Professional Data Science TrainingGorakhpurBeginner to Advanced

Turn Data IntoIntelligenceData Science Course in Gorakhpur

Python • Analytics • SQL • Machine Learning • AI

Learn how to collect, clean, analyse and interpret data. Build your foundation in Python, statistics, data visualisation, SQL and Machine Learning. Explore practical projects that connect data analysis with real-world problem solving.

Learning Modules

Python

Programming

Machine Learning

Predictive Models

Projects

Practical Learning

Understand data. Discover insights.

Data Science Course Training in Gorakhpur

Learn Data Science at Dazzling Skill through a structured, practical learning roadmap. Start with Python programming, statistics and data handling. Progress into NumPy, Pandas, SQL, data cleaning, exploratory analysis, visualisation, Power BI and Machine Learning. Understand how to train and evaluate models and apply your knowledge through portfolio-oriented projects.

Python Programming

Build the programming foundation for data work.

Data Analysis

Clean, transform and analyse datasets.

Visualisation

Communicate findings with charts and dashboards.

Machine Learning

Explore prediction and pattern recognition.

Course coverage, fees, timings and availability are subject to confirmation with Dazzling Skill.

Structured learning roadmap

Complete Data Science Course Syllabus

Progress from Python and statistics to data analysis, visualisation, machine learning and practical applications.

01–03

Foundations

04–06

Data Handling

07–09

Analytics

10–14

AI & ML

Projects

01. Introduction to Data Science

Understand Data Science, its applications and the complete process of converting raw data into useful insights.

3–4 Hours

What is Data Science?
Data Science vs Data Analytics vs Data Engineering
Introduction to Artificial Intelligence
Introduction to Machine Learning
Types of data: structured and unstructured
Understanding datasets and data sources
Data Science lifecycle
Real-world applications of Data Science
Roles of a Data Analyst and Data Scientist
Setting up the development environment
Installing Python and Anaconda
Introduction to Jupyter Notebook

02. Python Programming for Data Science

Build a strong Python programming foundation for data analysis, automation and machine learning.

8–10 Hours

Python introduction and installation
Variables and data types
Operators and expressions
Conditional statements
Loops: for and while
Strings and string methods
Lists, tuples, sets and dictionaries
Functions and lambda functions
List comprehensions
Modules and packages
File handling: CSV, TXT and JSON
Exception handling
Object-oriented programming fundamentals
Working with Python libraries
Virtual environments and pip

03. Mathematics, Statistics & Probability

Learn the mathematical and statistical concepts used to understand datasets and build reliable models.

6–8 Hours

Basic mathematics for Data Science
Mean, median and mode
Range, variance and standard deviation
Percentiles and quartiles
Probability fundamentals
Conditional probability
Independent and dependent events
Probability distributions
Normal distribution
Sampling and population
Correlation and covariance
Hypothesis testing introduction
P-values and statistical significance
Basic linear algebra concepts
Vectors and matrices

04. NumPy for Numerical Computing

Use NumPy to perform fast numerical calculations and work efficiently with multidimensional data.

4–5 Hours

Introduction to NumPy
Creating NumPy arrays
Array dimensions and shapes
Indexing and slicing
Array data types
Reshaping and flattening arrays
Array operations
Broadcasting
Mathematical functions
Aggregation functions
Random number generation
Sorting and filtering arrays
Working with matrices
NumPy vs Python lists

05. Pandas & Data Manipulation

Learn to load, inspect, clean, transform and analyse structured datasets using Pandas.

6–8 Hours

Introduction to Pandas
Series and DataFrame
Reading CSV and Excel files
Inspecting datasets
Selecting rows and columns
Filtering records
Sorting data
Handling missing values
Removing duplicate records
Changing data types
String operations
Date and time operations
GroupBy and aggregation
Merging and joining DataFrames
Pivot tables
Exporting cleaned datasets

06. Data Cleaning & Exploratory Data Analysis

Discover patterns, identify data quality problems and explore datasets before building analytical models.

5–7 Hours

What is data preprocessing?
Understanding data quality
Handling missing and incorrect values
Identifying duplicate records
Detecting outliers
Exploratory Data Analysis (EDA)
Univariate analysis
Bivariate analysis
Multivariate analysis
Distribution analysis
Correlation analysis
Feature relationships
Data profiling
Exploratory analysis reports
Preparing datasets for modelling

07. SQL & Database Fundamentals

Retrieve and analyse data from relational databases using SQL queries and database concepts.

