DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE COURSE LEAD MENTORS

DATA SCIENCE COURSE FEE IN ITANAGAR

Live Virtual

Instructor Led Live Online

110,000
59,451

  • 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
34,951

  • 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
64,451

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

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UPCOMING DATA SCIENCE ONLINE CLASSES IN ITANAGAR

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 ITANAGAR

MODULE 1: DATA SCIENCE ESSENTIALS 

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

MODULE 2: DATA SCIENCE DEMO

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

MODULE 3: ANALYTICS CLASSIFICATION 

 • Types of Analytics
 • Descriptive Analytics
 • Diagnostic Analytics
 • Predictive Analytics
 • Prescriptive Analytics
 • EDA and insight gathering demo in Tableau

MODULE 4: 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 5: DATA SCIENCE ROLES & WORKFLOW

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

MODULE 6: MACHINE LEARNING INTRODUCTION

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

MODULE 7: 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 Variables
 • Python basic data types
 • Number & Booleans, strings
 • Arithmetic Operators
 • Comparison Operators
 • Assignment Operators

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
 • Basics of List
 • List: Object, methods
 • Tuple: Object, methods
 • Sets: Object, methods
 • Dictionary: Object, methods

MODULE 4: PYTHON FUNCTIONS 

 • Functions basics
 • Function Parameter passing
 • Lambda functions
 • Map, reduce, filter functions

MODULE 1: OVERVIEW OF STATISTICS 

 • Introduction to 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
 • Types of Sampling
 • Simple Random Sampling
 • Stratified Random Sampling
 • Cluster Random Sampling
 • Systematic Random Sampling
 • Multi stage Sampling
 • 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 & Properties
 • Z Value / Standard Value
 • Empirical Rule and Outliers
 • Central Limit Theorem
 • Normality Testing
 • Skewness & Kurtosis
 • Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
 • Covariance & Correlation

MODULE 4: HYPOTHESIS TESTING 

 • Hypothesis Testing Introduction
 • P- Value, Critical Region
 • Types of Hypothesis Testing
 • Hypothesis Testing Errors : Type I And Type II
 • Two Sample Independent T-test
 • Two Sample Relation T-test
 • One Way Anova Test
 • Application of Hypothesis testing

 

MODULE 1: MACHINE LEARNING INTRODUCTION 

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

MODULE 2:  PYTHON NUMPY  PACKAGE 

 • Introduction to Numpy Package
 • Array as Data Structure
 • Core Numpy functions
 • Matrix Operations, Broadcasting in Arrays

MODULE 3:  PYTHON PANDAS PACKAGE 

 • Introduction to Pandas package
 • Series in Pandas
 • Data Frame in Pandas
 • File Reading in Pandas
 • Data munging with Pandas

MODULE 4: VISUALIZATION WITH PYTHON - Matplotlib

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

MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN

 • Seaborn: Basic Plot
 • Advanced Python Data Visualizations

MODULE 6: ML ALGO: LINEAR REGRESSSION

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

MODULE 7: ML ALGO: LOGISTIC REGRESSION

 • Introduction to Logistic Regression
 • How it works: Classification & Sigmoid Curve
 • Modeling and Evaluation 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 9: ML ALGO: KNN

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

MODULE 1: FEATURE ENGINEERING 

 • Introduction to Feature Engineering
 • Feature Engineering Techniques: Encoding, Scaling, Data Transformation
 • Handling Missing values, handling outliers
 • Creation of Pipeline
 • Use case for feature engineering

MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

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

MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)

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

MODULE 4:  ML ALGO: DECISION TREE 

 • Introduction to Decision Tree & Random Forest
 • How it works
 • Modeling and Evaluation in Python

MODULE 5: ENSEMBLE TECHNIQUES - BAGGING 

 • Introduction to Ensemble technique 
 • Bagging and How it works
 • Modeling and Evaluation in Python

MODULE 6: ML ALGO: NAÏVE BAYES

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

MODULE 7: GRADIENT BOOSTING, XGBOOST

 • Introduction to Boosting and XGBoost
 • How it works?
 • Modeling and Evaluation of in Python

MODULE 1: TIME SERIES FORECASTING - ARIMA 

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

MODULE 2: SENTIMENT ANALYSIS 

 • Introduction to Sentiment Analysis
 • NLTK Package
 • Case study: Sentiment Analysis on Movie Reviews

MODULE 3: REGULAR EXPRESSIONS WITH PYTHON 

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

MODULE 4:  ML MODEL DEPLOYMENT WITH FLASK 

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

MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL

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

MODULE 6:  AWS CLOUD FOR DATA SCIENCE

 • Introduction of cloud
 • Difference between GCC, Azure, AWS
 • AWS Service ( EC2 instance)

