DATA ANALYTICS CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYTICS LEAD MENTORS

DATA ANALYTICS COURSE FEE IN NOIDA

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 NOIDA

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 NOIDA

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 NOIDA

DATA ANALYTICS TRAINING REVIEWS

ABOUT DATA ANALYTICS TRAINING IN NOIDA

Is data analytics an area that will allow you to use your inherent abilities while also allowing you to feel fulfilled at the end of each day? DataMitesTM is a global institute with a pre-planned, in-depth curriculum for data analytics programmes. Join DataMites to get sharp-end data analytics training in Noida for affordable rates. Since the programme is so long, candidates are trained from the very outset. Since the course is comprehensive on its own, applicants are taught practically everything from the ground - up. In Noida, the cost of the data analytics course is 42,000 INR. 

If you want to work as a data analyst, you may have wondered if you should obtain a data analytics certification in Noida or just add relevant data abilities to your CV. At DataMites, we offer the best data analytics course in Noida - Certified Data Analyst. With the knowledge and instruction needed to use contemporary analytics technologies, DataMites data analyst's certification in Noida proves your competence. Normally 55,000 INR, our certified data analyst training in Noida is really only 44,900 INR. There are certificates from IABAC and JainX.

The three-phase cognitive learning is used during the four-month-long DataMites Data Analytics Certification Courses in Noida, which are divided into classroom and online sessions.

The first step, also known as Phase 1, entails giving students access to videos and other resources for independent study in addition to the requisite study materials.

The interactive capstone project, the prestigious IABAC Data Analytics Credential, and online data analytics training are all included in Phase 2.

Phase 3 is characterised by projects, internships, and the Job Ready Program.

Uttar Pradesh, the most northern state of India, contains the planned city of Noida. The "Best City Awards" presented by ABP News in 2015 recognised Noida as the Best City in Uttar Pradesh and the Best City for Housing in India. Presently, 77% of top companies believe that data analytics is an essential component of company performance. This shows that big data specialists have a tremendous influence on business strategy and marketing strategies. 

As per Glassdoor, a data analyst's average salary in Noida is 5,33,810 and the data analyst in Noida earns an average amount of 4,18,379 LPA (Payscale)! Gift yourself our certified Data Analytics training in Noida!

Along with the data analytics courses, DataMites also provides machine learning, data engineer, python training, deep learning, tableau, data science, r programming, and artificial intelligence courses in Noida.

ABOUT DATA ANALYTICS COURSE IN NOIDA

Getting insights from data is the goal of the field of data analytics. It includes the methods, equipment, and instruments used in data management and analysis, as well as the procedures for gathering, arranging, and storing data. Data analytics' primary goal is to use statistical analysis and technology to look for patterns and address issues in data.

Both business analysts and data analysts support data-driven decision-making inside their businesses. Business analysts are typically more active in addressing business issues and making recommendations while data analysts typically work more directly with the data itself. Both positions are in high demand and can pay well.

The simplest response is that anyone who is willing to learn data analytics, whether they are seasoned professionals or total amateurs, should do so. Engineers, software developers, IT professionals, and marketers may all enrol in a  Data Analytics Course in Noida.

Possibly not. It is always preferable to have knowledge of SQL, Excel, and Python. However, you may undoubtedly get better by starting with the fundamentals.

A great profession in data analytics exists. To put it simply, there has never been a better time to work with data. Data is generated at a rate of 2.5 quintillion bytes per day, and this rate is only increasing. The price would change depending on the type of instruction you want. From 30,000 to 1,000,000 Indian rupees are charged for the Data Analytics Training in Noida.

A degree is typically not required for a position as a data analyst, but getting the right certification from an accredited provider is crucial. It could take anywhere from six weeks to two years to learn the skills needed for success in data analytics. DataMites 4-month Data Analytics Training Programme in Noida is an efficient way to learn about and master data analytics. Because there are so many different paths one might take to become a data analytics specialist, the variability is explained by this.

Given the high need for data analytics, the employment prognosis for data analysts is excellent. Before the end of 2020, IBM predicted that there would be 2,720,000 more employment for data professionals in the U.S. alone.

  • Data Analyst Consultant

  • Business Intelligence Analyst

  • Data Analyst

  • Operations Analyst

  • Marketing Analyst

  • Quantitative Analyst

  • Data Scientist

  • Data Engineer

  • Project Manager

  • IT Systems Analyst

It would be beneficial to learn data analytics if you had technical abilities like data analysis, statistical knowledge, data narrative, communication, and problem-solving. Data analysts who frequently collaborate with business stakeholders are said to benefit from having strong business intuition and strategic thinking.

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

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

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

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

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

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

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

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

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

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

Competent data analysts are among the most in-demand professionals globally. Due to the great demand for their services and the relatively small pool of skilled candidates, data analysts enjoy excellent salaries and perks even at the entry-level. As per Glassdoor, a data analyst's salary in Noida is 5,33,810 and the data analyst in Noida earns an average amount of 4,18,379 LPA (Payscale)!

