Course Overview

Data Science using Python and SQL is a comprehensive training program designed to equip professionals with the skills required to collect, manage, analyze, visualize, and interpret data for business intelligence, research, predictive analytics, and data-driven decision-making. As organizations increasingly rely on data to drive innovation, improve efficiency, and gain competitive advantage, the demand for expertise in Data Science, Python programming, SQL database management, machine learning, big data analytics, business intelligence, predictive modeling, and data visualization continues to grow. This course provides participants with practical knowledge and hands-on experience in modern data science techniques using two of the most powerful tools in the analytics ecosystem: Python and SQL.

The training explores the complete data science lifecycle, including data acquisition, database querying, data cleaning, exploratory data analysis, statistical modeling, machine learning, and reporting. Participants will learn how to use SQL to retrieve and manage data from relational databases and leverage Python libraries such as Pandas, NumPy, Matplotlib, Scikit-learn, and Seaborn to perform advanced analytics and predictive modeling. The course combines theoretical foundations with practical applications using real-world datasets and business scenarios.

Participants will gain hands-on experience in database design, SQL querying, data wrangling, statistical analysis, machine learning model development, dashboard creation, and data storytelling. The course examines how Python and SQL can be integrated to support business analytics, financial analysis, customer intelligence, healthcare analytics, supply chain optimization, research projects, and organizational performance monitoring. Through practical exercises and relevant case studies, participants will develop confidence in solving complex analytical problems using data science methodologies.

The training further addresses emerging trends in data science, including artificial intelligence, deep learning, cloud analytics, big data platforms, natural language processing, automated machine learning, real-time analytics, and responsible AI practices. Participants will develop the competencies required to transform raw data into actionable insights and support strategic decision-making across diverse industries and sectors.

Course Objectives

1.      Understand the fundamentals of data science and analytics.

2.      Develop proficiency in Python programming for data analysis.

3.      Master SQL for data extraction, manipulation, and database management.

4.      Perform data cleaning, transformation, and exploratory analysis.

5.      Apply statistical methods and machine learning techniques to datasets.

6.      Build predictive models for business and research applications.

7.      Create effective visualizations and data-driven reports.

8.      Integrate Python and SQL for end-to-end analytics workflows.

9.      Strengthen problem-solving and data-driven decision-making skills.

10.  Apply modern data science tools and methodologies in real-world projects.

Organizational Benefits

1.      Enhanced ability to leverage organizational data for decision-making.

2.      Improved operational efficiency through data-driven insights.

3.      Better forecasting, planning, and predictive analytics capabilities.

4.      Increased productivity through automation of analytical processes.

5.      Improved customer intelligence and market analysis.

6.      Enhanced risk management and performance monitoring.

7.      Better reporting and business intelligence capabilities.

8.      Reduced reliance on manual data processing and reporting.

9.      Increased innovation through advanced analytics and machine learning.

10.  Strengthened organizational competitiveness and digital transformation initiatives.

Target Participants

·         Data analysts and business intelligence professionals

·         Database administrators and SQL developers

·         Researchers and research assistants

·         Monitoring and Evaluation (M&E) specialists

·         IT professionals and software developers

·         Financial and business analysts

·         Data scientists and machine learning practitioners

·         Government and NGO data management personnel

·         Consultants and digital transformation specialists

·         Academic researchers and university faculty

·         Graduate and postgraduate students

·         Anyone interested in data science, analytics, and programming

Course Outline

Module 1: Foundations of Data Science, Python, and SQL

1.      Introduction to data science concepts and applications

2.      Overview of Python and SQL in data analytics

3.      Setting up Python development environments and database systems

4.      Data science workflow and project lifecycle

5.      Introduction to relational databases and SQL fundamentals

6.      Ethical considerations and data governance in data science

Case Study:
Developing a data science strategy to support organizational decision-making and performance improvement.

Module 2: SQL for Data Management and Analysis

1.      Database design and relational database concepts

2.      Writing SQL queries for data retrieval and filtering

3.      Data aggregation, grouping, and summarization techniques

4.      SQL joins, subqueries, and advanced querying methods

5.      Database optimization and performance considerations

6.      Preparing data for analytical and machine learning applications

Case Study:
Analyzing customer and transaction data stored in a relational database to generate business insights.

Module 3: Python Programming for Data Analysis

1.      Python fundamentals for data science

2.      Working with data using Pandas and NumPy

3.      Data cleaning, transformation, and preprocessing techniques

4.      Exploratory data analysis and descriptive statistics

5.      Data visualization using Matplotlib and other libraries

6.      Automating data processing workflows with Python

Case Study:
Cleaning and analyzing a large operational dataset to identify trends and performance indicators.

Module 4: Statistical Analysis and Machine Learning

1.      Statistical concepts and hypothesis testing

2.      Correlation and regression analysis

3.      Supervised and unsupervised machine learning techniques

4.      Classification, clustering, and predictive modeling

5.      Model evaluation and performance measurement

6.      Feature engineering and model optimization

Case Study:
Developing a predictive model to forecast customer behavior and improve service delivery.

Module 5: Data Visualization, Reporting, and Business Intelligence

1.      Principles of effective data visualization

2.      Creating interactive charts and dashboards

3.      Data storytelling and communication techniques

4.      Business intelligence reporting frameworks

5.      Integrating SQL and Python for automated reporting

6.      Presenting analytical findings to stakeholders

Case Study:
Building a management dashboard that integrates SQL data extraction and Python-based analytics for executive reporting.

Module 6: Advanced Data Science Applications and Emerging Trends

1.      Artificial intelligence and deep learning fundamentals

2.      Natural language processing and text analytics

3.      Big data analytics and cloud-based data science platforms

4.      Real-time analytics and data streaming concepts

5.      Responsible AI, ethics, and governance considerations

6.      Future trends in data science, analytics, and intelligent systems

Case Study:
Designing an end-to-end data science solution that integrates SQL database management, Python-based analytics, machine learning, business intelligence dashboards, predictive forecasting, and automated reporting to improve organizational performance, innovation, and strategic decision-making.

 

 

 

Essential Information

 

1. Our courses are customizable to suit the specific needs of participants.
2. Participants are required to have proficiency in the English language.
3. Our training sessions feature comprehensive guidance through presentations, practical exercises, web-based tutorials, and collaborative group activities. Our facilitators boast extensive expertise, each with over a decade of experience.
4. Upon fulfilling the training requirements, participants will receive a prestigious Global King Project Management certificate.
5. Training sessions are conducted at various Global King Project Management Centers, including locations in Nairobi, Mombasa, Kigali, Dubai, Lagos, and others.
6. Organizations sending more than two participants from the same entity are eligible for a generous 20% discount.
7. The duration of our courses is adaptable, and the curriculum can be adjusted to accommodate any number of days.
8. To ensure seamless preparation, payment is expected before the commencement of training, facilitated through the Global King Project Management account.
9. For inquiries, reach out to us via email at training@globalkingprojectmanagement.org or by phone at +254 114 830 889.
10. Additional amenities such as tablets and laptops are available upon request for an extra fee. The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a certificate of successful completion. Participants are responsible for arranging and covering their travel expenses, including airport transfers, visa applications, dinners, health insurance, and any other personal expenses.