DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE COURSE LEAD MENTORS

DATA SCIENCE COURSE FEE IN MYSORE

Live Virtual

Instructor Led Live Online

110,000
68,900

  • 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
41,900

  • 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
78,900

  • 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 MYSORE

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 MYSORE

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 MYSORE

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN MYSORE

Organizations are gaining data from different sectors, channels, and platforms, including smartphones, e-commerce sites, social media, healthcare surveys, internet searches, connected devices, and more. The abundance of data available opened the door to a new field called Data Science. Data science is a study based on these big data which can effectively contribute to the creation of advanced operational tools in the future. Data scientists provide useful insights for their organization after analyzing large amounts of complex data or big data. They use a combination of different fields of work in statistics and computation to interpret data for decision-making purpose.

Candidates with Data Science certification can secure a quick job in this highly competitive job market. However, It is essential to remember that they need to be get trained from the best Data Science training institute in Mysore to attain those crucial job-oriented skills involved in the technology. DataMites™ is the top training provider accredited by the International Association of Business Analytics Certifications (IABAC) who is offering Data Science course through online in Mysore. Top faculties impart the Data Science training sessions by providing practical insights into real-life applications. Furthermore, Candidates gain hands-on exposure to quickly solve the business problems after practicing numerous real-time examples in 24/7 cloud lab. The Data Science courses are available online, and on demand, the classroom training will be conducted in Mysore. Whether you are thinking to spend a couple of hours per day or extended hours per week, you can work on enhancing your new skills at the pace that is right for you.

A data science course is increasingly valuable in today's digital era as it equips individuals with the skills to unlock the power of data. By mastering statistical analysis, machine learning, and data visualization techniques, learners can derive insights and make informed decisions across various industries. With the rising demand for data-driven solutions, a data science course opens doors to lucrative career opportunities and allows individuals to contribute to innovation and problem-solving in a data-driven world.

It is a complete course with a detailed learning that covers a 9 course bundle of

1) Python for Data Science

2) Statistics for Data Science

3) Machine Learning Associate

4) Machine Learning expert

5) Time series foundation

6) Model deployment (Flask-API)

7) Deep Learning -CNN Foundation

8) Tableau Foundation

9) Data Science business concepts that helps the aspirants in specialising the area

Certified Data Scientist course that is being conducted in Mysore comes with 8-months/700+ hours course duration.

The structured three phase modules of this course are

Phase 1 (15 Days)

Pre-course study helps you to develop your knowledge on the basics of Data Science and Machine Learning. It is a self-study phase that needs to be completed before entering to phase 2 module. Phase 1 includes high-quality videos, E-books covering the syllabus of Basic Python Language, Basic Mathematics for Data Science, Statistics essentials for Data Science, Beginners guide to Machine Learning (E-book) and Practice Materials. Furthermore, it facilitates the candidates to practice scripts at a cloud lab conveniently.

Phase 2 (2 Months)

This is the most crucial part of the training that comes with fulltime intensive training sessions through any of the convenient channels, Traditional Classroom Training, Live Instructor-Led Online Training, and Self Paced Learning / E-learning. This phase covers the next higher level syllabus of Python/R Programming, Statistics, Machine Learning Associate and expert.

Phase 3 PAT Services (4 Months)

This is a dedicated part for candidates to make them market ready after the series of intensive coaching and learning. It covers 4-month Project Mentoring, exposure to 5+ detailed Industry related projects, revision sessions, access to an extensive collection of interview questions, resume support, mock interview sessions, job updates and experience certificate.

On completion of these structured three phase DataMites™ Data Science training, you are assured of gaining the essential skills and confidence to perform your "Super Hero duty" as Data Scientist.

A data science course in Mysore offers a promising opportunity for individuals in the region to gain expertise in the field of data science. Mysore, known for its emerging tech industry and educational institutions, provides a conducive environment for learning and professional growth. By enrolling in a data science course in Mysore, learners can acquire in-demand skills, tap into local job prospects, and become part of a vibrant data science community in the city.

The Key Features of Data Scientist Training in Mysore

Project Mentoring: You can gain experience by working on live projects from global AI and ML Solution providers.

Revision Sessions: Lots of revisions and multiple opportunities to clarify your doubts with our chief Data Scientist even after course completion.

Resume Support: You can curate a customized resume at the hands of experts to make your first impression the best one.

Interview Questions: Equip yourself with the latest interview questions and answers to face the interviews confidently.

Mock Interviews: Our experts will help you to increase your job interview success rate and get hired quickly by practicing numerous mock interview sessions.

Job Updates: All latest job updates which are validated and perspective are posted regularly by PAT Team in PAT Facebook group.

Along with the data science courses, DataMites also provides artificial intelligence, data engineer, data analytics, deep learning, tableau, python training, r programming, and machine learning courses in Mysore.

DESCRIPTION OF DATA SCIENCE COURSE IN MYSORE

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 to 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 Mysore 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 offers Live Online modes of training in Mysore at a fee of Rs 88000.  

Data Science is a vast subject for study, it is a mix of Statistics and Computer Science. DataMites in Mysore, offers quality training sessions in Data Science, Artificial Intelligence, Machine Learning etc. The data science courses provided by DataMites in Mysore 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 Mysore, 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.

Mysore 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 Mysore 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 Mysore. As an acknowledgement of this rising demand, DataMites has come with the Certified Data Scientist course in Mysore. The course covers all the areas of Data Science, Machine Learning, the 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. 

Mysore, 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. 

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

Mysore 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 Mysore.

As per the reports published by Indeed.com, the average salary of Data Scientists in Mysore 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 with which you are associated 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 the Data Science course in Mysore 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 MYSORE

DataMites in Mysore offers globally recognised 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 the EDISON Data Science Framework (EDSF). Machine Learning, Artificial Intelligence. All the data science certifications offered by DataMites are structured based on industry trends.

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  Online, Classroom and Self Learning. Data Science courses in India are offered at an affordable price of Rs 88000 for Online training.

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 the cancellation request

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

DataMites in Mysore 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. The sessions are conducted by experienced industry professionals. 

 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 up to 1 year.

DataMites offer various modes of training in Mysore, 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 Mysore includes 25 capstone projects and 1 client project.

The training sessions provided by DataMites in Mysore 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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