Since there is a growing need for data specialists but a limited supply, there are many excellent work prospects in this industry. If you want to pursue a career in data analytics, DataMites is the finest institute for you. The primary mentors are knowledgeable and industry-oriented, and the course curriculum is skillfully laid out. With projects and internship opportunities, we provide practical training!

The concept of data analytics has become more well-known in recent years as a result of the rise in data generation. Since the DataMites Data Analytics Course in Noida is intended to train candidates beginning at level 1, the knowledge base of programming languages, databases, data structures, mathematics, and algorithms is simply helpful. However, there aren't any formal qualifications for the course.

The top qualification in data analytics is Certified Data Analyst, which attests to your competence in confidently evaluating data utilising a range of technologies. A Certified Data Analyst credential demonstrates your proficiency in handling data, conducting exploratory research, comprehending the fundamentals of analytics, and visualising, presenting, and elaborating on your findings. The DataMites Certified Data Analyst Course in Noida is recognised by both IABAC and the renowned Jain University.

Your greatest option in the field is the DataMites data analyst certification course in Noida. Our data analytics course in Noida provides you with substantial proof that you are qualified to help companies, including well-known multinationals, interpret the data at hand. It is evidence that you are qualified to carry out the responsibilities of a particular employment role in conformance with industry standards, as opposed to a data analytics certificate.

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FAQ’S OF DATA ANALYTICS TRAINING IN NOIDA

Selecting a certain ability you wish to develop professionally is the greatest method to succeed in this field of employment. Making the most of the resources at your disposal to learn data analysis is your best bet.

Freshmen and students alike are welcome to enrol in the course. The best career path for you to take if you want to go from an IT to a business profile is to become a data analyst. You will be well-positioned to succeed in this sector if you have strong coding and IT skills. DataMites Data Analytics Certification Courses in Noida is open to individuals who do not work in information technology, including those in the human resources, banking, marketing, and sales sectors.

It's both challenging and lucrative to work in data analytics. Finding employment in this sector is difficult, and success in it requires a great deal of perseverance. Data analysts do not emerge out of thin air. Particularly if you're hoping to begin a career in data science as a novice, DataMites gives you the knowledge, experience, and understanding of the ideas.

  • The International Association of Business Analytics Certifications has approved DataMitesTM, a global institute for data science (IABAC).

  • Trained more than 50,000 candidates

  • To provide the finest instruction possible, the three-phase learning technique was meticulously planned.

  • Participate in worthwhile case studies and real-world projects.

  • Obtain the global IABAC and JainX Data Analytics Certification.

  • Assistance with internships and employment

For its data analytics training programmes, DataMites charges roughly 42,000 Indian rupees in Noida.

The sky is the limit for a data analyst who possesses the necessary quantity of experience on your part and the appropriate data analytics training. DataMites offers four-month long data analytics courses.

You can choose from a variety of flexible learning choices at DataMites, including live online classes, self-study courses, and classroom training in data analytics. Each training session is specifically designed to help participants become experts in their chosen field.

You should absolutely finish the DataMites Certified Data Analyst Training if you're thinking about a profession in data analysis. Our curriculum promises to offer the knowledge, assurance, and qualifications necessary to start a data analysis career from zero.

One of the top data analytics programmes offered by DataMites is the Certified Data Analyst curriculum, which has been accredited by the IABAC and JainX extremely prominent agencies, whose credentials you would receive after completing the course. The best way to begin a data analytics career is to obtain the DataMites Certified Data Analyst certification.

Candidates may participate in Datamites sessions for a three-month period regarding any question or revision they wish to clear with our Flexi-Pass for Data Analytics Certification Training in Noida.

Once you have been validated by IABAC and Jain University, you will obtain an IABAC® certification and a JainX certification, opening the door for your future job in the industry and ensuring that your skills are recognised globally.

A three-phase learning process is offered by DataMites. Candidates will be given books and self-study materials to use throughout Phase 1 to assist them to learn everything there is to know about the programme. The main part of the intensive live online training is Phase 2, and it culminates in the awarding of the IABAC Data Analytics Certification, a universal credential. Additionally, we will assign tasks and placements during the third phase.

Yes, we offer free trial sessions to give potential students a broad idea of what the upcoming course would entail. You are more than welcome to participate in these sessions to acquire a sense of the programme before deciding whether to continue with it or not.

Bring your photo identification with you when you register for the certification exams and when we issue you a participation certificate, such as a national ID card and a driver's licence.

We take payments via; 

  • Cash

  • Credit Card

  • PayPal

  • American Express

  • Net Banking

  • Cheque

  • Debit Card

  • Visa

  • Master Card

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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