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Data Science & Analytics
Overview
Curriculum
FAQs
Instructor
Reviews
About Data Science & Analytics
Data Science & Analytics focuses on extracting meaningful insights from data to support informed decision-making. This course builds a strong foundation in data analysis, statistics, and data science concepts with a practical, industry-oriented approach.
Learners will gain hands-on experience in working with real-world datasets, performing data cleaning and analysis, and creating insightful reports and dashboards. The course emphasizes problem-solving, analytical thinking, and the use of modern tools and techniques commonly used by data analysts and data scientists.
This program is designed to help students and professionals develop job-ready skills for data-driven roles across industries.
Importance of Data Science & Analytics in Modern Industries
Data has become one of the most valuable assets for organizations. Industries such as IT, finance, healthcare, e-commerce, marketing, and manufacturing rely on data science and analytics to understand customer behavior, optimize operations, and predict future trends.
Data-driven decision-making helps businesses improve efficiency, reduce costs, and gain a competitive advantage. Skilled data professionals are in high demand as companies continue to adopt analytics and AI-driven strategies.
Learning data science and analytics opens opportunities in roles such as Data Analyst, Business Analyst, and Data Scientist.
What Will You Learn?
- Fundamentals of data science and analytics
- Data collection, cleaning, and preprocessing techniques
- Exploratory Data Analysis (EDA) and data visualization
- Basic statistics and analytical concepts
- Working with structured and unstructured data
- Creating reports, dashboards, and data insights
- Understanding business problems using data
- Industry-oriented data analysis workflows
Training Schedule
Weekend Batch
Duration: 50 Hours
Training Mode: Online Live
Training Platform: Microsoft Teams
Who is this course for
- Engineering students and graduates from any discipline
- Diploma holders interested in data and analytics
- Freshers aiming to start a career in data science or analytics
- Working professionals planning to shift into data-driven roles
- Software or IT professionals expanding into data roles
- Anyone interested in working with data and insights
- 1 Section
- 0 Lessons
- 40 Hours
Expand all sectionsCollapse all sections
- Data Science & Analytics
Introduction to Data Science & Analytics
Overview of data science and analytics
Data-driven decision making
Role of data scientist and data analyst
Applications of data science across industries
Career opportunities in data science & analytics
Python Programming for Data Science
Introduction to Python
Python syntax and data types
Control structures and functions
Working with libraries for data analysis
Data manipulation using Python
Statistics & Probability for Data Analysis
Descriptive statistics
Inferential statistics
Probability concepts
Random variables and distributions
Hypothesis testing
Correlation and regression analysis
Data Collection & Data Cleaning
Data sources and data types
Data collection methods
Handling missing and inconsistent data
Outlier detection and treatment
Data transformation techniques
Exploratory Data Analysis (EDA)
Understanding data patterns
Data summarization techniques
Data visualization fundamentals
Identifying trends and anomalies
EDA using Python libraries
Data Visualization Tools
Principles of data visualization
Charts and graphs for analysis
Data storytelling
Visualization using Python
Introduction to BI tools
SQL for Data Analytics
Introduction to databases
SQL queries and commands
Data filtering and aggregation
Joins and subqueries
Performance optimization basics
Machine Learning for Data Science
Introduction to machine learning
Supervised learning algorithms
Unsupervised learning algorithms
Model training and evaluation
Feature selection and engineering
Business Analytics & Reporting
Understanding business problems
Key performance indicators (KPIs)
Data-driven business decisions
Dashboard design concepts
Reporting best practices
Big Data & Advanced Analytics (Overview)
Introduction to big data
Big data tools overview
Structured and unstructured data
Real-time vs batch processing
Data Science Tools & Technologies
NumPy
Pandas
Matplotlib
Seaborn
Scikit-learn
Power BI / Tableau overview
Data Interpretation & Communication
Interpreting analytical results
Data storytelling techniques
Presenting insights to stakeholders
Visualization best practices
Data Ethics & Governance
Ethical use of data
Data privacy regulations
Bias and fairness in analytics
Responsible data practices
Data Science Projects
End-to-end data analysis project
Real-world datasets
Business case studies
Model building and evaluation
Project documentation
Career Preparation & Interview Readiness
Resume building for data roles
Portfolio project development
Interview question preparation
Industry-oriented guidance
Career roadmap planning
0
This course covers essential topics such as engineering fundamentals, construction materials, surveying, structural analysis, building design, and project management. You will also learn practical skills used in real construction and infrastructure projects.
No, the course starts from basic civil engineering concepts, making it suitable for beginners. As you progress, you will be introduced to advanced topics and practical applications commonly used in the industry.
The course is designed to build practical, job-ready skills. It includes hands-on modules, real-world examples, and industry-relevant topics that help students prepare for roles in construction, site supervision, design, and infrastructure development.
Depending on the module, you may learn industry-standard tools such as AutoCAD, STAAD Pro, Revit, MS Project, and other construction management or design applications used by engineers in real projects.
Yes. The course includes practical exercises, case studies, and real-site examples to help learners understand how civil engineering concepts are applied in actual construction and infrastructure projects.
Price
Free
Get unlimited access to all learning content and premium assets Membership Pro
Featured Review
Control Panel Design involves creating the electrical panels that operate, monitor, and protect industrial machines, process equipment, and plant systems.
Data Science & Analytics
Description
About Data Science & Analytics
Data Science & Analytics focuses on extracting meaningful insights from data to support informed decision-making. This course builds a strong foundation in data analysis, statistics, and data science concepts with a practical, industry-oriented approach.
