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  • 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

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  • 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

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  • Instructor-Led & Self-Paced training
  • Customized Learning Options
  • Industry Expert Trainers
  • Case Study Approach
  • Enterprise Grade Learning
  • 24*7 Cloud Lab

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UPCOMING DATA ANALYTICS COURSE ONLINE CLASSES

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 COURSES

Why DataMites Infographic

SYLLABUS OF DATA ANALYTICS COURSES

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

DATA ANALYTICS CAREER SUCCESS STORIES

DATA ANALYTICS COURSE REVIEWS

ABOUT DATA ANALYTICS COURSE

DataMites™ is the global institute for Data Analytics Courses, through a syllabus meticulously sketched out to prove 360-degree training. DataMites offers a range of flexible learning alternatives, including live online classes, high-quality recorded sessions, and classroom instruction. Our Data Analytics Certification Course is accredited by  IABAC, a European Union framework. Get hold of a first-rate training experience in a cost-efficient setting through DataMites.

DataMites Data Analytics Training is for 4 months time frame with both Data Analytics Online Training and Data Analytics Classroom Training that would be imparted within a three-phase learning method. 

Phase 1: Candidates will be given top-notch self-study videos and materials to aid them in completing the complete curriculum as they prepare ready for the forthcoming course.

Phase 2 is the primary live intensive training phase and it contains the IABAC Data Analytics Certification, a global certification, as well as practical capstone projects.

Projects, internships, and the Job Ready Program comprise Phase 3.

DataMites Data Analytics Certification modules are designed to provide a complete overview of the Data Analytics topic and its lifecycle. The Data Analytics Coursewares are expertly designed to help you become adept with Data Analytics tools and approaches. There are no prerequisites for studying DataMites Data Analytics Course Online

The course is full-fledged in itself thereby teaching the candidates everything from the ground up. The data analytics course fee is 538 USD in the US, the course fee for the data analytics course in the UK is 435.72 GBP and the data analytics course fee in India is 42,000 INR. 

You can receive the knowledge, self-assurance, and certifications necessary to start a career in data analytics by enrolling in the top data analytics program offered by DataMites - the 4-month Certified Data Analyst Training programme, which will lead you through the entire process.

Our Certified Data Analyst Course Fee is 900 USD with certifications from IABAC & JainX, however, with a 20% reduction, the Certified Data Analyst Training Fee is now only 790 USD. Although it is decreased to 467.31 GBP, the Certified Data Analyst Training Fee in the UK is 572.43 GBP. The cost of Certified Data Analyst Training in India is 55,000 INR, yet it can be had for 44,900 INR.

Moreover, According to a report by the US Bureau of Labour and Statistics, a median annual salary of 86,200 USD is received by a Data Analyst per annum. As per Payscale, a data analyst in the UK receives an average salary of £28060 a year.  And a data analyst's average salary in India is 4,64,926!  

According to Forbes Magazine, Data-driven companies are 23 times more likely to acquire customers than their peers. Analytics has advanced the business landscape in many ways, bestowing organizations with actionable insights that yield positive results. Data Analytics has proven to be indispensable in assisting businesses to optimize their performance. For businesses to achieve a strategic edge, Data Analytics performs a major role. 

According to Technavio's recent market study report, the potential growth difference in the data analytics industry would be USD 196.47 billion from 2021 to 2026. During the projected period, the market is expected to increase at a CAGR of 13.54 per cent, according to the research.

A well-designed data analytics course may set you on the road to becoming a professional data analyst in a variety of fields. Over the last decade, the need for data specialists has increased dramatically across sectors, and data analytics training is one of the best ways to keep up. Make no provision. Join Data Analytics Certification Training at DataMites right away!

It's not surprising that more people are thinking about pursuing a career in data because it offers promising employment options. However, we understand that changing careers isn't just about money and job stability; you also want to be sure you'll enjoy and be successful at what you're doing.

Data Analytics is a tool used for analysing raw data in order to make conclusions about the information that it contains. So, what does it take to succeed in this fast-growing field? What signs can you look for to see if it's the right career for you?

ABOUT DATAMITES DATA ANALYTICS COURSES

The term data analytics refers to the process of scrutinizing datasets to derive conclusions about the information they contain. Data analytic techniques enable you to take raw data and discover patterns to extract valuable insights from it.

Anyone who wants to learn more about data science and analytics is welcome to enrol in the course. A Bachelor's degree with at least 50% overall or an equivalent grade from a reputable university, ideally in the fields of science or computer science, is the minimal requirement for admission to a postgraduate Data Analytics study.

  • Data Analytics is a supreme skill for top organizations
  • Increasing job opportunities
  • Escalating pay for Data Analytics professionals
  • Big Data Analytics is in every corner
  • Innumerable career paths to choose from
  • You will be at the heart of decision-making in the company

 

The fee would differ from institute to institute and the level of training you are looking for. The Data Analytics training fee ranges from 403 USD to 1286.31 USD.

