Instructor Led Live Online
Self Learning + Live Mentoring
In - Person Classroom Training
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.
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
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
• 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
• 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: DATA SCIENCE ESSENTIALS
• Introduction to Data Science
• Data Science Terminologies
• Classifications of Analytics
• Data Science Project workflow
MODULE 2: DATA ENGINEERING FOUNDATION
• Introduction to Data Engineering
• Data engineering importance
• Ecosystems of data engineering tools
• Core concepts of data engineering
MODULE 3: PYTHON FOR DATA ANALYSIS
• Introduction to Python
• Python Data Types, Operators
• Flow Control statements, Functions
• Structured vs Unstructured Data
• Python Numpy package introduction
• Array Data Structures in Numpy
• Array operations and methods
• Python Pandas package introduction
• Data Structures : Series and DataFrame
• Pandas DataFrame key methods
MODULE 4: VISUALIZATION WITH PYTHON
• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
• Advanced Python Data Visualizations
MODULE 5: STATISTICS
• Descriptive And Inferential statistics
• Types Of Data, Sampling types
• Measures of Central Tendencies
• Data Variability: Standard Deviation
• Z-Score, Outliers, Normal Distribution
• Central Limit Theorem
• Histogram, Normality Tests
• Skewness & Kurtosis
• Understanding Hypothesis Testing
• P-Value Method, Types Of Errors
• T Distribution, One Sample T-Test
• Independent And Relational T Tests
• Direct And Indirect Correlation
• Regression Theory
MODULE 6: MACHINE LEARNING INTRODUCTION
• Machine Learning Introduction
• ML core concepts
• Unsupervised and Supervised Learning
• Clustering with K-Means
• Regression and Classification Models.
• Regression Algorithm: Linear Regression
• ML Model Evaluation
• Classification Algorithm: Logistic Regression
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
• 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
• 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
• 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
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
• The Formatting Pane
• Trend Lines & Reference Lines
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
MODULE 1: ARTIFICIAL INTELLIGENCE OVERVIEW
• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence.
• Why Artificial Intelligence Now?
• Ai Terminologies
• Areas Of Artificial Intelligence
• Ai Vs Data Science Vs Machine Learning
MODULE 2: DEEP LEARNING INTRODUCTION
• Deep Neural Network
• Machine Learning vs Deep Learning
• Feature Learning in Deep Networks
• Applications of Deep Learning Networks
MODULE 3: TENSORFLOW FOUNDATION
• TensorFlow Installation and setup
• TensorFlow Structure and Modules
• Hands-On: ML modeling with TensorFlow
MODULE 4: COMPUTER VISION INTRODUCTION
• Image Basics
• Convolution Neural Network (CNN)
• Image Classification with CNN
• Hands-On: Cat vs Dogs Classification with CNN Network
MODULE 5: NATURAL LANGUAGE PROCESSING (NLP)
• NLP Introduction
• Bag of Words Models
• Word Embedding
• Language Modeling
• Hands-On: BERT Algorithm
MODULE 6: AI ETHICAL ISSUES AND CONCERNS
• Issues And Concerns Around Ai
• Ai And Ethical Concerns
• Ai And Bias
• Ai: Ethics, Bias, And Trust
Data analytics is the art of unraveling the secrets hidden within data, empowering businesses to make data-driven decisions and gain a competitive edge. With the rise of big data, organizations have access to vast amounts of information that, when analyzed strategically, can reveal valuable insights and trends. In fact, according to Gartner, by 2022, 90% of corporate strategies will explicitly mention data as a critical asset and analytics as an essential competency.
DataMites Institute brings its renowned Data Analytics Course to Agartala, empowering aspiring data analysts with in-depth knowledge and practical skills. The comprehensive certified data analyst in Agartala extends over 4 months, ensuring that students receive over 200 hours of immersive learning. Covering essential topics such as statistical analysis, data visualization, machine learning, and predictive modeling, this course equips students with the necessary tools to excel in the data analytics field. With the inclusion of 10 Capstone Projects and 1 Client Project, students have ample opportunities to apply their newfound skills to real-world scenarios and gain hands-on experience.
Here is why choosing DataMites for Data Analytics Training in Agartala is a smart decision:
Experienced Faculty: Learn from industry experts and experienced faculty members who bring their practical knowledge and expertise to the classroom.
