DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE COURSE LEAD MENTORS

DATA SCIENCE COURSE FEE IN GANDHINAGAR

Live Virtual

Instructor Led Live Online

110,000
72,345

  • IABAC® & NASSCOM® Certification
  • 8-Month | 700 Learning Hours
  • 120-Hour Live Online Training
  • 25 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

66,000
43,995

  • Self Learning + Live Mentoring
  • IABAC® & NASSCOM® Certification
  • 1 Year Access To Elearning
  • 25 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Leaner assistance and support

Classroom

In - Person Classroom Training

110,000
82,845

  • IABAC® & NASSCOM® Certification
  • 8-Month | 700 Learning Hours
  • 120-Hour Classroom Sessions
  • 25 Capstone & 1 Client Project
  • Cloud Lab Access
  • Internship + Job Assistance

ARE YOU LOOKING TO UPSKILL YOUR TEAM ?

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UPCOMING DATA SCIENCE ONLINE CLASSES IN GANDHINAGAR

BEST DATA SCIENCE 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 SCIENCE COURSE

Why DataMites Infographic

SYLLABUS OF DATA SCIENCE COURSE IN GANDHINAGAR

MODULE 1: DATA SCIENCE COURSE INTRODUCTION 

  • CDS Course Introduction
  • 3 Phase Learning
  • Learning Resources
  • Assessments & Certification Exams
  • DataMites Mobile App
  • Support Channels

MODULE 2: DATA SCIENCE ESSENTIALS 

  • Introduction to Data Science
  • Evolution of Data Science
  • Data Science Terminologies
  • Data Science vs AI/Machine Learning
  • Data Science vs Analytics

MODULE 3: DATA SCIENCE DEMO 

  • Business Requirement: Use Case
  • Data Preparation
  • Machine learning Model building
  • Prediction with ML model
  • Delivering Business Value

MODULE 4: ANALYTICS CLASSIFICATION 

  • Types of Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics

MODULE 5: DATA SCIENCE AND RELATED FIELDS

  • Introduction to AI
  • Introduction to Computer Vision
  • Introduction to Natural Language Processing
  • Introduction to Reinforcement Learning
  • Introduction to GAN
  • Introduction to  Generative Passive Models

MODULE 6: DATA SCIENCE ROLES & WORKFLOW

  • Data Science Project workflow
  • Roles: Data Engineer, Data Scientist, ML Engineer and MLOps Engineer
  • Data Science Project stages

MODULE 7: MACHINE LEARNING INTRODUCTION

  • What Is ML? ML Vs AI
  • ML Workflow, Popular ML Algorithms
  • Clustering, Classification And Regression
  • Supervised Vs Unsupervised

MODULE 8: DATA SCIENCE INDUSTRY APPLICATIONS 

  • Data Science in Finance and Banking
  • Data Science in Retail
  • Data Science in Health Care
  • Data Science in Logistics and Supply Chain
  • Data Science in Technology Industry
  • Data Science in Manufacturing
  • Data Science in Agriculture

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 PANDASPACKAGE

  • Pandasfunctions
  • 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: MACHINE LEARNING INTRODUCTION 

  • What Is ML? ML Vs AI
  • ML Workflow, Popular ML Algorithms
  • Clustering, Classification And Regression
  • Supervised Vs Unsupervised

MODULE 2: PYTHON NUMPY & PANDAS PACKAGE 

  • NumPy & Pandas functions
  • Array – Data Structure
  • Core Numpy functions
  • Matrix Operations
  • Data Frame and Series – Data Structure
  • Data munging with Pandas
  • Imputation and outlier analysis

MODULE 3: VISUALIZATION WITH PYTHON 

  • Visualization Packages (Matplotlib)
  • Components Of A Plot, Sub-Plots
  • Basic Plots: Line, Bar, Pie, Scatter
  • Advanced Python Data Visualizations

MODULE 4: ML ALGO: LINEAR REGRESSION

  • Introduction to Linear Regression
  • How it works: Regression and Best Fit Line
  • Modeling and Evaluation in Python

MODULE 5: ML ALGO: KNN 

  • Introduction to KNN
  • How It Works: Nearest Neighbor Concept
  • Modeling and Evaluation in Python

MODULE 6: ML ALGO: LOGISTIC REGRESSION 

  • Introduction to Logistic Regression
  • How it works: Classification & Sigmoid Curve
  • Modeling and Evaluation in Python