MODULE 7: AZURE FOR DATA SCIENCE

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

MODULE 8:  INTRODUCTION TO DEEP LEARNING

 • Introduction to Artificial Neural Network, Architecture
 • Artificial Neural Network in Python
 • Introduction to Convolutional Neural Network, Architecture
 • Convolutional Neural Network in Python

MODULE 1: DATABASE INTRODUCTION 

 • DATABASE Overview
 • Key concepts of database management
 • Relational Database Management System
 • CRUD operations

MODULE 2:  SQL BASICS

 • Introduction to Databases
 • Introduction to SQL
 • SQL Commands
 • MY SQL workbench installation

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
 • Self Join, Cross join
 • Windows function: Over, Partition, Rank

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

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
 • Git Essentials: Copy & User Setup
 • Mastering Git and GitHub

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
 • Editing Commits
 • Commit command Amend flag
 • Git reset and revert

MODULE 5: GIT WITH GITHUB AND BITBUCKET

 • Creating GitHub Account
 • Local and Remote Repo
 • Collaborating with other developers

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

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

MODULE 1: TABLEAU FUNDAMENTALS 

 • Introduction to Business Intelligence & Introduction to Tableau
 • Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
 • Bar chart, Tree Map, Line Chart
 • Area chart, Combination Charts, Map
 • Dashboards creation, Quick Filters
 • Create Table Calculations
 • Create Calculated Fields
 • Create Custom Hierarchies

MODULE 2:  POWER-BI BASICS

 • Power BI Introduction 
 • Basics Visualizations
 • Dashboard Creation
 • Basic Data Cleaning
 • Basic DAX FUNCTION

MODULE 3 : DATA TRANSFORMATION TECHNIQUES 

 • Exploring Query Editor
 • Data Cleansing and Manipulation:
 • Creating Our Initial Project File
 • Connecting to Our Data Source
 • Editing Rows
 • Changing Data Types
 • Replacing Values

MODULE 4: CONNECTING TO VARIOUS DATA SOURCES 

• Connecting to a CSV File
 • Connecting to a Webpage
 • Extracting Characters
 • Splitting and Merging Columns
 • Creating Conditional Columns
 • Creating Columns from Examples
 • Create Data Model

OFFERED DATA SCIENCE COURSES IN ITANAGAR

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN ITANAGAR

The comprehensive Data Science training in Itanagar is designed to equip you with the knowledge and skills needed to thrive in this rapidly growing field. The market size of data science is estimated to grow by USD 322.9 Billion by the year 2026 at a CAGR rate of 27.7 % according to the Markets and Markets report. 

DataMites is a globally recognized institute that provides Data Science course in Itanagar and covers recognized courses in artificial intelligence, machine learning, data analytics, and deep learning. At DataMites, students are provided with on-demand data science offline classes in Itanagar and the course duration is 8 months with 700 learning hours and 120 hours of live online training. DataMites offers IABAC-certified courses, and their training programs have benefited learners worldwide. DataMites provides internship/ job assistance and offers an exclusive Certified Data Scientist Course in Itanagar for career enhancement. 

DataMites provides salient features for Data Science Training Course in Itanagar that include:

  1. Faculty and Ashok Veda as Lead Mentor: Ashok Veda is the lead mentor at DataMites consists of experienced professionals with a collective mentorship experience of over 20,000 students, offering industry insights and enriching the learning process through their research involvement.
  2. Course Curriculum: Datamites provides all-encompassing data science training programs that encompass Python programming, deep learning and data visualization, offering a blend of theory and practical application.
  3. Flexible Training Modes: Datamites offers flexible training modes including on-demand classroom-based and online programs to cater to diverse learning preferences.
  4. Hands-on Projects: Datamites emphasizes practical implementation in data science training by integrating hands-on projects and assignments, enabling students to gain skills through application.
  5. Certification: Upon course completion, Datamites awards a data science certification in Itanagar that validates acquired skills and knowledge in the data science field.

Itanagar, the capital of Arunachal Pradesh, is nestled amidst the serene beauty of the Eastern Himalayas, offering a harmonious blend of traditional tribal cultures and modern development. Data science has the potential to bring significant opportunities and advancements and offer promising data science careers in Itanagar, with rising demand for skilled professionals proficient in areas like data analysis and data visualization. The salary of a data scientist in India ranges from INR 11,30,556 per year according to a Glassdoor report. DataMites offers online data science training in Itanagar with a comprehensive syllabus, study material, job training, and mock tests. At DataMites, the students get data science certification in Raipur after the completion of the training program. Join DataMites, Data Science Institute in Itanagar today and take the first step towards a rewarding and fulfilling career in this dynamic field.

