Hands-on projects
Practise with realistic business scenarios and datasets.
DATA SCIENCE & ANALYTICSCURRICULUM PREVIEW
Data Science & Predictive Analytics
Course fee: INR 22,000 + 18% GST (INR 3,960). Total including GST: INR 25,960.
Confirm exclusions, materials, assessments and payment options with admissions.
Turn Data into Decisions. Predict the Future.
Learn how to transform raw data into meaningful insights, build predictive models, uncover business opportunities, and make data-driven decisions using Python, statistics, machine learning, and modern analytics tools.
Python, SQL, statistics, machine learning and visualization.
120 hourscourse duration
Contact admissionsupcoming batch
Confirm online / offline availabilitylearning mode
Course duration: 120 hours. Contact admissions for batch dates, daily timings and delivery arrangements.
Program Highlights
End-to-end toolkit
Explore Python, SQL, statistics, machine learning, and visualization.
Predictive analytics
Develop practical forecasting and model-evaluation skills.
Career preparation
Build projects and receive career guidance.
Program Overview
Learn the science behind smarter business decisions
Data Science & Predictive Analytics helps organizations understand what happened, why it happened, what may happen next, and what actions they can take.
At Ecareerpluz, this program introduces learners to the complete data science workflow, from collecting and cleaning data to exploring patterns, building machine learning models, evaluating predictions, and communicating insights through compelling visualizations.
Through guided practice and project-based learning, you will explore how data science is applied in industries such as banking, healthcare, retail, education, manufacturing, marketing, logistics, and technology.
What you will learn
- Data collection, cleaning, preparation, and exploratory data analysis.
- Python programming for data science.
- SQL for querying and managing data.
- Probability, statistics, and analytical thinking.
- Data visualization and business intelligence.
- Machine learning algorithms and predictive modeling.
- Forecasting, model evaluation, and communicating results.
Who should enroll?
Graduates
Build applied skills across data preparation, analysis, and predictive modeling.
Students
Connect coursework with guided analytical projects and portfolio practice.
Working professionals
Explore data workflows and tools relevant to business decision-making.
Aspiring data scientists
Develop foundations in Python, SQL, statistics, and machine learning.
Business owners
Learn how analytics and forecasting can inform business decisions.
Curriculum Preview
The curriculum progresses from foundational concepts to practical predictive analytics and machine learning.
Module 1: Python Programming Fundamentals
- Python syntax, variables and data types.
- Conditions, loops and functions.
- Core data structures and file handling.
- Introductory programming problem-solving.
Module 2: SQL and Data Management
- Relational databases and SQL queries.
- Filtering, joins, grouping and subqueries.
- Aggregations and data extraction for analysis.
Module 3: Statistics and Exploratory Data Analysis
- Descriptive statistics, probability and distributions.
- Correlation, outliers and missing values.
- Hypothesis testing and identifying patterns in datasets.
Module 4: Data Cleaning and Feature Engineering
- Use Pandas and NumPy to clean and transform data.
- Combine and prepare datasets for analysis.
- Create useful features for analytical and machine learning tasks.
Module 5: Data Visualization and Business Intelligence
- Create charts, interactive dashboards and analytical reports.
- Use Matplotlib and Seaborn to explore data.
- Present business insights with Power BI or Tableau, where included.
Module 6: Machine Learning Fundamentals
- Supervised and unsupervised learning.
- Regression, classification and clustering.
- Decision trees, random forests and model training.
Module 7: Predictive Analytics and Forecasting
- Demand forecasts and sales predictions.
- Customer churn models and trend analysis.
- Time-series forecasting solutions.
Module 8: Model Evaluation and Deployment Basics
- Train-test splitting and cross-validation.
- Performance metrics, overfitting and model interpretation.
- Introductory deployment with suitable Python frameworks.
Module 9: Capstone Project and Portfolio
- Complete an end-to-end project using a suitable dataset.
- Document analysis, model evaluation and limitations.
- Present findings and evidence-based business recommendations.
The final module sequence, duration, and depth depend on the selected course level. Confirm the approved syllabus with ECAREERPLUZ before enrolling.
Tools & Technologies
Master the essential tools for data analysis and predictive modeling
Build a practical toolkit for collecting, analyzing, visualizing, and modeling data. The final tools depend on the approved syllabus and training environment.
Programming
Python
Programming for data processing, analytics, and machine learning workflows.
Data management
SQL & Databases
Retrieve, filter, combine, and analyze structured relational data.
Notebooks and visualization
Jupyter Notebook & Google Colab
Write Python code, test analytical ideas, visualize findings, and document experiments.
Matplotlib & Seaborn
Create statistical charts, distribution plots, correlation heatmaps, and visual reports.
Business intelligence
Power BI & Tableau
Build interactive dashboards, KPI visualizations, and business intelligence reports.
Machine learning
Scikit-learn
Develop regression, classification, clustering, model-selection, and evaluation workflows.
