DATA ANALYTICS CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYTICS LEAD MENTORS

DATA ANALYTICS COURSE FEE IN GANDHINAGAR

Live Virtual

Instructor Led Live Online

110,000
62,423

  • IABAC® 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® 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® 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 GANDHINAGAR

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 GANDHINAGAR

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 GANDHINAGAR

DATA ANALYTICS TRAINING REVIEWS

ABOUT DATA ANALYTICS TRAINING IN GANDHINAGAR

Data analytics is a transformative force that holds the key to unlocking valuable insights from the vast sea of data surrounding us. Every minute, approximately 188 million emails are sent, 18 million texts are exchanged, and 4.5 million videos are watched on YouTube. In this data-driven world, the ability to analyze and derive meaning from such massive volumes of information is crucial. It is estimated that by 2026, the global data analytics market will reach a value of $132.9 billion, a testament to its growing importance and impact across industries. (Market Research Future)

DataMites Institute, a renowned training provider in the field of data analytics, offers an extensive Data Analytics Course in Gandhinagar. The Certified Data Analyst Training program provided by DataMites Institute is a comprehensive 4-month course with over 200 hours of immersive learning. Covering essential topics like statistical analysis, data visualization, machine learning, and predictive modeling, students can expect to dedicate around 20 hours per week to their studies, ensuring a deep understanding of the subject matter. What sets this course apart is the incorporation of 10 Capstone Projects and 1 Client Project, allowing students to apply their knowledge to real-world scenarios and develop practical solutions for data analytics challenges.

When it comes to choosing DataMites for Data Analytics Training in Gandhinagar, there are the compelling reasons that set it apart. 

  • The institute boasts a team of industry-seasoned faculty members, including renowned expert Ashok Veda, who bring a wealth of experience and expertise to the program. 

  • The course curriculum is thoughtfully crafted, covering essential topics and the latest tools and techniques in data analytics. Successful completion of the program earns students prestigious global certifications such as IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing their professional credibility.

  • DataMites believes in flexible learning including online data analytics courses in Gandhinagar and ON DEMAND data analytics offline training in Gandhinagar, allowing students to tailor their learning schedule to fit their busy lives. 

  • The program emphasizes hands-on learning, with real-world projects that enable students to apply their knowledge and skills to practical scenarios. Data Analytics Internship opportunities further enhance students' practical experience, while data analytics courses with placement assistance and job references help connect them with exciting career opportunities in the field of data analytics.

  • To support the learning journey, DataMites provides students with hardcopy learning materials and books, augmenting their access to online resources. Students also become part of the DataMites Exclusive Learning Community, where they can engage with like-minded individuals, collaborate on projects, and foster a supportive network.

  • DataMites is committed to making data analytics training affordable for everyone, offering competitive pricing options and scholarships to eligible students.

In Gandhinagar, a data analytics certification from DataMites sets individuals on a path to exciting career prospects. The data analytics certification in Gandhinagar equips them with the skills and expertise to harness the power of data and contribute to the digital transformation of businesses and industries. With Gandhinagar's growing focus on technology and innovation, the demand for skilled data analysts is on the rise, making it an opportune time to embark on a data analytics journey with DataMites.

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

ABOUT DATA ANALYTICS COURSE IN GANDHINAGAR

Data Analytics refers to the process of examining, transforming, and modeling raw data to extract meaningful insights and make informed decisions. It involves using various techniques, tools, and algorithms to analyze large volumes of data and uncover patterns, trends, and correlations that can drive business strategies and improve decision-making.

Data Analytics is utilized in a wide range of industries, including but not limited to:

  • Finance and banking

  • Healthcare and pharmaceuticals

  • Retail and e-commerce

  • Telecommunications

  • Manufacturing and supply chain

  • Marketing and advertising

  • Government and public sector

  • Energy and utilities

  • Sports analytics

  • Transportation and logistics

The scope of Data Analytics is vast and continues to expand with the increasing availability of data and advancements in technology. It encompasses various domains such as descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Data Analytics professionals are in high demand to help organizations make data-driven decisions, improve operational efficiency, enhance customer experiences, and gain a competitive advantage.

