DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE COURSE LEAD MENTORS

DATA SCIENCE COURSE FEE IN KOZHIKODE

Live Virtual

Instructor Led Live Online

110,000
72,345

  • IABAC® & NASSCOM® Certification
  • 8-Month | 700 Learning Hours
  • 120-Hour Live Online Training
  • 25 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

66,000
43,995

  • Self Learning + Live Mentoring
  • IABAC® & NASSCOM® Certification
  • 1 Year Access To Elearning
  • 25 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Leaner assistance and support

Classroom

In - Person Classroom Training

110,000
82,845

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

ARE YOU LOOKING TO UPSKILL YOUR TEAM ?

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UPCOMING DATA SCIENCE ONLINE CLASSES IN KOZHIKODE

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 KOZHIKODE

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 KOZHIKODE

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN KOZHIKODE

The Data Science course in Kozhikode has been meticulously crafted to empower students with the essential skills and knowledge required to thrive in the dynamic field of Data Science. Based on a report by Precedence Research, the data science market is anticipated to achieve a market size of USD 378.7 Billion by 2030, demonstrating a steady compound annual growth rate (CAGR) of 16.43%.

DataMites is a well-known institute renowned for its comprehensive Data Science course in Kozhikode. The institute offers a wide range of recognized courses in artificial intelligence, machine learning, data analytics, and deep learning. Students at DataMites can enjoy the flexibility of on-demand data science offline classes in Kozhikode, with a course duration of 8 months and a total of 700 learning hours, including 120 hours of live online training. With courses certified by IABAC, DataMites ensures a global impact on learners, providing internship and job assistance to enhance career prospects. Additionally, DataMites provides valuable assistance in internships and job placements, aiming to enhance the career prospects of its students. DataMites provides an exclusive Certified Data Scientist Course in Kozhikode to enhance the learning experience of the students.

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

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

Kozhikode, also known as Calicut, is a vibrant city in the state of Kerala, India, famous for its historic sites, beaches, and delicious cuisine. Data Science is emerging in the city of Kozhikode which holds immense potential in transforming industries and driving innovation through advanced analytics, machine learning, and artificial intelligence. Data science institutes in Kozhikode play a crucial role in nurturing and training skilled professionals for this rapidly evolving field. According to a PayScale report, the salary of a data scientist in India ranges from INR 9,10,238 per year. DataMites offers online data science training in Kozhikode with a comprehensive syllabus, study material, job training, and mock tests. Join DataMites and take the first step towards a rewarding and in-demand career by enrolling in the Data Science training course in Kozhikode today.

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

ABOUT DATAMITES DATA SCIENCE COURSE IN KOZHIKODE

Data science is a multidisciplinary field that involves extracting insights and knowledge from structured and unstructured data using various techniques such as data analysis, statistics, machine learning, and data visualization.

Learning data science is important because it enables individuals and organizations to make data-driven decisions, uncover hidden patterns and trends, and gain valuable insights that can lead to improved business strategies, efficiency, and innovation.

Essential skills for becoming a data scientist include proficiency in programming languages like Python or R, knowledge of statistics and mathematics, data manipulation and analysis skills, machine learning expertise, data visualization, and effective communication and storytelling abilities.

Effective learning of data science involves a combination of theoretical understanding, hands-on practice with real-world datasets, participating in projects and competitions, continuous learning and updating of skills, and leveraging online resources, courses, and mentorship.

Data scientists often face challenges such as dealing with large and complex datasets, data cleaning and preprocessing, selecting appropriate algorithms and models, handling missing or noisy data, interpreting results accurately, and staying updated with the rapidly evolving field.

The cost of a data science course in Kozhikode can vary depending on the institution and program, but it generally ranges from INR 40,000 to INR 50,000.

The requirements to enrol in a data science course can vary but typically include a basic understanding of programming, mathematics, and statistics. Some courses may have prerequisites or recommended knowledge in specific areas.

Data science offers promising career prospects with a high demand for skilled professionals in various industries such as technology, finance, healthcare, marketing, and more. Job roles in data science include data scientist, data analyst, machine learning engineer, data engineer, and business analyst, among others.