5–7 Hours

Database fundamentals
Relational database concepts
Tables, rows and columns
Primary and foreign keys
SELECT and DISTINCT
WHERE and ORDER BY
AND, OR and IN operators
LIKE and BETWEEN
Aggregate functions
GROUP BY and HAVING
INNER and OUTER JOIN
Subqueries
CASE statements
Common table expressions introduction
Connecting Python with databases
Extracting data for analysis

08. Data Visualisation & Reporting

Present analytical findings through meaningful charts, graphs and interactive reports.

5–7 Hours

Principles of data visualisation
Matplotlib introduction
Line charts and bar charts
Histograms and scatter plots
Pie charts and box plots
Customising chart labels and legends
Seaborn introduction
Distribution plots
Heatmaps and correlation plots
Choosing the right visualisation
Creating analytical dashboards
Power BI introduction
Connecting data sources in Power BI
Data transformation in Power Query
Visuals, filters and slicers
Dashboard design fundamentals

09. Data Analytics & Business Intelligence

Translate data into business insights using KPIs, reporting and analytical thinking.

4–6 Hours

Introduction to business analytics
Understanding business requirements
Key Performance Indicators (KPIs)
Descriptive analytics
Diagnostic analytics
Trend and comparative analysis
Customer and sales analysis
Revenue and profitability analysis
Cohort analysis introduction
Dashboard storytelling
Business reporting
Communicating analytical findings
Data-driven decision making

10. Machine Learning Fundamentals

Understand machine learning workflows and train models for prediction and pattern recognition.

7–9 Hours

What is Machine Learning?
AI vs Machine Learning vs Deep Learning
Supervised learning
Unsupervised learning
Reinforcement learning overview
Features and target variables
Training and testing datasets
Feature preprocessing
Scikit-learn introduction
Linear regression
Logistic regression
Decision trees
Random forests
K-nearest neighbours
Support vector machines introduction
Model training and prediction

11. Machine Learning Model Evaluation

Measure model performance, compare algorithms and understand how to improve predictions.

5–7 Hours

Train-test split
Cross-validation
Regression evaluation metrics
MAE, MSE and RMSE
R-squared
Classification accuracy
Precision and recall
F1-score
Confusion matrix
ROC curve and AUC
Overfitting and underfitting
Bias and variance
Feature scaling
Feature selection introduction
Hyperparameter tuning
Model comparison

12. Unsupervised Learning & Advanced ML

Explore techniques for discovering hidden patterns and grouping similar records.

4–6 Hours

Clustering fundamentals
K-means clustering
Hierarchical clustering overview
DBSCAN introduction
Choosing the number of clusters
Elbow method
Principal Component Analysis (PCA)
Dimensionality reduction
Anomaly detection introduction
Recommendation system concepts
Ensemble learning overview
Practical clustering exercises

13. Artificial Intelligence & Deep Learning

Understand neural networks and explore the foundations of deep learning and modern AI applications.

5–7 Hours

Introduction to Artificial Intelligence
Introduction to deep learning
Neural network fundamentals
Neurons and layers
Activation functions
Loss functions and optimisation
Backpropagation concept
TensorFlow and Keras introduction
Building a basic neural network
Image classification overview
Natural Language Processing introduction
Text preprocessing fundamentals
Generative AI concepts
Large Language Model overview
Responsible AI fundamentals

14. Data Science Workflow & Model Deployment

Understand how to organise analytical projects and make trained models accessible to applications.

4–6 Hours

Structuring Data Science projects
Reusable Python scripts
Jupyter Notebook best practices
Saving trained models
Pickle and joblib introduction
Building a simple prediction interface
Streamlit introduction
Creating a basic data application
API concepts for model serving
Model deployment overview
Git and GitHub fundamentals
Documentation and reproducibility
Monitoring concepts
Data privacy and responsible handling

15. Real-World Projects & Portfolio

Apply the complete Data Science workflow to practical datasets and portfolio-oriented projects.

Variable

Understanding project requirements
Finding and understanding datasets
Data collection and preparation
Data cleaning and preprocessing
Exploratory Data Analysis
Visualisation and insight generation
Feature engineering fundamentals
Selecting suitable ML algorithms
Training and evaluating models
Interpreting model results
Creating dashboards and reports
Building a simple model application
GitHub project documentation
Presenting project findings
Portfolio preparation

Important Course Information

Module hours are indicative learning estimates, not a guaranteed class schedule or total course duration. The depth of mathematics, deep learning, model deployment and projects may be adjusted according to the selected batch and learning plan.

What Is Data Science?

Data Science combines programming, statistics, mathematics and analytical techniques to extract meaningful information from data. It helps organisations understand past performance, discover patterns, make informed decisions and develop predictive models. A Data Science workflow can involve collecting data, cleaning it, exploring patterns, building models and communicating results.

