4.9 (24,680) reviews 110,000
59,451*
NO COST EMI : 9,909 p.m for 6 Months
*Offer valid till 26th 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!
Anyone with a basic understanding of mathematics and programming can learn data science. Chandigarh residents interested in a data-driven career can pursue these courses. Students, professionals, and career changers can benefit from data science training.
As per AmbitionBox, Data Scientists in Chandigarh have an annual salary range of INR 3 Lakhs to INR 22 Lakhs, with professionals having 1 to 6 years of experience earning an average of INR 20 Lakhs, according to 117 recent salary submissions.
Focus on building a strong foundation in mathematics, statistics, and programming. Practice with real-world datasets and contribute to open-source projects. Supplement learning with online courses, workshops, and networking with data science professionals.
The duration of Data Science courses in Chandigarh varies from 3 months to 1 year, depending on the program type. Short-term courses focus on fundamentals and hands-on training, while longer programs offer in-depth learning and advanced concepts. Learners can choose based on their career goals and experience level.
There are several institutes offering Data Science courses in Chandigarh, but DataMites stands out as the best. It provides comprehensive training, industry-recognized certifications, and hands-on projects to enhance practical skills. With expert mentorship and career support, DataMites helps learners build a strong foundation in Data Science.
The Data science Course in Chandigarh has a strong scope, with growing demand across IT, startups, and government sectors. Skilled professionals can expect competitive salaries and diverse roles, while local universities and training institutes support industry-ready education. The future outlook remains promising due to increasing adoption of AI and data-driven technologies.
The Certified Data Scientist Course is one of the best Data Science programs available in Chandigarh. It covers machine learning, deep learning, statistics, and real-world projects, providing industry-relevant skills. This certification enhances career prospects by preparing learners for data-driven roles across various industries.
Yes, data science jobs are still in high demand globally, driven by AI and data-driven decision-making across industries. Skilled professionals in Python, machine learning, and data visualization have strong career prospects. The field is expected to grow rapidly in the coming years.
Yes, non-engineering graduates can transition into data science. Relevant skills like statistics and programming can be learned through various courses and self-study. Demonstrating these skills through projects and portfolios is key for career transitions.
To become a data scientist in Chandigarh, start with a relevant degree and build skills in Python, machine learning, and data analysis. Join reputed training institutes for hands-on projects and certifications, then gain practical experience through internships or real-world projects. Networking and applying for local data science roles can help launch your career.
Data science programs in Chandigarh typically require a bachelor's degree in a related field like computer science, statistics, or mathematics. Some institutions may also consider candidates with relevant work experience. Specific program requirements vary, so checking with individual institutions is recommended.
The Data Science course fees in Chandigarh typically range from INR 15,000 to INR 2,50,000, depending on the institute, program duration and whether the course is online, classroom-based or hybrid.
Yes, coding proficiency is essential for a career in Data Science, as it allows professionals to work with data manipulation, analysis, and machine learning models. Python and R are the most commonly used languages. While some roles may require less coding, a basic understanding of programming will always be beneficial in this field.
To become a Data Scientist, key skills include programming (primarily in Python or R), data analysis using tools like Pandas and SQL, and proficiency in machine learning algorithms. A solid understanding of statistics and data visualization is also crucial for interpreting and presenting data insights. Strong problem-solving abilities and domain knowledge will further enhance career success.
The key components of Data Science include data collection and data cleaning to ensure quality data, data analysis to derive meaningful insights, and machine learning to build predictive models. Statistics plays a crucial role in understanding and interpreting data patterns. Additionally, data visualization is essential for effectively communicating findings.
Data Science is widely used across industries like finance, where it helps with fraud detection and risk management. The healthcare industry leverages it for predictive analytics and personalized treatment plans. E-commerce, retail, and manufacturing also rely on data science for customer insights, supply chain optimization, and predictive maintenance.
Data science ethics involves concerns like data privacy and security, ensuring responsible use of personal information. Bias in algorithms and models can lead to unfair or discriminatory outcomes. Transparency and explainability are crucial for building trust and accountability in data-driven decisions.
A data scientist analyzes and interprets data to generate insights and build predictive models, focusing on statistics and machine learning. A data engineer, on the other hand, builds and maintains the data infrastructure and pipelines needed to store and process data efficiently.
Common challenges in Data Science include dealing with incomplete or dirty data, which can lead to inaccurate analysis. Ensuring data privacy and security is another concern, especially with sensitive information. Additionally, model interpretability and bias in algorithms can hinder trust and fairness in the results.
AI and Machine Learning play a central role in data science by enabling systems to analyze large datasets, identify patterns, and make predictions automatically. They help data scientists build predictive models, automate decision-making, and extract actionable insights from complex data efficiently.
Data science freshers can explore roles such as Data Analyst, Junior Data Scientist, Business Intelligence Analyst, Machine Learning Intern, and Data Engineer Trainee. These positions allow beginners to work on data cleaning, analysis, visualization, and basic model building while gaining industry experience.