TensorFlow & PyTorch
Explore neural networks and advanced analytical applications where included in the course.
Development workflow
Git & GitHub
Track code changes, maintain repositories, collaborate, and present a project portfolio.
Discuss tool access and any separate provider accounts with admissions. Suggested teaching technologies; display only tools included in the approved course syllabus and training environment. Third-party product names and trademarks belong to their respective owners. Confirm software access, subscriptions, and any additional charges with admissions.
Projects & Practical Work
Build projects that demonstrate your skills
Learn by solving practical problems inspired by real business situations. Each project connects data analysis with a measurable business question.
PROJECT 1
Sales Forecasting System
Analyze historical sales, identify seasonal patterns, and forecast future demand to support inventory and business planning.
Skills: Python, Pandas, time-series analysis, visualization.
PROJECT 2
Customer Segmentation
Group customers based on purchasing behavior and engagement to help businesses plan targeted marketing campaigns.
Skills: Exploratory data analysis, clustering, Scikit-learn.
PROJECT 3
Fraud Detection Model
Build a classification model to identify potentially suspicious transactions using an appropriately anonymized dataset.
Skills: Classification, feature engineering, precision, recall.
PROJECT 4
Customer Churn Prediction
Identify customer characteristics associated with leaving a service and recommend strategies for customer retention.
Skills: Logistic regression, decision trees, model evaluation.
PROJECT 5
Healthcare Data Analysis
Explore anonymized healthcare datasets to understand trends, compare groups, and communicate statistical findings responsibly.
Skills: Statistics, data cleaning, visualization.
PROJECT 6
Student Performance Analytics
Analyze attendance, assessments, and academic performance to identify patterns and develop evidence-based improvement recommendations.
Skills: SQL, Python, Power BI, reporting.
Capstone project steps
- Define a practical business or analytical question.
- Collect, clean, and explore an appropriate dataset.
- Select and train a suitable analytical or predictive model.
- Evaluate results, limitations, and responsible data use.
- Document findings and present evidence-based recommendations.
Capstone experience: complete an end-to-end project, from defining the problem and preparing the dataset to training a model, evaluating results, and presenting business recommendations.
Learning Goals
By the end of the program, learners should be able to:
Develop practical skills to analyze datasets, identify patterns, build predictive models, and present evidence-based recommendations.
Analyze data confidently
Organize datasets, investigate trends, and identify meaningful patterns.
Apply Python and SQL
Extract, clean, transform, and analyze data efficiently.
Understand statistics
Use statistical reasoning to support reliable conclusions.
Visualize insights
Develop clear charts, reports, and interactive dashboards.
Build predictive models
Apply suitable machine learning algorithms to practical problems.
Evaluate model performance
Understand errors, limitations, and the importance of validating predictions.
Communicate findings
Explain technical results in clear business language.
Build a professional portfolio
Present practical projects that demonstrate applied skills.
Industry Salary Growth & Business Opportunities
Turn analytical skills into career opportunities
Data science skills can support work in IT services, financial services, healthcare, retail, e-commerce, manufacturing, education, and consulting. Employers commonly seek combinations of Python, SQL, statistics, machine learning, visualization, and problem-solving skills.
Career pathways
Data Analyst
Prepare reports, analyze business performance, and communicate trends using SQL, Python, and BI dashboards.
Junior Data Scientist
Explore datasets, develop statistical models, and assist with predictive analytics projects.
Predictive Analytics Analyst
Build forecasts and analytical models to help organizations anticipate trends and plan decisions.
Machine Learning Engineer
Develop and deploy machine learning systems; this path typically requires stronger software engineering and deployment skills.
Business Intelligence Analyst
Convert operational data into dashboards, KPIs, and recommendations for business teams.
Indicative salary progression in India
Compare these planning ranges with current local opportunities. Pay varies by role, qualifications, experience and employer.
Entry-level Data Analyst
INR 3-6 LPAIndicative annual salaryExperienced Data Analyst
INR 6-12 LPAIndicative annual salaryJunior Data Scientist
INR 5-10 LPAIndicative annual salaryExperienced Data Scientist
INR 10-20+ LPAIndicative annual salarySenior / Specialist roles
INR 20-35+ LPAIndicative annual salaryBusiness opportunities
- Business reporting and dashboard development.
- Sales and demand forecasting services.
- Customer analytics and market research.
- Data cleaning and reporting automation.
- Marketing performance and customer segmentation analysis.
- Predictive analytics consulting for small and medium-sized businesses.
Build your future: Build a strong foundation in Python, SQL, statistics, and practical projects before specializing. A portfolio and the ability to explain business impact can strengthen applications.
Placement Assistance
From classroom learning to career readiness
Ecareerpluz's placement-support approach is designed to help learners prepare for opportunities in data analytics, business intelligence, and data science.
Career support services
Resume & Portfolio Development
Build a professional resume that highlights technical skills, projects, tools, and practical achievements.