Where Is Data Science Used?

Banking & Finance

Explore transaction patterns, risk and fraud detection concepts.

Healthcare

Analyse healthcare datasets and understand predictive modelling applications.

E-Commerce

Study customer behaviour, product trends and recommendation concepts.

Marketing

Understand campaign performance, customer segments and conversion trends.

Manufacturing

Explore quality data, process patterns and predictive maintenance concepts.

Business Intelligence

Create reports and dashboards to support data-driven decisions.

Data Science Course Learning Objectives

Develop a structured understanding of data processing, analysis, visualisation and machine learning through practical exercises.

Understand the Data Science lifecycle and its applications.

Develop Python programming skills for data analysis.

Understand statistics, probability and mathematical concepts.

Work with NumPy arrays and numerical operations.

Manipulate datasets using Pandas.

Clean missing, duplicate and inconsistent data.

Perform Exploratory Data Analysis.

Retrieve information using SQL queries.

Create charts and visualisations using Python.

Build analytical reports and Power BI dashboards.

Understand supervised and unsupervised learning.

Train regression and classification models.

Evaluate models using appropriate performance metrics.

Explore clustering and dimensionality reduction.

Understand neural networks and deep learning basics.

Build practical data science applications.

Understand model deployment workflows.

Document and present data science projects.

Data Science Training Features

🐍

Python-Based Learning

Build programming foundations and use Python for practical data analysis.

📊

Real Dataset Practice

Work with datasets to clean, explore and communicate useful insights.

🧮

Statistics & Mathematics

Understand the concepts behind analytical methods and ML models.

🤖

Machine Learning

Explore algorithms, model training and performance evaluation.

📈

Visualisation & BI

Present findings through charts, reports and dashboard exercises.

🚀

Portfolio Projects

Apply concepts through practical project-based learning.

Tools & Technologies You Will Explore

Explore the programming languages, libraries, databases and visualisation tools used in common Data Science workflows.

🐍

Python

📓

Jupyter Notebook

🧪

Anaconda

🔢

NumPy

🐼

Pandas

📈

Matplotlib

🎨

Seaborn

🗄️

SQL

📊

Power BI

🤖

Scikit-learn

🧠

TensorFlow

🐙

Git & GitHub

💻

VS Code

🚀

Streamlit

🐘

PostgreSQL

🏆

Kaggle

Tool versions and installation methods may vary. Specific tools depend on the selected learning modules.

Learn with purpose

Why Choose Dazzling Skill?

Data Science is best learned by working with data, asking meaningful questions and testing your findings. Follow a structured learning path from Python and statistics to analytics and machine learning. Focus on understanding the process behind the results, not just running code or copying models.

Structured Data Science learning roadmap

Python programming practice

Statistics and data analysis concepts

Dataset cleaning and exploration

SQL and database fundamentals

Machine Learning exercises

Visualisation and dashboard practice

Portfolio and project guidance

Who Should Join This Data Science Course?

🎓

BCA & MCA Students

Develop practical skills in Python, analytics and machine learning.

💻

B.Tech Students

Explore data-driven problem solving alongside academic learning.

📚

Graduates

Build a foundation for exploring data-focused technology roles.

🐍

Python Learners

Extend Python knowledge into data analysis and machine learning.

📊

Aspiring Data Analysts

Learn data cleaning, SQL, visualisation and reporting.

💼

Working Professionals

Explore analytical techniques and data-driven decision making.

Recommended Prerequisites

Basic computer knowledge is recommended. Familiarity with mathematics and logical problem-solving is helpful. Python programming starts from the fundamentals, so advanced programming knowledge is not required to begin the foundation modules.

Flexible Learning Options

Plan Your Data Science Learning Journey

Discuss your programming background, learning goals and preferred schedule before selecting a batch.

Classroom Training🏫

Offline Learning

Explore classroom learning options in Gorakhpur.

Structured Data Science learning roadmap
Python and statistics foundations
Hands-on data analysis exercises
Machine Learning concepts
Visualisation and reporting practice
Project-oriented learning

Online Learning🌐

Online Learning

Ask about online learning availability and the current class format.

Python and data analysis roadmap
Statistics and data handling
SQL and visualisation concepts
Machine Learning fundamentals
Practical coding exercises
Project-oriented learning

ℹ️

Admission & Fee Information

Contact Dazzling Skill to confirm the current Data Science course fees, monthly payment options, batch timings, schedules, course coverage and admission availability. The actual duration depends on your starting level and selected learning plan.

