DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE COURSE LEAD MENTORS

DATA SCIENCE COURSE FEE IN BANGALORE

Live Virtual

Instructor Led Live Online

110,000
70,623

  • 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
42,948

  • 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
80,873

  • 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 BANGALORE

UPCOMING DATA SCIENCE CLASSROOM CLASSES IN BANGALORE

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 BANGALORE

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 BANGALORE

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN BANGALORE

The data science sector is experiencing a notable surge, especially in Bangalore, often called India's Silicon Valley. As per Mordor Intelligence, the Data Science Platform Market is expected to increase from USD 276.68 billion in 2023 to USD 278.89 billion by 2028, with a CAGR of 0.16% from 2023 to 2028. In response to this growth, DataMites is providing hands-on data science training in Bangalore, inclusive of internships and job placements, to cultivate a new generation of proficient data science experts.

DataMites is globally recognized for its excellence in data science courses in Bangalore. The Certified Data Scientist Course, suitable for beginners and intermediates, focuses on building a strong base in data science, covering essential areas such as statistics, mathematics, Python, and machine learning. This career-focused program aims to provide learners with the necessary skills and knowledge to thrive in the dynamic field of data science.

DataMites 3-Phase Learning Methodology

At DataMites, we follow a unique and structured 3-Phase Learning Methodology to ensure a well-rounded education in data science:

Phase 1 - Pre-Course Self-Study: We offer high-quality video content that lays a strong foundation in the basic concepts of data science.

Phase 2 - Interactive Training: Students can choose between live online data science training in Bangalore and data science classroom training in Bangalore. This phase involves 20 hours of training per week for three months, focusing on a comprehensive syllabus, hands-on projects, and guidance from expert trainers and mentors.

Phase 3 - Internship & Placement Support: In this essential phase, students tackle 20 capstone projects and a client-specific project, earning a valuable internship certificate. Our Placement Assistance Team (PAT) provides thorough career guidance and support within our data science program. The data science training with placement in Bangalore, is designed to hone practical skills, preparing students for a smooth entry into careers in AI and data science.

The Demand for Data Scientists 

Data science is not just a field of study; it's a career path that leads to numerous opportunities. In Bangalore, known as the IT capital of India, the demand for data scientists is particularly high. This demand is mirrored in the attractive salaries and diverse job roles available in the field. DataMites data science courses in Bangalore are specifically designed to prepare students for these high-demand roles, ensuring that they are not only knowledgeable but also industry-ready.

Top DataMites Certifications in Data Science 

DataMites takes pride in offering some of the most sought-after data science certifications:

Statistics for Data Science: This course delves into the crucial statistical methods and tools necessary for data analysis within the Data Science realm.

Data Science for Managers: Specifically crafted for managers, this course explores the tactical implementation of Data Science in business decision-making.

Python Applications in Data Science: This course focuses on the application of Python, a vital programming language, in diverse Data Science contexts.

Data Science Foundation: An all-encompassing beginner's course establishing the basic concepts of Data Science.

Data Science for Marketing: Offers specialized training on using Data Science techniques to gain strategic insights in the marketing field.

Data Science with R: Aims to provide in-depth knowledge of using R for data analysis and statistical modeling in Data Science.

Data Science for Operation: Teaches the application of data science to enhance operational efficiency, optimize supply chains, and improve processes across industries.

Data Science for Finance: A deep dive into financial data analysis and risk management, focusing on skills for informed decision-making and financial performance enhancement.

Data Science in Human Resources: This course explores how data science intersects with HR, focusing on using data for effective talent management and workforce planning.

Data Science Foundation: Offers a basic understanding of data science principles, techniques, and tools, suitable for beginners in data analysis.

Diploma in Data Science: Provides extensive training in data science, covering areas like data analysis, machine learning, and data visualization, to prepare for advanced roles in the field.

Each certification caters to specific career goals and industry needs, ensuring our students receive the best training to advance their careers in data science in Bangalore.