Technical Interview Preparation
Practise Python, SQL, statistics, data analysis, machine-learning fundamentals, and project-based interview questions.
Communication & Mock Interviews
Develop confidence in explaining technical concepts, presenting project results, and answering interview questions.
Job Search Guidance
Receive guidance on relevant job titles, application strategies, professional networking, and employer requirements.
Project Portfolio Support
Organize capstone and practical projects to demonstrate the ability to solve data-related problems.
Our goal is to help learners prepare for interviews and entry-level opportunities through practical training and structured career guidance.
Example learning journeys
Every learner has a story. Start building yours.
Explore example data science and predictive analytics projects and learning journeys.
Example learning journey01
From a graduate to a data professional
Learner
The practical exercises helped me understand how Python and SQL can be used to solve real data problems. Developing projects improved my confidence in presenting my skills.
Practice focus: Python, SQL, and project communication.
Example learning journey02
Learning through real-world projects
Learner
Working on data analysis and predictive modeling helped me connect classroom concepts with business applications. I now have projects to discuss during interviews.
Practice focus: Data analysis and predictive modeling.
Example learning journey03
Building confidence with analytics
Learner
Learning to clean datasets, create visualizations, and explain findings helped me approach analytical problems in a more structured way.
Practice focus: Data preparation, visualization, and communication.
Data-Driven Learning, Delivered with Quality
Learn by doing, improve through practice
Our learning approach combines technical foundations, guided exercises, and project-based application to help learners develop skills progressively.
Our learning approach
Structured Learning Path
Move from programming and statistics to data analysis, predictive modeling, and project development.
Practical Training
Apply concepts through coding exercises, analytical assignments, datasets, and guided labs.
Mentor-Led Guidance
Receive guidance on problem-solving, project design, model evaluation, and technical questions.
Continuous Assessment
Track progress through assignments, quizzes, practical exercises, and project reviews.
Industry-Relevant Practice
Work with realistic datasets and business scenarios to understand how analytical techniques support decisions.
Learn, analyse, predict, and improve. Develop the confidence to turn data into meaningful outcomes.
Meet Your Mentors
Learn from Industry Experts and Experienced Professionals.
Mr. P. PRAKASH RAJ
Software Developer
Mrs. S. SIVANISHA
IT Trainer
Mr. S. MOHAMED RIYAS KHAN
UI & UX Designer
Mr. S. SABARINATHAN
AI AnalystFrequently Asked Questions
Everything You Need to Know Before Enrolling
What is Data Science & Predictive Analytics?
Data science combines programming, statistics, data analysis, and machine learning to extract useful insights. Predictive analytics uses historical data and suitable models to estimate future outcomes.
Who can join this course?
The program is intended for graduates, students, working professionals, aspiring data scientists, and business owners. Confirm any entry requirements with admissions.
Do I need programming experience?
Python fundamentals are included in the curriculum. Ask admissions whether the selected batch is suitable for complete beginners and what preparation is recommended.
Do I need strong mathematics skills?
The curriculum introduces probability, statistics, and analytical reasoning. Ask admissions about the mathematical prerequisites and depth for the selected course level.
Which tools will I learn?
Suggested tools include Python, SQL, Jupyter or Google Colab, Matplotlib, Seaborn, Power BI or Tableau, Scikit-learn, and Git. TensorFlow and PyTorch may depend on the approved syllabus.
How long does the course take?
The course duration is 120 hours. Contact admissions for batch dates and the detailed timetable.
Will I work on real-world projects?
These practice projects use realistic business scenarios. Confirm the project scope, datasets, and assessment arrangements for the current batch. Real business data requires appropriate permission and privacy safeguards.
Can beginners become data scientists?
Beginners can build foundational skills through structured learning and practice, but career readiness depends on prior experience, continued learning, project work, and employer requirements.
What career opportunities are available after the course?
Possible pathways include data analyst, junior data scientist, predictive analytics analyst, machine-learning engineer, and business intelligence analyst. Roles have different experience and technical requirements.
Does Ecareerpluz provide placement assistance?
The program describes career guidance, interview preparation, job-search support, and project-portfolio assistance.
Will I receive a certificate?
Confirm the certificate issuer, completion requirements, assessment criteria, and terms with Ecareerpluz before enrolling. No third-party accreditation is implied.
Can I use these skills to start a business?
These skills may support services such as reporting, dashboards, customer analytics, forecasting, and data preparation. Business success depends on client needs, experience, legal requirements, and other factors.
What is the course fee, including GST?
Course fee: INR 22,000 + 18% GST (INR 3,960). Total including GST: INR 25,960. Confirm exclusions, materials, assessments and payment options with admissions.
What is the course duration?
The course duration is 120 hours. Contact admissions for batch dates and the detailed timetable.
Build your next chapter in data science and predictive analytics.
Confirm course duration, fees, upcoming batch, learning mode, and final curriculum with our team.
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