Data Science & Big Data Analytics with Python

Course Introduction

Data is the new oil, and organizations across all industries are leveraging it to drive innovation and decision-making. This course provides a hands-on, practical introduction to data science and big data analytics using Python. Learners will explore the data science lifecycle, from data collection and cleaning to modeling, visualization, and insight generation. The course also covers working with big data frameworks such as Hadoop and Spark, enabling participants to handle large-scale data analytics tasks.

Participants will build real-world projects using Python libraries like Pandas, NumPy, Matplotlib, Scikit-learn, and integrate with big data tools for scalable analytics.

After Study Job Opportunities

• Data Analyst
• Data Scientist (Entry-Level)
• Big Data Analyst
• Business Intelligence Developer
• Machine Learning Engineer (Junior)

List of Required Documents

• Resume
• Passport or ID Photo

Admission Tests

• Study of the school file
• Tests: general knowledge, English, logic and practical case study
• Individual motivational interview

Professional Training

This course emphasizes:

• Foundational understanding of data science principles
• Hands-on Python programming for data manipulation and analysis
• Real-world data visualization and storytelling
• Introduction to machine learning algorithms
• Working with big data tools: Hadoop, Spark, PySpark
• Preparing for industry certifications like Microsoft Data Analyst Associate, IBM Data Science Professional

Objectives and Context of Certification

After completing this course, learners will understand the data science lifecycle (discover, prepare, model, evaluate, deploy), write Python scripts for data preprocessing and statistical analysis, apply machine learning models to real datasets, use Spark and PySpark for big data processing, visualize data for business insights using matplotlib, seaborn, and dashboards, and be awarded a certification validating skills in data science and big data analytics using Python. This certification serves as a foundation for deeper specialization in AI/ML, cloud analytics, and data engineering.

Graduate Responsibilities

After completing the training, participants will be able to:

• Analyze structured and unstructured datasets
• Build, train, and evaluate basic predictive models
• Generate actionable insights and present findings
• Work in data teams using version control, Jupyter notebooks, and Python packages
• Clean and prepare data for advanced modeling and AI use

Post-Study Job Opportunities

Graduates can work in industries such as Banking, E-commerce, Healthcare, Telecom, and Manufacturing. Roles include Junior Data Scientist, BI Analyst, Python Analyst, and Data Analytics Associate in domains like Predictive Analytics, Customer Insights, Fraud Detection, and Supply Chain Analytics.

Evaluation Methodology

• Weekly Quizzes (20%) – Understanding of key concepts
• Hands-on Assignments (30%) – Application to practical scenarios
• Final Project / Mini Capstone (30%) – Cumulative understanding project
• Participation & Interaction (10%)
• Final Assessment Test (10%) – Summary of key takeaways

Course Summary

• Duration: 2 weeks (60 hours) or part-time: 80 hours over 8 weeks
• Format: Online / In-person / Hybrid (ILT)
• Modules Include: Data Science Lifecycle, Python for Data Analysis, Machine Learning, Big Data Tools (Hadoop, Spark), Data Visualization
• Assessment: Real-world projects and final evaluation
• Outcome: Professional certification in Data Science & Big Data Analytics with Python

This course provides a hands-on introduction to data science and big data analytics, preparing participants for roles in the rapidly growing field of data-driven decision-making.

Starting intake: According to customer request

Tuition Fees:

• €3,000 per participant (60 hours)
• Inhouse corporate training: €30,000 per cohort (10-15 participants)