Obtaining a certification in data science is important as it demonstrates proficiency and credibility in the field, enhances job prospects, and provides validation of skills and knowledge to potential employers.

Yes, data science courses are in high demand as organizations increasingly recognize the value of data-driven decision-making and require skilled professionals to extract insights from data and drive business success.

Data science is considered a secure career choice due to the growing demand for data professionals across industries. However, it is important to continuously update skills and stay abreast of industry trends to maintain a competitive edge.

The difficulty level in studying data science can vary depending on individual aptitude, prior knowledge, and the depth of the subject matter. It requires a strong foundation in mathematics, statistics, and programming, but with dedication and practice, it can be mastered.

Python is widely used and highly recommended for data science due to its rich ecosystem of libraries and tools for data analysis, machine learning, and visualization. However, proficiency in other languages like R or SQL can also be beneficial depending on specific requirements.

SQL (Structured Query Language) is important for data science as it allows for efficient querying and manipulation of relational databases. It is useful for data extraction, cleaning, and aggregation tasks, which are common in data science projects.

A strong background in statistics is crucial for data science as it provides the foundation for understanding and interpreting data, selecting appropriate statistical techniques and models, and evaluating the significance and reliability of results.

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FAQ’S OF DATA SCIENCE TRAINING IN KOZHIKODE

DataMites in Kozhikode stands out as an excellent choice for those interested in pursuing a Data Science course. It sets itself apart with its highly skilled instructors, an extensive curriculum that encompasses various data science subjects, an emphasis on practical learning through hands-on exercises, industry-oriented projects, and dedicated support in finding placement opportunities.

The DataMites Certified Data Scientist Course in Kozhikode extends a warm welcome to individuals with a strong foundation in mathematics and programming, as well as those with prior experience in statistics, engineering, or related fields. This inclusive approach enables a diverse range of participants to pursue their career aspirations in the dynamic field of Data Science.

Opting for the DataMites data science course in Kozhikode is a wise choice, given its thoughtfully designed curriculum, knowledgeable faculty, engaging hands-on learning opportunities, practical project assignments, and industry-focused training. This all-encompassing program significantly improves your comprehension and expertise in the field of data science, thereby bolstering your chances of securing employment.

The course extends for a duration of 8 months, comprising 700 learning hours, out of which 120 hours are dedicated to live online training.

Upon the successful completion of the data science course in Kozhikode, students receive the prestigious IABAC certification, which holds substantial global recognition. This esteemed certification serves as a valuable credential, enhancing job prospects and enabling participation in internship programs, thus unlocking numerous opportunities within the field of data science.

After completing the course, DataMites provides strong support and guidance for placements through their dedicated Placement Assistance Team (PAT). The PAT offers personalized assistance to individuals, ensuring they receive comprehensive support in finding suitable job placements. This tailored support greatly improves employment prospects and opens up a wide range of opportunities in the field of data science.

DataMites provides a wide selection of data science courses in Kozhikode, encompassing a diverse range of topics. These courses include 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 widely recognized for its outstanding team of industry-expert educators who have deep expertise and extensive experience in the field of data science. These instructors are highly qualified, hold prestigious certifications, and bring their wealth of knowledge to the classroom, delivering exceptional instruction. With their guidance, students are empowered to develop a comprehensive understanding of the subject matter

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

DataMites provides a comprehensive overview of its training approach, ensuring students have a clear understanding of the training process and its elements. Additionally, they offer a complimentary demo class, allowing individuals to fully grasp the training methodology. This enables prospective students to evaluate the quality and suitability of the training before making a commitment, ensuring an informed decision.

Learning Through Case Study Approach

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

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

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

DataMites provides its Data Science Course in Kozhikode at various price points, offering a variety of options to accommodate different preferences. These include INR 35,000 for live online training, INR 21,000 for blended learning, and INR 44,000 for on-demand classroom training. This flexible pricing structure allows individuals to choose the plan that suits their budget and preferred mode of learning.

To obtain the participation certificate and book the certification exam, it is essential to submit valid photo identification proofs, such as a National ID card or a Driving license. These identification proofs play a crucial role in ensuring the authenticity and accuracy of the certification process.

According to a PayScale report, the salary of a data scientist in India ranges from INR 9,10,238 per year.

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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