DataMites Data Science Course Curriculum in Bangalore

The Certified Data Scientist Course in Bangalore offered by DataMites provides an in-depth and comprehensive curriculum, expertly crafted to cover a broad spectrum of subjects in data science. This extensive training is segmented into 9 distinct modules, each dedicated to a specific area of data science, ensuring a well-rounded educational experience. 

Python Essentials

Fundamentals of Python
Control Structures in Python
Python's Data Structures
Function Implementation in Python
Utilizing Python's Numpy Library
Exploring Python's Pandas Library

Fundamentals of Data Science

Core Principles of Data Science
Basics of Data Engineering
Python Applications in Data Science
Data Visualization using Python
Fundamentals of R Programming
Statistical Methods
Introduction to Machine Learning

Expertise in Machine Learning

Overview of Machine Learning
Linear Regression Techniques
Implementation of Logistic Regression
K-nearest Neighbors Algorithm
K Means for Cluster Analysis
Exploring Principal Component Analysis (PCA)
Decision Trees Methodology
Naïve Bayes Approach
Techniques in Gradient Boosting & Xgboost
Support Vector Machine (SVM) Applications
Basics of Artificial Neural Networks (ANN)
Advanced Machine Learning Concepts

Advanced Topics in Data Science

Time Series Analysis with ARIMA
Techniques in Feature Engineering
Conducting Sentiment Analysis
Regular Expressions in Python
Deploying ML Models Using Flask
Advanced Data Analysis Techniques in MS Excel
Utilizing AWS Cloud for Data Science
Exploring Azure in Data Science

Version Control Using Git

Introduction to Git
Managing Git Repositories and GitHub
Git Operations: Commits, Pull, Fetch, Push
Techniques in Tagging, Branching, Merging
Reversing Changes in Git
Integrating Git with GitHub and Bitbucket

Basics of Big Data

Introduction to Big Data
Understanding HDFS and MapReduce
Foundations of PySpark
Spark SQL and Integration with Hadoop Hive
Machine Learning Implementation with Spark ML
Exploring Kafka and Spark

Certified Business Intelligence Analyst

Introductory Concepts in Business Intelligence
Basics of BI Using Tableau
Connecting Data Sources in Tableau
Generating Business Insights with Tableau
Creating Dashboards, Stories, and Pages in Tableau
Business Intelligence with Power BI

Databases: SQL and MongoDB

Database Fundamentals
SQL Basics for Beginners
Understanding Data Types and Constraints
Working with Databases and Tables in MySQL
SQL Join Operations
Command and Clause Usage in SQL
Document-Oriented and NoSQL Databases

Foundation in Artificial Intelligence

Overview of Artificial Intelligence
Introduction to Deep Learning
Fundamentals of Tensorflow
Basics of Computer Vision
Introduction to Natural Language Processing (NLP)
Ethical Considerations and Issues in AI

DataMites Data Science Course Tools in Bangalore

In the Certified Data Scientist Training in Bangalore, a diverse array of tools integral to the field of data science is thoroughly covered. These tools are essential for practical applications in data analysis, machine learning, and big data processing. The proficiency in these data science tools equips learners with the necessary skills to tackle real-world data science challenges. The course includes:

  1. Anaconda
  2. Python
  3. Git
  4. Hadoop
  5. MongoDB
  6. Amazon SageMaker
  7. Apache Pyspark
  8. Google Bert
  9. Google Colab
  10. Advanced Excel
  11. Scikit Learn
  12. Azure Machine Learning
  13. Flask
  14. Apache Kafka
  15. GitHub
  16. Numpy
  17. MySQL
  18. TensorFlow
  19. Pandas
  20. Tableau
  21. PyCharm
  22. Atlassian BitBucket
  23. Power BI
  24. Natural Language Toolkit

Why Choose DataMites for Data Science Training in Bangalore 

Selecting DataMites for data science courses in Bangalore means:

Expert-Guided Teaching: Our curriculum is crafted and delivered by seasoned professionals in the industry, including the distinguished AI specialist Ashok Veda, who has a rich 19-year background in Analytics and Data Science. Learning under the guidance of such experts provides invaluable real-world insights and hands-on knowledge in data science.