The field of Data Analytics offers promising career prospects. Data analysts, data scientists, business analysts, and data engineers are among the popular job roles in this domain. With the growing adoption of data-driven strategies, the demand for skilled professionals in Data Analytics is expected to rise. Career opportunities exist in diverse industries and sectors, providing individuals with the chance to work on exciting projects and contribute to business growth.

  • 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 C$58,843 per year in Canada. (Payscale)

  • 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 INR 6,00,000 per year in India. (Glassdoor)

  • 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 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 ZAR 286,090 per year in South Africa. (Payscale.com)

  • 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 46,328 EUR per annum in Germany. (Payscale)

The salary of a data analyst in Gandhinagar, like in any other location, can vary based on factors such as experience, skills, industry, and company size. On average, a data analyst in Gandhinagar earns 3,81,223 lakhs per year. (Indeed) However, it's important to note that salaries can vary and may be higher or lower depending on individual circumstances and market conditions.

The "Certified Data Analyst" course at DataMites is highly recommended for those looking to venture into the field of data analytics. This well-structured course encompasses essential subjects such as data analysis techniques, statistical analysis, data visualization, and machine learning. By completing this program, participants gain the necessary expertise and proficiency to efficiently work with data and generate valuable insights.

The fee for a Data Analytics Course can vary depending on factors such as the institute, course duration, curriculum, mode of delivery, and additional features offered. Typically, the fee for a Data Analytics Course can range from INR 40,000 to INR 80,000 or more. It is recommended to research and compare the offerings of different institutes to find a course that fits your budget and offers value for money.

Yes, coding is often required for a career as a data analyst. Proficiency in programming languages such as Python, R, SQL, and SAS is beneficial for data analysts to manipulate, clean, and analyze data efficiently. Coding skills also enable data analysts to write scripts and develop automated processes for data extraction, transformation, and loading (ETL). While it is possible to perform certain data analysis tasks using graphical user interfaces (GUI) or specialized tools, having coding skills expands the range of analyses and enhances job prospects in the field.

The monthly salary of an entry-level Data Analyst in India can vary depending on factors such as location, company size, industry, and skills. As per the insights shared by Ambitionbox, the average starting salary for Data Analysts in India is around ₹1.6 Lakhs per year, amounting to an approximate monthly salary of ₹13.3k.

Yes, Data Analytics can be a good career option for freshers. The demand for skilled data analysts is increasing as organizations increasingly rely on data-driven insights for decision-making. Starting a career in Data Analytics allows freshers to work with diverse datasets, gain practical experience with data analysis tools, and contribute to impactful projects. With continuous learning, skill development, and industry experience, freshers can progress and advance their careers in the field of Data Analytics.

While a graduation degree is not always mandatory, it is typically preferred or considered advantageous for becoming a data analyst. Many organizations and employers prefer candidates with a bachelor's degree in fields such as computer science, mathematics, statistics, economics, or related disciplines. However, individuals with relevant certifications, practical experience, and strong analytical skills can also pursue a career in data analytics without a formal degree. It is important to demonstrate proficiency in data analysis concepts, tools, and techniques to enhance job prospects in the field.

Being a data analyst can be a challenging job as it requires a combination of analytical skills, critical thinking, and problem-solving abilities. Data analysts need to work with complex datasets, analyze large volumes of data, and derive meaningful insights from it. They also need to stay updated with emerging technologies, tools, and industry trends. However, with the right training, skills, and experience, individuals can overcome these challenges and excel in their role as data analysts.

There are several popular tools utilized in the field of Data Analytics. Some widely used ones include:

  • SQL: Structured Query Language (SQL) is commonly used for managing and manipulating relational databases.

