Data Science & AI Certificate (Live Online)

Overview

Learn Python, SQL, automation, and machine learning to become a data scientist. Gain Python programming, data analysis, SQL querying, and predictive modeling skills. Perfect for beginners, this program prepares you for entry-level data science and Python engineering roles.

Syllabus

Python for Data Science Bootcamp

Handle different types of data, such as integers, floats, and strings
Control the flow of your programs with conditional statements, loops, and functions
Reuse and simplify code with object-oriented programming
Analyze tabular data with Numpy and Pandas
Create graphs and visualizations with Matplotlib
Make predictions with linear regression, using scikit-learn

SQL Bootcamp

Explore information stored in a database (tables, columns, rows, etc.) using the graphical interface of DBeaver (a popular free database app)
Write SQL queries to retrieve data from tables in a database
Combine information from multiple tables with JOIN statements
Filter data, group it, and sort it to extract the specific info you need
Advanced techniques like Subqueries, String Functions, and IF‑Else logic with CASE
How to use Views and Functions with parameters instead of directly querying tables

Python for Automation

Scrape (extract) text and images from websites
Schedule Python scripts to run automatically
Automate browser interactions, reporting, and messaging

Python Data Visualization & Interactive Dashboards

Plan & present a data story
Gather and manipulate data from different sources
Find data stories through exploratory data analysis
Manipulate data with NumPy and Pandas.
Use advanced Python visualization libraries Plotly and Dash
Build a dashboard
Apply the rules of effective dashboard design to create professional data science solutions
Go live with your project & deploy the dashboard on a live server

Python Machine Learning Bootcamp

How to clean and balance your data using the Pandas library
Applying machine learning algorithms such as logistic regression and random forest using the scikit-learn library
Choosing good features to use as input for your algorithms
Properly splitting data into training, test and cross-validation sets
Important theoretical concepts like overfitting, variance and bias
Evaluating the performance of your machine learning models