Instructor Led Live Online
Self Learning + Live Mentoring
In - Person Classroom Training
The entire training includes real-world projects and highly valuable case studies.
IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.
MODULE 1: DATA SCIENCE ESSENTIALS
• Introduction to Data Science
• Evolution of Data Science
• Big Data Vs Data Science
• Data Science Terminologies
• Data Science vs AI/Machine Learning
• Data Science vs Analytics
MODULE 2: DATA SCIENCE DEMO
• Business Requirement: Use Case
• Data Preparation
• Machine learning Model building
• Prediction with ML model
• Delivering Business Value.
MODULE 3: ANALYTICS CLASSIFICATION
• Types of Analytics
• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics
• EDA and insight gathering demo in Tableau
MODULE 4: DATA SCIENCE AND RELATED FIELDS
• Introduction to AI
• Introduction to Computer Vision
• Introduction to Natural Language Processing
• Introduction to Reinforcement Learning
• Introduction to GAN
• Introduction to Generative Passive Models
MODULE 5: DATA SCIENCE ROLES & WORKFLOW
• Data Science Project workflow
• Roles: Data Engineer, Data Scientist, ML Engineer and MLOps Engineer
• Data Science Project stages.
MODULE 6: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
MODULE 7: DATA SCIENCE INDUSTRY APPLICATIONS
• Data Science in Finance and Banking
• Data Science in Retail
• Data Science in Health Care
• Data Science in Logistics and Supply Chain
• Data Science in Technology Industry
• Data Science in Manufacturing
• Data Science in Agriculture
MODULE 1: PYTHON BASICS
• Introduction of python
• Installation of Python and IDE
• Python Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
MODULE 2: PYTHON CONTROL STATEMENTS
• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements
MODULE 3: PYTHON DATA STRUCTURES
• Basic data structure in python
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods
MODULE 4: PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions
MODULE 1: OVERVIEW OF STATISTICS
• Introduction to Statistics
• Descriptive And Inferential Statistics
• Basic Terms Of Statistics
• Types Of Data
MODULE 2: HARNESSING DATA
• Random Sampling
• Sampling With Replacement And Without Replacement
• Cochran's Minimum Sample Size
• Types of Sampling
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
• Multi stage Sampling
• Sampling Error
• Methods Of Collecting Data
MODULE 3: EXPLORATORY DATA ANALYSIS
• Exploratory Data Analysis Introduction
• Measures Of Central Tendencies: Mean,Median And Mode
• Measures Of Central Tendencies: Range, Variance And Standard Deviation
• Data Distribution Plot: Histogram
• Normal Distribution & Properties
• Z Value / Standard Value
• Empirical Rule and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
• Covariance & Correlation
MODULE 4: HYPOTHESIS TESTING
• Hypothesis Testing Introduction
• P- Value, Critical Region
• Types of Hypothesis Testing
• Hypothesis Testing Errors : Type I And Type II
• Two Sample Independent T-test
• Two Sample Relation T-test
• One Way Anova Test
• Application of Hypothesis testing
MODULE 1: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
MODULE 2: PYTHON NUMPY PACKAGE
• Introduction to Numpy Package
• Array as Data Structure
• Core Numpy functions
• Matrix Operations, Broadcasting in Arrays
MODULE 3: PYTHON PANDAS PACKAGE
• Introduction to Pandas package
• Series in Pandas
• Data Frame in Pandas
• File Reading in Pandas
• Data munging with Pandas
MODULE 4: VISUALIZATION WITH PYTHON - Matplotlib
• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN
• Seaborn: Basic Plot
• Advanced Python Data Visualizations
MODULE 6: ML ALGO: LINEAR REGRESSSION
• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python
MODULE 7: ML ALGO: LOGISTIC REGRESSION
• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python
MODULE 8: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Modeling in Python
MODULE 9: ML ALGO: KNN
• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python
MODULE 1: FEATURE ENGINEERING
• Introduction to Feature Engineering
• Feature Engineering Techniques: Encoding, Scaling, Data Transformation
• Handling Missing values, handling outliers
• Creation of Pipeline
• Use case for feature engineering
MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python
MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)
• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python
MODULE 4: ML ALGO: DECISION TREE
• Introduction to Decision Tree & Random Forest
• How it works
• Modeling and Evaluation in Python
MODULE 5: ENSEMBLE TECHNIQUES - BAGGING
• Introduction to Ensemble technique
• Bagging and How it works
• Modeling and Evaluation in Python
MODULE 6: ML ALGO: NAÏVE BAYES
• Introduction to Naive Bayes
• How it works: Bayes' Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python
MODULE 7: GRADIENT BOOSTING, XGBOOST
• Introduction to Boosting and XGBoost
• How it works?
