DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE COURSE LEAD MENTORS

DATA SCIENCE COURSE FEE IN PANAJI

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 PANAJI

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 PANAJI

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 PANAJI

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN PANAJI

Data science provides opportunities to embark on a transformative career journey in Panaji, harnessing the power of data to drive innovation and make informed decisions in diverse industries. The data Science course in Panaji is meticulously crafted to cover the fundamental concepts, tools, and techniques in data science. According to the Mordor Intelligence report, the global data science market size is forecasted to reach USD$ 230.80 Billion by the year 2026 at a CAGR rate of 39.7%.

DataMites is a prestigious institute with a global reputation for its comprehensive Data Science training in Panaji. Their curriculum encompasses renowned courses in artificial intelligence, machine learning, data analytics, and deep learning. Students at DataMites can take advantage of flexible on-demand data science offline classes in Panaji, tailored to their individual needs. The course spans 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. Aspiring data scientists can also benefit from internship/job assistance, enhancing their career prospects. For professional growth in the field, DataMites offers an exclusive Certified Data Scientist Course in Panaji.

DataMites provides key features for Data Science Training in Panaji 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

Panaji, the capital city of Chhattisgarh, blends tradition and modernity in a vibrant tapestry of cultural diversity. Panaji presents a promising future for Data Science professionals in Panaji with its growing emphasis on technology and innovation. Data Science Certification in Panaji provides an ideal setting for career growth with its increasing demand for skilled professionals. 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 Panaji with a comprehensive syllabus, study material, job training, and mock tests. Seize the opportunity by joining DataMites and starts a rewarding journey towards becoming a certified data scientist by unlocking a promising future in the data science field.

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

ABOUT DATAMITES DATA SCIENCE COURSE IN PANAJI

Data Science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines elements of mathematics, statistics, computer science, and domain knowledge to analyze and interpret complex data sets.

Learning Data Science is important for several reasons. Firstly, it allows businesses to make data-driven decisions, leading to improved efficiency and profitability. Secondly, data science enables the discovery of patterns and trends in data, which can uncover valuable insights and opportunities. Additionally, data scientists play a crucial role in developing predictive models and machine learning algorithms, contributing to advancements in various fields such as healthcare, finance, marketing, and more.

To become a data scientist, you need a combination of technical and soft skills. Technical skills include proficiency in programming languages like Python or R, knowledge of statistical analysis and machine learning techniques, data visualization, and database queries.

 Learning data science effectively involves a combination of theoretical understanding and practical application. Here are some tips:

  • Gain a strong foundation in mathematics and statistics.
  • Learn programming languages commonly used in data science, such as Python or R.
  • Understand key concepts in machine learning and statistical modeling.

Data scientists often encounter challenges such as:

  • Acquiring and cleaning large and complex datasets.
  • Dealing with missing or inconsistent data.
  • Choosing the appropriate statistical or machine learning techniques for a given problem.

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

The eligibility criteria for data science courses require a bachelor's degree in a relevant field such as computer science, mathematics, statistics, or engineering is preferred. However, some courses may also accept individuals with equivalent work experience or those who have completed prerequisite courses in mathematics, programming, or statistics.

Data science has a wide scope and is applicable across various industries and domains. Organizations today generate vast amounts of data, and the need for professionals who can extract insights from this data is growing rapidly. Data scientists can find opportunities in fields such as finance, healthcare, marketing, e-commerce, telecommunications, and more. They can work on tasks like predictive modeling, data visualization, recommendation systems, fraud detection, and optimization.

Obtaining a data science certification is important as it validates one's skills and knowledge in the field, enhancing career prospects and increasing credibility among employers and peers.

Yes, there is a high demand for data science courses as organizations across industries increasingly recognize the value and potential of leveraging data to drive informed decision-making and gain a competitive edge.

Securing a job as a Data Scientist without prior experience is challenging but not impossible; it typically requires a strong educational background, relevant skills, and a demonstrated aptitude for data analysis and problem-solving.

Pursuing a career in data science is generally considered secure due to the high demand for skilled professionals and the increasing reliance on data-driven decision-making across industries. However, it is important to stay updated with emerging technologies and continuously upskill to remain competitive in this rapidly evolving field.

Python is the most recommended programming language for data science due to its extensive libraries, such as NumPy, Pandas, and Scikit-learn, which provide powerful tools for data manipulation, analysis, and machine learning.

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

Yes, knowledge of statistics is essential for data science as it forms the foundation for understanding and analyzing data, making inferences, and building statistical models for predictive and inferential purposes.

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FAQ’S OF DATA SCIENCE TRAINING IN PANAJI

DataMites offers comprehensive Data Science courses in Panaji with a strong focus on practical learning, industry-relevant curriculum, and experienced faculty, 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 Panaji is open to individuals with a background in mathematics, statistics, engineering, or any other relevant field, making it accessible to a wide range of professionals seeking to enhance their data science skills.

DataMites in Panaji provides industry-oriented data science courses, with a focus on practical applications, hands-on experience, and expert faculty, making it an excellent choice for individuals looking to acquire in-demand data science skills and advance their careers in the field.

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

Upon completing the data science course in Panaji, students receive an IABAC certification that is globally recognized, providing them with an advantage in job placements and internship programs.

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

DataMites in Panaji provides a wide variety of data science courses, covering 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.

DataMites is well-known for its team of immensely experienced data science educators. These instructors bring a wealth of expertise, qualifications, and certifications to the table. Leveraging their extensive industry knowledge, they deliver exceptional instruction, ensuring students develop a comprehensive understanding of the subject matter.

DataMites provides flexible learning options to accommodate the preferences of students. They offer a diverse range 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 aligns with their needs, making it convenient for them to pursue their data science education.

DataMites offers a comprehensive training approach and provides a complimentary demo class, enabling students to gain a better understanding of the training process and its various components. This allows individuals to enhance their knowledge and make informed decisions about their data science education.

Learning Through Case Study Approach

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

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

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

DataMites offers its Data Science Course in Panaji at varying price points: INR 35,000 for live online training, INR 21,000 for blended learning, and INR 44,000 for on-demand classroom training. Students have the flexibility to choose the pricing option that best suits their preferences and learning requirements.

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

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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