DATA ANALYTICS CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYTICS LEAD MENTORS

DATA ANALYTICS COURSE FEE IN AGRA

Live Virtual

Instructor Led Live Online

110,000
63,945

  • IABAC® & JAINx® Certification
  • 6-Month | 200+ Learning Hours
  • 20 HOURS LEARNING A WEEK
  • 10 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

55,000
36,645

  • Self Learning + Live Mentoring
  • IABAC® & JAINx® Certification
  • 1 Year Access To Elearning
  • 10 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Learner assistance and support

Classroom

In - Person Classroom Training

110,000
69,195

  • IABAC® & JAINx® Certification
  • 6-Month | 200+ Learning Hours
  • 20 HOURS LEARNING A WEEK
  • 10 Capstone & 1 Client Project
  • Cloud Lab Access
  • Internship +Job Assistance

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UPCOMING DATA ANALYTICS ONLINE CLASSES IN AGRA

BEST DATA ANALYTICS CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

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WHY DATAMITES INSTITUTE FOR DATA ANALYTICS COURSE

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SYLLABUS OF DATA ANALYTICS CERTIFICATION IN AGRA

MODULE 1: DATA ANALYSIS FOUNDATION

• Data Analysis Introduction
• Data Preparation for Analysis
• Common Data Problems
• Various Tools for Data Analysis
• Evolution of Analytics domain

MODULE 2: CLASSIFICATION OF ANALYTICS

• Four types of the Analytics
• Descriptive Analytics
• Diagnostics Analytics
• Predictive Analytics
• Prescriptive Analytics
• Human Input in Various type of Analytics

MODULE 3: CRIP-DM Model

• Introduction to CRIP-DM Model
• Business Understanding
• Data Understanding
• Data Preparation
• Modeling
• Evaluation
• Deploying
• Monitoring

MODULE 4: UNIVARIATE DATA ANALYSIS

• Summary statistics -Determines the value’s center and spread.
• Measure of Central Tendencies: Mean, Median and Mode
• Measures of Variability: Range, Interquartile range, Variance and Standard Deviation
• Frequency table -This shows how frequently various values occur.
• Charts -A visual representation of the distribution of values.

MODULE 5: DATA ANALYSIS WITH VISUAL CHARTS

• Line Chart
• Column/Bar Chart
• Waterfall Chart
• Tree Map Chart
• Box Plot

MODULE 6: BI-VARIATE DATA ANALYSIS

• Scatter Plots
• Regression Analysis
• Correlation Coefficients

MODULE 1: PYTHON BASICS

• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity

MODULE 2: PYTHON CONTROL STATEMENTS

• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements

MODULE 3: PYTHON DATA STRUCTURES

• Basic data structure in python
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions

MODULE 4: PYTHON FUNCTIONS

• Functions basics
• Function Parameter passing
• Iterators
• Generator functions
• Lambda functions
• Map, reduce, filter functions

MODULE 5: PYTHON NUMPY PACKAGE

• NumPy Introduction
• Array – Data Structure
• Core Numpy functions
• Matrix Operations

MODULE 6: PYTHON PANDAS PACKAGE

• Pandas functions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis

MODULE 1 : OVERVIEW OF STATISTICS 

  • Descriptive And Inferential Statistics
  • Basic Terms Of Statistics
  • Types Of Data

MODULE 2 : HARNESSING DATA 

  • Random Sampling
  • Sampling With Replacement And Without Replacement
  • Cochran's  Minimum Sample Size
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Biased Random Sampling Methods
  • Sampling Error
  • Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS 

  • Exploratory Data Analysis Introduction
  • Measures Of Central Tendencies: Mean, Median And Mode
  • Measures Of Central Tendencies: Range, Variance And Standard Deviation
  • Data Distribution Plot: Histogram
  • Normal Distribution
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance

MODULE 4 : HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Confidence Interval
  • Parametric Hypothesis Testing Methods
  • Hypothesis Testing Errors : Type I And Type Ii
  • One Sample T-test
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test

MODULE 5 : CORRELATION AND REGRESSION

  • Correlation Introduction
  • Direct/Positive Correlation
  • Indirect/Negative Correlation
  • Regression
  • Choosing Right Method
     

