Course Introduction
Data science has emerged as one of the most in-demand fields, and programming with Python and SQL forms the foundation of any data-driven career. This course equips learners with essential programming skills and practical knowledge to analyze, manage, and extract insights from large datasets effectively.
Python has become the leading language for data science due to its flexibility, simplicity, and rich ecosystem of libraries. Participants will explore Python programming essentials, data structures, and libraries such as NumPy, Pandas, and Matplotlib to perform advanced data analysis and visualization tasks.
SQL remains a cornerstone of data management, allowing professionals to query, organize, and manipulate structured data. The course introduces core SQL commands, joins, subqueries, and advanced concepts to help learners work with relational databases efficiently in real-world projects.
Emphasis will be placed on integrating Python and SQL to create robust data pipelines and conduct end-to-end analysis. Practical assignments will ensure learners can handle both structured and unstructured datasets, build workflows, and develop reproducible, data-driven insights.
To prepare participants for modern data challenges, the course also covers emerging topics such as database optimization, cloud-based data systems, and best practices in version control using Git and GitHub for collaborative projects.
By completion, learners will have built a strong foundation in data science programming, enabling them to pursue careers in analytics, machine learning, and business intelligence or advance toward specialized data science pathways.
Who Should Attend
- Aspiring data scientists and analysts beginning their data career journey.
- Professionals transitioning into data-driven roles across industries.
- Business analysts seeking to strengthen technical data handling skills.
- IT professionals wanting to expand into data science and analytics.
- University students and graduates preparing for careers in data fields.
- Researchers working with structured and unstructured datasets.
- Software developers aiming to transition into data-focused roles.
- Database administrators learning integration with analytics workflows.
- Managers overseeing data-driven projects and technical teams.
- Consultants offering business intelligence and analytics solutions.
- Educators and trainers developing data science teaching capacity.
- Entrepreneurs leveraging data for business strategy and innovation.
Duration
10 days
Course Objectives
- Gain a strong foundation in Python programming tailored for data science applications and analytics.
- Learn to use core Python libraries such as NumPy, Pandas, and Matplotlib for analysis and visualization.
- Acquire SQL skills to query, manage, and manipulate structured data within relational databases.
- Develop ability to combine Python and SQL workflows for seamless data pipeline creation and management.
- Strengthen capacity to clean, preprocess, and transform raw data into usable formats for modeling.
- Apply programming techniques to real-world datasets for insights and business intelligence.
- Explore cloud-based data solutions and integration with Python and SQL for scalable workflows.
- Learn Git and GitHub version control for collaboration in data science projects.
- Understand best practices in database optimization, indexing, and query performance improvement.
- Gain exposure to working with large datasets using Python and SQL efficiently.
- Build readiness to transition into advanced areas like machine learning, AI, and predictive analytics.
- Develop problem-solving and critical thinking skills for tackling data-driven challenges.
Comprehensive Course Outline
Module 1: Introduction to Data Science Programming
- Overview of data science ecosystem.
- Role of Python and SQL in analytics.
- Essential programming concepts.
- Tools and platforms for data science.
Module 2: Python Fundamentals
- Data types, variables, and operators.
- Control structures and loops.
- Functions and modular programming.
- File handling basics in Python.
Module 3: Python Data Structures
- Lists, tuples, and dictionaries.
- Working with sets and arrays.
- Nested structures and slicing.
- Practical exercises with data.
Module 4: Working with Python Libraries
- Introduction to NumPy for arrays.
- Pandas for data manipulation.
- Matplotlib and Seaborn for visualization.
- Case studies in data analysis.
Module 5: SQL Fundamentals
- Structure of relational databases.
- Basic SELECT queries and filtering.
- Sorting and aggregating data.
- Practical SQL exercises.
Module 6: Advanced SQL Techniques
- Joins and subqueries.
- Window functions and views.
- Stored procedures and triggers.
- Indexing and optimization basics.
Module 7: Python and SQL Integration
- Connecting Python to databases.
- SQLAlchemy and database APIs.
- Building data pipelines with Python and SQL.
- Practical integration projects.
Module 8: Data Cleaning and Preprocessing
- Identifying and handling missing data.
- Normalization and transformations.
- Feature extraction with Python.
- Ensuring data consistency.
Module 9: Exploratory Data Analysis
- Descriptive statistics with Python.
- SQL queries for EDA.
- Visualization of trends and patterns.
- Case study on dataset exploration.
Module 10: Handling Large Datasets
- Optimizing SQL queries for scale.
- Python tools for big data handling.
- Working with cloud-based datasets.
- Performance monitoring and tuning.
Module 11: Version Control with Git
- Introduction to Git basics.
- Branching and merging workflows.
- Collaboration on GitHub.
- Managing project versions.
Module 12: Database Design and Optimization
- Principles of relational schema design.
- Normalization and denormalization.
- Query performance tuning.
- Best practices for scalable databases.
Module 13: Data Visualization Projects
- Advanced charting with Matplotlib.
- Interactive visuals with Seaborn.
- Combining SQL and Python visualization.
- Presenting insights effectively.
Module 14: Cloud and Data Science Workflows
- Cloud databases and storage systems.
- Connecting Python and SQL to cloud.
- Serverless data analysis solutions.
- Security considerations in cloud workflows.
Module 15: Case Studies in Business Intelligence
- SQL for business reporting.
- Python for trend forecasting.
- Building BI dashboards.
- End-to-end data pipeline project.
Module 16: Project – Building a Data Science Pipeline
- Designing a Python and SQL workflow.
- Cleaning and preprocessing real data.
- Creating analysis and visualization outputs.
- Final project presentation.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.
Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment
Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