MODULE 7: PRINCIPLE COMPONENT ANALYSIS (PCA) 

  • Building Blocks Of PCA
  • How it works: Finding Principal Components
  • Modeling PCA in Python

MODULE 8: ML ALGO: K MEANS CLUSTERING 

  • Understanding Clustering (Unsupervised)
  • K Means Algorithm
  • How it works: K Means theory
  • Modeling in Python

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
  • Modeling and Evaluation in Python

MODULE 3: ML ALGO: LOGISTIC REGRESSION 

  • Introduction to Logistic Regression
  • How it works: Classification & Sigmoid Curve
  • Modeling and Evaluation in Python

MODULE 4: ML ALGO: KNN 

  • Introduction to KNN
  • How It Works: Nearest Neighbor Concept
  • Modeling and Evaluation in Python

MODULE 5: ML ALGO: K MEANS CLUSTERING 

  • Understanding Clustering (Unsupervised)
  • K Means Algorithm
  • How it works : K Means theory
  • Modeling in Python

MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA) 

  • Building Blocks Of PCA
  • How it works: Finding Principal Components
  • Modeling PCA in Python

MODULE 7: ML ALGO: DECISION TREE 

  • Random Forest Ensemble technique
  • How it works: Bagging Theory
  • Modeling and Evaluation in Python

MODULE 8 : ML ALGO: NAÏVE BAYES 

  • Introduction to Naive Bayes
  • How it works: Bayes' Theorem
  • Naive Bayes For Text Classification
  • Modeling and Evaluation in Python

MODULE 9: GRADIENT BOOSTING, XGBOOST 

  • Introduction to Boosting and XGBoost
  • How it works: weak learners' concept
  • Modeling and Evaluation of in Python

MODULE 10: ML ALGO: SUPPORT VECTOR MACHINE  (SVM) 

  • Introduction to SVM
  • How It Works: SVM Concept, Kernel Trick
  • Modeling and Evaluation of SVM in Python

MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN) 

  • Introduction to ANN
  • How It Works: Back prop, Gradient Descent
  • Modeling and Evaluation of ANN in Python

MODULE 12: ADVANCED ML CONCEPTS 

  • Adv Metrics (Roc_Auc, R2, Precision, Recall)
  • K-Fold Cross-validation
  • Grid And Randomized Search CV In Sklearn
  • Imbalanced Data Set: Smote Technique
  • Feature Selection Techniques

MODULE 1: TIME SERIES FORECASTING - ARIMA 

  • What is Time Series?
  • Trend, Seasonality, cyclical and random
  • Autoregressive Model (AR)
  • Moving Average Model (MA)
  • Stationarity of Time Series
  • ARIMA Model
  • Autocorrelation and AIC 

MODULE 2: FEATURE ENGINEERING 

  • Introduction to Features Engineering
  • Transforming Predictors
  • Feature Selection methods
  • Backward elimination technique
  • Feature importance from ML modeling

MODULE 3: SENTIMENT ANALYSIS 

  • Introduction to Sentiment Analysis
  • Python packages: TextBlob, NLTK
  • Case study: Twitter Live Sentiment Analysis

MODULE 4: REGULAR EXPRESSIONS WITH PYTHON 

  • Regex Introduction
  • Regex codes
  • Text extraction with Python Regex

MODULE 5: ML MODEL DEPLOYMENT WITH FLASK

  • Introduction to Flask
  • URL and App routing
  • Flask application – ML Model deployment

MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL 

  • MS Excel core Functions
  • Pivot Table
  • Advanced Functions (VLOOKUP, INDIRECT..)
  • Linear Regression with EXCEL
  • Goal Seek Analysis
  • Data Table
  • Solving Data Equation with EXCEL
  • Monte Carlo Simulation with MS EXCEL

MODULE 7: AWS CLOUD FOR DATA SCIENCE

  • Introduction of cloud
  • Difference between GCC, Azure,AWS
  • AWS Service ( EC2 and S3 service)
  • AWS Service (AMI), AWS Service (RDS)
  • AWS Service (IAM), AWS (Athena service)
  • AWS (EMR), AWS, AWS (Redshift)
  • ML Modeling with AWS Sage Maker 

MODULE 8: AZURE FOR DATA SCIENCE 

  • Introduction to AZURE ML studio
  • Data Pipeline and ML modeling with Azure

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: 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: 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 SCIENCE COURSES IN GANDHINAGAR

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN GANDHINAGAR

According to a Polaris Market Research report, the market size of the data science platforms is expected to reach USD 695.0 Billion and grow at a CAGR rate of 27.6%. The data science course in Gandhinagar offers excellent opportunities with a thriving tech ecosystem, including government initiatives, research institutions, and growing industries, making it an ideal hub for data scientists seeking impactful career prospects.

