DATA ANALYTICS CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYTICS LEAD MENTORS

DATA ANALYTICS COURSE FEE IN AMRITSAR

Live Virtual

Instructor Led Live Online

110,000
62,423

  • 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
35,773

  • 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
67,548

  • 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 AMRITSAR

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 AMRITSAR

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: 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
• 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

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

OFFERED DATA ANALYTICS COURSES IN AMRITSAR

DATA ANALYTICS TRAINING REVIEWS

ABOUT DATA ANALYTICS TRAINING IN AMRITSAR

Data analytics is not just a buzzword; it's an exhilarating journey into the realm of data, where hidden insights and patterns hold the key to unlocking success. Imagine being able to predict customer behavior, optimize processes, and make data-driven decisions that propel businesses to new heights. Data analytics is the superhero that empowers organizations across industries to harness the power of data and transform it into actionable intelligence, driving innovation and staying ahead of the competition. According to IBM, job opportunities in data analytics are projected to grow by 15% over the next few years. The demand for skilled data analysts is consistently rising across industries, making it a lucrative career choice.

In the vibrant city of Amritsar, DataMites Institute stands as a leading provider of data analytics training in Amritsar. Their Data Analytics Course in Amritsar is designed to equip learners with the essential skills and knowledge required to thrive in the field. The Certified Data Analyst Course is a comprehensive program spanning over 4 months, encompassing more than 200 hours of rigorous learning. Covering topics such as statistical analysis, data visualization, machine learning, and predictive modeling, the course ensures a deep understanding of data analytics concepts.

What sets the Certified Data Analyst Training apart is the emphasis on practical application. Students engage in 10 Capstone Projects and 1 Client Project, where they tackle real-world data analytics challenges and develop tangible solutions. This hands-on experience enables learners to gain confidence and expertise in applying their knowledge to real-life scenarios. DataMites offers Data Analytics Offline Courses On Demand in Amritsar, providing learners with the flexibility to access course materials and resources at their convenience.

Here are ten reasons why DataMites stands out as a premier choice for Data Analytics Courses in Amritsar:

Ashok Veda and Faculty: DataMites boasts a team of experienced trainers, including industry expert Ashok Veda, who provide expert guidance and mentorship throughout the course.

Comprehensive Course Curriculum: The course curriculum covers a wide range of topics, ensuring learners gain a deep understanding of data analytics techniques, tools, and methodologies.

Global Certification: DataMites offers globally recognized certifications from reputable bodies like IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing the credibility of learners' skills.

Flexible Learning: Learners can choose from various learning modes, including data analytics classroom training in Amritsar, data analytics training online in Amritsar, or a blended learning approach, catering to different preferences and schedules.

Projects with Real-World Data: DataMites emphasizes practical learning by incorporating real-world projects, enabling learners to apply their skills to real-life scenarios.

Internship Opportunity: The institute provides data analytics internship opportunities, allowing learners to gain hands-on experience and further enhance their practical skills.

Placement Assistance and Job References: DataMites offers data analytics courses with placement assistance and job references, connecting learners with potential employers and career opportunities in the field of data analytics.

Hardcopy Learning Materials and Books: Learners receive hardcopy learning materials and books, providing valuable resources to support their learning journey.

DataMites Exclusive Learning Community: Learners become part of DataMites' exclusive learning community, where they can engage with peers, share insights, and collaborate on projects.

Affordable Pricing and Scholarships: DataMites offers competitive pricing for its courses, making quality data analytics training accessible. Scholarships may also be available for eligible candidates.

Amritsar, known for its rich cultural heritage and historical significance, is a vibrant city in the state of Punjab, India. It is home to iconic landmarks like the Golden Temple and attracts tourists from around the world. Amritsar also boasts a thriving educational ecosystem, with renowned institutions and a growing emphasis on skill development. When it comes to data analytics certification in Amritsar, DataMites stands out as a trusted and reputable provider. By choosing DataMites for data analytics training in Amritsar, learners have the opportunity to benefit from the city's dynamic environment while acquiring in-demand skills.

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

ABOUT DATA ANALYTICS COURSE IN AMRITSAR

Data Analytics refers to the process of analyzing and transforming raw data to extract valuable insights and make informed decisions. It involves using techniques and tools to examine large amounts of data and identify patterns, trends, and correlations.

Data Analytics encompasses descriptive, diagnostic, predictive, and prescriptive analytics. It helps organizations make data-driven decisions, improve efficiency, enhance customer experiences, and gain a competitive advantage.

Data Analytics is used in various industries such as finance, healthcare, retail, telecommunications, manufacturing, marketing, government, energy, sports, transportation, and logistics.

Data Analytics offers promising career opportunities in roles such as data analysts, data scientists, business analysts, and data engineers. Skilled professionals are in high demand across diverse industries.