Globally Acknowledged Certifications: The data science certifications provided by our course, notably from IABAC and NASSCOM FutureSkills, hold international recognition. They significantly enhance your professional standing and boost your global employability, showcasing your high level of skill and understanding in data science.

Advanced Educational Resources: DataMites ensures students have access to the latest and most advanced learning materials, including cutting-edge software, contemporary data science tools, and relevant industry case studies. This keeps you in line with the latest industry trends and methods.

Practical Learning Approach: At DataMites, emphasis is placed on experiential learning. Our course offers numerous hands-on projects and case studies that mirror the challenges faced in the real world of data science. With 20 capstone projects and 1 client project included, our students are well-prepared for real data science roles.

Flexible Study Modes: We accommodate various learning preferences by offering both online data science courses in Bangalore and data science training offline in Bangalore in the prominent cities of Kudlu Gate, Marathahalli, and BTM. This flexibility allows students and professionals to manage their education alongside other personal and professional commitments.

The Importance of Data Science Internships in Bangalore 

Internships in data science in Bangalore are key to translating academic learning into practical, real-world skills. They provide essential hands-on experience and a deeper insight into the workings of the industry.

At DataMites, our data science courses with internship in Bangalore offer a comprehensive blend of theoretical knowledge and crucial practical exposure. This combination of classroom learning and real-world experience, augmented with a data science internship certification from a renowned AI company, greatly enhances the capabilities and job prospects of our students in the ever-evolving field of data science in Bangalore.

Data Science Placement in Bangalore

The job market in Bangalore, particularly in data science, demands well-rounded professionals who can smoothly transition from academic environments to the workforce. 

At DataMites, we address this requirement by providing in-depth data science courses with placement in Bangalore, through our expert Placement Assistance Team (PAT). Our curriculum is structured as an all-encompassing 'data science job ready' program, where we train students not only in technical aspects but also in essential soft skills. This dual focus ensures that our graduates are not just knowledgeable, but also highly employable in the thriving data science industry of Bangalore.

Bangalore, often hailed as India's Silicon Valley, leads the nation's IT revolution, blending traditional culture with cutting-edge tech innovation. It's a prime spot for IT professionals and enterprises, thanks to its strong infrastructure, rich talent pool from renowned educational institutions, and supportive government policies. These factors collectively establish Bangalore as a top destination for IT and data science.

Bangalore's job market thrives in data science, offering high-demand data science job roles in Bangalore like Data Scientist, Data Analyst, Machine Learning Engineer, and Business Intelligence Analyst. Other sought-after positions include Data Engineer, Data Science Consultant, Statistician, Quantitative Analyst, AI Research Scientist, and Big Data Engineer. These roles offer competitive salaries and vast prospects for career advancement in the city's dynamic IT sector.

Bangalore is a hub for data science professionals, with LinkedIn reporting over 23,000 Data Scientist job openings in Bangalore. This underscores the city's high demand for expertise in this area. The average national salary for data scientists in India is around INR 12,00,000, but in Bangalore, it's notably higher with a data scientists salary in Bangalore at INR 17,00,000 per year. (Glassdoor) This reflects Bangalore's role as a center for tech innovation and a prime location for data science careers.

DataMites in data scientist courses in Bangalore is your pathway to mastering data science. Our educational portfolio extends beyond our in-depth data science curriculum, encompassing a wide array of courses aligned with the ever-changing industry requirements. This includes expert-led training in areas such as data analytics, machine learning, Python programming, artificial intelligence, MLOps, and data engineering, ensuring a well-rounded educational experience. Join us now and embark on your journey to become a skilled data science professional.

