4.8 (25,900) reviews 110,000
59,451*
NO COST EMI : 9,909 p.m for 6 Months
*Offer valid till 12th October 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!
The Data Science course in Bangalore is open to anyone with a basic understanding of mathematics and statistics. It is suitable for fresh graduates, working professionals, and career switchers aiming to enter the field. While prior programming knowledge can be helpful, it is not mandatory to enroll in most courses. Individuals with analytical thinking and problem-solving skills can quickly adapt to data science training and gain hands-on experience.
Several institutes offer Data Science courses in Bangalore, but DataMites is widely regarded as a top option. The institute provides in-depth training, industry-recognized certifications, and practical projects that strengthen real-world skills. Expert trainers and career support services ensure learners build a strong foundation and are ready for data-driven roles in IT, finance, healthcare, and other sectors.
To excel in Data Science in Bangalore, learners need proficiency in Python, R, and SQL for handling and analyzing datasets. A solid grasp of statistics, machine learning, and data visualization is essential. Familiarity with big data technologies, AI frameworks, and cloud platforms can further expand career opportunities in this growing tech hub.
The Certified Data Scientist Course is among the leading programs in Bangalore. It covers machine learning, deep learning, statistical analysis, and includes hands-on projects for practical exposure. This certification equips learners with industry-focused skills, enhancing employability in data-driven roles across sectors.
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.
Yes, data science careers in Bangalore remain in high demand. Organizations are increasingly leveraging analytics, AI, and machine learning to make strategic decisions. The city’s IT ecosystem and startup culture create numerous opportunities, ensuring steady growth in roles for data scientists, analysts, and machine learning engineers.
In a Data Science course in Bangalore, learners gain comprehensive knowledge and hands-on experience in:
Data Science courses in Bangalore typically last between 3 months and 1 year. Short-term programs emphasize hands-on skills, while longer courses include theoretical learning, projects, and practical exercises. Flexible schedules such as full-time, part-time, or online formats are commonly offered to suit learners’ needs.
The Data Science course fee in Bangalore typically ranges from Rs 15,000 to Rs 2,50,000, depending on the institute, course duration, and mode of training. Learners can choose from online, classroom, or self-paced formats according to their convenience.
Data Science is a vast subject for study, it is a mix of Statistics and Computer Science. DataMites in Bangalore, offers quality training sessions in Data Science, Artificial Intelligence, Machine Learning etc. The data science courses provided by DataMites in Bangalore are exclusively designed in tune with the current industry requirements. Also with many projects to work on, under the mentoring of industry experts.
Yes, coding is an essential part of data science. Python and SQL are the most commonly used languages, and learners can start with basic programming knowledge, gradually advancing to complex data manipulation and model building.
After completing the Certified Data Scientist Course in Bangalore, an individual will be well equipped with the following:-
Intense knowledge of the workflow, of a Data Science project.
Learn the basics of the use of Statistics in Data Science.
Gain knowledge of the various Machine Learning Algorithms.
Knowledge of Data Forecasting, Data Mining and Data Visualization.
Ways to deliver end to end Data Science projects.
Bangalore is known as the technological hub of India, with lots of business opportunities and large corporate houses adorning the city. This, in turn, contributes to new employment opportunities being created. Hence opting for a Data Science course in Bangalore will help an individual to leverage the available possibilities in the best manner, to land a career in Data Science.
Data Scientists have been in great demand in Bangalore. As an acknowledgement to this rising demand, DataMites has come with the Certified Data Scientist course in Bangalore. The course covers all the areas of Data Science, Machine Learning, basics of Mathematics and Statistics, etc. Also, the Certified Data Scientist course, covers all the practical aspects of the knowledge required to become a Data Scientist.
Bangalore, in India, is known as the technological hub of India, with lots of business opportunities. It consists of many large companies, business houses, with large amounts of transactions happening every day, as a result of which there is an equally large amount of data generated daily. Also, India is known for many recognised universities. Learning Data Science in India will be a great opportunity for students as well as professionals. Graduates freshers and employees working in organisations can leverage these opportunities to easily land a Data Science job.
Bangalore offers diverse roles like Data Scientist, Data Analyst, ML Engineer, BI Developer, and Big Data Engineer across IT, finance, e-commerce, and healthcare. According to Mordor Intelligence, the global data science platform market is USD 111.23B in 2025 and is expected to reach USD 275.67B by 2030 at a CAGR of 21.43%, reflecting strong demand for skilled professionals.
Bangalore is experiencing strong growth in data science across IT, finance, e-commerce, and healthcare. Institutes offer specialized courses with practical projects, creating ample opportunities for data scientists, analysts, and machine learning professionals in the city.
As per AmbitionBox, the salary of a Data Scientist in Bangalore ranges between ₹4 Lakhs and ₹30 Lakhs annually, with an average annual salary of ₹15 Lakhs for professionals with 1 to 7 years of experience.
Data science involves data collection and cleaning to maintain accuracy, analysis to extract insights, predictive modeling with machine learning, statistical pattern identification, and visualization for easy interpretation.
