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 duration of data science courses in Belgaum varies from a few months to a year, depending on the course type. Short-term certification programs last 3 to 6 months, while diploma or postgraduate programs can take up to a year. Some self-paced online courses also offer flexible durations.
According to AmbitionBox reports, Data Scientist salaries in Belgaum range from ₹1 Lakh to ₹14 Lakhs per year, with an average of ₹7 Lakhs annually. Entry-level data scientists typically earn between ₹3 LPA to ₹6 LPA, depending on skills, experience, and the company. Earning potential can increase with additional certifications and hands-on projects.
The demand for data science is growing in Belgaum as industries adopt data-driven strategies. Sectors like healthcare, finance, and IT are hiring skilled professionals. With AI and machine learning advancements, the field offers promising career growth.
Most courses require a background in mathematics, statistics, or computer science. Graduates from any discipline with analytical skills can apply for beginner-level programs. Advanced courses may require programming knowledge or prior experience in data analytics.
The best data science course depends on individual goals, learning preferences, and career aspirations. The Certified Data Scientist Course is highly recommended as it covers Python, machine learning, and data visualization in depth. A program with hands-on projects and industry exposure ensures practical learning and career growth.
The cost of data science courses in Belgaum ranges from ₹20,000 to ₹2,00,000. Fees vary based on course duration, institute, and certification level. Online courses are generally more affordable than full-time classroom programs.
Yes, data science jobs remain in demand in Belgaum as businesses rely on data-driven decision-making. Companies in various industries seek skilled professionals to analyze and interpret data. The growing digital economy further boosts job opportunities.
Many institutes offer quality data science programs in Belgaum, each with different teaching approaches. DataMites stands out as the best institute, offering comprehensive course content, expert faculty, and strong placement support. Choosing DataMites ensures practical training and real-world projects for a successful learning experience.
The best approach is a combination of structured courses, self-learning, and hands-on projects. Online resources, bootcamps, and internships help gain practical experience. Regular practice of coding, statistics, and data analysis tools is essential.
Yes, non-engineers can transition to data science with proper training in statistics, programming, and data analysis. Many courses cater to beginners from diverse educational backgrounds. Developing strong analytical and problem-solving skills is crucial.
Key skills include statistical analysis, programming (Python/R), data visualization, and machine learning. Problem-solving, critical thinking, and communication skills are equally important. Hands-on experience with real-world datasets enhances expertise.
Yes, coding proficiency is essential for data science, especially in Python, R, or SQL. Basic programming knowledge is needed for data manipulation, analysis, and model building. However, no-code tools also exist for beginners entering the field.
Anyone with an interest in data analysis, statistics, and programming can learn data science. Professionals from IT, finance, healthcare, and business backgrounds can upskill. Enthusiasts with logical thinking and problem-solving abilities can excel in the field.
Essential technical skills include programming (Python, R), SQL, machine learning, and data visualization. Knowledge of statistical methods, cloud computing, and big data tools is beneficial. Hands-on practice with tools like Tableau, Power BI, and TensorFlow helps.
To become a data scientist, one should learn programming, statistics, and machine learning. Enrolling in a structured course, working on projects, and gaining certifications help. Building a strong portfolio and networking with professionals can improve job prospects.
Statistical analysis helps in understanding data patterns, making predictions, and validating models. It ensures accurate decision-making based on data-driven insights. Strong statistical knowledge enhances a data scientist's analytical abilities.
Yes, Python is widely used in data science due to its simplicity and powerful libraries. Most courses teach Python for data manipulation, visualization, and machine learning. However, R and SQL are also useful in certain data science applications.
Graduates in mathematics, computer science, engineering, or related fields are typically eligible. Some courses accept students from any background with basic analytical skills. Prior programming knowledge is beneficial but not always mandatory.
A Certified Data Scientist course provides industry-recognized credentials upon completion. It covers key topics like machine learning, data analytics, and visualization. Such certifications help in career advancement and job opportunities.
