Advanced Certification in Data Science & Analytics Training

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Course Highlights

Advanced Certification in Data Science & Analytics - Key Learning Outcomes

Python Essentials: Learn Python from the ground up to code, analyse, and build data-driven solutions.
Numerical Programming in Python: Master libraries like NumPy and Pandas for data manipulation and numerical computing.
Relational Databases: Understand SQL and database management for effective data storage, retrieval, and analysis.
Data Visualisation Tools: Gain expertise in tools like Matplotlib, Seaborn, Power BI, and Tableau to present insights visually.
Applied Statistics: Build a strong foundation in probability, hypothesis testing, and statistical modeling for real-world data problems.
Introduction to Machine Learning (ML): Learn supervised and unsupervised algorithms, model building, and evaluation techniques.

By the end of this programme, learners will be able to:

Build a strong foundation in Python programming for data manipulation and analytics.
Apply numerical programming with libraries like NumPy and Pandas for efficient data handling.
Work with relational databases (SQL) to manage and analyse structured data.
Create impactful Data Visualisations using Matplotlib, Seaborn, Power BI, and Tableau.
Apply statistical methods for data-driven, decision-making and business insights.
Understand and implement Machine Learning algorithms for predictive modeling.
Gain real-world exposure through projects, case studies, and capstone assignment.

Collaboration with FutureSkills adds industry credibility
Comprehensive 6-month, more than 200 hours (workload of the student) programme with blended classroom and hands-on learning
Covers end-to-end Data Science pipeline - from data collection to Machine Learning models
Practical, project-driven training ensures real-world applicability
Learn from Industry-certified trainers with strong academic and corporate expertise
Placement support with resume building, interview prep, and career guidance
Designed to make learners job-ready for Data Analyst, Data Scientist, and ML Engineer roles

College students who want to enter the Data Science and Analytics domain.
Fresh graduates from IT, Computer Science, Statistics, or Mathematics backgrounds seeking career opportunities in Data Science.
Working professionals looking to upskill or switch to Analytics, Business Intelligence, or Data Science roles.
Software developers and IT professionals who want to integrate Data Science into their work.
Business professionals aiming to make data-driven decisions using analytics tools.
Tech enthusiasts and career changers aspiring to transition into the high-demand Data Science field.

Key modules covered in the programme include:

Python Essentials - Core Python, functions, OOPs, scripting
Numerical Programming - NumPy, Pandas, SciPy
Relational Databases and SQL - Database concepts, queries, joins, normalisation
Data Visualisation - Matplotlib, Seaborn, Tableau, Power BI
Applied Statistics - Probability, hypothesis testing, regression analysis
Machine Learning - Supervised and unsupervised learning, model evaluation
Capstone Project - End-to-end Data Science project simulating industry challenges

After completion, learners will gain expertise in:

Programming and Data Handling: Python, NumPy, Pandas, SciPy
Databases: SQL (MySQL, PostgreSQL)
Visualisation Tools: Matplotlib, Seaborn, Excel, Tableau, Power BI
Statistical Techniques: Probability, hypothesis testing, regression, correlation
Machine Learning: Scikit-learn, supervised and unsupervised models
Industry Practices: Data wrangling, feature engineering, model evaluation