4.9 (26,680) reviews 110,000
59,451*
NO COST EMI : 9,909 p.m for 6 Months
*Offer valid till 9th November 2025
Instructor Led Live Online
Self Learning + Live Mentoring
In - Person Classroom Training
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
Enrolling at DataMites was one of the best decisions I made for my career. Coming from a freelance writing background with no prior technical experience, I was initially unsure about stepping into the field of data analytics. But DataMites made the transition smooth and achievable. I’d highly recommend them to anyone looking for a guided, industry-ready data analytics course. A true DataMites success story!
To pursue data science in Trivandrum, candidates typically require a background in mathematics, statistics, or computer science. A basic understanding of programming languages like Python or R is often essential. Additionally, some institutes may ask for a bachelor's degree or equivalent qualification in related fields.
Data science courses in Trivandrum typically range from 3 to 1 year, depending on the depth and structure of the program. Some may offer flexible schedules, allowing part-time or weekend options. Course duration can also vary based on whether it’s a certification or a comprehensive degree program.
As per AmbitionBox, Data Scientists in Trivandrum earn annual salaries ranging from INR 3 Lakhs to INR 13 Lakhs, with an average of INR 12 Lakhs, based on insights from professionals possessing 1 to 6 years of experience.
The scope of data science in Trivandrum is rapidly expanding as the city evolves into a prominent IT and analytics hub. With major companies driving innovation in AI and big data, professionals skilled in data science can expect strong career opportunities and long-term growth.
The Certified Data Scientist course in Trivandrum is one of the top choices for aspiring data scientists. It provides comprehensive training in key data science concepts and tools, backed by industry-recognized certification. This course is highly recommended for those looking to build a solid foundation and advance their career in data science.
Coding proficiency is highly beneficial for a data science career, as it enables efficient data manipulation and algorithm implementation. However, it's possible to succeed in the field with a focus on analytical skills and domain knowledge, while gradually learning programming. Balancing coding with problem-solving abilities is key to thriving in data science.
Yes, non-engineering graduates can pursue a career in data science in Trivandrum. Many institutes offer training programs and certifications tailored to individuals from diverse backgrounds. With the right skills and knowledge, non-engineering graduates can successfully transition into the data science field.
The Data Science Course fees in Trivandrum generally range from INR 15,000 to INR 2,50,000. The exact fee depends on factors such as the training institute, course duration, and chosen learning mode whether online, classroom-based, or hybrid.
To study data science in Trivandrum, consider enrolling in reputable institutes offering structured courses and certifications. You can also explore online learning platforms for flexibility and comprehensive content. Additionally, participate in local meetups and workshops to network with professionals and gain practical exposure.
A successful data science career requires proficiency in programming languages like Python or R, strong statistical and analytical skills, and a solid understanding of machine learning algorithms. Additionally, data wrangling and visualization expertise are crucial for interpreting and presenting data insights effectively. Collaboration and problem-solving abilities help in translating business needs into actionable data-driven solutions.
Yes, data science job opportunities remain strong in Trivandrum, supported by its rapidly growing tech ecosystem and expanding presence of companies at Technopark. With rising demand for expertise in analytics, machine learning, and AI, skilled professionals can find abundant career opportunities in the city.
Among the various institutes in Trivandrum, DataMites stands out as a top choice for aspiring data professionals. The institute provides in-depth training, globally accredited certifications, and practical project experience. With guidance from expert mentors and strong career support, learners gain the essential, industry-relevant skills needed to excel in data science.
Data science involves using advanced algorithms, machine learning, and statistical models to predict outcomes and uncover deeper insights from data. In contrast, data analytics focuses on interpreting existing data to identify trends and support business decisions. While both are data-driven fields, data science is broader and more technical, whereas data analytics is more application-oriented.
Data science combines several key components: data collection and cleaning to ensure quality inputs, statistical analysis and modeling to uncover patterns and insights, and visualization to communicate findings effectively. Machine learning algorithms enhance predictive capabilities, while programming skills streamline the process. Collaboration across disciplines ensures comprehensive results.
Python is widely used in data science for tasks such as data manipulation, analysis, and visualization, thanks to its extensive libraries like Pandas, NumPy, and Matplotlib. It also supports machine learning through libraries like Scikit-learn and TensorFlow. Its versatility and ease of use make it a top choice for professionals in the field.
A Certified Data Scientist course is a structured program designed to equip individuals with the necessary skills in data analysis, machine learning, and statistical modeling. It provides hands-on training and theoretical knowledge to prepare learners for roles in data science. Upon completion, participants receive certification validating their expertise in the field.