MODULE 1: COMPARISION AND CORRELATION ANALYSIS

• Data comparison Introduction
• Concept of Correlation
• Calculating Correlation with Excel
• Comparison vs Correlation
• Performing Comparison Analysis on Data
• Performing correlation Analysis on Data
• Hands-on case study 1: Comparison Analysis
• Hands-on case study 2 Correlation Analysis

MODULE 2: VARIANCE AND FREQUENCY ANALYSIS

• Concept of Variability and Variance
• Data Preparation for Variance Analysis
• Business use cases for Variance and Frequency Analysis
• Performing Variance and Frequency Analysis
• Hands-on case study 1: Variance Analysis
• Hands-on case study 2: Frequency Analysis

MODULE 3: RANKING ANALYSIS

• Introduction to Ranking Analysis
• Data Preparation for Ranking Analysis
• Performing Ranking Analysis with Excel
• Insights for Ranking Analysis
• Hands-on Case Study: Ranking Analysis

MODULE 4: BREAK EVEN ANALYSIS

• Concept of Breakeven Analysis
• Make or Buy Decision with Break Even
• Preparing Data for Breakeven Analysis
• Hands-on Case Study: Procurement Decision with break even

MODULE 5: PARETO (80/20 RULE) ANALSYSIS

• Pareto rule Introduction
• Preparation Data for Pareto Analysis
• Insights on Optimizing Operations with Pareto Analysis
• Performing Pareto Analysis on Data
• Hands-on case study: Pareto Analysis

MODULE 6: Time Series and Trend Analysis

• Introduction to Time Series Data
• Preparing data for Time Series Analysis
• Types of Trends
• Trend Analysis of the Data with Excel
• Insights from Trend Analysis
• Hands-on Case Study: Trend Analysis

MODULE 7: DATA ANALYSIS BUSINESS REPORTING

• Management Information System Introduction
• Various Data Reporting formats
• Creating Data Analysis reports as per the requirements
• Presenting the reports
• Hands-on case study: Create Data Analysis Reports

MODULE 1: DATA ANALYTICS FOUNDATION

• Business Analytics Overview
• Application of Business Analytics
• Visual Perspective
• Benefits of Business Analytics
• Challenges
• Classification of Business Analytics
• Data Sources
• Data Reliability and Validity
• Business Analytics Model

MODULE 2: OPTIMIZATION MODELS

• Prescriptive Analytics with Low Uncertainty
• Mathematical Modeling and Decision Modeling
• Break Even Analysis
• Product Pricing with Prescriptive Modeling
• Building an Optimization Model
• Case Study 1 : WonderZon Network Optimization
• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity

MODULE 3: PREDICTIVE ANALYTICS WITH REGRESSION

• Mathematics beyond Linear Regression
• Hands on: Regression Modeling in Excel
• Case Study 2 : Sales Promotion Decision with Regression Analysis
• Assignment 2 : Design Marketing Decision board for QuikMark Inc.

MODULE 4: DECISION MODELING

• Prescriptive Analytics with High Uncertainty
• Comparing Decisions in Uncertain Settings
• Decision Trees for Decision Modeling
• Case Study 3 : Decision modeling of Internet Plans, Monte Carlo Simulation
• Case Study 4 : Kickathlon Sports Retailer Supplier Decision Modeling

MODULE 1: MACHINE LEARNING INTRODUCTION

• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised

MODULE 2: ML ALGO: LINEAR REGRESSSION

• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Hands-on Linear Regression with ML Tool

MODULE 3: ML ALGO: LOGISTIC REGRESSION

• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool

MODULE 4: ML ALGO: KNN

• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Hands-on KNN with ML Tool

MODULE 5: ML ALGO: K MEANS CLUSTERING

• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Hands-on K Means Clustering with ML Tool

MODULE 6: ML ALGO: DECISION TREE

• Random Forest Ensemble technique
• How it works: Bagging Theory
• Hands-on Decision Tree with ML Tool

MODULE 7: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python

MODULE 8: ARTIFICIAL NEURAL NETWORK (ANN)

• Introduction to ANN
• How It Works: Back prop, Gradient Descent
• Modeling and Evaluation of ANN in Python

MODULE 9: PROJECT: PREDICTIVE ANALYTICS WITH ML

• Project Business requirements
• Data Modeling
• Building Predictive Model with ML Tool
• Evaluation and Deployment
• Project Documentation and Report