• Modeling and Evaluation of in Python
MODULE 1: TIME SERIES FORECASTING - ARIMA
• What is Time Series?
• Trend, Seasonality, cyclical and random
• Stationarity of Time Series
• Autoregressive Model (AR)
• Moving Average Model (MA)
• ARIMA Model
• Autocorrelation and AIC
• Time Series Analysis in Python
MODULE 2: SENTIMENT ANALYSIS
• Introduction to Sentiment Analysis
• NLTK Package
• Case study: Sentiment Analysis on Movie Reviews
MODULE 3: REGULAR EXPRESSIONS WITH PYTHON
• Regex Introduction
• Regex codes
• Text extraction with Python Regex
MODULE 4: ML MODEL DEPLOYMENT WITH FLASK
• Introduction to Flask
• URL and App routing
• Flask application – ML Model deployment
MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL
• MS Excel core Functions
• Advanced Functions (VLOOKUP, INDIRECT..)
• Linear Regression with EXCEL
• Data Table
• Goal Seek Analysis
• Pivot Table
• Solving Data Equation with EXCEL
MODULE 6: AWS CLOUD FOR DATA SCIENCE
• Introduction of cloud
• Difference between GCC, Azure, AWS
• AWS Service ( EC2 instance)
MODULE 7: AZURE FOR DATA SCIENCE
• Introduction to AZURE ML studio
• Data Pipeline
• ML modeling with Azure
MODULE 8: INTRODUCTION TO DEEP LEARNING
• Introduction to Artificial Neural Network, Architecture
• Artificial Neural Network in Python
• Introduction to Convolutional Neural Network, Architecture
• Convolutional Neural Network in Python
MODULE 1: DATABASE INTRODUCTION
• DATABASE Overview
• Key concepts of database management
• Relational Database Management System
• CRUD operations
MODULE 2: SQL BASICS
• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
MODULE 3: DATA TYPES AND CONSTRAINTS
• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment
MODULE 4: DATABASES AND TABLES (MySQL)
• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table
MODULE 5: SQL JOINS
• Inner Join, Outer Join
• Left Join, Right Join
• Self Join, Cross join
• Windows function: Over, Partition, Rank
MODULE 6: SQL COMMANDS AND CLAUSES
• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries
MODULE 7 : DOCUMENT DB/NO-SQL DB
• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
MODULE 1: GIT INTRODUCTION
• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture
MODULE 2: GIT REPOSITORY and GitHub
• Git Repo Introduction
• Create New Repo with Init command
• Git Essentials: Copy & User Setup
• Mastering Git and GitHub
MODULE 3: COMMITS, PULL, FETCH AND PUSH
• Code Commits
• Pull, Fetch and Conflicts resolution
• Pushing to Remote Repo
MODULE 4: TAGGING, BRANCHING AND MERGING
• Organize code with branches
• Checkout branch
• Merge branches
• Editing Commits
• Commit command Amend flag
• Git reset and revert
MODULE 5: GIT WITH GITHUB AND BITBUCKET
• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
MODULE 1: BIG DATA INTRODUCTION
• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction
MODULE 2 : HDFS AND MAP REDUCE
• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners,
• Combiners, Shuffle, and Sort
MODULE 3: PYSPARK FOUNDATION
• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs
MODULE 4: SPARK SQL and HADOOP HIVE
• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
MODULE 1: TABLEAU FUNDAMENTALS
• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies
MODULE 2: POWER-BI BASICS
• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION
MODULE 3 : DATA TRANSFORMATION TECHNIQUES
• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values
MODULE 4: CONNECTING TO VARIOUS DATA SOURCES
• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model
The cost of data science courses in Kolhapur typically ranges from INR 30,000 to INR 2,00,000 depending on the course duration and the provider. It can vary based on the depth of the program and the certification offered. Some institutes may also offer flexible payment options.
