Specialization - 12 course series
Introduction to Data Science
Establishes foundational understanding of Data Science, lifecycle, roles, and real-world applications.
Tools / Technologies: Python, Jupyter Notebook, Google Colab
Assignments : Write a brief on Data Science lifecycle; identify real-world use cases
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Python for Data Science
Covers Python basics, data structures, and introduction to libraries for data analysis.
Tools / Technologies : Python, NumPy, Pandas
Assignments : Perform data manipulation tasks; build basic scripts
Data Collection & Cleaning
Focuses on data gathering, handling missing values, and cleaning datasets.
Tools / Technologies : Pandas
Assignments : Clean a raw dataset and document preprocessing steps
Exploratory Data Analysis (EDA)
Covers statistical summaries and visualization techniques to understand data.
Tools / Technologies : Pandas, Matplotlib, Seaborn
Assignments : Perform EDA and create visual insights report
Statistics for Data Science
Introduces descriptive and inferential statistics, probability distributions.
Tools : Python (SciPy, Statsmodels)
Outcome: Solve statistical problems; analyze dataset statistically
SQL for Data Analysis
Teaches querying, joins, aggregations, and database interaction.
Tools : SQL (MySQL/PostgreSQL)
Outcome: Write SQL queries for data extraction and analysis
Data Visualization & Storytelling
Focuses on presenting data insights effectively using visuals and dashboards.
Tools : Tableau / Power BI
Outcome : Create an interactive dashboard
Introduction to Machine Learning
Covers ML basics, supervised vs unsupervised learning, and simple models.
Tools : Scikit-learn
Outcome : Build a basic ML model
Supervised Learning
Deep dive into regression and classification algorithms and evaluation.
Tools : - Scikit-learn
Outcome: Build and evaluate ML models
Unsupervised Learning
Covers clustering and dimensionality reduction techniques.
Tools : Scikit-learn
Outcome : Perform clustering project
Big Data & Tools
Introduces big data concepts and tools for handling large datasets.
Tools : Apache Spark, Hadoop (intro)
Outcome : Process large dataset using Spark (basic task)
Capstone Project & Career Development
End-to-end project covering data collection, analysis, and modeling.
Tools : - Python, SQL, Visualization Tools, GitHub
Outcome: Complete project; submit report and portfolio
Data Science Curriculum
LEVEL:
Beginner to Intermediate
FOCUS :
Core IT, Cloud Fundamentals, and Hands-on Cloud Deployment Skills
GOAL:
Prepare learners for foundational cloud roles and certifications (AWS CCP, AZ-900, GCP Digital Leader)
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