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
To study data science in Marathahalli, explore local training institutes or online platforms. DataMites provides a well-structured curriculum with hands-on projects to develop skills effectively. They also offer offline classes in nearby areas like BTM Layout and Kudlu Gate.
The Data Science syllabus at Marathahalli encompasses essential topics like Python programming, data analysis, and machine learning. It features modules on data visualization, statistical techniques, and real-world projects, focusing on equipping learners with practical skills and hands-on experience in data science.
DataMites in Marathahalli offers a comprehensive Data Science course covering Python, statistics, machine learning, and Tableau. Accredited by IABAC and NASSCOM FutureSkills, it ensures industry recognition with flexible learning options.
Data science courses in Marathahalli generally range from 4 to 12 months, depending on the type of program. The duration may vary based on whether the course is full-time or part-time, online or offline. It is recommended to check the course details for more accurate information.
Yes, offline data science courses are available at the DataMites Marathahalli branch located at 1st Floor, 761/1, Outer Ring Rd, near KLM Mall, Marathahalli Village, Marathahalli, Bengaluru, Karnataka 560037. Individuals from nearby areas like Kundalahalli (560037), Whitefield (560066), HAL (560008), Mahadevapura (560048), Bellandur (560103), and Brookefield (560066) can also enroll in these courses. The center provides hands-on training in a professional environment, perfect for those looking to gain practical experience.
DataMites Institute in Marathahalli is a highly regarded center for data science education, providing in-depth training in essential fields such as machine learning, AI, and data analysis. The curriculum is tailored to industry requirements, equipping students with valuable, real-world skills. With expert trainers and practical projects, DataMites is a top choice for those pursuing a career in data science.
The Data Science course in Marathahalli can be suitable for freshers if it covers fundamental concepts and hands-on experience. It is important to evaluate the course content, faculty expertise, and student reviews. Choosing a program that offers strong support and practical exposure would be beneficial for beginners.
In Bangalore, Data Scientists can earn between ₹4 Lakhs and ₹36 Lakhs annually, with the average salary being around ₹15 Lakhs. Salaries can differ based on factors like experience, skill set, and the employer. Entry-level positions generally offer salaries on the lower end of this spectrum.
Individuals with logical, mathematical, and analytical skills are eligible for the Data Science course at the Marathahalli branch. This includes professionals from various domains, statisticians, economists, mathematicians, software programmers, business analysts, Six Sigma consultants, and freshers with strong analytical abilities.
Coding proficiency is highly beneficial for a career in data science, as it enables effective data manipulation and analysis. While not always required, knowledge of programming languages like Python or R is often essential for tasks such as data cleaning and modeling. Additionally, coding skills improve the ability to automate and optimize processes in data-driven environments.
To pursue a career in data science, a strong foundation in mathematics, statistics, and programming is essential. A degree in fields like computer science, engineering, or data science is commonly preferred. Practical experience with data analysis tools and machine learning techniques is also highly valuable.
Bangalore remains a key hub for data science, with a growing demand for skilled professionals across industries like IT, finance, and healthcare. The city's strong tech ecosystem and numerous startups continue to fuel this demand. As data-driven decision-making expands, the outlook for data science roles is expected to stay positive and evolve with emerging technologies.
Yes, learning Python is highly recommended for data science students. It is a versatile language with a rich ecosystem of libraries like NumPy, pandas, and scikit-learn, essential for data analysis and machine learning. Python's simplicity and widespread use make it an ideal tool for aspiring data scientists.
Essential skills for a career in data science include strong proficiency in programming languages like Python or R, statistical analysis, and data manipulation. A solid understanding of machine learning algorithms and data visualization techniques is also crucial. Effective problem-solving and communication skills are key to translating data insights into actionable business strategies.
A data scientist is a professional who analyzes and interprets complex data to help organizations make informed decisions. They use statistical methods, machine learning, and programming skills to extract insights. Their role includes data collection, cleaning, modeling, and visualizing results.
Mastering data science can be challenging due to its broad scope, requiring knowledge in statistics, programming, and domain expertise. Continuous learning is necessary as the field evolves with new tools and techniques. Success depends on persistence, practical experience, and a strong foundation in core concepts.
Common tools used in data science include programming languages like Python and R for data analysis and modeling. Data visualization tools like Tableau and Power BI help in presenting insights. Additionally, libraries such as TensorFlow and scikit-learn are widely used for machine learning tasks.
