabled tamper-proof certificate(s)
  • Free Learning Management System Access
  • Real-life industry-based projects with AiSPRY

 

Professional Data Analytics Training

360DigiTMG Professional Data Analytics course equips you with a clear understanding of data processing tools like Excel, SQL/NoSQL, and Data Visualization tools like Tableau and PowerBI. While SQL/NoSQL is used to work with the data stored in the Database Management software, Tableau and PowerBI 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 working hours.

Professional Data Analytics Training Learning Outcomes

The course is designed keeping in mind all the latest market trends and it aims at equipping you with all the tools and techniques to handle huge data sets via Data Analytics. Course participants will get to assess the applications of these technologies that are used in storing and processing huge amounts of data. Each module is intensely designed to cover all the important concepts and to analyze structured and unstructured data, building visual stories using Tableau and or PowerBI capabilities. Professional Data Analytics Course is ideal for professionals who want to acquire in-depth knowledge of daily used Data frameworks. The six-month Data Analytics training will cover essential tools like SQL, NoSQL, Tableau, PowerBI, 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. The course contains multiple applied case studies that enable the participants to solve complex business problems improving profitability in their companies.

 

Work with various data generation sources
Perform load, retrieve, update, and delete the data in RDBMS
Analyse Structured and Unstructured data using different SQL and NoSQL queries
Develop an understanding of row-oriented and document-based database systems
Apply data-driven, visual insights for business decisions
Build dashboards and reports for day-to-day applicability
Develop live reports from streaming data to take proactive business decisions
Use Advanced Excel concepts to represent data for easy understanding
Block Your Time
data analytics course in USA - 360digitmg

200+ hours

Classroom Sessions

data analytics training in USA - 360digitmg

100+ hours

Assignments

data analytics training in USA

80+ hours

Projects

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

Professional Data Analytics Course Syllabus

Python
  • 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
Tableau
  • 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
  • Power BI Tips and Tricks & ChatGPT Prompts
  • Overview of Power BI
  • Architecture of PowerBI
  • PowerBI and Plans
  • Installation and introduction to PowerBI
  • Importing data
  • Changing Database
  • Data Types in PowerBI
  • 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
Excel
SQL
Google Looker Studio
  • 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
AWS Data Analytics Speciality
  • AWS infrastructure
  • Understanding AWS services
  • AWS Budget and Alerts
  • Data Analytics
  • Different Data Types
  • When to use Data Analytics?
  • Different types of Data Analytics
  • Data Analytics Pipeline
  • Database vs Data Lake vs Data Warehouse
  • Relational Database services (RDS)
  • DynamoDB
  • Redshift
  • ElastiCache
  • Kinesis Overview
  • DynamoDB
  • Data Migration Service (DMS)
  • Internet of Things (IoT) Platform on AWS
  • AWS KafKa
  • AWS Managed Streaming for Apache Kafka
  • Kinesis Data Streams
  • Kinesis Producers
  • Kinesis Consumers
  • Kinesis Enhanced FanOut
  • Kinesis Scaling
  • Handling Duplicate records using Kinesis
  • CloudWatch Subscription Filter with Kinesis
  • Kinesis Firehose
  • Simple Queue Service(SQS)
  • Kinesis Data Streams vs Kinesis firehose vs SQS
  • Amazon Simple Storage Service (Amazon S3) Overview
  • S3 Storage Classes
  • S3 Lifecycle Rules
  • Versioning in S3 Bucket
  • Replication Rules ins S3
  • S3 Performance
  • S3 Encryption
  • S3 Security and Bucket Policies
  • S3 Glacier Select
  • S3 Event Notifications
  • Static WebHosting
  • Transfer Acceleration
  • Server Access logging
  • Access Points
  • Amazon DynamoDB Overview
  • DynamoDB Provisioned Throughput
  • DynamoDB Partitions
  • DynamoDB APIs
  • Local Secondary Index (LSI) and Global Secondary Index (GSI)
  • DynamoDB Accelerator (DAX)
  • Streams in DynamoDB
  • Time to Live (TTL)
  • Security
  • Storing Large Objects
  • Inserting and Querying Data
  • AWS Lambda
  • Lambda Integration
  • Lambda Costs, Promises, and Anti-Patterns
  • What is Glue? + Partitioning your Data Lake
  • Glue, Hive, and Extract, Transform, and Load (ETL)
  • Glue ETL: Developer Endpoints, Running ETL Jobs with Bookmarks
  • Glue Costs and Anti-Patterns
  • AWS Glue Studio
  • AWS Glue DataBrew
  • AWS Glue Elastic Views
  • AWS Lake Formation
  • Amazon Elastic MapReduce (EMR) Architecture and Usage
  • Amazon Elastic MapReduce (EMR), Amazon Web Services (AWS) Integration, and Storage
  • Amazon Elastic MapReduce (EMR) Promises and Introduction to Hadoop
  • Introduction to Apache Spark
  • Spark Integration with Kinesis and Redshift
  • Hive on Amazon Elastic MapReduce (EMR)
  • Apache Pig on Amazon Elastic MapReduce (EMR)
  • Apache HBase on Amazon Elastic MapReduce (EMR)
  • Presto on Amazon Elastic MapReduce (EMR)
  • Apache Zeppelin and Amazon Elastic MapReduce (EMR) Notebooks
  • Hue, Splunk, and Flume
  • S3DistCp and Other Services
  • Amazon Elastic MapReduce (EMR) Security and Instance Types
  • Amazon Web Services (AWS) Data Pipeline
  • Amazon Web Services (AWS) Step Functions
  • Analysis
  • Introduction to Kinesis Analytics
  • Kinesis Analytics Costs and RANDOM_CUT_FOREST
  • Introduction to Amazon Elasticsearch
  • Amazon Elasticsearch Service
  • Amazon Elasticsearch Service Performance
  • Introduction to Amazon Athena
  • Athena and Glue, Costs, and Security
  • Amazon Redshift Introduction and Architecture
  • Redshift Spectrum and Performance Tuning
  • Amazon Redshift Durability and Scaling
  • Amazon Redshift Distribution Styles
  • Amazon Redshift Sort Keys
  • Amazon Redshift Data Flows and the COPY Command
  • Amazon Redshift Integration/Workload Management (WLM)/Vacuum/Anti-Patterns
  • Amazon Redshift Resizing (Elastic vs. Classic) and new Redshift features
  • Amazon Redshift Security Concerns
  • Introduction to Amazon QuickSight
  • Amazon QuickSight Pricing and Dashboards
  • Choosing Visualization Types
  • Other Visualization Tools (HighCharts, D3, and so on)
  • Encryption 101
  • S3 Encryption (Reminder)
  • Amazon Web Services Key Management Service (AWS KMS) Overview
  • Amazon Web Services Key Management Service (AWS KMS) Key Rotation
  • Amazon Web Services (AWS) CloudHSM Overview
  • Amazon Web Services Security Token Service (AWS STS) and Cross-Account Access
  • Identity Federation
  • Policies – Advanced
  • Amazon Web Services (AWS) CloudTrail
  • Virtual Private Cloud (VPC) Endpoints
SUNY University Syllabus
  • Business Intelligence with TIBCO Spotfire
  • Data Analysis Application
  • Microsoft Power BI
  • Business Intelligence Development and Maintenance
  • Designing Business Intelligence Solutions
  • Operational Intelligence Fundamentals with Splunk
  • Base SAS 9 Programming
  • Predictive Modelling Best Practices
  • Data Management
  • Graph Analytics
  • Data Mining
  • Data Nuts and Bolts
  • Math
  • Big Data Basics and Analytics
  • Data Analytics with Snowflake
  • Business & Leadership for Data Infrastructure with Snowflake
  • Productivity Tools for Data Infrastructure with Snowflake
  • Data Analyst to Data Scientist
  • Graph Analytics
  • Azure Data Analytics