It can take anywhere from 6 weeks to two years to develop the abilities needed to be successful in data analytics. 4 months of training can be an excellent way to become well-versed in data analytics and be adept in it. The fact that there are many distinct paths to a career in data analytics explains the wide range.

Every industry needs data analysts, and they have a variety of job designations. Typical sectors include retail, healthcare, banking and finance, transportation, education, construction, and technology. You can be a Data Analytics, Data scientist, Business Intelligence Analyst, Data Engineer, Quantitative Analyst, Data Analytics Consultant, Operations Analyst, Marketing Analyst, Project Manager, IT Systems Analyst, and Transportation Logistics Specialist to name a few.

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 is USD 69,517 per year in the United States. (Glassdoor)
  • The national average salary for a Data Analyst is £36,535 per annum in the UK.  (Glassdoor)
  • The national average salary for a Data Analyst is INR 6,00,000 per year in India. (Glassdoor)
  • The national average salary for a Data Analyst is C$58,843 per year in Canada. (Payscale)
  • The national average salary for a Data Analyst is AUD 85,000 per year in Australia. (Glassdoor)
  • The national average salary for a Data Analyst is 46,328 EUR per annum in Germany. (Payscale)
  • The national average salary for a Data Analyst is CHF 95,626 per year in Switzerland. (Glassdoor)
  • The national average salary for a Data Analyst is AED 106,940 per year in UAE. (Payscale)
  • The national average salary for a Data Analyst is SAR 95,960 per year in Saudi Arabia. (Payscale.com)
  • The national average salary for a Data Analyst is ZAR 286,090 per year in South Africa. (Payscale.com)

DataMites is the best institute for you if you are willing to make a career out of the analytics domain. The course curriculum is aptly sketched out and the lead mentors are industry-oriented with expert knowledge. We offer hands-on practice with projects and internship opportunities!

There are no prerequisites as such for DataMites Data Analytics Training as the syllabus is sketched out to train candidates from level 1, however, having prior knowledge of programming language, databases, data structures, mathematics and algorithms will only be an added advantage. 

 

The greatest credential in data analytics is the Certified Data Analyst course, which certifies your ability to confidently assess data using a number of technologies. Certification demonstrates your proficiency in handling data, conducting exploratory research, comprehending the fundamentals of analytics, and visualizing, presenting, and elaborating on your findings. The DataMites CDA Course is recognized by IABAC, and the prestigious Jain University.

 

DataMites data analyst certification training is your best bet in the domain. Our data analytics training gives you concrete evidence that you are qualified to assist businesses, even well-known multinationals, in deciphering the meaning of the data at hand. In contrast to a data analytics certificate, it is proof that you are qualified to perform the duties of a certain job role in accordance with industry standards.

 

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FAQ’S OF DATA ANALYTICS TRAINING COURSE

The DataMites Data Analytics Training is meticulously designed and organised in accordance with the fact that the entire domain is expertly explained to newcomers to the field. With that said, you can enlist without a second thought if learning analytics interests you.

DataMites™ is the global institute for Data Science accredited by the International Association of Business Analytics Certifications (IABAC). 

  • Trained over 50,000 candidates
  • 3 Phase learning method - scrupulously designed to impart the best possible training. 
  • Engage in real-world projects and highly valuable case studies. 
  • Get IABAC Data Analytics Certification which is a global certification.
  • Chance to do an internship with an AI company Rubixe, which is a global technology company.

At DataMites the fee for Data Analytics Training will be approximately 538 USD in the US, 501.84 Euro in the European Countries and 42,000 INR in India.

At DataMites you will have data analytics training for 4 months.

If you're interested in a career as a data analyst, you should definitely complete the DataMites Certified Data Analyst Training. Our programme guarantees to provide the information, assurance, and credentials required to launch a data analyst profession from scratch.

The IABAC and JainX international recognised authorities, whose credentials you would acquire following the course completion, have accredited the Certified Data Analyst curriculum, one of the best data analytics programmes provided by DataMites. The DataMites Certified Data Analyst credential is the best approach to launch a career in data analytics.

DataMites offers flexible learning methods for candidates, ranging from online data analyst training to highly interactive classroom training in data analytics. The choice is all yours.

We are adamant about providing you with instructors who are certified and highly qualified with decades of experience in the industry and well versed in the subject matter.

Our Flexi-Pass for Data Analytics Training grants candidates to attend sessions from Datamites for a period of 3 months related to any query or revision you wish to clear.

 

We will issue you with IABAC® certification that would provide global recognition of the relevant skills.

 

Indeed, DataMites Data Analytics Training does come with the course completion certificate which will be handed to you after completion of the course.

You don't have to stress about it. To arrange a class that fits your schedule, simply contact your trainers. For online training, each session will be recorded and uploaded so you can simply catch up on anything you skipped at your own speed and comfort.

You can make payments to us through Cash, Credit Card, PayPal, Visa, Master Card, American Express, Net Banking, Cheque and Debit 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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