MODULE 1: GIT INTRODUCTION

• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture

MODULE 2: GIT REPOSITORY and GitHub

• Git Repo Introduction
• Create New Repo with Init command
• Copying existing repo
• Git user and remote node
• Git Status and rebase
• Review Repo History
• GitHub Cloud Remote Repo

MODULE 3: COMMITS, PULL, FETCH AND PUSH

• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo

MODULE 4: TAGGING, BRANCHING AND MERGING

• Organize code with branches
• Checkout branch
• Merge branches

MODULE 5: UNDOING CHANGES

• Editing Commits
• Commit command Amend flag
• Git reset and revert

MODULE 6: GIT WITH GITHUB AND BITBUCKET

• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
• Bitbucket Git account

MODULE 1: DATABASE INTRODUCTION

• DATABASE Overview
• Key concepts of database management
• CRUD Operations
• Relational Database Management System
• RDBMS vs No-SQL (Document DB)

MODULE 2: SQL BASICS

• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
• Comments
• import and export dataset

MODULE 3: DATA TYPES AND CONSTRAINTS

• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment

MODULE 4: DATABASES AND TABLES (MySQL)

• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table

MODULE 5: SQL JOINS

• Inner join
• Outer join
• Left join
• Right join
• Cross join
• Self join

MODULE 6: SQL COMMANDS AND CLAUSES

• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries

MODULE 7: DOCUMENT DB/NO-SQL DB

• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
• MongoDB data management

MODULE 1: BIG DATA INTRODUCTION

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
• Hands-on Map Reduce task

MODULE 3: PYSPARK FOUNDATION

• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE

• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
• Working with Spark SQL Query Language

MODULE 5: MACHINE LEARNING WITH SPARK ML

• Introduction to MLlib Various ML algorithms supported by Mlib
• ML model with Spark ML.
• Linear regression
• logistic regression
• Random forest

MODULE 6: KAFKA and Spark

• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations

MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION

• What Is Business Intelligence (BI)?
• What Bi Is The Core Of Business Decisions?
• BI Evolution
• Business Intelligence Vs Business Analytics
• Data Driven Decisions With Bi Tools
• The Crisp-Dm Methodology

MODULE 2: BI WITH TABLEAU: INTRODUCTION

• The Tableau Interface
• Tableau Workbook, Sheets And Dashboards
• Filter Shelf, Rows And Columns
• Dimensions And Measures
• Distributing And Publishing

MODULE 3: TABLEAU: CONNECTING TO DATA SOURCE

• Connecting To Data File , Database Servers
• Managing Fields
• Managing Extracts
• Saving And Publishing Data Sources
• Data Prep With Text And Excel Files
• Join Types With Union
• Cross-Database Joins
• Data Blending
• Connecting To Pdfs

MODULE 4: TABLEAU : BUSINESS INSIGHTS

• Getting Started With Visual Analytics
• Drill Down And Hierarchies
• Sorting & Grouping
• Creating And Working Sets
• Using The Filter Shelf
• Interactive Filters
• Parameters
• The Formatting Pane
• Trend Lines & Reference Lines
• Forecasting
• Clustering

MODULE 5: DASHBOARDS, STORIES AND PAGES

• Dashboards And Stories Introduction
• Building A Dashboard
• Dashboard Objects
• Dashboard Formatting
• Dashboard Interactivity Using Actions
• Story Points
• Animation With Pages

MODULE 6: BI WITH POWER-BI

• Power BI basics
• Basics Visualizations
• Business Insights with Power BI

OFFERED DATA ANALYTICS COURSES IN AGRA

DATA ANALYTICS TRAINING REVIEWS

ABOUT DATA ANALYTICS TRAINING IN AGRA

Data analytics is the ultimate crystal ball that predicts the future of businesses. Did you know that organizations that use data analytics are 2.5 times more likely to make proactive decisions rather than reactive ones? By analyzing historical data and trends, data analysts can anticipate market shifts, identify emerging opportunities, and mitigate risks. It's like having a superpower that enables businesses to stay ahead of the curve and make data-driven choices that fuel growth and innovation.