Learners will gain hands-on experience in working with real-world datasets, performing data cleaning and analysis, and creating insightful reports and dashboards. The course emphasizes problem-solving, analytical thinking, and the use of modern tools and techniques commonly used by data analysts and data scientists.
This program is designed to help students and professionals develop job-ready skills for data-driven roles across industries.
Importance of Data Science & Analytics in Modern Industries
Data has become one of the most valuable assets for organizations. Industries such as IT, finance, healthcare, e-commerce, marketing, and manufacturing rely on data science and analytics to understand customer behavior, optimize operations, and predict future trends.
Data-driven decision-making helps businesses improve efficiency, reduce costs, and gain a competitive advantage. Skilled data professionals are in high demand as companies continue to adopt analytics and AI-driven strategies.
Learning data science and analytics opens opportunities in roles such as Data Analyst, Business Analyst, and Data Scientist.
What Will You Learn?
- Fundamentals of data science and analytics
- Data collection, cleaning, and preprocessing techniques
- Exploratory Data Analysis (EDA) and data visualization
- Basic statistics and analytical concepts
- Working with structured and unstructured data
- Creating reports, dashboards, and data insights
- Understanding business problems using data
- Industry-oriented data analysis workflows
Training Schedule
Weekend Batch
Duration: 50 Hours
Training Mode: Online Live
Training Platform: Microsoft Teams
Who is this course for
- Engineering students and graduates from any discipline
- Diploma holders interested in data and analytics
- Freshers aiming to start a career in data science or analytics
- Working professionals planning to shift into data-driven roles
- Software or IT professionals expanding into data roles
- Anyone interested in working with data and insights
Features
- Course Management
- Communication and Collaboration
- User-Friendly Interface
Target Audiences
- Educational Institutions
- Businesses and Organization
- Individual Learners
Requirements
- User-Friendly Interface
- Robust Course Management Capabilities
- Effective Communication and Collaboration
Curriculum
- 1 Section
- 0 Lessons
- 40 Hours
Expand all sectionsCollapse all sections
- Data Science & Analytics
Introduction to Data Science & Analytics
Overview of data science and analytics
Data-driven decision making
Role of data scientist and data analyst
Applications of data science across industries
Career opportunities in data science & analytics
Python Programming for Data Science
Introduction to Python
Python syntax and data types
Control structures and functions
Working with libraries for data analysis
Data manipulation using Python
Statistics & Probability for Data Analysis
Descriptive statistics
Inferential statistics
Probability concepts
Random variables and distributions
Hypothesis testing
Correlation and regression analysis
Data Collection & Data Cleaning
Data sources and data types
Data collection methods
Handling missing and inconsistent data
Outlier detection and treatment
Data transformation techniques
Exploratory Data Analysis (EDA)
Understanding data patterns
Data summarization techniques
Data visualization fundamentals
Identifying trends and anomalies
EDA using Python libraries
Data Visualization Tools
Principles of data visualization
Charts and graphs for analysis
Data storytelling
Visualization using Python
Introduction to BI tools
SQL for Data Analytics
Introduction to databases
SQL queries and commands
Data filtering and aggregation
Joins and subqueries
Performance optimization basics
Machine Learning for Data Science
Introduction to machine learning
Supervised learning algorithms
Unsupervised learning algorithms
Model training and evaluation
Feature selection and engineering
Business Analytics & Reporting
Understanding business problems
Key performance indicators (KPIs)
Data-driven business decisions
Dashboard design concepts
Reporting best practices
Big Data & Advanced Analytics (Overview)
Introduction to big data
Big data tools overview
Structured and unstructured data
Real-time vs batch processing
Data Science Tools & Technologies
NumPy
Pandas
Matplotlib
Seaborn
Scikit-learn
Power BI / Tableau overview
Data Interpretation & Communication
Interpreting analytical results
Data storytelling techniques
Presenting insights to stakeholders
Visualization best practices
Data Ethics & Governance
Ethical use of data
Data privacy regulations
Bias and fairness in analytics
Responsible data practices
Data Science Projects
End-to-end data analysis project
Real-world datasets
Business case studies
Model building and evaluation
Project documentation
Career Preparation & Interview Readiness
Resume building for data roles
Portfolio project development
Interview question preparation
Industry-oriented guidance
Career roadmap planning
0
FAQs
This course covers essential topics such as engineering fundamentals, construction materials, surveying, structural analysis, building design, and project management. You will also learn practical skills used in real construction and infrastructure projects.
No, the course starts from basic civil engineering concepts, making it suitable for beginners. As you progress, you will be introduced to advanced topics and practical applications commonly used in the industry.
The course is designed to build practical, job-ready skills. It includes hands-on modules, real-world examples, and industry-relevant topics that help students prepare for roles in construction, site supervision, design, and infrastructure development.
Depending on the module, you may learn industry-standard tools such as AutoCAD, STAAD Pro, Revit, MS Project, and other construction management or design applications used by engineers in real projects.
Yes. The course includes practical exercises, case studies, and real-site examples to help learners understand how civil engineering concepts are applied in actual construction and infrastructure projects.
Instructor
0 Students26 Courses
Reviews
Price
Free
Get unlimited access to all learning content and premium assets Membership Pro
Featured Review
Control Panel Design involves creating the electrical panels that operate, monitor, and protect industrial machines, process equipment, and plant systems.
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