Comprehensive Course Curriculum: Gain in-depth knowledge through a comprehensive curriculum that covers all aspects of data analytics, ensuring a holistic learning experience.
Global Certification: Earn globally recognized certifications from prestigious organizations like IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing your professional credibility.
Flexible Learning: DataMites offers flexible learning options, allowing you to balance your training with other commitments and learn at your own pace.
Real-world Projects: Work on projects with real-world data to apply your skills and gain hands-on experience in solving practical data analytics challenges.
Internship Opportunity: Get the chance to data analytics internship with leading organizations, gaining invaluable industry experience and building a strong professional network.
Placement Assistance: DataMites provides data analytics course with placement assistance, guiding you through the job search process and connecting you with relevant job opportunities.
Hardcopy Learning Materials: Access hardcopy learning materials and books, providing you with comprehensive study resources to support your learning journey.
DataMites Exclusive Learning Community: Become part of an exclusive learning community, where you can network, collaborate, and exchange knowledge with fellow data enthusiasts.
Affordable Pricing and Scholarships: DataMites offers competitive pricing for their courses, making quality data analytics training accessible to a wide range of learners. Scholarships are also available for deserving candidates, further enhancing affordability.
Agartala, the capital city of the northeastern state of Tripura in India, serves as a vibrant and culturally rich location for pursuing a data analytics certification in Agartala. Nestled in the lush landscapes of the region, Agartala offers a unique blend of historical charm and modern development. The city is known for its architectural landmarks, such as the Ujjayanta Palace and Neermahal, which provide a glimpse into the region's royal heritage.
Along with the data analytics courses, DataMites also provides artificial intelligence, mlops, data science, deep learning, data engineer, IoT, AI expert, data mining, tableau, python, r programming and machine learning courses in Agartala.
Data Analytics involves the process of gathering, arranging, analyzing, and interpreting large datasets to identify patterns, trends, and valuable insights that aid in decision-making and drive business improvements.
Studying Data Analytics offers several advantages, including enhanced decision-making capabilities, increased efficiency and productivity, gaining a competitive edge, better understanding of customer behavior, and a wide range of career opportunities.
Individuals from diverse educational backgrounds, such as mathematics, statistics, computer science, engineering, economics, and business, can pursue a career in Data Analytics. A passion for data analysis, problem-solving, and critical thinking is also valuable in this field.
Data Analytics is utilized in various industries, including 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.
To excel in Data Analytics, one needs proficiency in programming languages like 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, and effective communication and storytelling abilities.
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 varies across different countries. In the UK, it is £36,535 per annum; in Canada, it is C$58,843 per year; in the United States, it is USD 69,517 per year; in India, it is INR 6,00,000 per year; in Australia, it is AUD 85,000 per year; in Germany, it is 46,328 EUR per annum; in the UAE, it is AED 106,940 per year; in South Africa, it is ZAR 286,090 per year; in Switzerland, it is CHF 95,626 per year, and in Saudi Arabia, it is SAR 95,960 per year.
The scope of Data Analytics encompasses various areas such as data mining, data visualization, predictive modeling, machine learning, and artificial intelligence.
The average data analyst salary in Agartala is ₹3,36024 per annum according to Indeed.
The typical cost for Data Analytics training in Agartala falls within the range of 40,000 to 80,000 INR. However, specific institutes may have different pricing structures based on various factors.
While a mathematics background can be advantageous, it is not always a mandatory requirement to pursue a career in data analytics. Individuals with strong logical thinking and problem-solving skills can still enter the field without an extensive mathematics background.
The difficulty level of a Data Analytics Course in Agartala can vary depending on the curriculum, topics covered, and individual aptitude. Data Analytics involves complex concepts, but with dedication, practice, and guidance, it is possible to grasp the concepts and excel in the field.
A bachelor's degree in a relevant field such as mathematics, statistics, computer science, engineering, economics, or business is typically required for a career in data analytics. However, specific requirements may vary based on the job position and company. Advanced degrees or certifications in data analytics or related fields may be required for some roles. Continuous learning and upskilling are also important to stay updated with evolving tools and techniques in data analytics.
DataMites is a highly recommended institute for studying data analytics. They offer comprehensive data analytics courses taught by experienced faculty, provide practical experience, and have a strong industry reputation. DataMites also provides placement assistance and has a track record of helping students secure rewarding career opportunities in the field of data analytics.