Embark on a transformative journey in data analytics with DataMites' Data Analytics Course in Agra. DataMites Institute offers a Certified Data Analyst Training program renowned for its comprehensive curriculum. Over the course of 4 months, students will delve into various subjects, including statistical analysis, data visualization, machine learning, and predictive modeling. With an average commitment of 20 hours per week, students can ensure a thorough understanding of the course material. The program stands out for its integration of 10 Capstone Projects and 1 Client Project, empowering students to tackle real-world data analytics challenges and deliver tangible results.

Here is why you should with DataMites for Data Analytics Training in Agra:

  • Expert Faculty: Learn from industry experts, including renowned data analytics professional Ashok Veda, who brings a wealth of knowledge and experience to the classroom.

  • Comprehensive Course Curriculum: Gain a holistic understanding of data analytics through a well-structured and up-to-date curriculum that covers essential concepts and practical applications.

  • Global Certification: Earn globally recognized certifications from prestigious institutions such as IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing your professional credibility.

  • Flexible Learning: Benefit from flexible learning options, including online data analytics training in Agra and ON DEMAND data analytics offline classes in Agra, allowing you to balance your training with other commitments.

  • Real-World Projects: Gain hands-on experience by working on projects with real-world datasets, enabling you to apply your knowledge to practical scenarios.

  • Internship Opportunity: Get the chance to do data analytics internship with industry-leading organizations, further enhancing your practical skills and gaining valuable work experience.

  • Placement Assistance: Receive dedicated data analytics courses with placement assistance and access to job references, connecting you with potential employers in the field of data analytics.

  • Learning Materials: Access comprehensive learning materials and books in both digital and hardcopy formats to support your learning journey.

  • DataMites Exclusive Learning Community: Join a vibrant learning community of like-minded professionals, fostering collaboration and networking opportunities.

  • Affordable Pricing and Scholarships: Benefit from affordable pricing options and the opportunity to avail scholarships, making the course accessible to a wide range of learners.

Agra, a city nestled on the banks of the Yamuna River in the northern state of Uttar Pradesh, is renowned worldwide for its architectural masterpiece, the Taj Mahal. This UNESCO World Heritage site attracts millions of visitors every year, mesmerizing them with its grandeur and timeless beauty. Beyond the Taj Mahal, Agra is steeped in history and culture, with several other iconic landmarks like Agra Fort and Fatehpur Sikri showcasing the city's rich Mughal heritage.

Opting for a data analytics certification in Agra allows you to immerse yourself in a city that seamlessly blends historical significance with modern development. The presence of world-class educational institutions, such as Agra University and various management institutes, underscores Agra's commitment to providing quality education. This makes Agra an ideal destination for pursuing advanced analytics training, combining the allure of historical landmarks with a conducive learning environment.

Along with the data analytics courses, DataMites also provides machine learning, data engineer, IOT, deep learning, mlops, data science, AI expert, data mining, tableau, python, r programming and artificial intelligence courses in Agra.

ABOUT DATA ANALYTICS COURSE IN AGRA

Data Analytics refers to the process of collecting, organizing, analyzing, and interpreting large sets of data to uncover patterns, trends, and insights that can inform decision-making and drive business improvements. It involves using statistical and quantitative techniques and various tools and technologies to extract valuable information from data.

Data Analytics is utilized across various industries such as finance and banking, healthcare and pharmaceuticals, retail and e-commerce, manufacturing and logistics, telecommunications, marketing and advertising, energy and utilities, government and public sector, and sports and entertainment.

Studying Data Analytics offers several significant benefits, including improved decision-making, enhanced efficiency and productivity, competitive advantage, better customer understanding, and diverse career opportunities.

Proficiency in programming languages such as Python, R, or SQL, strong analytical and problem-solving skills, knowledge of statistical analysis and data visualization techniques, familiarity with database management systems, understanding of machine learning and predictive modeling, ability to work with large datasets and perform data manipulation, and effective communication and storytelling skills are essential for success in Data Analytics.

A career in Data Analytics is open to individuals from diverse educational backgrounds such as mathematics, statistics, computer science, engineering, economics, business, and other related fields. Passion for data analysis, problem-solving, and critical thinking is also valuable for entering this field.

The scope of Data Analytics is vast and expanding rapidly. It includes areas like data mining, data visualization, predictive modeling, machine learning, and artificial intelligence.

Data Analytics offers promising career prospects, with job opportunities available in technology companies, consulting firms, financial institutions, healthcare organizations, e-commerce companies, and government agencies. Job titles may include Data Analyst, Data Scientist, Business Intelligence Analyst, Data Engineer, Machine Learning Engineer, and Data Consultant, among others.