What Will You Learn?

Understand the Data Science lifecycle.

Write Python programs for data analysis.

Apply statistics and probability concepts.

Work with NumPy arrays.

Clean and manipulate datasets using Pandas.

Perform Exploratory Data Analysis.

Write SQL queries and retrieve data.

Create visualisations using Matplotlib and Seaborn.

Build basic Power BI reports and dashboards.

Understand regression and classification.

Train models using Scikit-learn.

Evaluate models using appropriate metrics.

Explore clustering and dimensionality reduction.

Understand neural networks and deep learning basics.

Build a simple data application.

Document projects and maintain code with GitHub.

Present analytical findings.

Develop a practical project portfolio.

Actual proficiency depends on practice, project work and the topics covered in your selected learning plan.

Explore your possibilities

Data Science Skills & Career Paths

Data Science skills can support several analytical and technology career paths. Each role may require additional tools, domain knowledge, practical experience and specialised training.

Data Analyst

SQL, Excel, Python, data cleaning, visualisation and reporting

Junior Data Scientist

Python, statistics, EDA, machine learning and model evaluation

Business Intelligence Analyst

Power BI, SQL, KPIs, dashboards and business reporting

Machine Learning Engineer

Python, ML algorithms, model evaluation and software engineering

Python Developer – Data Applications

Python, data processing, APIs and application development

AI / ML Associate

Machine learning foundations, neural networks and AI concepts

Career opportunities are not guaranteed and depend on your skills, portfolio, experience and market conditions. Some roles require advanced mathematics, software engineering or specialised ML knowledge.

Practical Data Science Projects

Apply your knowledge through different types of data analysis and machine learning exercises. These are suggested project ideas; confirm the guided projects included in your batch.

📊Beginner

Sales Data Analysis

Analyse sales records to discover revenue trends, top-performing products and monthly business performance.

Skills Applied

Python, Pandas, NumPy and Matplotlib

👥Beginner

Customer Insights Dashboard

Explore customer records and create an interactive dashboard to communicate important business KPIs.

Skills Applied

SQL, Power BI and Data Visualisation

🏠Intermediate

House Price Prediction

Prepare housing data, identify useful features and train a regression model to estimate property prices.

Skills Applied

Pandas, EDA, Scikit-learn and Regression

📉Intermediate

Customer Churn Prediction

Analyse customer behaviour and build a classification model to identify customers at risk of leaving.

Skills Applied

Data Cleaning, Classification and Model Evaluation

🎯Advanced

Customer Segmentation

Group customers according to their behaviour and purchasing patterns using clustering techniques.

Skills Applied

EDA, Feature Scaling and K-means

🤖Advanced

End-to-End ML Prediction App

Build a complete workflow from data preparation and model training to a simple prediction interface.

Skills Applied

Python, Scikit-learn, Streamlit and Model Deployment

Data Science Learning Resources

Continue learning with official documentation, Python references, machine learning guides and data science practice platforms.

🐍

Python reference

Python Documentation

Learn Python syntax, modules and programming fundamentals.

Explore Resource

🔢

Numerical computing

NumPy Documentation

Explore numerical computing and multidimensional arrays.

Explore Resource

🐼

Data analysis

Pandas Documentation

Learn DataFrames, data cleaning and data manipulation.

Explore Resource

📈

Visualisation

Matplotlib

Create charts and visualisations using Python.

Explore Resource

🎨

Statistical charts

Seaborn

Explore statistical data visualisation techniques.

Explore Resource

🤖

Machine Learning

Scikit-learn

Explore machine learning algorithms and model evaluation.

Explore Resource

🧠

Deep Learning

TensorFlow

Learn deep learning and neural network development.

Explore Resource

📊

Business intelligence

Power BI

Explore dashboards, reports and business intelligence.

Explore Resource

🐘

Database

PostgreSQL

Learn relational databases and SQL fundamentals.

Explore Resource

🏆

Datasets and practice

Kaggle

Explore datasets, notebooks and data science competitions.

Explore Resource

Everything you need to know

Data Science Course FAQs

Find answers to common questions about Python, statistics, data analysis, SQL, Machine Learning, projects, learning options, fees and admission.

Data Science is an interdisciplinary field that uses programming, statistics, mathematics and analytical techniques to extract insights and knowledge from data.

Please contact the institute to verify current admission details, fees, batch schedules and course coverage.

Turn curiosity into practical skills

Ready to Start Your Data Science Journey?

Learn Python, explore data, understand Machine Learning and build practical projects with Dazzling Skill in Gorakhpur.

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