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 a data science course in Ghaziabad varies between INR 30,000 to INR 2,50,000, depending on the institute, duration, and mode of learning. Online courses are generally more affordable than in-person programs. Additional costs may apply for certifications and advanced modules.
The duration of data science courses in Ghaziabad ranges from a few months to two years. Short-term courses last 3–6 months, while diploma and degree programs can take 1–2 years. The duration depends on course content, depth, and learning mode.
According to AmbitionBox reports, the salary for a Data Scientist in Ghaziabad ranges between ₹4 Lakhs to ₹22 Lakhs per year. The average annual salary is approximately ₹14 Lakhs, depending on factors such as experience, skills, and company size. Senior professionals with advanced expertise and a strong track record may earn salaries on the higher end of the range.
Most data science courses require a background in mathematics, statistics, or computer science. Graduates from any stream with analytical and programming skills can apply. Some advanced programs may require prior knowledge of coding or machine learning.
The demand for data science professionals is increasing in Ghaziabad due to the rise of tech-driven businesses. Industries like IT, healthcare, e-commerce, and finance require skilled data scientists. With AI and big data growth, the future of data science remains promising.
Anyone with an interest in data analysis, programming, and problem-solving can enroll. Students, professionals, and career switchers from diverse backgrounds can pursue data science. Basic knowledge of statistics and coding is beneficial but not always mandatory.
The best way to study data science is through structured courses, hands-on projects, and continuous practice. Online platforms, self-paced learning, and mentorship programs can be helpful. Practical exposure to real-world datasets enhances understanding and job readiness.
Several institutes offer quality data science courses in Ghaziabad, both online and offline. DataMites is one of the best institutes, known for its comprehensive curriculum, experienced faculty, and strong placement support. The best choice depends on curriculum, faculty, placement assistance, and hands-on training. Candidates should compare course reviews, fees, and certification value before enrolling.
Yes, coding is an essential skill for a data science career, with Python and R being the most commonly used languages. However, some beginner courses may allow learning data science concepts with minimal coding. Strong programming skills enhance job prospects and analytical capabilities.
Anyone with an interest in data analysis, programming, and problem-solving can enroll in a data science course in Ghaziabad. Students, working professionals, and career switchers from various backgrounds can pursue data science. Basic knowledge of statistics and coding is beneficial but not always mandatory.
Data science trends in Ghaziabad include AI-driven analytics, automation, and cloud-based data management. Companies are adopting machine learning, big data, and predictive analytics for decision-making. The demand for AI specialists and data engineers is also rising.
The Certified Data Scientist Course is the best option for aspiring data professionals in Ghaziabad. It provides hands-on projects, industry-relevant tools, and placement assistance. This course includes real-world case studies, machine learning, and internship opportunities for practical experience.
Ethical concerns in data science include data privacy, bias in AI models, and responsible data handling. Transparency in algorithms and fairness in decision-making are critical. Organizations must ensure compliance with data protection laws and ethical AI practices.
Data science consists of data collection, data cleaning, exploratory data analysis, machine learning, and visualization. Statistical modeling and programming play a crucial role in extracting insights. Deployment and model evaluation complete the data science workflow.
Popular tools in data science include Python, R, SQL, Tableau, Power BI, and Jupyter Notebook. Technologies such as machine learning frameworks (TensorFlow, Scikit-learn) and big data tools (Hadoop, Spark) are widely used. Cloud computing platforms like AWS and Google Cloud are also essential.
Yes, Python is one of the most widely used programming languages in data science. Many courses emphasize Python for data analysis, visualization, and machine learning. However, alternatives like R and SQL are also useful in data science roles.
Yes, non-engineering graduates can transition into data science with the right skills. Strong analytical thinking, mathematics, and programming knowledge help in this switch. Many courses provide beginner-friendly training for candidates from diverse educational backgrounds.
Ghaziabad’s key localities include Indirapuram (201014), a well-developed residential and commercial center, and Vaishali (201010), known for its excellent metro connectivity and modern amenities. Kaushambi (201012) is a thriving area with premium housing and business hubs, while Raj Nagar Extension (201017) is emerging as a hotspot for affordable and spacious living. Crossings Republik (201016) offers a self-sufficient township environment, whereas Sahibabad (201005) and Vasundhara (201012) are preferred for their strategic location and accessibility. Rapidly expanding areas like NH-24 (201102), Pratap Vihar (201009), and Kavi Nagar (201002) make Ghaziabad a desirable destination for both homebuyers and professionals.
