Data Analysis Foundations
Turn raw datasets into business and research insights.
Course overview
Data analysts extract clarity from raw numbers. In this applied course, you practice the complete analytical workflow: defining analytical questions, wrangling data with Python/Pandas, performing exploratory data analysis (EDA), identifying patterns, and delivering actionable summary reports.
Prerequisites
What you'll achieve
- Apply structured analytical thinking to real-world business and research questions
- Perform thorough Exploratory Data Analysis (EDA) on unknown datasets
- Identify trends, outliers, correlations, and business opportunities
- Synthesize analytical findings into clear presentations and written summaries
Curriculum
- 1The data analysis process & problem definition
- 2Data collection, inspection & quality assessment
- 3Data cleaning and transformation workflows
- 4Univariate and bivariate exploratory analysis
- 5Detecting anomalies, outliers & missing patterns
- 6Feature aggregation, KPI calculation & business metrics
- 7Structuring professional analytical reports and findings
- 8Project: Complete end-to-end analysis on a real business or research dataset
Who is this for?
Aspiring data analysts, business analysts, students, and research scholars.
Contact admissions for current fee and available learning formats
Enrol in Data Analysis Foundations
- 6 weeks
- Beginner to Intermediate
- Live online · One-to-one available · Group batches when scheduled
- 1-on-1 & small batches
Related courses
NumPy
Master N-dimensional arrays, vectorized computations, broadcasting, slicing, indexing, and linear algebra operations.
Pandas
Master DataFrames, Series, data ingestion, handling missing values, filtering, groupby aggregations, and merging.
Matplotlib & Data Visualization
Learn line charts, bar plots, histograms, scatter plots, subplots, Seaborn styling, and visual storytelling.
Ready to start your learning journey?
Apply today and our admissions team will review your application and guide you through the next steps.