View More >

Data Analytics Panel of Coaches

data science faculty and mentors Faculty and Mentors
Bharani Kumar Depuru

Areas of expertise

Data Analytics, Digital Transformation,

Industrial Revolution 4.0.

 

 

data science faculty and mentors Faculty and Mentors
Sharat Chandra Kumar

Areas of expertise

Data Science, Machine Learning,

Business Intelligence and Data Visualisation.

 

 

Data Analytics Faculty and Mentors Faculty and Mentors
Bhargavi Kandukuri

Areas of expertise

Data Analytics,

Business Intelligence and Data Visualisation.

 

 

Alumni Speak

"Coming from a psychology background, I was looking for a Data Science certification that can add value to my degree. The 360DigiTMG program has such depth, comprehensiveness, and thoroughness in preparing students that also looks into the applied side of Data Science."

"I'm happy to inform you that after 4 months of enrolling in a Professional Diploma in Full Stack Data Science, I have been offered a position that looks into applied aspects of Data Science and psychology."

Nur Fatin

Associate Data Scientist

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"360DigiTMG has an outstanding team of educators; who supported and inspired me throughout my Data Science course. Though I came from a statistical background, they've helped me master the programming skills necessary for a Data Science job. The career services team supported my job search and, I received two excellent job offers. This program pushes you to the next level. It is the most rewarding time and money investment I've made-absolutely worth it.”

Thanujah Muniandy

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"360DigiTMG’s Full Stack Data Science programme equips its graduates with the latest skillset and technology in becoming an industry-ready Data Scientist. Thanks to this programme, I have made a successful transition from a non-IT background into a career in Data Science and Analytics. For those who are still considering, be bold and take the first step into a domain that is filled with growth and opportunities.”

Ann Nee, Wong

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"360DigiTMG is such a great place to enhance IR 4.0 related skills. The best instructor, online study platform with keen attention to all the details. As a non-IT background student, I am happy to have a helpful team to assist me through the course until I have completed it.”

