DATA ANALYTICS CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYTICS LEAD MENTORS

DATA ANALYTICS COURSE FEE IN DEHRADUN

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 DEHRADUN

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 DEHRADUN

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 DEHRADUN

DATA ANALYTICS TRAINING REVIEWS

ABOUT DATA ANALYTICS TRAINING IN DEHRADUN

Data analytics is the modern-day superpower that enables organizations to unlock the hidden potential of data. Did you know that data analytics can increase customer satisfaction rates by 10% and reduce customer churn by 15%? By delving into vast datasets, data analysts uncover valuable insights about customer preferences, needs, and sentiments. They help businesses personalize their offerings, optimize marketing campaigns, and deliver exceptional customer experiences. Data analytics empowers organizations to make strategic decisions that drive customer loyalty and business success.

DataMites Institute presents an immersive Data Analytics Course in Dehradun, designed to equip students with essential skills in data analysis. The Certified Data Analyst Training program offered by DataMites spans across 4 months, providing over 200 hours of comprehensive learning. Through this program, students will gain expertise in statistical analysis, data visualization, machine learning, and predictive modeling. By investing an average of 20 hours per week, students can ensure a solid understanding of the course content. Notably, the course integrates 10 Capstone Projects and 1 Client Project, enabling students to apply their knowledge to real-world data analytics scenarios and deliver practical solutions.

Here are the reasons to choose DataMites for Data Analytics Training in Dehradun:

  • Expert Faculty: DataMites boasts a team of experienced instructors, including industry experts like Ashok Veda, who bring their real-world insights and expertise to the classroom.

  • Comprehensive Course Curriculum: The curriculum is carefully designed to cover all the essential concepts and techniques of data analytics, ensuring a well-rounded learning experience.

  • Global Certification: DataMites offers globally recognized certifications such as IABAC, NASSCOM FutureSkills Prime, and JainX, validating your skills and enhancing your career prospects.

  • Flexible Learning Options: DataMites provides flexible learning options, including both ON DEMAND data analytics classroom courses in Dehradun and online data analytics training in Dehradun, allowing students to choose the mode of learning that suits them best.

  • Real-World Projects: Students get the opportunity to work on projects with real-world data, enabling them to apply their skills and gain hands-on experience in solving data analytics challenges.

  • Internship Opportunity: DataMites offers data analytics internship opportunities, allowing students to gain practical exposure and work on real-life data analytics projects under the guidance of industry professionals.

  • Placement Assistance and Job References: DataMites provides data analytics course with placement assistance to help students kickstart their careers in data analytics, including job references and networking opportunities with industry professionals.

  • Hardcopy Learning Materials and Books: Students receive comprehensive learning materials and books in hardcopy format, serving as valuable resources throughout their data analytics journey.

  • DataMites Exclusive Learning Community: Students become part of the DataMites exclusive learning community, where they can collaborate, exchange ideas, and network with fellow data enthusiasts.

  • Affordable Pricing and Scholarships: DataMites offers competitive pricing for its courses and also provides scholarships to deserving candidates, making quality data analytics training accessible to a wider audience.

Nestled in the picturesque Doon Valley, Dehradun is the capital city of Uttarakhand, a state in northern India known for its scenic beauty and tranquil environment. Surrounded by the majestic Himalayan ranges, Dehradun offers a serene and conducive setting for pursuing a data analytics certification. The city is renowned for its pleasant climate, lush greenery, and proximity to popular hill stations like Mussoorie, making it an ideal location for both learning and rejuvenation.

Dehradun is home to prestigious educational institutions, including renowned engineering and management colleges, research centers, and technology hubs. The city's focus on quality education and its status as an emerging educational hub make it an excellent choice for individuals seeking advanced analytics training. By pursuing a data analytics certification in Dehradun, you gain access to a diverse learning ecosystem that fosters innovation, collaboration, and academic excellence.

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

ABOUT DATA ANALYTICS COURSE IN DEHRADUN

Data Analytics is the process of gathering, organizing, analyzing, and interpreting large datasets to discover patterns, trends, and insights that can guide decision-making and drive business improvements. It involves utilizing statistical and quantitative techniques, as well as various tools and technologies, to extract valuable information from data.

Data Analytics is employed across a wide range of industries, including finance and banking, healthcare and pharmaceuticals, retail and e-commerce, manufacturing and logistics, telecommunications, marketing and advertising, energy and utilities, government and public sector, and sports and entertainment.

Studying Data Analytics offers numerous benefits, including improved decision-making, enhanced efficiency and productivity, gaining a competitive edge, better understanding of customers, and access to diverse career opportunities.

To excel in Data Analytics, it is important to have proficiency in programming languages like Python, R, or SQL, strong analytical and problem-solving skills, knowledge of statistical analysis and data visualization techniques, familiarity with database management systems, understanding of machine learning and predictive modeling, ability to work with large datasets and perform data manipulation, and effective communication and storytelling skills.

