
Master data science with SQL and Python takes you from Python foundations to machine learning, covering NumPy, Pandas, MySQL, SQL fundamentals, visualization, statistics, and a capstone on Kenya's housing prices.
Develop a versatile data scientist who masters statistics, mathematics, Python and SQL, and domain knowledge. Communicate results through visualization and apply machine learning techniques to drive business decisions.
Discover essential data science tools, including Python, R, and SQL, databases and NoSQL, cloud platforms, Tableau and Power BI dashboards, and Jupyter notebook workflows.
Explore the data science life cycle—from defining the problem and collecting data to cleaning, exploring, modeling, interpreting results, and deploying solutions—illustrated with a game-like workflow.
Explore ethical considerations in data science, including privacy, consent, bias, transparency, accountability, and social impact, while outlining the data science ecosystem and roles from data engineers to machine learning engineers.
Explore how probability underpins data science, with practical discussions of Bayes' theorem and posterior probability, and how to apply statistics like chi test and ANOVA in real data.
Explore Python basics, including what Python is, how to install it, and how to use Anaconda and Jupyter notebooks for data science with Python.
Explore data structures in Python, including lists, sets, tuples, and dictionaries, learning how each structure stores and accesses data, order, mutability, and operations like append, insert, remove, add, update.
Master Python data structures and string manipulation, including lists, sets, tuples, dictionaries, and replace, join, split, and reverse, then master arithmetic, assignment, logical, and identity operators.
Define Python functions as reusable blocks with def, parameters, and an optional return. Call them to perform tasks, show scope rules, default arguments, docstrings, and lambda alternatives.
Learn Pandas essentials for data science, including series and data frames, reading and writing CSV and Excel, data manipulation, cleaning, transformation, time series, and basic analysis with numpy integration.
Learn to connect Python with MySQL by installing the MySQL connector, testing connections, and using a cursor to execute queries, then fetch results and convert them to a data frame.
This course comprehensively introduces data science using Python and MySQL, combining essential data manipulation, analysis, and database management skills. You'll learn Python's powerful data science libraries, including Pandas, NumPy, and Matplotlib, to clean, analyze, and visualize data. Additionally, you'll gain hands-on experience with MySQL, mastering SQL queries, database creation, and data management techniques to store and retrieve data efficiently.
Through real-world projects, you will explore the entire data science pipeline—data collection, storage, analysis, and visualization—while understanding how Python and MySQL integrate to solve practical problems. By the end of this course, you’ll be able to manipulate and analyze datasets, create insightful visualizations, and work confidently with databases, equipping you with the skills to handle diverse data science challenges in any professional setting. Suitable for beginners and intermediates.
You'll also delve into key statistical concepts and machine learning basics, applying predictive models to extract insights from data. The course emphasizes practical applications, guiding you through structured exercises and real-world datasets to reinforce your understanding. With Python, you'll automate data workflows and create interactive dashboards, while MySQL enables you to efficiently manage and query complex datasets. By the end, you’ll have a robust toolkit for tackling data science tasks, from initial data exploration to delivering data-driven insights and actionable recommendations across various industries.