Instructor Led Live Online
Self Learning + Live Mentoring
In - Person Classroom Training
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.
MODULE 1: DATA SCIENCE ESSENTIALS
• Introduction to Data Science
• Evolution of Data Science
• Big Data Vs Data Science
• Data Science Terminologies
• Data Science vs AI/Machine Learning
• Data Science vs Analytics
MODULE 2: DATA SCIENCE DEMO
• Business Requirement: Use Case
• Data Preparation
• Machine learning Model building
• Prediction with ML model
• Delivering Business Value.
MODULE 3: ANALYTICS CLASSIFICATION
• Types of Analytics
• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics
• EDA and insight gathering demo in Tableau
MODULE 4: DATA SCIENCE AND RELATED FIELDS
• Introduction to AI
• Introduction to Computer Vision
• Introduction to Natural Language Processing
• Introduction to Reinforcement Learning
• Introduction to GAN
• Introduction to Generative Passive Models
MODULE 5: DATA SCIENCE ROLES & WORKFLOW
• Data Science Project workflow
• Roles: Data Engineer, Data Scientist, ML Engineer and MLOps Engineer
• Data Science Project stages.
MODULE 6: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
MODULE 7: DATA SCIENCE INDUSTRY APPLICATIONS
• Data Science in Finance and Banking
• Data Science in Retail
• Data Science in Health Care
• Data Science in Logistics and Supply Chain
• Data Science in Technology Industry
• Data Science in Manufacturing
• Data Science in Agriculture
MODULE 1: PYTHON BASICS
• Introduction of python
• Installation of Python and IDE
• Python Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
MODULE 2: PYTHON CONTROL STATEMENTS
• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements
MODULE 3: PYTHON DATA STRUCTURES
• Basic data structure in python
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods
MODULE 4: PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions
MODULE 1: OVERVIEW OF STATISTICS
• Introduction to Statistics
• Descriptive And Inferential Statistics
• Basic Terms Of Statistics
• Types Of Data
MODULE 2: HARNESSING DATA
• Random Sampling
• Sampling With Replacement And Without Replacement
• Cochran's Minimum Sample Size
• Types of Sampling
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
• Multi stage Sampling
• Sampling Error
• Methods Of Collecting Data
MODULE 3: EXPLORATORY DATA ANALYSIS
• Exploratory Data Analysis Introduction
• Measures Of Central Tendencies: Mean,Median And Mode
• Measures Of Central Tendencies: Range, Variance And Standard Deviation
• Data Distribution Plot: Histogram
• Normal Distribution & Properties
• Z Value / Standard Value
• Empirical Rule and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
• Covariance & Correlation
MODULE 4: HYPOTHESIS TESTING
• Hypothesis Testing Introduction
• P- Value, Critical Region
• Types of Hypothesis Testing
• Hypothesis Testing Errors : Type I And Type II
• Two Sample Independent T-test
• Two Sample Relation T-test
• One Way Anova Test
• Application of Hypothesis testing
MODULE 1: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
MODULE 2: PYTHON NUMPY PACKAGE
• Introduction to Numpy Package
• Array as Data Structure
• Core Numpy functions
• Matrix Operations, Broadcasting in Arrays
MODULE 3: PYTHON PANDAS PACKAGE
• Introduction to Pandas package
• Series in Pandas
• Data Frame in Pandas
• File Reading in Pandas
• Data munging with Pandas
MODULE 4: VISUALIZATION WITH PYTHON - Matplotlib
• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN
• Seaborn: Basic Plot
• Advanced Python Data Visualizations
MODULE 6: ML ALGO: LINEAR REGRESSSION
• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python
MODULE 7: ML ALGO: LOGISTIC REGRESSION
• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python
MODULE 8: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Modeling in Python
MODULE 9: ML ALGO: KNN
• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python
MODULE 1: FEATURE ENGINEERING
• Introduction to Feature Engineering
• Feature Engineering Techniques: Encoding, Scaling, Data Transformation
• Handling Missing values, handling outliers
• Creation of Pipeline
• Use case for feature engineering
MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python
MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)
• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python
MODULE 4: ML ALGO: DECISION TREE
• Introduction to Decision Tree & Random Forest
• How it works
• Modeling and Evaluation in Python
MODULE 5: ENSEMBLE TECHNIQUES - BAGGING
• Introduction to Ensemble technique
• Bagging and How it works
• Modeling and Evaluation in Python
MODULE 6: ML ALGO: NAÏVE BAYES
• Introduction to Naive Bayes
• How it works: Bayes' Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python
MODULE 7: GRADIENT BOOSTING, XGBOOST
• Introduction to Boosting and XGBoost
• How it works?
