Data Science & Artificial intelligence (AI)
Data science is an interdisciplinary field that combines scientific methods, processes, and algorithms to extract meaningful insights and knowledge from large volumes of structured and unstructured data. It blends expertise from several domains, primarily mathematics, statistics, computer science, and domain-specific knowledge, Recommended Skills for Beginners Languages: Python (preferred), R. Tools: Jupyter Notebooks, Excel, SQL, Power BI/Tableau. Concepts: Probability, Statistics, Data Preprocessing.
Career Relevance
Data science is a rapidly growing, high-demand field essential for data-driven decision-making across healthcare, finance, and tech, with job openings projected to grow 34%–36% by 2031-2034. It offers lucrative, stable careers, including roles like AI specialist and data scientist, due to the need for analyzing "big data" to gain competitive edges.
Schedule
Pricing
Curriculum Highlights
- Module 1: Introduction to Data Science: Understanding the lifecycle roles and the tools required.
- Module 2: Programming for Data Science: Python basics including data types functions and control flow.
- Module 3: Data Libraries (NumPy & Pandas): Arrays mathematical computations data cleaning and manipulation.
- Module 4: Mathematics & Statistics: Probability descriptive/inferential statistics and linear algebra.
- Module 5: Data Visualization: Using tools like Matplotlib Seaborn and Tableau/PowerBI to present data.
- Module 6: SQL & Database Management: Querying databases joins and data retrieval.
- Module 7: Exploratory Data Analysis (EDA): Techniques for handling missing values outliers and data cleaning.
- Module 8: Machine Learning Basics: Supervised (regression classification) and unsupervised (clustering) algorithms.
- Module 9: Specialized Topics (Optional for Beginners): Introduction to Big Data Deep Learning or AI.
- Module 10: Capstone Project: Real-world application such as sales forecasting or customer churn prediction.
Learning Outcomes
- A beginner data science learning outcomes focus on building a foundational understanding of data manipulation.
- Analysis and visualization using Python (libraries like Pandas NumPy scikit-learn).
- Learners will acquire skills in exploratory data analysis (EDA)
- basic statistics (mean median variance) and supervised/unsupervised machine learning techniques (regression classification clustering).
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