DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE COURSE LEAD MENTORS

DATA SCIENCE COURSE FEE IN ALLAHABAD

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 ?

Enquire Now

UPCOMING DATA SCIENCE ONLINE CLASSES IN ALLAHABAD

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 ALLAHABAD

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 ALLAHABAD

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN ALLAHABAD

The data science course in Allahabad offers excellent opportunities with a growing demand for skilled professionals in the field. The city provides a vibrant ecosystem for data scientists to thrive and contribute to various industries through their expertise. Based on a report by Precedence Research, the data science market is anticipated to achieve a market size of USD 378.7 Billion by 2030, demonstrating a steady compound annual growth rate (CAGR) of 16.43%.

DataMites is a renowned institute globally recognized for its extensive Data Science Training in Allahabad. It offers a wide range of prestigious courses, including artificial intelligence, machine learning, data analytics, and deep learning. Students can choose from on-demand data science offline classes in Allahabad, which are customized to their specific requirements. The program spans over 8 months and comprises 700 hours of learning, including 120 hours of live online training. With IABAC-certified courses, DataMites ensures that learners have a global impact and also provides internship and job assistance to enhance career prospects. Moreover, DataMites offers an exclusive Certified Data Scientist Course in Allahabad to provide students with an enriched learning experience.

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

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

Allahabad, also known as Prayagraj, is a historical city located in Uttar Pradesh, India. It is renowned for its cultural heritage and the confluence of the holy rivers Ganges, Yamuna, and Saraswati, making it a popular pilgrimage site. The future scope for data science professionals with the presence of reputable data science institutes in Allahabad facilitates skill development and knowledge enhancement in the field. According to a PayScale report, the salary of a data scientist in India ranges from INR 9,10,238 per year.  DataMites offers online data science training in Allahabad with a comprehensive syllabus, study material, job training, and mock tests. Join DataMites exciting data science training course in Allahabad and explore the exciting opportunities that enhance the career prospect of the students.

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

ABOUT DATAMITES DATA SCIENCE COURSE IN ALLAHABAD

Data science is an interdisciplinary field that involves extracting insights and knowledge from data using various techniques such as statistical analysis, machine learning, and data visualization.

Learning data science is important because it equips individuals with the skills to extract valuable insights from data, make data-driven decisions, and solve complex problems across industries.

Necessary skills for becoming a data scientist include proficiency in programming languages (such as Python or R), statistical analysis, machine learning, data manipulation, and data visualization.

Effective methods for learning data science include online courses, bootcamps, self-study through books and tutorials, and practical application of skills on real-world projects.

Some common challenges encountered by data scientists include data quality issues, dealing with large and complex datasets, selecting appropriate models and algorithms, and interpreting and communicating results effectively.

The cost of a data science course in Allahabad can vary depending on the institution and program, but it generally ranges from INR 40,000 to INR 50,000.

The eligibility criteria for enrolling in a data science course can vary, but typically it requires a bachelor's degree in a relevant field (such as computer science, mathematics, or statistics) and a basic understanding of programming and statistics.

The scope of data science is vast and expanding rapidly, with increasing demand in various industries like finance, healthcare, e-commerce, and technology, offering opportunities for data scientists to solve complex problems, make informed decisions, and drive innovation.

Certification in data science is significant as it provides a recognized validation of one's skills and knowledge in the field, enhancing career prospects and credibility among employers and peers.

Yes, there is a significant demand for data science courses due to the increasing need for data-driven insights and decision-making in organizations across industries.

Pursuing a career in data science is generally considered safe due to the growing demand for data scientists, the wide range of industries that require their expertise, and the potential for career growth and attractive salary prospects

Data science can be challenging as it requires a combination of skills from various disciplines, including programming, statistics, and machine learning. However, with dedication and continuous learning, it is an achievable field of study.

Python is a powerful programming language commonly used in data science due to its extensive libraries and frameworks for data manipulation, analysis, and machine learning. However, proficiency in other tools and languages like R and SQL can also be beneficial in certain data science contexts.

SQL is often used for managing and querying relational databases, which can be an important skill for data scientists working with structured data. While not a strict requirement, familiarity with SQL can be valuable for accessing and manipulating data efficiently.

A solid understanding of statistics is necessary for data science as it forms the foundation for various statistical techniques, hypothesis testing, and model evaluation. Statistical knowledge helps in making informed decisions and drawing meaningful insights from data.

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FAQ’S OF DATA SCIENCE TRAINING IN ALLAHABAD

DataMites stands out as an excellent choice for a Data Science course in Allahabad due to its skilled faculty, comprehensive curriculum that encompasses diverse data science subjects, emphasis on practical learning with hands-on experience, industry-oriented projects, and dedicated support for placement opportunities.

DataMites Certified Data Scientist Course in Allahabad is open to individuals with a strong foundation in mathematics and programming, as well as those with previous experience in statistics, engineering, or related disciplines. This inclusive approach caters to a diverse range of participants who have aspirations to establish a thriving career in the dynamic field of Data Science.

One should opt for the DataMites data science course in Allahabad for its meticulously designed curriculum, experienced faculty, interactive hands-on learning opportunities, practical project assignments, and industry-focused training. This comprehensive program can significantly enhance your knowledge and skills in data science, thereby improving your job prospects in the field.

The course duration spans over 8 months with 700 learning hours, which includes 120 hours of live online training.

Upon completion of the data science course in Allahabad, students receive the prestigious IABAC certification, widely acknowledged globally. This certification serves as a valuable credential, enhancing employment prospects and enabling participation in internship programs, thereby significantly enriching opportunities in the field of data science.

DataMites provides strong support and guidance for placements through their dedicated Placement Assistance Team (PAT) upon successful completion of the course. The personalized assistance offered by PAT ensures individuals receive comprehensive support in securing suitable job placements, significantly boosting their employment prospects and opening up a multitude of opportunities.

DataMites provides a wide array of data science courses in Allahabad that encompass diverse areas 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.

DataMites is widely recognized for its team of highly experienced and knowledgeable educators who are industry experts in the field of data science. With their exceptional qualifications, certifications, and extensive expertise, these instructors deliver outstanding instruction, empowering students to develop a comprehensive understanding of the subject matter.

DataMites offers flexible learning options to accommodate the preferences of students, providing a range of choices such as live online sessions, self-paced learning, and on-demand classroom training. This flexibility empowers individuals to choose the learning approach that best fits their needs, making it convenient for them to pursue their data science education.

DataMites provides a comprehensive overview of its training approach and offers a complimentary demo class, giving students an opportunity to fully grasp the training process and its components. This allows individuals to evaluate the quality and suitability of the training before making a commitment.

Learning Through Case Study Approach

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

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

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

DataMites offers its Data Science Course in Allahabad at various price points, providing options such as INR 35,000 for live online training, INR 21,000 for blended learning, and INR 44,000 for on-demand classroom training. This flexible pricing structure allows individuals to choose the plan that best suits their budget and preferred mode of learning.

To acquire the participation certificate and book the certification exam, it is mandatory to present valid photo identification proofs such as a National ID card or a Driving license. These identification proofs are crucial in ensuring the authenticity and accuracy of the certification process.

According to a PayScale report, the salary of a data scientist in India ranges from INR 9,10,238 per year.

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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