• Modeling and Evaluation of in Python
MODULE 1: TIME SERIES FORECASTING - ARIMA
• What is Time Series?
• Trend, Seasonality, cyclical and random
• Stationarity of Time Series
• Autoregressive Model (AR)
• Moving Average Model (MA)
• ARIMA Model
• Autocorrelation and AIC
• Time Series Analysis in Python
MODULE 2: SENTIMENT ANALYSIS
• Introduction to Sentiment Analysis
• NLTK Package
• Case study: Sentiment Analysis on Movie Reviews
MODULE 3: REGULAR EXPRESSIONS WITH PYTHON
• Regex Introduction
• Regex codes
• Text extraction with Python Regex
MODULE 4: ML MODEL DEPLOYMENT WITH FLASK
• Introduction to Flask
• URL and App routing
• Flask application – ML Model deployment
MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL
• MS Excel core Functions
• Advanced Functions (VLOOKUP, INDIRECT..)
• Linear Regression with EXCEL
• Data Table
• Goal Seek Analysis
• Pivot Table
• Solving Data Equation with EXCEL
MODULE 6: AWS CLOUD FOR DATA SCIENCE
• Introduction of cloud
• Difference between GCC, Azure, AWS
• AWS Service ( EC2 instance)
MODULE 7: AZURE FOR DATA SCIENCE
• Introduction to AZURE ML studio
• Data Pipeline
• ML modeling with Azure
MODULE 8: INTRODUCTION TO DEEP LEARNING
• Introduction to Artificial Neural Network, Architecture
• Artificial Neural Network in Python
• Introduction to Convolutional Neural Network, Architecture
• Convolutional Neural Network in Python
MODULE 1: DATABASE INTRODUCTION
• DATABASE Overview
• Key concepts of database management
• Relational Database Management System
• CRUD operations
MODULE 2: SQL BASICS
• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
MODULE 3: DATA TYPES AND CONSTRAINTS
• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment
MODULE 4: DATABASES AND TABLES (MySQL)
• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table
MODULE 5: SQL JOINS
• Inner Join, Outer Join
• Left Join, Right Join
• Self Join, Cross join
• Windows function: Over, Partition, Rank
MODULE 6: SQL COMMANDS AND CLAUSES
• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries
MODULE 7 : DOCUMENT DB/NO-SQL DB
• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
MODULE 1: GIT INTRODUCTION
• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture
MODULE 2: GIT REPOSITORY and GitHub
• Git Repo Introduction
• Create New Repo with Init command
• Git Essentials: Copy & User Setup
• Mastering Git and GitHub
MODULE 3: COMMITS, PULL, FETCH AND PUSH
• Code Commits
• Pull, Fetch and Conflicts resolution
• Pushing to Remote Repo
MODULE 4: TAGGING, BRANCHING AND MERGING
• Organize code with branches
• Checkout branch
• Merge branches
• Editing Commits
• Commit command Amend flag
• Git reset and revert
MODULE 5: GIT WITH GITHUB AND BITBUCKET
• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
MODULE 1: BIG DATA INTRODUCTION
• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction
MODULE 2 : HDFS AND MAP REDUCE
• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners,
• Combiners, Shuffle, and Sort
MODULE 3: PYSPARK FOUNDATION
• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs
MODULE 4: SPARK SQL and HADOOP HIVE
• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
MODULE 1: TABLEAU FUNDAMENTALS
• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies
MODULE 2: POWER-BI BASICS
• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION
MODULE 3 : DATA TRANSFORMATION TECHNIQUES
• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values
MODULE 4: CONNECTING TO VARIOUS DATA SOURCES
• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model
The cost of a data science course in Thrissur varies between INR 20,000 and INR 2,00,000, depending on the course level and duration. Fees may differ based on factors like curriculum depth, certification, and additional support. It is advisable to compare options and choose a course that aligns with your learning goals and budget.