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

ABOUT DATAMITES DATA SCIENCE COURSE IN ITANAGAR

Data science is a multidisciplinary field that involves extracting insights and knowledge from data using various techniques, tools, and algorithms.

Learning data science is crucial as it equips individuals with the skills to analyze and interpret large volumes of data, enabling informed decision-making, predictive modeling, and valuable insights for businesses and organizations.

Proficiency in programming languages like Python or R, statistical analysis, machine learning algorithms, data visualization, problem-solving, and domain knowledge are essential skills for aspiring data scientists.

To learn data science effectively, a combination of theoretical knowledge, practical implementation through projects, hands-on experience with real-world datasets, and continuous learning and practice is recommended.

Common challenges for data scientists include data quality issues, data cleaning and preprocessing, managing and analyzing large datasets, selecting appropriate algorithms, and effectively communicating insights to non-technical stakeholders.

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

The eligibility criteria for data science courses can vary, but typically a background in mathematics, statistics, computer science, or a related field is preferred.

Data science has a vast scope across industries as it helps organizations make data-driven decisions, improve operational efficiency, develop predictive models, optimize marketing strategies, enhance customer experience, and drive innovation.

A data science certification provides credibility and validates one's skills and knowledge in the field, enhancing job prospects and career opportunities in the competitive data science industry.

Data science courses are in high demand due to the increasing need for data-driven insights and skilled professionals who can harness the power of data to drive business growth and innovation.

While it may be more challenging for a fresher to land a job as a data scientist directly, there are entry-level positions such as data analyst, data engineer, or junior data scientist that can provide valuable experience and pave the way for a career in data science.

Data science is considered a safe career choice as the demand for skilled professionals continues to grow across industries as technologies are emerging and continuous learning is crucial to remain competitive.

Python is widely regarded as the best programming language for data science due to its extensive libraries, ecosystem, ease of use, and flexibility for tasks such as data manipulation, analysis, and machine learning.

While coding is an integral part of data science, the extent of coding can vary depending on the specific tasks and projects. Data scientists typically use programming languages for tasks like data preprocessing, analysis, and implementing machine learning algorithms.

Data science can be challenging due to the interdisciplinary nature of the field, requiring a blend of technical, mathematical, and analytical skills. However, with dedication, continuous learning, and practice, it is achievable for motivated individuals.

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FAQ’S OF DATA SCIENCE TRAINING IN ITANAGAR

DataMites offers a comprehensive Data Science course in Itanagar that covers all essential topics, provides hands-on experience with real-world projects, and offers industry-recognized certifications, making it an ideal choice for individuals aspiring to excel in the field of Data Science.

The Certified Data Scientist Course offered by DataMites in Itanagar is open to individuals with a strong foundation in mathematics and programming, as well as those with a background in statistics, engineering, or related fields, making it suitable for a wide range of participants interested in pursuing a career in Data Science.

Considering a data science course offered by DataMites in Itanagar would be beneficial as it provides comprehensive training, practical exposure through real-world projects, and industry-recognized certifications, enabling individuals to acquire the necessary skills and knowledge to succeed in the field of data science.

The course duration for data science is 8 months with 700 learning hours and 120 hours of live online training

Yes, after completion of the data science course, the students are certified with globally recognized IABAC certification which helps them during job and internship programs.

Upon completing the course, DataMites offers dedicated placement assistance through their Placement Assistance Team (PAT), ensuring that individuals receive support and guidance in securing employment opportunities.

DataMites offers 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 provides highly experienced educators who possess extensive expertise in the field of data science, along with the necessary qualifications and certifications. Leveraging their years of experience, they offer superior instruction, ensuring a comprehensive understanding of the subject matter.

DataMites provide flexible learning options to suit student’s preferences, offering a range of choices from live online sessions to self-paced learning methods and on-demand classroom training.

Yes, DataMites provide an overview of the training approach and a complimentary demo class will be arranged, enabling the students to gain a better understanding of the training process and its components.

Learning Through Case Study Approach

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

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

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

The price of the Data Science Course offered by DataMites in Itanagar ranges from INR 35,000 for live online training, INR 21,000 for blended learning and INR 44,000 for on-demand classroom training.

Yes. Photo ID proofs like a National ID card, Driving license etc. are needed for issuing the participation certificate and booking the certification exam as required.

The salary of a data scientist in India ranges from INR 11,30,556 per year according to a Glassdoor report.

With the Flexi-Pass for Data Science training, the students will have the flexibility to attend Datamites sessions for a duration of 3 months. This pass allows you to address any queries or engage in revision as per your preferences.

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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