The best approach includes enrolling in structured courses that focus on practical learning. Self-study through online resources, coding practice, and participating in data science competitions are also effective methods. Local meetups or workshops can help in building a strong network.
Entry-level data scientists in Kolhapur can expect a salary ranging from ₹3 to ₹7 lakhs per annum. The exact salary may vary based on the company and individual qualifications.
Data science courses in Kolhapur usually range from 3 to 6 months for short-term certification programs. Full-fledged degree courses may take 1 to 2 years, depending on the educational level. The time can vary based on course depth and intensity.
The demand for data science professionals in Kolhapur is expected to grow with increased reliance on data-driven decision-making. Industries such as agriculture, manufacturing, and IT are likely to drive this growth. There will be plenty of opportunities for skilled professionals in the future.
Typically, candidates need a bachelor’s degree in any field, though a background in mathematics, statistics, computer science, or engineering is preferred. Basic programming skills are often necessary, but some courses may not have strict eligibility criteria.
The best Data Science course available in Kolhapur is the Certified Data Scientist program. This course offers a comprehensive curriculum that includes 700+ hours of learning, with live online sessions and practical projects. It is designed to provide in-depth knowledge and hands-on experience, making it an ideal choice for aspiring data scientists.
Yes, coding proficiency, especially in Python and R, is essential for data science. It helps in data manipulation, model building, and automation of processes. However, it's not mandatory to be an expert at the beginning, as you can learn on the job.
Key skills for data science include statistical analysis, programming, data wrangling, and machine learning. Problem-solving, communication, and domain knowledge also play a crucial role in delivering impactful insights. A strong foundation in mathematics is equally important.
Yes, non-engineering graduates can pursue data science by gaining proficiency in mathematics, programming, and analytics. Many courses are designed for students from various academic backgrounds, offering entry-level skills required for the field.
Job opportunities for data scientists are expanding in Kolhapur, particularly in sectors like agriculture, IT, and healthcare. With growing investments in technology, companies are seeking skilled data scientists for analysis and predictive modeling roles.
While there are several institutes in Kolhapur, DataMites is considered one of the best for data science training. It offers a balanced mix of theory and practical knowledge, with a focus on hands-on experience and strong industry connections. The quality content, along with a highly experienced faculty team, makes it an excellent choice for aspiring data scientists.
A typical data science course covers topics like data preprocessing, machine learning algorithms, data visualization, statistical analysis, and data ethics. Advanced topics such as deep learning, natural language processing, and big data technologies may also be included.
The essential elements of data science are data collection, cleaning, analysis, and modeling. Machine learning and statistical techniques are employed to extract insights and predict trends. Data visualization and communication are key for sharing results effectively.
Learning data science is important as it equips individuals with skills to analyze and interpret data, driving better decision-making and innovation. It is a valuable asset across many industries.
Data science is broader, incorporating data engineering, machine learning, and advanced algorithms for prediction and automation. Data analytics focuses primarily on analyzing historical data to inform decisions. Data science involves more complex problem-solving techniques and predictive modeling.
To become a data scientist, one must have skills in programming (Python, R), statistics, data manipulation, machine learning, and data visualization. Communication skills are equally important to explain findings clearly. A strong understanding of algorithms is also valuable.
AI and machine learning are pivotal in automating data analysis and creating predictive models. They enhance the capabilities of data science by enabling systems to learn from data and make decisions without explicit programming. These technologies drive innovation in various industries.