Challenges in data science include handling messy or incomplete data, ensuring privacy and security, avoiding algorithmic bias, understanding complex models, and translating insights into actionable business strategies.
Data analysts focus on interpreting information and identifying trends, while data scientists design predictive models, build algorithms, and provide actionable solutions. In short, analysts deliver insights, and data scientists convert insights into innovative, data-driven decisions.
Common tools include Python, R, SQL, Excel, Tableau, Power BI, and cloud platforms. Machine learning often relies on libraries such as scikit-learn, TensorFlow, and PyTorch for model development and analysis.
Yes, graduates from any discipline can pursue data science in Bangalore. Strong analytical skills and basic programming knowledge are helpful, and completing specialized courses or certifications can bridge skill gaps and strengthen career readiness.
DataMites Bangalore welcomes working professionals, fresh graduates, and career changers who wish to build expertise in data science. A basic understanding of mathematics and programming is usually sufficient to start, though specific prerequisites may vary depending on the course level and specialization.
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
Yes, DataMites Bangalore provides Data Science courses with dedicated placement assistance. The program includes hands-on training, resume preparation, mock interviews, and career guidance to help learners secure roles in the data-driven industry.
The DataMites Data Science course fee in Bangalore varies based on the chosen learning mode. The live online course costs around INR 60,000, the blended learning option is INR 35,000, and classroom training is available for approximately INR 65,000. For the latest fee details or offers, it’s best to contact the Bangalore center directly.
DataMites Bangalore offers a full refund if a cancellation request is made within one week of the course start date, provided the student has attended at least two sessions. Refunds are processed within 5–7 business days and are not applicable after six months from the date of enrollment.
Yes, DataMites Bangalore includes internship opportunities as part of its Data Science courses. These internships help students gain real-world exposure, apply their knowledge in practical settings, and enhance their employability.
Yes, DataMites Bangalore provides flexible EMI options for Data Science courses, allowing learners to pay fees in convenient monthly installments. For specific EMI plans and eligibility, students are advised to contact the Bangalore center for assistance.
Enrolling for online training online is very simple. The payment can be done using your debit/credit card that includes Visa Card, MasterCard; American Express or via PayPal. You will receive the receipt after the payment is successful. In case of more queries you can get in touch with our educational counselor who will guide you with the same.
You have access to the online study materials from 6 months upto 1 year.
The DataMites Data Science course in Bangalore spans around 8 months, including approximately 700 learning hours. The curriculum is structured to balance conceptual understanding, tool-based learning, and hands-on project experience.
Yes, DataMites Bangalore offers a free demo class for Data Science courses. This session allows prospective students to understand the training methodology, interact with mentors, and get clarity on the course structure before enrolling.
The trainers at DataMites Bangalore are experienced industry professionals and certified data science experts. They bring in-depth practical knowledge from working in analytics, AI, and machine learning roles, offering both technical guidance and career mentorship.
The DataMites Data Science syllabus in Bangalore includes key modules such as statistics, Python programming, machine learning, data visualization, and data mining. Advanced programs also introduce deep learning and big data concepts, aligning with current industry demands.
Yes, DataMites Bangalore provides live project opportunities that allow students to apply theoretical learning to real business problems. This hands-on approach strengthens understanding and prepares learners for practical data science roles.
DataMites Bangalore offers both live and recorded online classes. Live sessions provide direct interaction with instructors, while recorded videos allow learners to review and revise topics at their own pace, ensuring a flexible and thorough learning experience.
The Certified Data Scientist course at DataMites Bangalore covers data analysis, statistics, machine learning, and programming using Python or R. It emphasizes real-world applications, live projects, and model deployment, preparing students for professional certification and data-centric careers.
DataMites Bangalore accepts multiple payment methods, including debit/credit cards (Visa, MasterCard, American Express), UPI, bank transfer, and PayPal. Once payment is completed, students receive course access details and dedicated counselor support.
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.
Yes, upon successful completion of the Data Science program, DataMites Bangalore provides globally recognized certifications accredited by IABAC® and NASSCOM® FutureSkills. These certifications validate the learner’s competence and enhance their credibility in the data science job market.
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.
DataMites Bangalore is easily accessible from key areas such as Marathahalli (560037) and Koramangala (560095), which are prominent educational and IT hubs, along with BTM Layout (560076), a popular student locality. Surrounding neighborhoods like Whitefield (560066), Indiranagar (560038), and Kudlu Gate (560068) offer convenient access to the training center. The branch is also well connected to nearby areas including Electronic City, HSR Layout, Jayanagar, and Hebbal, making it easy for students and professionals to enroll in DataMites courses in Bangalore.
DataMites Bangalore provides both online and offline Data Science courses, giving students the flexibility to choose the learning mode that best suits their schedule and preferences. The curriculum and quality of training remain consistent across both formats. The Bangalore center is located at Bajrang House, 7th Mile, C-25, Bengaluru - Chennai Highway, Kudlu Gate, Garvebhavi Palya, Bengaluru, Karnataka 560068, offering convenient access for students and working professionals.
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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