The salary of a data analyst in Amritsar depends on factors like experience, skills, industry, and company size. On average, a data analyst in Amritsar earns 3,81,223 lakhs per year.

The average salary of a Data Analyst varies globally. Here are approximate figures from different countries: UK (£36,535), Canada (C$58,843), US (USD 69,517), India (INR 6,00,000), Australia (AUD 85,000), Switzerland (CHF 95,626), UAE (AED 106,940), South Africa (ZAR 286,090), Saudi Arabia (SAR 95,960), Germany (46,328 EUR).

The fee for a Data Analytics Course varies based on factors such as institute, duration, curriculum, and mode of delivery. Generally, it ranges from INR 40,000 to INR 80,000 or more.

DataMites is widely regarded as an excellent institute for learning Data Analytics. They offer comprehensive courses and training programs in various locations.

The monthly salary of an entry-level Data Analyst in India varies based on location, company size, industry, and skills. On average, it is around ₹1.6 Lakhs per year, approximately ₹13.3k per month.

The "Certified Data Analyst" course at DataMites is highly recommended for those pursuing a career in Data Analytics. It covers essential subjects like data analysis techniques, statistical analysis, data visualization, and machine learning.

Yes, coding is often required for a data analyst career. Proficiency in programming languages like Python, R, SQL, and SAS is beneficial for data manipulation, analysis, and developing automated processes.

Being a data analyst can be challenging as it involves working with complex datasets, applying analytical techniques, and staying updated with emerging technologies. Strong analytical and problem-solving skills are essential.

Yes, Data Analytics offers a good career option for freshers. The demand for skilled data analysts is increasing, providing opportunities to work with diverse datasets and contribute to impactful projects.

While not always mandatory, a graduation degree is typically preferred for becoming a data analyst. However, relevant certifications, practical experience, and strong analytical skills can also lead to a career in data analytics.

Forge a career as a data analyst by pursuing a bachelor's degree in computer science, statistics, or a related field. Develop a strong foundation in programming languages like Python and R, and master data manipulation, analysis, and visualization techniques. Gain practical experience through internships or freelance projects. Continuously enhance your skills through online courses and certifications. Network with industry professionals through events and online platforms. Proactively search for data analyst job opportunities and showcase your portfolio of projects.

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FAQ’S OF DATA ANALYTICS TRAINING IN AMRITSAR

DataMites is the preferred choice for Data Analytics Courses in Amritsar due to its industry relevance, comprehensive curriculum, experienced trainers, and practical learning approach. They emphasize hands-on experience and real-world projects, enhancing the overall learning experience.

DataMites is recommended for Certified Data Analyst Training in Amritsar due to its reputation for high-quality training, globally recognized certifications, and practical industry-focused skills. Their experienced trainers create a supportive learning environment.

Prerequisites for data analytics training in Amritsar may vary based on the course. However, having a basic understanding of mathematics, statistics, and computer usage is beneficial.

The DataMites Certified Data Analyst Course in Amritsar is open to aspiring data analysts, working professionals seeking to enhance their skills, graduates, and individuals interested in data analysis and its applications.

The cost of the Data Analytics Course in Amritsar at DataMites varies based on course duration, delivery mode, and additional services. The fee for the certified data analyst training can range from INR 28,178 to INR 76,000, depending on course details.

The DataMites Certified Data Analytics Course in Amritsar is designed to be completed within 4 months, involving over 200 learning hours. This comprehensive training includes practical exercises and hands-on projects.

The Flexi-Pass from DataMites allows learners to access multiple courses at a discounted price, offering flexibility in choosing different courses according to individual learning preferences and needs.

The DataMites Certified Data Analyst Training in Amritsar covers data analysis techniques, statistical analysis, data visualization, machine learning, predictive analytics, and data mining, providing a comprehensive understanding of data analytics principles.

Yes, upon successful completion of Data Analytics training at DataMites, learners receive globally recognized certifications from IABAC, NASSCOM FutureSkills Prime, and JainX, validating their expertise in data analytics.

DataMites accepts various payment methods, including online payment gateways, bank transfers, and other convenient options to ensure a smooth payment process for learners.

Yes, DataMites conducts ON DEMAND classroom training for data analytics in Amritsar, providing interactive and instructor-led sessions for effective learning.

Trainers responsible for conducting Data Analytics Courses at DataMites are experienced professionals with industry knowledge and practical expertise, ensuring high-quality training delivery.

DataMites may offer trial classes or demo sessions for prospective learners to experience the training and teaching methodology before finalizing the fee payment.

DataMites offers various training options, including classroom training, online training, corporate training, self-paced learning, and blended learning programs, catering to different learning preferences and schedules.

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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