Mohd Basri

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"I think the Full Stack Data Science Course overall was great. It helped me formalize and think more deeply about ways to tackle the projects from a Data Science perspective. Also, I was remarkably impressed with the instructors, specifically their ability to make complicated concepts seem very simple."

"The instructors from 360DigiTMG were great and it showed how they engaged with all the students even in a virtual setting. Additionally, all of them are willing to help students even if they are falling behind. Overall, a great class with great instructors. I will recommend this to upcoming deal professionals going forward.”

Ashner Novilla

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Our Alumni Work At

Our Alumni

"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 for Professional Data Analytics

While there are a number of roles pertaining to Data Professionals, most of the responsibilities overlap. However, the following are some basic job descriptions for each of these roles.

 

As a Data Analyst, you will be dealing with Data Cleansing, Exploratory Data Analysis and Data Visualisation, among other functions. The functions pertain more to the use and analysis of historical data for understanding the current state.

 

As a Data Scientist, you will be building algorithms to solve business problems using statistical tools such as Python, R, SAS, STATA, Matlab, Minitab, KNIME, Weka etc. A Data Scientist also performs predictive modelling to facilitate proactive decision-making. Machine learning algorithms are used to build predictive models using Regression Analysis.A Data Scientist has to develop expertise in Neural Networks and Feature Engineering.

 

A Data Engineer primarily does programming using Spark, Python, R etc. It often complements the role of a Data Scientist.

 

A Data Architect has a much broader role that involves establishing the hardware and software infrastructure needed for an organisation to perform Data Analysis. They help in selecting the right Database, Servers, Network Architecture, GPUs, Cores, Memory, Hard disk etc.

Different organisations use different terms for Data Professionals. You will sometimes find these terms being used interchangeably. Though there are no hard rules that distinguish one from another, you should get the role descriptions clarified before you join an organisation.

With growing demand, there is a scarcity of Data Science professionals in the market. If you can demonstrate strong knowledge of Data Science concepts and algorithms, then there is a good chance that you can carve a successful career in this domain.

 

360DigiTMG provides internship opportunities through Innodatatics, our USA-based consulting partner, for deserving participants to help them gain real-life experience. This greatly helps students to bridge the gap between theory and practice.

There are plenty of jobs available for Data Professionals. Once you complete the training, assignments and the live projects you can enroll for placement guarantee. We help our students in resume preparation. Once the resume is ready we will float it organisations with whom we have formal agreements on job placements.

 

We also conduct webinars to help you with your resume and job interviews. We cover all aspects of post-training activities that are required to get a successful placement. After placement we provide technical assistance for the first project on the job.

After you have completed the classroom sessions, you will receive assignments through the online Learning Management System that you can access at your convenience. You will need to complete the assignments in order to obtain your Data Scientist certificate.

If you miss a class, we will arrange for a recording of the session. You can then access it through the online Learning Management System.

We assign mentors to each student in this programme. Additionally, during the mentorship session, if the mentor feels that you require additional assistance, you may be referred to another mentor or trainer.

No, The cost of the certificate is included in the programme package.

Why are Analytics Internships important for professionals?

Why are Analytics Internships important for professionals?

Internships are an ideal way to kick-start a career in any sector. However, for professionals to work in data-driven analytics, it is also beneficial to have relevant experience and knowledge of the domain. Therefore, an internship in analytics would be a potent add-on to starting a career in data analytics. You will get familiar with the latest data science tools and technologies and gain experience working on industry-level projects, which will ultimately help you advance in your career.

What will be the salaries for Data Analytics in USA?

What will be the salaries for Data Analytics in USA?

Salaries in Data Analytics vary based on experience. In the USA, freshers might start with a basic pay of around $48,000 per year, while experienced professionals can earn between $84,000 and $120,000 per year. Entry-level positions often require strong analytical skills and proficiency in data tools. A Junior Data Analyst can earn an average salary of $70,933 per year. Senior Data Analysts can earn significantly more, with top salaries reaching $24.1 Lakhs per year.

How to get jobs in the Data Stream industry through 360digiTMG?

How to get jobs in the Data Stream industry through 360DigiTMG?

A data scientist's work is intellectually challenging, technologically advanced, and rewarding. Those considering data science for their career tune in to 360digiTMG course training. All you need to do is start enroll in your subject, take classes, complete assignments, accomplish the final project, and get certification to build your resume. Then, start applying for the jobs with the skills gained by us. Therefore, the job is assured!

What will be the career track for Analytics?

What will be the career track for Analytics?

Becoming a data analyst is a stellar career option if you're looking for something stable and long-term. Depending on your goals and objectives, you could advance into data science, management, consulting, or a more specialized data career. As a data analyst, you can make a high salary and work in various areas, including food, technology, business, the environment, and the public sector. You can build a long and successful career in this field with the relevant skills and training.

Talk to your program advisors today!

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