Yes, data science roles remain in high demand in Bangalore.
Data Science is crucial today due to its ability to extract valuable insights from massive datasets. This enables businesses to make data-driven decisions, improve operations, and gain a competitive edge. Furthermore, it plays a vital role in addressing complex challenges across various fields, such as healthcare, finance, and environmental science.
A Data Scientist focuses on building models, algorithms, and predictive analytics using advanced statistical and machine learning techniques. A Data Analyst, on the other hand, primarily works with data visualization, reporting, and descriptive analysis to inform business decisions. The key difference lies in the complexity and scope of tasks they handle.
DataMites offers internship opportunities alongside their Data Science course in Marathahalli. These internships provide practical experience to enhance your learning. For more information, you can visit their website or get in touch with their support team.
In Bangalore, Data Science course fees generally vary from INR 15,000 to INR 2,50,000. At DataMites' Marathahalli branch, fees for different courses range from INR 40,000 to INR 1,20,000. The Certified Data Scientist Program, an 8-month course, is priced at INR 59,451 for online, INR 64,451 for offline, and INR 34,951 for blended learning. Other offerings like the Data Science Foundation and Data Science for Managers start at INR 24,000.
Yes, DataMites offers EMI options for their Data Science courses in Marathahalli. This allows students to pay the course fees in convenient monthly installments. You can inquire directly with the DataMites support team for further details on the EMI plans available.
To enroll in the DataMites Data Science course, visit our website and choose the course you wish to pursue. Complete the registration form and proceed with payment via debit/credit card or PayPal. After payment, you will receive your course materials, schedule, and receipt. For assistance, feel free to contact our educational counselor.
When you enroll in the Data Science course in Marathahalli, you'll receive comprehensive learning materials, including course books, online resources, and practice datasets. You'll also have access to recorded lectures, real-world projects, and dedicated support throughout the course. Additionally, you'll be provided with guidance on industry tools and techniques.
Yes, DataMites Marathahalli offers Data Science courses that include live projects. These courses are designed to provide practical experience, enhancing your learning. For more details, you can visit our official website.
Upon completing the Data Science course at DataMites, you will receive recognized certifications. These include the IABAC® certification, globally acknowledged, and the NASSCOM FutureSkills certification. Both demonstrate your proficiency in data science.
The DataMites branch in Marathahalli is situated at:
1st Floor, 761/1, Outer Ring Rd, close to KLM Mall, Marathahalli Village, Marathahalli, Bengaluru, Karnataka 560037.
People from nearby locations such as Kundalahalli (560037), Whitefield (560066), HAL (560008), Mahadevapura (560048), Bellandur (560103), and Brookefield (560066) are also eligible to enroll in these courses.
The trainers at DataMites Institute are seasoned professionals with deep expertise in data science, Python programming, and AI. They bring valuable industry insights and hands-on experience to enrich the learning process. Committed to delivering thorough guidance, DataMites trainers ensure a strong grasp of the course material.
Yes, DataMites in Marathahalli offers free data science demo sessions. These sessions are available both online and offline, with flexible scheduling on weekdays and weekends. For more details or to register, please contact your education counselor.
DataMites offers a 100% refund if you request it within one week of the course start date and have attended at least two sessions. Refunds are not available after six months or if more than 30% of the course material has been accessed. To request a refund, email care@datamites.com from your registered email.
DataMites has three offline training centers in Bangalore:
Kudlu Gate: Nestled in a fast-growing tech hub, this center provides a modern, well-equipped environment for data science learning.
Marathahalli: Positioned in a key tech zone, it offers top-notch courses to professionals and students, with proximity to major IT parks like RMZ Ecospace.
BTM Layout: Located in South Bangalore, this center offers easy access to extensive data science training for learners in the region.
DataMites in Marathahalli provides Data Science training with internationally recognized certifications, ensuring a high standard of education. Courses are taught by industry professionals, offering practical insights and opportunities for internships and job placements. With flexible schedules, the program caters to various learning needs, making it an ideal option for those looking to advance in data science.
Yes, DataMites Marathahalli offers data science courses with placement assistance. They provide comprehensive training in data science, machine learning, and related fields. The institute also offers placement assistance to help students secure job opportunities.
Yes, if you miss a class, you can make up for it by reviewing the recorded session. All online sessions are recorded and will be shared with the candidates. You can access the recordings at your convenience.
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.