DataMites is a globally renowned institute recognized for its comprehensive Data Science training in Gandhinagar. Their curriculum covers various prestigious courses such as artificial intelligence, machine learning, data analytics, and deep learning. Students can take advantage of flexible-on-demand data science offline classes in Gandhinagar tailored to their needs, spanning the duration of 8 months with 700 learning hours, including 120 hours of live online training. With IABAC-certified courses, DataMites ensures a positive impact on learners worldwide, providing internship/job assistance and enhancing career prospects. Enhance your learning experience with DataMites exclusive Certified Data Scientist Course in Gandhinagar.

DataMites provides key features for Data Science Training in Gandhinagar that include:

  1. Faculty and Ashok Veda as Lead Mentor
  2. Course Curriculum
  3. Flexible Training Modes
  4. Hands-on Projects
  5. Global Certification
  6. Resume Preparation
  7. Live client project
  8. 24-hour job and placement assistance 
  9. Intensive live online training
  10. Hardcopy Learning materials and books
  11. DataMites Exclusive Learning Community
  12. Affordable pricing and Scholarships.

Gandhinagar, named after Mahatma Gandhi, is a city that seamlessly blends modernity with nature, featuring well-maintained gardens, wide roads, and a peaceful atmosphere. It is also home to prestigious educational institutions and serves as a major hub for government offices and industries. The future of data science looks promising, with increasing demand for skilled professionals and a growing ecosystem of technology companies, and data science training institutes in Gandhinagar for supporting its growth. The salary of data scientists in India ranges from INR 11,49,482 per year according to an Indeed report.  DataMites offers online data science training in Gandhinagar with a comprehensive syllabus, study material, job training, and mock tests. Join the DataMites Data Science Training Course in Gandhinagar and unlock a world of opportunities in the data-driven era.

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

ABOUT DATAMITES DATA SCIENCE COURSE IN GANDHINAGAR

Data science is a multidisciplinary field that involves extracting knowledge and insights from structured and unstructured data using various scientific methods, algorithms, and tools. It combines elements of statistics, mathematics, computer science, and domain expertise to uncover patterns, make predictions, and drive data-driven decision-making.

Learning data science is crucial in today's data-driven world because it equips individuals with the skills to analyze and interpret vast amounts of data. By leveraging data science techniques, organizations can gain valuable insights, improve efficiency, make informed decisions, and gain a competitive advantage in their respective industries.

To become a data scientist, several skills are essential, including:

  • Proficiency in programming languages such as Python or R.
  • Strong knowledge of statistics and mathematics.
  • Data manipulation and analysis using tools like SQL and Pandas.
  • Machine learning techniques and algorithms.
  • Data visualization and storytelling using tools like Tableau or Matplotlib.

To learn data science effectively, you can follow these steps:

  • Gain a solid foundation in mathematics, statistics, and programming.
  • Take online courses or enrol in a data science program.
  • Practice by working on real-world projects and datasets.
  • Join data science communities and participate in online forums.
  • Read books, research papers, and articles related to data science.

Data scientists often face several challenges, including:

  • Data quality issues and data cleaning.
  • Dealing with large and complex datasets.
  • Choosing the appropriate algorithms and models.
  • Interpretation and communication of results to non-technical stakeholders.
  • Ethical considerations and maintaining data privacy.

The cost of a data science course in Gandhinagar ranges from INR 40,000 to INR 50,000 depending on the institute, course duration, and curriculum.

The eligibility criteria for data science courses may vary depending on the institution or program. However, common requirements may include:

  • A bachelor's degree in a relevant field like computer science, mathematics, or engineering.
  • Basic knowledge of mathematics, statistics, and programming.
  • Proficiency in a programming language like Python or R.

Data science has a vast scope across various industries and sectors. As businesses continue to generate massive amounts of data, there is an increasing demand for skilled data scientists. Data science can be applied in fields such as finance, healthcare, marketing, e-commerce, manufacturing, and more. It offers opportunities for extracting insights, making predictions, improving processes, and driving innovation.