A career in Data Analytics is open to individuals from diverse educational backgrounds such as mathematics, statistics, computer science, engineering, economics, business, and related fields. Passion for data analysis, problem-solving, and critical thinking is also valuable for entering this field.

The scope of Data Analytics is extensive and rapidly expanding. It encompasses areas such as data mining, data visualization, predictive modeling, machine learning, and artificial intelligence.

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

The average salary of a Data Analyst in Dehradun is ₹19,606 per month according to Indeed.

In Dehradun, the cost of a Data Analytics Course can range from 40,000 to 80,000 INR, depending on the institute and the specific features and duration of the training program. It is advisable to research and compare different options to find the one that best fits your budget and learning requirements.

While a mathematics background can be advantageous for understanding certain concepts in data analytics, it is not always a mandatory requirement. Data analytics involves a combination of skills from various disciplines, including mathematics, statistics, programming, and business. Individuals with a strong aptitude for logical thinking and problem-solving can still pursue a career in data analytics, even without an extensive mathematics background.

Data Analytics presents promising career prospects, with job opportunities available in technology companies, consulting firms, financial institutions, healthcare organizations, e-commerce companies, and government agencies. Job titles may include Data Analyst, Data Scientist, Business Intelligence Analyst, Data Engineer, Machine Learning Engineer, and Data Consultant, among others.

The difficulty level of a Data Analytics course can vary depending on the curriculum, the depth of the topics covered, and the individual's prior knowledge and aptitude. Data Analytics does involve complex concepts and requires analytical thinking and technical skills. However, with dedication, practice, and proper guidance, it is possible to grasp the concepts and excel in the field.

DataMites is the recommended institute for studying data analytics. They offer comprehensive data analytics courses that cover a wide range of topics and provide hands-on practical experience. With experienced faculty members, a strong industry reputation, and a robust alumni network, DataMites is known for delivering high-quality training in data analytics. Additionally, DataMites provides placement assistance and has a track record of helping students secure rewarding career opportunities in the field of data analytics. It is advisable to explore the offerings of DataMites and consider it as the preferred institute for learning data analytics.

A career in data analytics typically requires a bachelor's degree in a relevant field such as mathematics, statistics, computer science, engineering, economics, or business. However, specific requirements may vary based on the job position and company. Some roles may require advanced degrees or certifications in data analytics or related fields. Continuous learning and upskilling are also crucial to stay updated with the evolving tools and techniques in data analytics.

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FAQ’S OF DATA ANALYTICS TRAINING IN DEHRADUN

DataMites is the preferred choice for Data Analytics Courses in Dehradun due to several reasons. They have expert faculty members with deep knowledge and experience in the field, providing comprehensive training. Their curriculum covers all essential topics and skills required for data analytics. Practical learning is emphasized through real-world projects, and industry-recognized certifications are awarded upon completion. DataMites offers placement support, flexible learning options, and affordable pricing, making it accessible to a wide range of individuals.

The DataMites Certified Data Analyst Course in Dehradun is open to individuals from diverse backgrounds, including fresh graduates, working professionals, and anyone interested in pursuing a career in data analytics. There are no specific eligibility criteria, making it accessible to a wide range of learners.

The fee for the Data Analytics Course at DataMites in Dehradun can vary based on factors like course duration, mode of delivery, and additional services included. Generally, the certified data analyst training fee in Dehradun ranges from INR 28,178 to INR 76,000.

The DataMites Certified Data Analytics Course in Dehradun has a duration of 4 months, providing over 200 learning hours. This extensive timeframe allows for comprehensive training, hands-on practical exercises, and project work.

The DataMites Certified Data Analyst Training in Dehradun covers a comprehensive range of topics, including data preprocessing, data visualization, statistical analysis, predictive modeling, machine learning, data mining, and database management systems. The exact curriculum may vary based on the course level and specialization.

DataMites offers multiple payment methods, including online payment through debit or credit cards, net banking, and other digital payment platforms. Offline modes like demand drafts or bank transfers may also be available.

Yes, DataMites provides support sessions to participants who require additional assistance or clarification on the topics covered in the Data Analytics training. These sessions aim to ensure a thorough understanding of the subject matter and address specific queries or challenges.

Yes, DataMites provides certifications from IABAC, NASSCOM FutureSkills Prime, and JainX upon successful completion of the Data Analytics training. These certifications are globally recognized and enhance career prospects.

Participants may be required to bring a valid ID proof (like Aadhaar card, passport, or driver's license) for verification purposes. It is advisable to check with DataMites or refer to specific instructions provided by them regarding any required documents.

Flexi-Pass is a unique feature offered by DataMites that provides students with flexibility in scheduling their training sessions. It allows learners to choose their preferred timing and attend the training at their convenience, accommodating their professional and personal commitments.

Yes, DataMites offers on-demand classroom training for Data Analytics in Dehradun. They provide interactive and instructor-led sessions in a traditional classroom setting, facilitating direct interaction with faculty and fellow participants.

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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