  • Python: Python is a versatile programming language that offers a wide range of libraries and frameworks for data manipulation, analysis, and visualization, such as Pandas, NumPy, and Matplotlib.

  • R: R is a statistical programming language widely used for data analysis, data visualization, and machine learning.

  • Tableau: Tableau is a powerful data visualization tool that allows analysts to create interactive and visually appealing dashboards and reports.

  • Power BI: Power BI is another popular data visualization tool that provides interactive dashboards, real-time analytics, and easy integration with various data sources.

  • Apache Hadoop: Hadoop is an open-source framework used for distributed storage and processing of large datasets, especially in big data environments.

DataMites is highly regarded as an excellent institute for those seeking to learn data analytics. Their wide range of courses and training programs, available in various locations, offer comprehensive education and hands-on experience, empowering individuals to thrive in the dynamic field of data analytics.

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FAQ’S OF DATA ANALYTICS TRAINING IN GANDHINAGAR

To pursue a career as a data analyst, start by obtaining a bachelor's degree in fields like computer science, mathematics, statistics, or economics. Gain proficiency in programming languages like Python and R, and learn data manipulation and analysis techniques. Gain practical experience through internships or entry-level positions. Continuously upgrade your skills through online courses and certifications. Network with professionals in the industry and actively search for data analyst roles to kickstart your career.

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

DataMites is recommended for Certified Data Analyst Training in Gandhinagar because of its reputation for delivering high-quality training, offering globally recognized certifications, and equipping learners with practical skills required in the industry. Their trainers bring valuable expertise and ensure a supportive learning environment.

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

The prerequisites for data analytics training in Gandhinagar may vary based on the specific course. However, having a basic understanding of mathematics, statistics, and computer usage can be beneficial.

The cost of the Data Analytics Course in Gandhinagar offered by DataMites is subject to variation based on factors like the duration of the course, the delivery mode, and the availability of additional services. The fee for the certified data analyst training in Gandhinagar can range from INR 28,178 to INR 76,000, depending on the specific course details and features.

The DataMites Certified Data Analytics Course in Gandhinagar is designed to be completed within a duration of 4 months, encompassing over 200 learning hours. This course structure allows for in-depth and comprehensive training, ensuring ample time for practical exercises and hands-on projects. The emphasis on practical application enables students to gain practical skills and experience in the field of data analytics, preparing them for real-world scenarios and challenges.

The Flexi-Pass offered by DataMites allows learners to access multiple courses at a discounted price. It provides flexibility in choosing and attending different courses according to individual learning needs and preferences. The Flexi-Pass offers an opportunity to explore a diverse range of topics within the field of data analytics.

The DataMites Certified Data Analyst Training in Gandhinagar covers a wide range of topics, including data analysis techniques, statistical analysis, data visualization, machine learning, predictive analytics, and data mining. The course curriculum is designed to provide a comprehensive understanding of data analytics principles and their practical applications.

DataMites accepts various payment methods, including online payment gateways, bank transfers, and other convenient modes of payment. They provide multiple options to ensure a smooth and hassle-free payment process for their learners.

Upon successful completion of the Data Analytics training at DataMites, you will receive globally recognized certifications from IABAC, NASSCOM FutureSkills Prime, and JainX. These certifications serve as a testament to your expertise and validate your proficiency in the field of data analytics. They hold immense value and can greatly enhance your career prospects by showcasing your skills to potential employers.

Yes, DataMites conducts ON DEMAND classroom training for data analytics in Gandhinagar. They provide interactive and instructor-led sessions in a traditional classroom setting to facilitate effective learning and practical application of concepts.

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

DataMites may offer trial classes or demo sessions for prospective learners to get a glimpse of the training and teaching methodology.

The trainers responsible for conducting Data Analytics Courses at DataMites are experienced professionals with expertise in the field of data analytics. They possess industry knowledge and practical experience, ensuring a high standard of training delivery.

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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