Obtaining a certification in data science is important as it provides formal recognition of one's skills and knowledge in the field, enhancing credibility and employability in the competitive job market.

Yes, there is a high demand for data science courses due to the increasing reliance on data-driven decision-making in various industries, leading to a growing need for skilled professionals who can effectively analyze and interpret complex data sets.

Yes, SQL is a necessary skill for data science as it is widely used for data extraction, manipulation, and analysis from relational databases.

Entry-level professionals in data science have promising career prospects, with opportunities to work as data analysts, data engineers, machine learning engineers, or data scientists in various industries.

Top companies hiring freshers in data science include Google, Microsoft, Amazon, Facebook, IBM, and many more, as they recognize the value of data-driven decision-making.

A solid understanding of statistics is essential for data science as it forms the foundation for data analysis, modeling, and interpretation, enabling meaningful insights to be derived from data.

Yes, data science involves a significant amount of math, including statistics, linear algebra, probability, and calculus, as these concepts are fundamental for data analysis, modeling, and machine learning algorithms.

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FAQ’S OF DATA SCIENCE TRAINING IN GANDHINAGAR

DataMites is a reputable institute known for its comprehensive data science courses, experienced faculty, hands-on training, industry-relevant curriculum, and placement assistance, making it a reliable choice for pursuing a data science course.

The eligibility criteria for enrolling in the Certified Data Scientist Course offered by DataMites in Gandhinagar may vary, but typically it requires a bachelor's degree in any discipline with a basic understanding of mathematics and statistics. However, it's recommended to contact DataMites directly for the most accurate and up-to-date information regarding eligibility.

Reasons to consider a data science course offered by DataMites in Gandhinagar include their strong industry connections, practical approach to learning, comprehensive curriculum covering various data science concepts and tools, and the opportunity to gain practical experience through real-world projects and case studies. Additionally, their track record of successful placements and career support services adds value to the overall learning experience.

The duration of the course is of 8 months with 700 learning hours, including 120 hours of live online training.

Upon successful completion of the data science course in Gandhinagar, students are awarded the prestigious IABAC certification, which is globally recognized. This certification greatly enhances their prospects during job applications and internship programs, providing them with a competitive edge in the industry.

After completing the course, DataMites offers dedicated support and guidance for placements through their Placement Assistance Team (PAT). This ensures that individuals receive comprehensive assistance in finding suitable job placements, increasing their chances of securing employment opportunities.

DataMites in Gandhinagar provides a wide array of data science courses, covering a diverse range of topics such as Data Science Foundation, Data Science for Managers, Data Science Associate, Diploma in Data Science, Python for Data Science, Statistics for Data Science, Data Science Marketing, Data Science Operations, Data Science Retail, Data Science for HR, Data Science with Finance, and Data Science. This extensive selection allows individuals to choose a course that aligns with their specific interests and career goals.

DataMites is widely recognized for its team of exceptionally experienced educators specializing in data science. These instructors possess extensive expertise, relevant qualifications, and certifications, allowing them to deliver exceptional instruction. Through their wealth of experience, students are equipped with a comprehensive understanding of the subject matter.

DataMites understands the diverse preferences of students and offers flexible learning options to cater to their needs. They provide a range of choices, such as live online sessions, self-paced learning methods, and on-demand classroom training. This flexibility empowers individuals to select the learning approach that aligns with their preferences and enables them to conveniently pursue their data science education.

DataMites provides a comprehensive training approach, which is accompanied by a complimentary demo class. This demo class enables students to improve their understanding of the training process and gain insights into its various components.

Learning Through Case Study Approach

Theory → Hands-on → Case Study → Project → Model Deployment

The payment mode available for the data science course in Gandhinagar through:

  • Cash
  • Net Banking
  • Check
  • Debit Card
  • Credit Card
  • PayPal
  • Visa
  • Master card
  • American Express

The DataMites Data Science Course in Gandhinagar offers flexible pricing options. The course is available at different price points: INR 35,000 for live online training, INR 21,000 for blended learning, and INR 44,000 for on-demand classroom training.

In order to receive the participation certificate and book the certification exam, it is required to submit valid photo identification proofs such as a National ID card or a Driving license.

The salary of data scientists in India ranges from INR 11,49,482 per year according to an Indeed report.  

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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