ABOUT DATAMITES DATA SCIENCE COURSE IN BANGALORE

Data Science is the art of collecting, classifying, summarizing data sets, and deriving valuable insights from these data sets. These insights are used to take further decisions. Data Science has become instrumental in adding value to the business.

There are no mandatory prerequisites. However, basic knowledge of Statistics would be an added advantage.

  • Analytical skills

  • Basic knowledge of Mathematics and Statistics 

  • Knowledge of coding

  • Skills of working with programming languages like ‘R’ and Python.

The various business skills required, to become a Data Scientist are as follows:-

  • Industry Knowledge

  • Problem Solving Skills

  • Communication Skills 

  • Curiosity  

Industry Knowledge:- A Data Scientist should have a clear understanding of the areas that need to be paid attention and the areas that need to be ignored. This is possible only if the Data Scientist has sound knowledge of the industry.

Problem Solving Skills:- A Data Scientist is known for finding solutions to problems. For doing so, a Data Scientist must understand the problem, which can be achieved only after a deep study of the scenario.

Communication Skills:- A Data Scientist often needs to communicate the findings arrived at, with regards to analytics and business insights. A Data Scientist should be a good conversationalist.

Curiosity:- A Data Scientist should always be curious enough while approaching a problem. Finding out the root of the problem depends upon the curiosity of a Data Scientist.

As far as Data Scientist is concerned Python is the most effective programming language, with a lot of libraries available. Python can be deployed at every phase of data science functions. It is beneficial in capturing data and importing it into SQL. Python can also be used to create data sets.

Data Science is all about managing a set of information received from various sources, to arrive at conclusions. The data that is acquired needs to be analysed and decisions need to be taken. Statistics makes it easier to work on data. Various statistical techniques such as Classification, Regression, Hypothesis Testing, Time Series Analysis is used to construct data models. With the help of Statistics, a Data Scientist can gain better insights, which enables to effectively streamline the decision-making process.

  • The different roles, Data Science is subjected to, in an organisation.

  • Analysing and managing projects.

  • Employing various data models.

  • Making use of sampling techniques

  • Prediction and Analysis

  • Segmentation through clustering technique

  • Making use of Linear and Logistics regression methods

The duration of the Data Science course in Bangalore is 8 months, a total of 120 hours of training. The training sessions are provided on weekdays and weekends. You can opt between the two, as per your convenience.

The Data Science course fee in India ranges from Rs 50000 to Rs 150000. DataMites DataMites offers three modes of training in Bangalore, namely Online, Classroom and Self Learning. Data Science courses in India are offered at an affordable price of Rs 88000 for Online and Classroom sessions and Self Learning at Rs 62000.

Data Science is a vast subject for study, it is a mix of Statistics and Computer Science. DataMites in Bangalore, offers quality training sessions in Data Science, Artificial Intelligence, Machine Learning etc. The data science courses provided by DataMites in Bangalore are exclusively designed in tune with the current industry requirements. Also with many projects to work on, under the mentoring of industry experts. 

 

Whether you need a P.G degree to pursue a data science certification can be better understood, based on your knowledge in the Science & Technology, Engineering and Management domain. If you have a strong knowledge base in any of the mentioned areas

After completing the  Certified Data Scientist Course in Bangalore, an individual will be well equipped with the following:-

  • Intense knowledge of the workflow, of a Data Science project.

  • Learn the basics of the use of Statistics in Data Science.

  • Gain knowledge of the various Machine Learning Algorithms.

  • Knowledge of Data Forecasting, Data Mining and Data Visualization.

  • Ways to deliver end to end Data Science projects.

Bangalore is known as the technological hub of India, with lots of business opportunities and large corporate houses adorning the city. This, in turn, contributes to new employment opportunities being created. Hence opting for a Data Science course in Bangalore will help an individual to leverage the available possibilities in the best manner, to land a career in Data Science.

