Data Science & Data Analysis
This 3-month intensive program equips learners with the essential skills to collect, analyze, and visualize data to solve business problems. Covering tools such as Excel, SQL, Python, and key libraries like pandas, seaborn, and scikit-learn, the curriculum blends statistical foundations with hands-on projects in exploratory data analysis, dashboards, and machine learning.
Professional Certificate in Data Science & Data Analysis
Duration: 3 Months (3 sessions/week) | Format: Live Online or In-Person | Total Estimated Learning Hours: ~144
For: Aspiring Data Analysts, Junior Data Scientists, Career Changers, Technical Professionals
Skill Level: Beginner to Intermediate
Program Overview
This 3-month intensive program equips learners with the essential skills to collect, analyze, and visualize data to solve business problems. Covering tools such as Excel, SQL, Python, and key libraries like pandas, seaborn, and scikit-learn, the curriculum blends statistical foundations with hands-on projects in exploratory data analysis, dashboards, and machine learning.
Graduates leave with a portfolio of projects demonstrating their ability to clean, model, and present insights using industry tools and workflows.
Core Courses
1. Data Thinking and Excel for Analysis
Hours: 18
Prerequisites: None
Summary:
Learn the fundamentals of data analysis using spreadsheets. Focus on interpreting business problems, cleaning data, and drawing insights.
Learning Outcomes
- Use Excel for data exploration and summaries
- Create pivot tables and dynamic dashboards
- Identify trends, outliers, and initial insights
- Structure business problems for analysis
2. Python for Data Analysis
Hours: 24
Prerequisites: None
Summary:
Gain practical experience using Python and libraries like pandas and numpy for data transformation and wrangling.
Learning Outcomes
- Write Python scripts to clean and analyze datasets
- Use pandas for grouping, merging, and filtering data
- Automate data handling workflows
- Import/export datasets from multiple formats
3. SQL for Data Exploration
Hours: 18
Prerequisites: None
Summary:
Master SQL queries to retrieve, clean, and analyze structured data stored in relational databases.
Learning Outcomes
- Query databases using SELECT, JOIN, and GROUP BY
- Perform nested queries and create views
- Analyze customer, sales, and operational datasets
- Use SQL to answer real business questions
4. Data Cleaning and Wrangling with Python
Hours: 18
Prerequisites: Python basics
Summary:
Tackle messy, real-world data problems. Learn techniques to reshape, sanitize, and validate data.
Learning Outcomes
- Handle missing values, duplicates, and outliers
- Normalize and convert data types
- Use regex and string methods for cleanup
- Document cleaning processes for reproducibility
5. Data Visualization and Storytelling
Hours: 18
Prerequisites: Python, pandas
Summary:
Create compelling visualizations to communicate insights. Learn how to choose the right chart for the right story.
Learning Outcomes
- Build plots with matplotlib, seaborn, and Plotly
- Create interactive dashboards
- Visualize trends, relationships, and comparisons
- Design visuals for business and technical audiences
6. Applied Statistics for Data Science
Hours: 18
Prerequisites: Basic math and Python
Summary:
Understand core statistical concepts needed for data analysis and machine learning.
Learning Outcomes
- Summarize data using descriptive statistics
- Conduct hypothesis tests and calculate confidence intervals
- Use probability distributions to model uncertainty
- Run statistical tests in Python
7. Introduction to Predictive Modeling and Machine Learning
Hours: 18
Prerequisites: Python, pandas, statistics
Summary:
Build and evaluate simple machine learning models using scikit-learn.
Learning Outcomes
- Split data into training and testing sets
- Train and evaluate regression and classification models
- Use metrics like accuracy, precision, and recall
- Apply models to real-world datasets
8. Capstone Project: End-to-End Data Analysis
Hours: 12
Prerequisites: Completion of prior courses
Summary:
Solve a real-world business problem using data science workflows from cleaning to modeling to presentation.
Learning Outcomes
- Apply the CRISP-DM framework to structure your project
- Build an analytical pipeline in Jupyter Notebook
- Develop visuals and narrative for business presentation
- Deliver a project report or interactive dashboard
Ready to Transform Your Career?
At TBS, we view data science and analysis as more than just number-crunching—it’s the engine behind smarter decisions and innovative solutions. Join the TBS Data Science & Data Analysis program to gain hands-on expertise in data exploration, statistical modeling, and predictive analytics using industry-standard tools. Step into the world of data-driven strategy with TBS. Enroll now and start your journey toward becoming a highly skilled data science and analytics professional.