SQL is essential for managing and querying structured data in databases. Data scientists use SQL to extract, manipulate, and analyze data before applying machine learning models. Proficiency in SQL enhances efficiency in handling large datasets.
Yes, data science jobs are in high demand in Ghaziabad, with companies seeking skilled professionals. Industries like IT, healthcare, and finance are actively hiring data scientists and analysts. The increasing adoption of AI and data-driven decision-making fuels job growth.
Yes, DataMites Ghaziabad offers a Data Science course that includes internship opportunities. The program provides hands-on experience to help students apply their skills in real-world projects. DataMites ensures a structured learning path with practical exposure.
Yes, DataMites Ghaziabad offers an EMI option for the Data Science course, making it easier for learners to manage fees. Flexible payment plans are available to suit different financial needs. You can contact DataMites for detailed information on EMI options and eligibility.
DataMites Data Science syllabus encompasses essential topics such as Python and R programming, statistics, machine learning algorithms, and data visualization techniques. It also delves into deep learning, big data concepts, and practical applications across various industries. This comprehensive curriculum is designed to equip learners with the skills necessary for a successful career in data science.
DataMites offers Data Science courses in Ghaziabad with fees varying based on the chosen learning mode. The Live Virtual (Instructor-Led Online) course is priced at INR 59,451, while the Blended Learning (Self Learning + Live Mentoring) option costs INR 34,951. For those preferring in-person instruction, the Classroom Training is available at INR 64,451.
Yes, DataMites offers a free demo class for its courses in Ghaziabad. This session helps you understand the training structure, curriculum, and mentor guidance. You can attend the demo to evaluate if DataMites meets your learning needs.
Anyone interested in learning Data Science can enroll in DataMites courses in Ghaziabad, including students, professionals, and beginners. DataMites offers training for individuals from diverse backgrounds, whether they are starting fresh or upgrading their skills. With expert guidance, DataMites ensures a structured learning path for all aspiring data professionals.
DataMites offers a Data Science course that includes placement assistance to help students secure job opportunities. Our program is designed to provide practical skills aligned with industry needs. Placement support is provided, but results may vary based on individual performance and market conditions.
DataMites offers a 100% refund if you request cancellation within one week of the course start date and have 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.
The DataMites Data Science course in Ghaziabad spans 8 months with a total of 700 learning hours. It also includes 120 hours of live online training. This program is designed to provide a comprehensive understanding of Data Science concepts and techniques.
Yes, DataMites in Ghaziabad offers course certifications accredited by IABAC® and NASSCOM® FutureSkills. These certifications are designed to enhance your professional credentials in data science and related fields. Upon successful completion of the training, you will receive globally recognized certifications, validating your expertise and skills.
DataMites offers a structured Data Science course in Ghaziabad with industry-relevant training and expert guidance. The program includes hands-on projects, certification, and career support to enhance learning. DataMites provides a flexible and practical approach to building Data Science skills.
DataMites offers Data Science courses in Ghaziabad, led by experienced trainers proficient in the field. These instructors bring practical industry knowledge to the classroom, ensuring a comprehensive learning experience. For detailed information about the trainers, please visit our DataMites website.
Yes, DataMites in Ghaziabad offers courses that include live projects for hands-on learning. These projects help students apply theoretical knowledge to real-world scenarios. DataMites focuses on practical experience to enhance industry readiness.
DataMites in Ghaziabad offers a variety of payment methods for their training programs. Accepted options include cash, credit cards (Visa, MasterCard, American Express), debit cards, net banking, cheques, and PayPal. These flexible payment solutions are designed to accommodate the diverse preferences of their participants.
The DataMites Flexi-Pass offers a 3-month period to attend Data Science training sessions at your convenience. It helps learners revisit topics, clear doubts, and strengthen their understanding. This flexible option ensures ongoing guidance and better retention of concepts.
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.