Data Scientists have been in great demand in Bangalore. As an acknowledgement to this rising demand, DataMites has come with the Certified Data Scientist course in Bangalore. The course covers all the areas of Data Science, Machine Learning, basics of Mathematics and Statistics, etc. Also, the Certified Data Scientist course, covers all the practical aspects of the knowledge required to become a Data Scientist.

Bangalore, in India, is known as the technological hub of India, with lots of business opportunities. It consists of many large companies, business houses, with large amounts of transactions happening every day, as a result of which there is an equally large amount of data generated daily. Also, India is known for many recognised universities. Learning Data Science in India will be a great opportunity for students as well as professionals. Graduates freshers and employees working in organisations can leverage these opportunities to easily land a Data Science job.

Bangalore has several large companies, Banking and Financial institutions, Insurance companies, Automobile companies, Manufacturing enterprises, as a result, Bangalore happens to be the most sought after city when it comes to career opportunities in Data Science.

Bangalore is a city that is always bustling with business activities, financial transactions happening in huge volumes. Hence it serves to be a great opportunity for starting a Data Science Career in Bangalore.

As per the reports published by Indeed.com, the average salary of Data Scientists in Bangalore is ₹ 8,37,198  annually.

A large amount of data is being generated through various activities daily. For instance, data of investments done in the stock market, data of the financial transactions, data with regards to the browsing history. The company which you are associated with records and maintains your data. For example, when you make regular online purchases, the provider collects all the information on your activity and stores it securely. It then makes use of the same data to make further product recommendations. Different companies use data in different ways.

  • Small-sized companies employ  Google Analytics for analyzing the small size of data.

  • Medium-sized companies have data that will need a Machine Learning Expert to work on it.

  • Big sized companies may need data science professionals who are experts in Machine Learning and Data Visualization.

Data Science is all about the collection and classification of information and using the same to derive insights. Python and R are the two programming languages that are used in the data science process. Some of the reasons, for python being the most preferred programming language in comparison to R:-

  • Easy to learn: Python is easier to understand and master, in comparison to R 

  • Flexible: The flexibility offered by Python offers is better when compared to the R programming language.

  • Availability of libraries: Python has a wide range of libraries available, such as pandas, scikit-learn, etc. This makes it easier in handling machine learning projects.

  • Data visualization: By using matplotlib in Python, you can do the plotting of complex data representations into 2D plots. Data visualization is a significant process in the job of a data scientist. Python can be used for Data Visualisation.

  • Globally Recognised Certification

  • Experienced Trainers

  • Industry aligned courses

  • Internship Opportunities

  • Job assistance

The mode of training offered by DataMites for Data Science course in Bangalore is online training.

  • Graduate Freshers 

  • Individuals looking to switch their career into Data Science.

  • Professionals who have experience in the Data Science domain.

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FAQ’S OF DATA SCIENCE TRAINING IN BANGALORE

DataMites in Bangalore provides a range of courses in Data Science, Machine Learning, Artificial Intelligence, with training sessions uncompromised of quality, conducted by industry experts, who possess intense knowledge of the subject matter. DataMites provides 3 different modes of training in India, namely Online, Classroom and Self Learning. The sessions are conducted by experienced industry professionals.

DataMites is a training provider that imparts quality training and upskilling in Data Science, for freshers who are data enthusiasts and professionals who wish to enhance their career possibilities. Above all DataMites offers the following;-

  • Industry aligned courses 

  • Online sessions that ensure good engagement.

  • Expert Trainers, who possess a vast knowledge of the subject matter.

  • Case studies approach, which delved deep into the practical application of the concepts.

  • Opportunity to get connected with a network of Data Science professionals.

  • Career Guidance

  • Opportunity to work on projects 

DataMites has a faculty of trainers who possess deep subject matter expertise and significant years of experience in the field of Data Science.

The Data Science course fee in India ranges from Rs 50000 to Rs 150000. DataMites offers three modes of training in Bangalore, namely Online, Classroom and Self Learning. Data Science courses in India are offered at an affordable price of Rs 88000 for Online and Classroom sessions and Self Learning at Rs 62000.