DataMites stands out as the preferred choice for Data Analytics Courses in Agartala due to several reasons. These include:
Expert Faculty: DataMites boasts a team of experienced faculty members who possess deep knowledge and expertise in the field of data analytics. They provide comprehensive training and guidance to students.
Comprehensive Curriculum: DataMites offers a well-structured and up-to-date curriculum that covers all the essential topics and skills required for data analytics. The course is designed to provide a thorough understanding of data analytics concepts and techniques.
Hands-on Experience: DataMites places a strong emphasis on practical learning by providing hands-on experience through real-world projects and case studies. This approach enables students to apply their knowledge and skills to solve industry-relevant problems.
Industry-Recognized Certification: Upon successful completion of the training, participants receive globally recognized certifications from DataMites. These certifications validate their skills and enhance their career prospects in the field of data analytics.
Placement Support: DataMites offers placement assistance to help students kick-start their careers in data analytics. They provide guidance, interview preparation, and job references to increase the chances of securing suitable job opportunities.
Flexible Learning Options: DataMites offers flexible learning options, including online and offline modes, to accommodate the diverse needs of learners. This flexibility allows individuals to balance their professional and personal commitments while pursuing the data analytics course.
Affordable Pricing: DataMites provides competitive and affordable pricing for their data analytics courses, making it accessible to a wide range of individuals who are looking to upskill or start a career in data analytics.
To attend data analytics training in Agartala, having a basic understanding of mathematics, statistics, and computer operations is generally beneficial. Familiarity with programming languages like Python or R and knowledge of database management systems can also be advantageous.
The fee for Data Analytics Course at DataMites in Agartala may differ based on factors like course duration, mode of delivery, and additional services. The cost of certified data analyst training in Agartala can range from INR 28,178 to INR 76,000.
In Agartala, the DataMites Certified Data Analytics Course is designed to span 4 months and includes more than 200 learning hours. This timeframe ensures ample opportunity for thorough training, practical exercises, and project-based learning.
The DataMites Certified Data Analyst Training in Agartala covers a comprehensive range of topics, including data preprocessing, data visualization, statistical analysis, predictive modeling, machine learning, data mining, and database management systems. The exact curriculum may vary based on the course level and specialization.
Flexi-Pass is a unique feature offered by DataMites that provides students with flexibility in scheduling their training sessions. With Flexi-Pass, learners can choose their preferred timing and attend the training at their convenience. This feature enables working professionals and individuals with other commitments to pursue data analytics training without disrupting their regular schedule.
DataMites offers multiple payment methods to make the enrollment process convenient for students. The available payment methods may include online payment through debit or credit cards, net banking, and other digital payment platforms. For specific information on the payment methods, it is recommended to visit the DataMites website or contact their admissions team.
The DataMites Certified Data Analyst Course in Agartala is open to individuals from diverse backgrounds, including fresh graduates, working professionals, and anyone interested in pursuing a career in data analytics. There are no specific eligibility criteria, making it accessible to a wide range of learners.
Yes, DataMites provides support sessions to participants who require additional assistance or clarification on the topics covered in the Data Analytics training. These support sessions aim to ensure a thorough understanding of the subject matter and address any specific queries or challenges faced by participants.
By completing the Data Analytics training program at DataMites, you will earn highly esteemed certifications from IABAC, NASSCOM FutureSkills Prime, and JainX. These certifications hold global recognition and serve as tangible proof of your competence in the field of data analytics, enabling you to stand out in the job market.
Typically, DataMites may require participants to carry certain documents to the training session, such as a valid ID proof (like Aadhaar card, passport, or driver's license) for verification purposes.
DataMites offers on-demand classroom training for Data Analytics in Agartala. They provide interactive and instructor-led sessions, allowing participants to learn in a traditional classroom setting. This mode of training facilitates direct interaction with the faculty and fellow learners, creating an immersive learning environment.
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: -
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.
IABAC Global Certifications
Comprehensive : Math, Stats, Machine Learning, Python, R, Tableau
8-month | 700 Learning Hours
Internship | Job Assistance
IABAC Global Certifications
Comprehensive: Computer vision, NLP,
Deep Learning, Reinforcement Learning
11-Month | 780 Learning Hours
Internship | Job assistance