Kolhapur’s popular areas include Rajarampuri (416008), a bustling residential and commercial hub, and Shahupuri (416001), known for its central location and connectivity. Tarabai Park (416003) offers upscale living with modern amenities, while Ujlaiwadi (416004) is growing rapidly as a residential and industrial zone. Laxmipuri (416002) and Kasba Bawada (416006) blend heritage with urban convenience, making them sought-after neighborhoods. Emerging localities like Nagala Park (416003), Rankala (416012), and Rajendra Nagar (416005) provide excellent infrastructure, ensuring Kolhapur remains a prime destination for families, professionals, and businesses.
Python is widely used in data science due to its simplicity and versatility. It supports libraries like NumPy, Pandas, and Scikit-learn, which are essential for data manipulation, analysis, and machine learning. Python’s flexibility makes it ideal for building data science applications.
Challenges in data science include dealing with messy and incomplete data, choosing the right algorithms, and ensuring the scalability of models. Data privacy and ethical issues are also critical concerns. Communicating complex results to non-technical stakeholders can be difficult.
Yes, DataMites Kolhapur provides EMI options for students to ease the payment process for the data science course. Flexible plans are available based on the course fee structure. Students can inquire about EMI details during the enrollment process.
You can enroll in the data science course at DataMites Kolhapur by visiting our official website or contacting our admission team. Enrollment can be done online or offline, depending on your preference. The team will guide you through the necessary steps to complete your registration.
DataMites offers Data Science courses in Kolhapur with flexible pricing options:
These fees include comprehensive course materials, access to online resources, and software tools necessary for practical sessions.
Yes, DataMites Kolhapur offers data science courses that include internship opportunities. This helps students gain practical exposure and enhance their learning. Internships are offered as part of the course to provide real-world industry experience.
DataMites provides industry-relevant training and ensures that students acquire practical skills in data science. With expert instructors and comprehensive course materials, DataMites prepares students for successful careers. The hands-on approach and career support make DataMites an ideal choice.
DataMites offers a Data Science course in Kolhapur with a duration of 8 months, totaling 700 learning hours. This includes 120 hours of live online training, 25 capstone projects, and 1 client project. The course is available in live virtual, blended learning, and classroom formats
The DataMites Flexi-Pass offers a 3-month flexible timeframe to participate in Data Science sessions. It enables learners to revisit lessons, address doubts, and solidify their understanding. This flexible structure ensures consistent support throughout the learning process.
Yes, DataMites Kolhapur offers free demo classes for students interested in the data science course. This allows potential learners to experience the teaching style and course content before enrolling. Demo classes help students make an informed decision about their learning path.
Yes, DataMites Kolhapur provides placement assistance to students who complete the data science course. The placement support includes job preparation, resume building, and interview guidance. DataMites connects students with industry recruiters and hiring partners.
Yes, DataMites Kolhapur includes live projects as part of their data science curriculum. These projects help students gain hands-on experience in solving real-world problems. Working on live projects enhances practical knowledge and prepares students for industry challenges.
DataMites Kolhapur provides comprehensive study materials, including textbooks, notes, and online resources. The materials are designed to cover all aspects of the data science syllabus. Students also get access to tools and resources to practice and apply their learning.
DataMites Kolhapur 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 offers course certification upon successful completion of their programs. The certifications are recognized by IABAC and NASSCOM FutureSkills. This adds value to your credentials and enhances career opportunities.
The DataMites data science syllabus covers a wide range of topics, including Python, Machine Learning, Deep Learning, Data Visualization, and Big Data Analytics. The curriculum is designed to provide a strong foundation in data science. Students learn key concepts and techniques essential for a career in data science.
DataMites Kolhapur provides multiple payment methods for course registration, including debit/credit cards (Visa, MasterCard, American Express), PayPal, and EMI options. After completing the payment, you will receive confirmation and course materials. For assistance, a dedicated educational counselor is available to help you with the process.
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.