₦200,000.00
Complete Data Science Bootcamp 2026
Description
Curriculum
Ready to embrace a new career in the tech industry? Or is your business craving the online visibility it deserves? Digital marketing can elevate your journey. By leveraging SEO, social media, and PPC, you can enhance your brand and connect with your audience.
What you’ll learn
- Clarify what Digital Marketing entail.
- Learn strategies top marketers use to drive traffic and conversions.
- Understand how to measure and analyze your marketing efforts for continuous improvement.
- Design and optimize marketing funnels for maximum conversions.
- Execute and present a multi-channel digital marketing strategy.
- Gain hands-on experience with essential digital marketing tools.
- Portfolio building with real case study.
In today’s technology-driven landscape, digital marketing is vital for modern business strategies, enabling companies to enhance their online presence and boost sales.
- 6 Sections
- 23 Lessons
- 12 Weeks
Expand all sectionsCollapse all sections
- Build a solid foundation in Python programming to effectively implement AI concepts and applications.5
- 1.1Introduction to Data Science and what it entail
- 1.2Learn how to install Python together and how it works
- 1.3Learn data underpins modern business by aligning data team, analytics, and data science roles.
- 1.4Clarify the differences between analysis and analytics
- 1.5Explains past events, while analytics explores future patterns using qualitative analytics and quantitative analytics
- Learn how transformer models revolutionize NLP tasks, and how to leverage them for various applications.3
- Learn how to utilize vector databases for efficient storage and retrieval of embeddings in AI projects. Explore the essentials of Large Language Models (LLMs) and their applications in generative tasks.4
- 3.1Explore how business intelligence analyzes past data within data science.
- 3.2Learn how machine learning and artificial intelligence enhance real-time dashboards, predictive analytics, fraud detection, and sales forecasting.
- 3.3Explore how traditional AI improves structured data tasks with automation, while generative AI creates new data and content, powered by machine learning in data science.
- 3.4Explore how generative AI enhances data science through text comprehension, content generation across text, code, audio and images, and AGI concepts with tools like DALL-E, ChatGPT, and Gemini.
- Explore the essentials of Large Language Models (LLMs) and their applications in generative tasks. Learn how Machine Learning & Deep Learning works.2
- Understand the complete pipeline of Natural Language Processing, from data preprocessing to model deployment.5
- 5.1Explore why data drives business decisions in data science, contrasting traditional methods with machine learning for predictive analytics, and using a timeline to map past and future data work.
- 5.2Explore techniques for traditional data, including data collection, pre-processing, labeling, cleansing, balancing, and shuffling, with ER diagrams and relational schemas for databases used in data science.
- 5.3Explore traditional data through customer and stock data to distinguish numerical from categorical variables, noting IDs as categorical, complaint counts as numerical, and dates as categorical in stock data.
- 5.4Explore techniques for collecting, pre-processing, and cleansing big data across text, image, audio, and video types, including missing value handling and text data mining.
- 5.5Facebook’s diverse user data and real time analytics show variety, velocity, and volume, with financial trading data and stock prices every second illustrating big data.
- Develop skills in crafting effective prompts to optimize model performance and achieve desired outputs.4
- 6.1Blend data skills with business knowledge to explain past performance and answer key questions, using observations, measures, metrics, KPIs, and business intelligence dashboards.
- 6.2Use BI to optimize pricing in real time based on demand and historical data.
- 6.3Apply BI to inventory management by analyzing past sales for seasonality and stock optimization.
- 6.4Explore traditional data science methods for predictive analytics, including linear and logistic regression, cluster and factor analysis, and time series applications for business decisions.
TUITION FEE PAYMENT STRUCTURE
Pay Your Fee At Once
First Instalment : 70%
Second Instalment : 30%
Splits Payment to Every Month At Your Pace
Registration Fee of ₦5,000. (This covers your Application Form, ID Card, Jotter, Pen & Sticker)
HOW TO APPLY FOR OUR COURSES
Enroll Today and enjoy the benefits of our ongoing 50% Scholarship Program.
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