Data science often faces challenges such as dealing with incomplete or noisy data, which can hinder accurate analysis. The complexity of choosing the right algorithms and models also poses difficulties in achieving optimal results. Additionally, ensuring the scalability and interpretability of solutions in real-world applications remains an ongoing hurdle.
Data science freshers can pursue roles such as Data Analyst, Junior Data Scientist, Business Analyst, Machine Learning Intern, or Data Engineer Trainee. These positions help them build foundational experience in data handling, analytics, and model development.
To become a Data Scientist in Trivandrum, develop strong skills in programming, statistics, and machine learning while gaining practical experience through real-world projects. Enrolling in a certified data science program and staying updated with the latest tools and technologies can open excellent career opportunities in the city’s growing tech industry.
AI and machine learning enhance data science by enabling the automation of data analysis, uncovering patterns, and making predictions. They optimize decision-making through algorithms that learn from data, improving accuracy and efficiency. These technologies transform raw data into valuable insights for better business strategies and solutions.
DataMites Trivandrum offers EMI options for their data science courses, allowing students to pay fees in manageable monthly installments. Payments can be made via debit/credit cards, including Visa, MasterCard, and American Express, or through PayPal. For credit card payments, EMI options are available.
To enroll in the Data Science course at DataMites Trivandrum, visit our official website and navigate to the course section. Fill out the registration form with your details and select your preferred batch. After submission, you will receive further instructions to complete the enrollment process.
The Data Science course fees at DataMites Trivandrum vary based on the chosen learning mode. Classroom training is priced at around INR 65,000, live online sessions cost approximately INR 60,000, and blended learning options are available for about INR 35,000. For the most updated details on fees, offers, and batch schedules, it is recommended to contact the Trivandrum center directly.
DataMites Trivandrum offers data science courses that include internship opportunities. These programs are designed to provide practical experience in the field. For detailed information on course offerings and internships, please contact DataMites directly.
DataMites offers a comprehensive data science course in Trivandrum, designed with expert trainers and hands-on learning. Their curriculum covers industry-relevant skills and provides practical exposure to real-world projects. Additionally, they offer flexible learning options and strong career support to enhance job readiness.
The Data Science course at DataMites has a duration of 8 months. It consists of 700 hours of comprehensive learning. This program is designed to equip students with the necessary skills for data science careers.
DataMites offers free demo classes for data science. While specific information about their Trivandrum location isn't available, you can book a free demo class through their website. For more details, please visit their official page.
Yes, DataMites Trivandrum offers a data science course that includes placement assistance. The program is designed to equip students with the necessary skills and provides support in securing job opportunities. It aims to enhance career prospects for learners in the data science field.
DataMites in Trivandrum offers multiple payment methods for course fees, including credit and debit cards (Visa, MasterCard, American Express), PayPal, net banking, and cash payments. For credit card payments, EMI options are available. A token advance is collected during registration, with the remaining balance due before course completion.
Yes, DataMites Trivandrum offers course certification upon successful completion of their programs. The certifications are recognized and supported by industry standards. Additionally, they align with accrediting bodies such as IABAC® and NASSCOM® FutureSkills.
DataMites Trivandrum offers a 100% money-back guarantee if you request a refund within one week of the batch start date, attend at least two training sessions during the first week, and have not accessed more than 30% of the study material or training sessions. Refund requests should be sent to care@datamites.com from your registered email.
Yes, DataMites in Trivandrum offers courses that include live projects. These projects are conducted under the guidance of industry experts, providing practical experience. The Data Science course, for example, includes 20 capstone projects and 3 real time projects.
The DataMites Flexi-Pass offers a 3-month flexible period to attend Data Science training sessions. It enables learners to revisit topics, clarify doubts, and strengthen their understanding. This adaptable approach ensures ongoing support for a smooth learning experience.
DataMites Trivandrum offers a variety of study resources, including video tutorials, e-books, and practice datasets. Additionally, learners gain access to real-world case studies and live project sessions to enhance practical knowledge. These materials support a well-rounded understanding of data science, AI, and machine learning concepts.
Data science courses are offered by DataMites for learners across Trivandrum and nearby areas through flexible online and blended learning modes. Students from Kazhakootam (695582), Pattom (695004), Ulloor (695011), Sreekariyam (695017), Medical College (695011), Kesavadasapuram (695004), Vattiyoorkavu (695013), Pangappara (695581), and Peroorkada (695005) can easily enroll in these programs. For enrollment details, batch timings, and fees, learners can contact DataMites directly.
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.
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