The cost of a data science course in Thrissur varies between INR 20,000 and INR 2,00,000, depending on the course level and duration. Fees may differ based on factors like curriculum depth, certification, and additional support. It is advisable to compare options and choose a course that aligns with your learning goals and budget.
To excel in data science in Thrissur, professionals need strong skills in programming (Python, R, or SQL), data analysis, and machine learning. Knowledge of data visualization, statistical modeling, and big data technologies is essential. Hands-on experience with cloud platforms and AI tools can enhance career opportunities.
To pursue data science in Thrissur, candidates typically need a bachelor's degree in engineering, computer science, mathematics, or a related field. Basic knowledge of programming, statistics, and data analysis is often required. Some courses may also have specific eligibility criteria, such as prior experience or entrance exams.
The scope of data science in Thrissur is growing, with increasing demand across industries like healthcare, finance, and retail. Businesses are adopting data-driven strategies, creating more opportunities for skilled professionals. The future looks promising as technology evolves, driving innovation and digital transformation in the region.
Datamites is a highly regarded institute for learning data science in Thrissur, known for its structured curriculum and industry-oriented training. It offers expert guidance, hands-on projects, and globally recognized certifications. With flexible learning options, it ensures a strong foundation for a successful data science career.
Data science courses in Thrissur vary in length, typically ranging from 4 to 12 months. Shorter programs often last about 4 to 6 months, while more comprehensive courses can extend up to a year. The exact duration depends on the specific curriculum and training provider.
To study data science in Thrissur, start with online courses and hands-on projects to build strong fundamentals. Join local tech meetups or communities to connect with professionals and stay updated. Practice real-world problems using open datasets to improve skills and gain experience.
Anyone interested in data science can enroll, including students, working professionals, and career changers. Basic knowledge of mathematics, statistics, and programming may be helpful but is not always required. Many courses offer beginner-friendly options, making it accessible to learners from various backgrounds.
The Certified Data Scientist course is widely regarded as the premier data science program in Thrissur. This comprehensive course covers essential topics such as Python programming, machine learning, and data visualization, equipping students with the skills needed to excel in the field. Graduates often find enhanced career opportunities in data analysis and related domains.
Data science roles are emerging in Thrissur, with job listings for positions such as Data Scientist and Data Science Trainer. The presence of Infopark Thrissur, an IT park housing various companies, contributes to the growing demand for data science professionals in the region. While opportunities are increasing, the market is still developing compared to larger Indian cities.
Yes, non-engineers can transition to data science in Thrissur with the right skills and dedication. Learning programming, statistics, and data analysis is essential for a smooth career shift. Gaining hands-on experience through projects and certifications can improve job prospects.
Yes, coding is important for a data science career in Thrissur, as it helps in data analysis, machine learning, and automation. Common languages like Python and R are widely used in the field. However, some entry-level roles may focus more on data visualization and reporting with minimal coding.
Yes, learning Python is highly recommended for a data science course in Thrissur. It is widely used for data analysis, machine learning, and visualization. A strong foundation in Python can improve problem-solving skills and career opportunities in data science.
Data science commonly uses programming languages like Python and R for analysis, along with SQL for managing databases. Tools such as Jupyter Notebook, TensorFlow, and Scikit-learn assist in data processing, machine learning, and visualization. Cloud platforms and big data technologies like AWS, Hadoop, and Spark enhance scalability and efficiency.
Thrissur is witnessing a surge in data science adoption across various sectors, with a focus on AI-driven analytics and real-time data processing. Emphasis on data privacy and ethical practices is growing, aligning with global standards. The city's strategic location near IT hubs enhances its appeal for data science professionals.
