Data Science Training Course
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100% practical with case studies and real-world datasets.
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Tool-oriented: Jupyter, Pandas, NumPy, Matplotlib, Scikit-learn.
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Create dashboards, predictive models, and data pipelines.
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Geared towards beginners, graduate students, and professionals.
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Delivered by practicing data science industry experts.
Learn from real-time data analysts and machine-learning engineers working on live client projects.
Unlock Your Data Science Potential: Enroll Today!
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Why Join this Program?
Why Choose This Data Science Course?
This course is designed to provide you with comprehensive training in data science, covering Python, machine learning, and real-time data analytics. You’ll gain practical experience through case studies and real-world datasets, using tools like Jupyter, Pandas, NumPy, Matplotlib, and Scikit-learn. The course is delivered by industry experts and is suitable for beginners, graduate students, and professionals.
Key Highlights
Python
Techniques for Data Analysis and Automation
Machine Learning and AI
Model Building using Machine Learning and AI
SQL
Data Querying and Joins using SQL
Power BI
Business Intelligence using Power BI
Statistics and EDA
Statistics, Data Cleansing, and EDA
Projects
Projects with Real Datasets, Resume Building + Mock Interview Preparation
Certification Program Advantage
Get Your Data Science Certification
- Recognized Certificate From Digi Edu Learning.
- Internship Certificate from STS Digital Solutions.
- Portfolio-ready projects to showcase your skills.
Stay Ahead with Hands-On Training
- Real-time industry datasets for analysis and prediction.
- Work on live dashboards, reports, and machine learning models.
- Hands-on with Python, Excel, Power BI, and Scikit-learn.
Online Data Science Course
The course is designed for practitioners who want to analyze data, find insights, and build predictive models. It provides the structure and mentoring needed to enter the analytics field or upskill your profile with sought-after tools and techniques.
Why Become an Expert in Data Science?
Becoming a Data Science expert opens doors to a highly global, in-demand, and top-paying job market. Data Science skills are versatile and applicable in tech, finance, marketing, and healthcare, enabling centralized decision-making in companies. This skill is globally recruitable and facilitates work-from-home, freelance, product, and start-up roles.
What Will You Learn in This Data Science Course?
This program covers essential skills and tools for data science and analytics.
- Python programming for data science
- Data visualization using Matplotlib & Seaborn
- Data cleaning, wrangling, and preprocessing
- Exploratory Data Analysis (EDA)
- Machine Learning algorithms & model evaluation
- SQL for databases and advanced queries
- Power BI for dashboard creation
- Working with Excel for analysis automation
- Business case projects and analytics solutions
What Are the Job Roles After This Course?
Data Analyst
Analyze and interpret complex data sets.
Analytics Consultant
Provide expert advice on data-driven solutions.
Junior Data Scientist
Develop and implement machine learning models.
Machine Learning Engineer
Design and deploy ML systems.
Business Intelligence Analyst
Create and manage BI tools and dashboards.
Python Data Developer
Build data applications using Python.
Research & Insights Specialist
Conduct research and gather insights from data.
Full Learning Path with Curriculum
- What is Data Science? Career Scope and Applications
- Overview of the Data Science Lifecycle
- Python Basics: Installation, IDEs, Syntax, Variables
- Control Structures: Loops, Conditional Statements
- Data Types: Strings, Lists, Tuples, Dictionaries
- Functions and Lambda Functions
- File Handling in Python
- Working in Jupyter Notebooks and Google Colab
- Mini Project: Create a student management system using Python functions and file handling.
- Introduction to NumPy: Arrays, Array Operations
- Indexing, Slicing, and Reshaping
- Pandas for DataFrames and Series
- Reading and Writing CSV, Excel, and JSON Files
- Handling Missing Data
- Data Filtering and Sorting
- Data Aggregation and GroupBy
- Data Wrangling Techniques
- Mini Project: Clean and structure a sales dataset from a retail company using Pandas.
- What is EDA, and why is it important?
- Data Distributions and Frequency Counts
- Using Matplotlib for Static Visuals
- Seaborn: Boxplots, Heatmaps, Pairplots
- Correlation Matrix and Statistical Summaries
- Visualizing Categorical vs Numerical Data
- Outlier Detection and Treatment
- Storytelling with Visuals
- Mini Project: Perform full EDA on the Titanic Dataset and present findings visually.
- Understanding Relational Databases
- Writing SELECT Queries
- Using WHERE, ORDER BY, and GROUP BY
- Performing INNER, LEFT, and RIGHT Joins
- Aggregation Functions (SUM, AVG, COUNT)
- Nested Queries and Subqueries
- Creating and Updating Tables
- Integrating SQL Data with Python (using SQLite)
- Mini Project: Design a customer order tracking database and extract key insights.
- What is Machine Learning? Types of ML
- Data Preprocessing: Encoding, Normalization
- Train-Test Splitting and Cross-Validation
- Linear Regression and Multiple Regression
- Classification Algorithms: Logistic Regression, Decision Tree, K-Nearest Neighbors
- Evaluation Metrics: Accuracy, Precision, Recall, F1 Score
- Clustering with K-means
- Model Tuning and Hyperparameter Optimization
- Mini Projects: 1. Predict Housing Prices using Regression, 2. Classify Emails into Spam/Not Spam using Naive Bayes
- Introduction to Business Intelligence
- Power BI Basics: Interface, Data Import
- Creating Visualizations: Charts, Tables, Maps
- Using DAX for Measures and Calculated Columns
- Creating Relationships Between Tables
- Building Dashboards and Reports
- Publishing Reports on Power BI Service
- Interpreting KPIs and Business Metrics
- Projects (Choose 1 or More): Customer Churn Analysis for Telecom, E-Commerce Product Sales Dashboard, Credit Risk Modeling for a Loan Company
- Final Capstone Submission & Review
- One-on-One Mentorship Feedback
- Resume and LinkedIn Portfolio Review
- Certification Test (MCQs + Project-Based)
- Course Completion Certificate
- Internship Certificate (optional, from STS Digital Solutions)
Comparison
| Feature | Digi Edu Learning  |
Other Institutes  |
|---|---|---|
| 1:1 Mentorship |  Dedicated Mentors |
✘ Group support only |
| Real-Time Case Studies | Based on real datasets |
✘ Outdated or dummy data |
| Interview Preparation | Resume + mock interviews |
✘ Only theory is covered |
| Career Support | Placement + freelancing |
✘ No career roadmap provided |
| Hands-on Projects | 5+ Portfolio Projects |
✘ Only classroom examples |
| Industry Tools Covered | Python, Power BI, ML, Excel |
✘ Limited or theoretical tools |
