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DATAANALYTICS · ML · BI

Course details

Data Analysis

From raw data to business decisions — Python, SQL, ML & Power BI.

  • BeginnerLevel
  • 36 Hours+Hours
  • 3 MonthsDuration
  • 620+Students
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Taught by Sneha Iyer · Data Analytics Mentor

Data Analysis
CertificateIncluded on completion
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Project-based learning

Build real work you can show in interviews.

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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

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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

Sneha IyerData Analytics Mentor

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