SQL is essential in data science for managing and querying structured data efficiently. It helps in extracting, filtering, and analyzing large datasets stored in databases. Strong SQL skills enhance data handling, making analysis and decision-making more effective.
Thrissur's most well-known areas include Swaraj Round (680001), the city's bustling commercial and cultural heart, and Punkunnam (680002), a prime residential locality with excellent connectivity. MG Road (680004) is a key business district, while East Fort (680005) blends historical significance with modern conveniences. Ayyanthole (680003) and Poothole (680004) are favored for their peaceful living environments and accessibility. Rapidly developing neighborhoods like Viyyoor (680010), Kuttanellur (680014), and Chembukkavu (680020) offer a mix of contemporary infrastructure and essential amenities, making Thrissur an attractive choice for both families and professionals.
Major ethical concerns in data science include privacy risks, as personal data can be misused or exposed. Bias in algorithms can lead to unfair outcomes, impacting decision-making in critical areas. Transparency and accountability are essential to ensure data-driven decisions are ethical and trustworthy.
The Data Science course in Thrissur is available with fees ranging from INR 34,951 to INR 64,451, based on the selected learning mode. The Live Virtual Instructor-Led Online course costs INR 59,451, while the Classroom In-Person Training is INR 64,451. The Blended Learning option, which includes self-paced study with live mentoring, is priced at INR 34,951.
To enroll in a data science course, visit the official website of the training provider and check the course details. Complete the registration process by filling out the application form and submitting the required documents. For further assistance, contact their support team or visit the nearest center.
Yes, EMI options are available for the data science course, making it easier to manage payments. Flexible installment plans help learners pursue the course without financial strain. Specific details on EMI plans can be obtained during the enrollment process.
The duration of data science courses in Thrissur varies based on the program's depth and structure. Generally, these courses span from 4 to 12 months, with shorter programs lasting around 4 to 6 months and more comprehensive ones extending up to a year. The specific length depends on the curriculum and training approach.
Yes, the data science course in Thrissur includes internships, offering practical experience to apply learned skills. These internships provide exposure to real-world projects, enhancing your understanding of data science applications. Additionally, the program offers placement assistance to support your career transition.
Yes, a data science course with placement assistance is available in Thrissur. The program covers key topics like machine learning, Python, and data visualization. Career support includes resume building, interview preparation, and job referrals.
Yes, a free demo class is available for data science to help you understand the course structure and teaching approach. This session provides insights into the curriculum and learning experience. It’s a great way to evaluate if the program meets your expectations.
Choose a training provider that offers expert-led courses, practical learning, and industry-recognized certifications. Ensure they provide hands-on projects, real-world case studies, and career support. Opt for a program with flexible learning options and strong placement assistance.
Multiple payment options are available for course enrollment, including debit/credit cards (Visa, MasterCard, American Express), PayPal, and EMI plans. Upon successful payment, you will receive course materials and a confirmation of your registration. For any assistance, a dedicated counselor is available to guide you through the process.
The refund policy allows a 100% refund if cancellation is requested within one week of the course start date, provided at least two sessions have been attended. Refunds are processed within 5-7 business days after the request. However, refunds are not available beyond six months from the course enrollment date.
The study materials include comprehensive course modules, hands-on project guides, and practice exercises. Additional resources such as case studies, mock tests, and reference materials support practical learning. Access to online study portals and expert mentorship enhances the learning experience.
Yes, the courses include live projects to provide hands-on experience. This helps learners apply concepts to real-world scenarios. Practical exposure enhances skills and prepares students for industry challenges.
A data science syllabus typically covers key topics such as Python programming, data analysis, and machine learning fundamentals. It also includes statistics, data visualization, deep learning, and big data concepts. Practical projects and case studies help in applying theoretical knowledge to real-world scenarios.
Yes, they provide course certification upon successful completion. The certification is accredited by IABAC and NASSCOM FutureSkills, adding industry recognition. This ensures credibility and enhances career prospects.
The Flexi-Pass provides a 3-month window to attend training sessions at your convenience. It allows learners to revisit concepts, clarify doubts, and reinforce their knowledge. This flexible learning option ensures continuous support for a better grasp of the subject.
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: -
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.