Belgaum features sought-after localities like Tilakwadi (590006), a bustling residential and commercial area, and Shahapur (590003), known for its educational institutions and business hubs. Udyambag (590008) is a growing industrial and tech center, making it ideal for professionals, including those in data science. The city is well-connected to nearby areas such as Kangrali (590010), Hindalga (590011), Vadgaon (590005), and Macche (590014). Prominent localities like Sadashiv Nagar (590019), Nehru Nagar (590020), Hanuman Nagar (590009), and Angol (590006) provide excellent infrastructure and accessibility for aspiring professionals.
Yes, DataMites Belgaum provides EMI options for the Data Science course, making it easier for learners to manage their payments. Flexible payment plans are available to suit different financial needs. You can contact DataMites for detailed information on EMI plans and eligibility.
To enroll in the Data Science course at DataMites Belgaum, visit their official website and explore the available courses. You can choose the program that suits your needs and complete the registration process online. For more details, contact our support team for guidance on enrollment.
DataMites offers a Certified Data Scientist course in Belgaum with fees varying by learning mode:
These options provide flexibility to suit different learning preferences.
DataMites offers a comprehensive Data Science course in Belgaum, featuring 700 hours of learning and 120 hours of live online training. The program includes 25 capstone projects and a client project, providing practical experience. Additionally, DataMites offers internship opportunities and job placement support to enhance career prospects.
DataMites offers comprehensive Data Science courses that are designed to equip learners with practical skills and industry-relevant knowledge. With experienced trainers and hands-on projects, DataMites ensures a thorough understanding of core concepts. The flexible learning options make it an ideal choice for professionals and beginners alike.
The Data Science course at DataMites lasts for 8 months, offering a total of 700 hours of detailed instruction. This course is structured to provide in-depth knowledge and practical experience. It aims to prepare students for successful careers in the data science field.
DataMites in Belgaum does offer a free demo class for Data Science. This session allows prospective learners to get an overview of the course content and teaching methodology. It helps individuals decide if the program aligns with their learning goals.
DataMites offers a Data Science course that includes placement assistance. The course is designed to equip students with essential skills for the data science industry. Placement support is provided to help graduates find relevant job opportunities after completion.
DataMites Belgaum offers various payment methods for course fees, including cash, credit card, PayPal, American Express, net banking, cheque, debit card, Visa, and MasterCard. These options provide flexibility to students for fee payments.
DataMites offers a 100% money-back guarantee if a refund request is made within one week from the batch start date, provided the candidate has attended at least two training sessions during the first week and has not accessed more than 30% of the study material or training sessions. Refund requests must be sent from the candidate's registered email to care@datamites.com. Please note that exam bookings are non-refundable, and no refunds will be issued after six months from the course enrollment date.
DataMites offers a range of courses that include hands-on experience with live projects. These courses feature 25 capstone projects, allowing learners to apply their knowledge in real-world scenarios. Additionally, DataMites incorporates a client project to further enhance practical skills.
DataMites Belgaum provides comprehensive study materials, including detailed course content, practical exercises, and industry-relevant case studies. These materials are designed to enhance learning and ensure a strong grasp of key concepts. Additionally, DataMites offers access to resources like practice questions and online support to assist learners throughout their journey.
DataMites provides course certification upon successful completion of their programs. The certification is recognized by accrediting bodies such as IABAC® and NASSCOM® FutureSkills. This ensures the validity and industry recognition of the skills gained during the course.
The DataMites Data Science syllabus covers topics such as data analysis, machine learning, statistical modeling, and data visualization. The curriculum is designed to provide a comprehensive understanding of data science.
The DataMites Flexi-Pass grants a 3-month flexible access to Data Science training, allowing learners to revisit content and resolve queries. This flexible option ensures ongoing guidance, helping strengthen understanding and enhance the overall learning experience.
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.