Project-based learning
Build real work you can show in interviews.
DATA
Data Analysis
From raw data to business decisions: Python, SQL, ML & Power BI.
Taught by Sneha Iyer · Data Analytics Mentor
CertificateIncluded on completion
Lifetime access
Revisit lessons anytime after you enroll.
Mentor support
Learn with guidance from industry experts.
Career-ready skills
Practical modules aligned to real roles.
Overview
What this course covers
A 3-month professional program (12 weeks, 2–3 live classes/week). Path: Python → Data Analysis → Statistics → Machine Learning → SQL → Power BI. Hands-on labs with real datasets and an industry capstone.
Outcome
Finish with practical skills and a portfolio-ready project in Data Analysis you can use for jobs, freelancing, or your next role.
Skills
What you'll learn
Python for analytics (Pandas, NumPy)
Data analysis & statistics
Machine learning for analysts
SQL & ETL pipelines
Power BI dashboards
Career paths: Data Analyst, BI Analyst, ML Analyst, Power BI Developer
Audience
Who this course is for
Beginners
Starting Data Analysis from scratch and want a clear path.
Students & grads
Building a portfolio and interview-ready skills.
Working pros
Upskilling into data roles with practical projects.
Before you start
Prerequisites
- Basic computer and internet skills
- Curiosity and consistency to practise weekly
- Open to learners of every background
Syllabus
Curriculum overview
12 modules · week-by-week topics · labs · certificate on completion
01Week 1: Python Foundations
Phase 01 · Data Analysis Foundations, start with a solid Python base.
Topics covered
- Anaconda
- Jupyter
- Variables
- Data types
Lab: Setup environment & coding exercises
02Week 2: Data Structures
Process collections of data with clean, reusable Python.
Topics covered
- Lists
- Tuples
- Dicts
- Loops
- Functions
Lab: Data processing program
03Week 3: File Handling
Read, write, and organize external datasets safely.
Topics covered
- CSV
- Text files
- Directories
Lab: External data processing utility
04Week 4: NumPy & Pandas
Analyse tabular data efficiently with NumPy and Pandas.
Topics covered
- Arrays
- Series
- DataFrames
- Filtering
Lab: Real-world dataset analysis
05Week 5: EDA & Visualization
Find patterns and communicate them with clear visuals.
Topics covered
- Matplotlib
- Seaborn
- Data patterns
Lab: EDA insights report
06Week 6: Statistics
Use descriptive stats to support reliable decisions.
Topics covered
- Mean
- Median
- Variance
- IQR
- Outliers
Lab: Statistical analysis & outlier detection
07Week 7: Data Preprocessing
Phase 02 · Machine Learning & BI, prepare data for modeling.
Topics covered
- Missing values
- Scaling
- Feature selection
Lab: Clean an ML-ready dataset
08Week 8: ML & Regression
Build and evaluate regression models for business prediction.
Topics covered
- Supervised learning
- Linear regression
- Metrics
Lab: Mini project: prediction system
09Week 9: Classification
Classify outcomes and report metrics that matter.
Topics covered
- Logistic regression
- Precision
- Recall
- F1
Lab: Mini project: classification system
10Week 10: Tree-Based Models
Compare tree ensembles and pick the right approach.
Topics covered
- Decision trees
- Random Forest
- Bagging
- Boosting
Lab: Model comparison lab
11Week 11: SQL for Analysis
Query relational data for analysis-ready extracts.
Topics covered
- SELECT
- Joins
- GROUP BY
- Aggregation
Lab: Business data queries
12Week 12: Power BI & BI
Ship a BI dashboard and complete the analytics capstone.
Topics covered
- ETL
- DAX
- Dashboards
- KPIs
Lab: Interactive dashboard + capstone
Open the full Syncpedia curriculum booklet in a popup, view only, no download.
After the course
You'll walk away ready
By completing this course, you will have the practical skills and a portfolio project in Data Analysis to confidently move forward in your career.
- Job-ready data fundamentals
- A completed capstone you can showcase
- Certificate of completion from Syncpedia
Faculty
Your course mentor
Programs
Explore more courses
Every lesson is hands-on, screen-shared, and built for real operators.
Next step
Enroll, or ask the team first.
Open another course, or call the Madhapur desk if you want a person to walk you through it.
Contact the team