Latest trends in Data Science include the growing use of Artificial Intelligence (AI) and Machine Learning for predictive analytics and automation. Natural Language Processing (NLP) is gaining traction in understanding human language and text. Additionally, edge computing and cloud-based analytics are enabling faster, real-time data processing and collaboration across industries.
SQL is crucial for data science, enabling efficient data retrieval and manipulation from databases. It allows data scientists to query, filter, and aggregate data for analysis and model building. SQL skills are essential for accessing and preparing data for data science projects.
DataMites Data Science courses cater to a broad audience, including professionals seeking career transitions and fresh graduates. Individuals with a basic understanding of mathematics and programming are generally eligible. Specific prerequisites may vary depending on the course level and focus.
DataMites Chandigarh offers a 100% refund if you request cancellation within one week of the course start date, provided you've attended at least two sessions. Refunds are processed within 5-7 business days after the request is made. Please note that refunds are not available after six months from the course enrollment date.
Yes, DataMites Chandigarh offers a data science course that includes an internship. This provides students with hands-on experience, applying theoretical knowledge to real-world problems. The internship helps enhance practical skills and increases employability in the data science field.
Yes, DataMites Chandigarh offers EMI options for the data science course. This allows students to pay the course fees in manageable monthly installments. It makes the learning experience more accessible and flexible for those who prefer an easy payment plan.
The data science course fee in Chandigarh depends on the chosen learning mode: live online training is around INR 60,000, classroom programs cost approximately INR 65,000, and blended options are available for about INR 35,000. For exact details, it is recommended to contact the Chandigarh center directly.
Yes, DataMites Chandigarh offers a data science course with placement assistance. The program is designed to equip students with the necessary skills and help them secure job opportunities in the field. Placement support includes guidance, interview preparation, and connections with top employers.
DataMites data science syllabus typically covers statistics, programming (like Python or R), and machine learning. It also includes modules on data visualization, data mining, and often big data technologies. Specific topics may vary depending on the course and certification level.
DataMites Chandigarh offers both online and offline Data Science courses, allowing students to select the learning mode that best fits their schedule and preferences. The curriculum and quality of instruction remain consistent across both formats. The Chandigarh center is located at Workcave Coworking, SCO 301-302, Level LG, 35B, Chandigarh, 160022, providing convenient access for students and working professionals.
Yes, DataMites Chandigarh offers a free demo class for its data science courses. This allows prospective students to get a feel for the course structure and teaching style before enrolling. It’s a great opportunity to make an informed decision about your learning path.
DataMites offers data science courses in Chandigarh with comprehensive curriculum, experienced instructors, and placement assistance. Their programs cover in-demand skills and tools, preparing individuals for data science roles. Choosing DataMites can provide a structured learning path and career support in this field.
Yes, DataMites Chandigarh offers courses that include live projects, providing students with practical experience in real-world scenarios. This hands-on approach enhances learning and prepares students for industry challenges. It ensures that learners gain valuable skills to excel in their data science careers.
DataMites' data science trainers are industry experts with extensive experience in the field. They possess in-depth knowledge of data science concepts and practical applications. Trainers are dedicated to providing comprehensive training and mentorship to students.
DataMites Chandigarh is easily accessible to individuals from nearby areas such as Mohali (160055), Panchkula (134109), Zirakpur (140603), Kharar (140301), and Dera Bassi (140507). The center is also conveniently located for those from various sectors within Chandigarh, including Sector 17 (160017), Sector 22 (160022), Sector 35 (160035), Sector 43 (160043), and Sector 47 (160047). These locations are well-connected, making it easy for students to travel for their data science courses. Chandigarh itself is a rapidly growing tech hub with increasing demand for data science professionals across industries like IT, healthcare, and finance. The scope for data science in Chandigarh is high, with many organizations seeking skilled experts in AI, machine learning, and analytics to drive business innovation.
The DataMites Data Science course in Chandigarh has a duration of 8 months, encompassing 700 learning hours. This comprehensive program covers various aspects of data science, providing students with in-depth knowledge and practical experience. It is designed to equip learners with the skills needed to excel in the data science field.
DataMites offers several payment options for course enrollment, including debit/credit cards (Visa, MasterCard, American Express) and PayPal. After the payment is completed, you will receive the course materials and a confirmation of your registration. Additionally, an educational counselor is available to assist you throughout the process.
A Certified Data Scientist course provides structured training in data science principles and techniques. These courses often cover programming, statistics, machine learning, and data visualization. Certification can validate skills and enhance career prospects in the data science field.
Yes, DataMites Chandigarh offers course certification upon successful completion of the Data Science program. The certification is awarded by recognized bodies such as IABAC® and NASSCOM® FutureSkills. This credential helps validate your skills and improves career opportunities in the data science field.
DataMites offers both live and recorded online classes for its data science courses. Live sessions provide real-time interaction with trainers, while recorded classes allow flexibility to review the material at your convenience. This blended approach ensures comprehensive learning for all students.
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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