DATA ANALYTICS CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYTICS LEAD MENTORS

DATA ANALYTICS COURSE FEE IN PANAJI

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 PANAJI

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

Why DataMites Infographic

SYLLABUS OF DATA ANALYTICS CERTIFICATION IN PANAJI

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 PANAJI

DATA ANALYTICS TRAINING REVIEWS

ABOUT DATA ANALYTICS TRAINING IN PANAJI

Data analytics is the catalyst driving the digital revolution, unraveling the power hidden within vast troves of data. It is estimated that by 2025, the global volume of data will reach a mind-boggling 175 zettabytes, highlighting the exponential growth of information. Data analytics enables businesses to extract valuable insights, identify patterns, and make data-driven decisions that fuel innovation and growth. From predicting customer behavior to optimizing supply chains, data analytics has become an indispensable tool across industries, shaping the future of businesses worldwide.

DataMites Institute, a renowned provider of data analytics training, presents an extensive Data Analytics Course in Panaji. The Certified Data Analyst Training offered by DataMites is a comprehensive program that extends over a period of 4 months, comprising more than 200 hours of immersive learning. This course encompasses a wide range of topics including statistical analysis, data visualization, machine learning, and predictive modeling. Students can expect to devote approximately 20 hours per week to their studies, ensuring a deep understanding of the subject matter. One of the standout features of the Certified Data Analyst Course is its incorporation of 10 Capstone Projects and 1 Client Project, enabling students to apply their knowledge and skills to real-world scenarios, tackling data analytics challenges and delivering practical solutions.

Choosing DataMites for Data Analytics Training in Panaji offers numerous advantages. 

  • The institute boasts a team of experienced faculty, including renowned expert Ashok Veda, who bring a wealth of industry knowledge to the classroom. 

  • The course curriculum is carefully crafted to cover all key topics, tools, and techniques essential for data analytics. Successful completion of the program grants students globally recognized certifications from leading organizations like IABAC, NASSCOM FutureSkills Prime, and JainX, providing them with a competitive edge in the job market.

  • DataMites prioritizes flexible learning online data analytics training in Panaji and ON DEMAND data analytics offline training in Panaji, allowing students to tailor their learning journey to their individual needs and schedules. 

  • Real-world projects integrated into the curriculum provide hands-on experience in working with actual data, bridging the gap between theory and practice. Moreover, the institute offers data analytics internship opportunities, enabling students to apply their skills in real-world scenarios.

  • DataMites also provides data analytics placement assistance and job references, supporting students in securing promising career opportunities.

  • To facilitate learning, DataMites provides hardcopy learning materials and books in addition to online resources, ensuring a comprehensive educational experience. 

  • Students become part of the DataMites Exclusive Learning Community, a platform that fosters collaboration, knowledge sharing, and networking. 

  • The institute is committed to making data analytics training accessible to all by offering affordable pricing options and scholarships, opening doors to learners from diverse backgrounds.

Panaji boasts a thriving economy driven by sectors such as tourism, information technology, pharmaceuticals, and financial services. The city's strategic location and infrastructure make it an attractive destination for businesses seeking growth and expansion. As a result, there is a rising demand for skilled professionals in emerging fields like data analytics, which play a vital role in driving innovation and decision-making in businesses. By pursuing a data analytics certification in Panaji, individuals can position themselves at the forefront of this exciting field and contribute to the growth and success of businesses and industries.

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

ABOUT DATA ANALYTICS COURSE IN PANAJI

Data Analytics involves the collection, organization, analysis, and interpretation of large datasets to uncover patterns, trends, and insights that can drive decision-making and improve business performance.

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

A career in Data Analytics is open to individuals from various educational backgrounds, including mathematics, statistics, computer science, engineering, economics, and business. Passion for data analysis, problem-solving, and critical thinking is also valuable.

Data Analytics is utilized in industries such as finance, healthcare, retail, manufacturing, telecommunications, energy, government, marketing, and entertainment.

Proficiency in programming languages like Python, R, or SQL, strong analytical and problem-solving skills, knowledge of statistics and data visualization, familiarity with databases, understanding of machine learning and predictive modeling, and effective communication skills are essential.

The scope of Data Analytics includes data mining, data visualization, predictive modeling, machine learning, and artificial intelligence.

Data Analytics offers career prospects in technology companies, consulting firms, finance, healthcare, e-commerce, government, and more. Job titles may include Data Analyst, Data Scientist, Business Intelligence Analyst, Data Engineer, Machine Learning Engineer, and Data Consultant.

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

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

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

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

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

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

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

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

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

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

The average data analyst salary in Panaji is ₹3,43,947 per annum according to Indeed.

The cost of a Data Analytics Course in Panaji can range between 40,000 and 80,000 INR, depending on factors such as the institute, course duration, curriculum, and additional features offered.

While a mathematics background can be beneficial, it is not always a mandatory requirement. Data Analytics requires a combination of skills from various disciplines, and individuals with strong problem-solving and critical thinking abilities can pursue a career in the field.