The registrations cancelled within 48 hrs of enrollment will be refunded in full. The processing time of the refund is within 30 days, from the date of the receipt of  cancellation request

Yes. You will receive a certificate from DataMites after the completion of the course.

DataMites in Bangalore offers certifications in collaboration with IABAC for courses in Data Science, Artificial Intelligence and Machine Learning. IABAC is a global body, which offers certifications in Business Analytics and Data Science. IABAC is founded on the principles of EDISON Data Science Framework (EDSF). All the data science certifications offered by DataMites are structured based on the industry trends.

Enrolling for online training online is very simple. The payment can be done using your debit/credit card that includes Visa Card, MasterCard; American Express or via PayPal. You will receive the receipt after the payment is successful. In case of more queries you can get in touch with our  educational counselor who will guide you with the same.

You have access to the online study materials from 6 months upto 1 year.

DataMites offer various modes of  training in Bangalore, namely Online, Classroom Self Learning mode.

DataMites offers data science sessions, both on weekdays and weekends. You can opt between the two, based on your convenience.

DataMites offers data science sessions, in the Morning and Evening. You can opt, based on your convenience.

Yes. DataMites does provide an online lab facility. You can visit prolab.datamites.com. When you visit the site, it asks for the password, you must enter the password given to you, to access the facility.

Yes. DataMites do provide live data science projects, which are done under the guidance of industry experts.

The data science course offered by DataMites in Bangalore includes 25 capstone projects and 1 client project.

The training sessions provided by DataMites in Bangalore are primarily online. However, classroom training can be made available if there is adequate demand.

DataMites is a training provider that imparts quality training and upskilling in Data Science, for freshers who are data enthusiasts and professionals who wish to enhance their career possibilities. Above all DataMites offers the following;-

  • Industry aligned courses 

  • Online sessions that ensure good engagement.

  • Expert Trainers, who possess a vast knowledge of the subject matter.

  • Case studies approach, which delved deep into the practical application of the concepts.

  • Opportunity to get connected with a network of Data Science professionals.

  • Career Guidance

  • Opportunity to work on projects

DataMites provides Flexi Pass, which gives you the privilege to attend unlimited batches in a year. The Flexi pass is specific to one particular course. Therefore if you have a Flexi pass for one particular course of your choice, you will be able to attend any number of sessions of that course. It is to be noted that a Flexi pass is valid for a particular period.

DataMites accepts all the online payments(Debit/Credit) through Razor pay. If you opt to pay through your credit card there will be an EMI option. DataMites collects token advance during the time of registration and the remaining payment should be settled in full before the completion of the course.

All the online sessions are recorded and will be shared with the candidates. If you miss any of the online sessions, you can still have access to the recordings later.

Yes. The Datamites certification exam fee is included in the total course fee. Therefore once you are registered for a course, you are also eligible to attend the exam.

Yes. DataMites offers internship opportunities along with the course. You will be mentored by industry experts through the internship. Once the internship is completed, DataMites provides you with the internship certificate along with the experience certificate.

The DataMites Placement Assistance Team(PAT)  helps the candidates to have an easy start in his/her career. The team will assist you in the following areas;-

  • Project Mentoring- 100 hrs Live mentoring in industry projects.

  • Interview Preparations- Mock Interview sessions.

  • Resume Support- Personal guidance in resume creation by professionals.

  • Doubt clearing sessions- Live doubt clearing sessions on 

  • Job updates- Interview connects.

No, DataMites doesn’t guarantee a job, but it will provide all the support and guidance needed, in getting a job, Resume Building, Interview preparations. DataMites internships offer a candidate to work with industry experts, which helps in knowing the corporate way of working. This proves as a stepping stone to an individual’s professional life.

DataMites internship programs are exclusively designed for a candidate to enable him/her to get a practical experience of working on live projects. The candidate gets an opportunity to work under the guidance of industry experts.

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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