  • The average salary for a Data Analyst is £36,535 per annum in the UK. (Glassdoor)

  • The average salary for a Data Analyst is INR 6,00,000 per year in India. (Glassdoor)

  • The average salary for a Data Analyst is C$58,843 per year in Canada. (Payscale)

  • The average salary for a Data Analyst is USD 69,517 per year in the United States. (Glassdoor)

  • The average salary for a Data Analyst is AUD 85,000 per year in Australia. (Glassdoor)

  • The average salary for a Data Analyst is 46,328 EUR per annum in Germany. (Payscale)

  • The average salary for a Data Analyst is CHF 95,626 per year in Switzerland. (Glassdoor)

  • The average salary for a Data Analyst is AED 106,940 per year in UAE. (Payscale)

  • The average salary for a Data Analyst is ZAR 286,090 per year in South Africa. (Payscale.com)

  • The average salary for a Data Analyst is SAR 95,960 per year in Saudi Arabia. (Payscale.com)

The average data analyst salary in Agra is ₹2,99,075 per annum according to Indeed.

In Agra, the fee for a Data Analytics Course can vary from 40,000 to 80,000 INR, depending on the institute and the specific features and duration of the training program. It is advisable to research and compare different options to find the one that best suits your budget and learning requirements.

The difficulty level of a Data Analytics course can vary depending on the curriculum, the depth of the topics covered, and the individual's prior knowledge and aptitude. Data Analytics does involve complex concepts and requires analytical thinking and technical skills. However, with dedication, practice, and proper guidance, it is possible to grasp the concepts and excel in the field.

The educational requirements for a career in data analytics typically include a bachelor's degree in a relevant field such as mathematics, statistics, computer science, engineering, economics, or business. However, it is important to note that the specific requirements may vary based on the job position and company. Some roles may require advanced degrees or certifications in data analytics or related fields. Continuous learning and upskilling are also crucial to stay updated with the evolving tools and techniques in data analytics.

While a background in mathematics can be beneficial for understanding certain concepts in data analytics, it is not always a mandatory requirement. Data analytics involves a combination of skills from various disciplines, including mathematics, statistics, programming, and business. Individuals with a strong aptitude for logical thinking and problem-solving can still pursue a career in data analytics, even without an extensive mathematics background.

The best institute for learning data analytics is DataMites. DataMites offers comprehensive data analytics courses that cover a wide range of topics and provide hands-on practical experience. With experienced faculty members, a strong industry reputation, and a robust alumni network, DataMites is known for delivering high-quality training in data analytics. Additionally, DataMites provides placement assistance and has a track record of helping students secure rewarding career opportunities in the field of data analytics. It is recommended to explore the offerings of DataMites and consider it as the preferred institute for learning data analytics.

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FAQ’S OF DATA ANALYTICS TRAINING IN AGRA

DataMites offers experienced faculty, a comprehensive curriculum, hands-on experience, industry-recognized certification, placement support, flexible learning options, and affordable pricing.

The course is open to fresh graduates, working professionals, and anyone interested in pursuing a career in data analytics without any specific eligibility criteria.

The fee for the course ranges from INR 28,178 to INR 76,000, depending on factors like course duration and additional services included.

The course has a duration of 4 months, providing over 200 learning hours.

Basic understanding of mathematics, statistics, and computer operations, familiarity with programming languages like Python or R, and knowledge of database management systems are beneficial.

The training covers data preprocessing, data visualization, statistical analysis, predictive modeling, machine learning, data mining, and database management systems, among other topics.

Flexi-Pass allows students to schedule their training sessions at their convenience, providing flexibility for working professionals and individuals with other commitments.

Yes, support sessions are available to provide additional assistance and clarification on the topics covered in the training.

Yes, participants receive certifications from IABAC, NASSCOM FutureSkills Prime, and JainX upon successful completion of the training.

Participants may need to carry a valid ID proof for verification purposes.

Online payment through debit or credit cards, net banking, and offline modes like demand drafts or bank transfers may be available.

Yes, DataMites offers on-demand classroom training in a traditional classroom setting in Agra.

DataMites offers online payment options through debit or credit cards, net banking, and other digital payment platforms.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

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