DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE COURSE LEAD MENTORS

DATA SCIENCE COURSE FEE IN AMRITSAR

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 AMRITSAR

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 AMRITSAR

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 AMRITSAR

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN AMRITSAR

The data science course in Amritsar offers ample opportunities with a range of specialized courses and training programs available for aspiring data scientists. The city's growing presence in industries such as healthcare, finance, and agriculture provides a platform for professionals to apply their data science skills and contribute to the development of innovative solutions. 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, a globally renowned institute, is widely recognized for its comprehensive Data Science training in Amritsar. Their curriculum encompasses prestigious courses in artificial intelligence, machine learning, data analytics, and deep learning. Students have the flexibility to attend on-demand data science offline classes in Amritsar, tailored to their specific needs. The program spans 8 months, offering 700 learning hours, including 120 hours of live online training. DataMites prides itself on providing IABAC-certified courses, ensuring a positive impact on learners worldwide. Additionally, the institute offers internship/job assistance, further enhancing career prospects for aspiring data scientists. Take your learning experience to the next level with DataMites' exclusive Certified Data Scientist Course in Amritsar.

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

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

Amritsar is a vibrant city located in the state of Punjab, India. It is renowned for its rich cultural heritage, including the iconic Golden Temple, and is a major centre for Sikhism. In recent years, data science has witnessed a growing presence and increased demand in the city which encouraged the establishment of a dedicated data science institute in Amritsar. 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 Amritsar with a comprehensive syllabus, study material, job training, and mock tests. At DataMites, the students get data science certification in Amritsar after the completion of the training program. Join us at DataMites and unlock your potential in the dynamic field of the Data Science Training Course in Amritsar.

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

ABOUT DATAMITES DATA SCIENCE COURSE IN AMRITSAR

Data science is a multidisciplinary field that involves extracting knowledge and insights from data through various techniques, such as statistics, machine learning, and data visualization, to make informed decisions and solve complex problems.

Learning data science is important because it enables individuals and organizations to analyze and interpret vast amounts of data, leading to better decision-making, improved efficiency, and the ability to uncover valuable insights and patterns that can drive innovation and growth.

To become a data scientist, one needs a combination of technical skills like programming (Python, R, etc.), statistics, machine learning, data visualization, and domain knowledge, as well as critical thinking, problem-solving, and communication skills.

Effective learning of data science involves a combination of theoretical understanding and practical application. It is recommended to pursue a structured curriculum or online courses, engage in hands-on projects, participate in Kaggle competitions, and collaborate with others in the field to gain experience and deepen knowledge.

Common challenges faced by data scientists include dealing with messy and incomplete data, selecting appropriate techniques for analysis, handling large datasets, ensuring data privacy and security, and effectively communicating complex findings to non-technical stakeholders.

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

The eligibility criteria for enrolling in a data science course may vary depending on the institution or program. Typically, a background in mathematics, statistics, computer science, or a related field is beneficial, but many courses also cater to beginners with no prior experience.

The scope of data science is vast and growing rapidly. Data scientists are in high demand across industries due to the increasing availability of data and the need to extract actionable insights from it. The field offers opportunities in areas like finance, healthcare, marketing, e-commerce, technology, and more.

Certification in data science is important as it provides credibility and validates one's knowledge and skills in the field. It can enhance job prospects, demonstrate competence to potential employers, and showcase a commitment to professional development.

Yes, there is a high demand for data science courses as organizations worldwide are recognizing the value of data-driven decision-making. This demand is driven by the need for skilled professionals who can analyze and interpret data effectively to derive actionable insights.

Data science is considered a safe career option due to the increasing demand for skilled professionals in the field. However, it is important to continuously update skills and stay updated with evolving technologies and techniques to remain competitive in the job market.

Data science can be challenging due to its multidisciplinary nature and the need to continuously learn and adapt to new tools and methodologies. It requires a strong foundation in mathematics and statistics, as well as the ability to work with large datasets and apply complex algorithms.

Python is one of the most popular programming languages in data science due to its rich ecosystem of libraries and tools. While Python is not the only language used in data science, having a strong understanding of Python is highly beneficial for tasks such as data manipulation, analysis, and machine learning.

SQL (Structured Query Language) is commonly used in data science for data extraction, manipulation, and querying from relational databases. Proficiency in SQL is valuable for accessing and managing large datasets efficiently, especially when working with structured data.

Statistics is an essential component of data science as it provides the foundation for understanding data and making meaningful inferences. It helps data scientists to analyze patterns, validate hypotheses, and make informed decisions based on data-driven insights.

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FAQ’S OF DATA SCIENCE TRAINING IN AMRITSAR

The reasons to choose DataMites for a Data Science course in Amritsar include their experienced faculty, comprehensive curriculum covering various data science topics, practical hands-on approach, industry-relevant projects, and support for placement assistance.

The Certified Data Scientist Course offered by DataMites in Amritsar welcomes individuals with a solid background in mathematics and programming, as well as those with prior experience in statistics, engineering, or related fields. This inclusive approach accommodates a diverse range of participants who aspire to build a successful career in the field of Data Science.

One should consider a data science course offered by DataMites in Amritsar because they provide a well-structured curriculum, expert faculty, hands-on learning experience, practical projects, and industry-oriented training that can enhance knowledge and skills in data science, leading to better job prospects in field.

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

Upon successfully finishing the data science course in Amritsar, students are awarded the highly regarded IABAC certification, recognized worldwide. This certification acts as a valuable asset when seeking employment or participating in internship programs, significantly improving their opportunities in the data science field.

Upon successfully finishing the course, DataMites offers robust placement support and guidance through their Placement Assistance Team (PAT). This personalized assistance ensures individuals receive comprehensive help in securing suitable job placements, greatly enhancing their prospects and opportunities for employment.

DataMites in Amritsar offers an extensive range of data science courses that cover various aspects, including 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 renowned for its team of exceptionally experienced educators who are experts in the field of data science. With their extensive expertise, qualifications, and certifications, these instructors provide exceptional instruction, enabling students to gain a thorough understanding of the subject matter.

DataMites offers flexible learning options to cater to the preferences of students. They provide a variety of choices, including live online sessions, self-paced learning methods, and on-demand classroom training. This flexibility empowers individuals to select the learning approach that suits their needs, making it convenient for them to pursue their data science education.

DataMites offers an overview of its training approach and provides a complimentary demo class, allowing students to gain a thorough understanding of the training process and its components. This opportunity enables individuals to assess 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 Amritsar through:

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

DataMites provides its Data Science Course in Amritsar at different price points, offering 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 enables individuals to select the plan that aligns with their budget and preferred learning mode.

To obtain the participation certificate and book the certification exam, it is necessary to provide valid photo identification proofs like a National ID card or a Driving license. These proofs are essential to ensure the authenticity and accuracy of the certification process.

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