The difficulty of a Data Analytics course can vary based on the curriculum, topics covered, and individual aptitude. With dedication, practice, and guidance, it is possible to grasp the concepts and excel in the field.

A bachelor's degree in mathematics, statistics, computer science, engineering, economics, or business is typically required. However, requirements may vary based on the job and company, and advanced degrees or certifications can be beneficial.

DataMites is a recommended institute for studying data analytics. They offer comprehensive courses, experienced faculty, practical experience, and placement assistance. It is advisable to explore their offerings and consider them as a preferred institute for learning data analytics.

Yes, a non-science student can learn data analytics. While a background in mathematics, statistics, or computer science can be advantageous, individuals from various educational backgrounds can acquire the necessary skills through relevant training and courses.

A graduation degree is often required for a data analyst position. Most employers prefer candidates with at least a bachelor's degree in a relevant field such as mathematics, statistics, computer science, economics, or business. However, some organizations may consider candidates with equivalent work experience or relevant certifications.

Yes, it is possible to enter the field of data analytics without prior experience. Many organizations offer entry-level positions or internships for individuals who are new to the field. Additionally, acquiring relevant certifications and completing data analytics projects or internships during your education can help you gain practical experience and increase your chances of starting a career in data analytics.

Yes, freshers can pursue a career as a data analyst. Many companies offer entry-level positions for recent graduates or individuals with limited work experience. By acquiring the necessary skills, completing internships or relevant projects, and demonstrating a strong aptitude for data analysis, freshers can establish themselves in the field of data analytics.

While having some prior experience or relevant internships can be beneficial, there are opportunities for individuals to secure data analyst positions without prior work experience. Entry-level roles or internships specifically designed for individuals with limited experience are available in the industry. Building a strong portfolio of data analysis projects and showcasing your skills and knowledge can also improve your chances of getting a data analyst job with no prior experience.

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FAQ’S OF DATA ANALYTICS TRAINING IN PANAJI

DataMites is a reputable institute renowned for its high-quality data analytics courses. Opting for DataMites in Panaji offers access to experienced trainers, a comprehensive curriculum, practical projects, and placement support. The institute provides flexible learning schedules, convenient location, and a supportive learning community, making it an excellent choice for data analytics training in Panaji.

The prerequisites for data analytics training in Panaji may vary depending on the specific course. However, having a basic understanding of mathematics, statistics, and computer applications can be beneficial for grasping the concepts effectively.

DataMites provides several compelling reasons to consider their Certified Data Analyst Training in Panaji. These include experienced faculty members, a comprehensive course curriculum, hands-on projects with real-world data, internship opportunities, placement assistance, flexible learning options, and a supportive learning community. DataMites also offers globally recognized certifications that enhance your resume.

The DataMites Certified Data Analyst Course in Panaji is open to individuals from diverse backgrounds, including graduates, working professionals, business analysts, IT professionals, and anyone interested in building a career in data analytics.

Depending on the course duration, mode of delivery, and additional services offered, the Data Analytics Course Fee at DataMites in Panaji can vary. Typically, the cost of certified data analyst training in Panaji falls between INR 28,178 and INR 76,000.

The DataMites Certified Data Analytics Course in Panaji has a duration of 4 months, comprising over 200 learning hours. This duration allows for comprehensive training, practical exercises, and project work.

The DataMites Certified Data Analyst Training in Panaji covers a wide range of topics, including data analysis techniques, statistical analysis, data visualization, data mining, machine learning, predictive analytics, and data-driven decision making. For a detailed curriculum, you can refer to DataMites' website or consult with their team during the counseling session.

Flexi-Pass in DataMites is a unique feature that provides learners with access to the course material and resources for 365 days from the date of enrollment. This allows learners to study at their own pace, review the content, and revisit the course material even after completing the training.

DataMites offers various payment methods to accommodate learners. These may include online payment options such as debit or credit cards, net banking, UPI, or other online payment gateways. They may also accept payment through bank transfers or demand drafts. DataMites will provide specific payment method details during the enrollment process.

Upon the successful completion of the Data Analytics training at DataMites, you will receive prestigious certifications from IABAC, NASSCOM FutureSkills Prime, and JainX. These internationally recognized certifications validate your expertise in data analytics, enhancing your career prospects and demonstrating your proficiency to potential employers.

Yes, DataMites provides support sessions for learners who require a deeper understanding of specific topics. You can reach out to their support team or faculty to schedule additional support sessions or seek clarification on any doubts or concepts you would like to explore further.

The specific documents required for the training session may vary depending on the institute's policies. However, it is advisable to carry a government-issued photo ID proof for identification purposes. It is recommended to contact DataMites directly to inquire about any specific documents or requirements for the training session.

DataMites offers various payment options for enrolling in their courses. These may include online payment methods such as debit or credit cards, net banking, UPI, or other online payment gateways. They may also accept payment through bank transfers or demand drafts. DataMites will provide specific payment options during the enrollment process.

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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