180 Days Mein Job-Ready Data Analyst Banne Ki Journey
Data Analysis ko sirf theory padhkar nahi, balki hands-on practice, real-world business questions aur portfolio projects ke through seekhiye. Spreadsheets se SQL, Statistics, Power BI aur Python/pandas tak — step by step, Dev Sir ke saath.
Data Analyst banne ke liye kya seekhenge?
Ek structured path jo tools ke saath analytical thinking bhi build karta hai. Goal sirf software chalana nahi, data se reliable answers nikalna aur unhe clearly communicate karna hai.
Data aur Business Thinking
Business question ko analysis plan mein convert karna, metrics samajhna aur data quality check karna.
Excel / Google Sheets
Data clean karna, formulas, lookups, PivotTables, charts aur clear spreadsheet reports banana.
SQL
Tables query karna, JOINs, aggregations, CTEs aur window functions se business questions answer karna.
Statistics
Distributions, variation, sampling, correlation aur uncertainty ko samajhkar conclusions ko validate karna.
Power BI & Data Visualization
Data transform karna, relationships aur measures banana, dashboards design karna aur insights explain karna.
Python & pandas
Notebook mein data load, inspect, clean, transform, analyze aur visualize karna.
Complete 180-Day Curriculum
Har stage ka apna purpose aur practical milestone hai. Kisi topic ko search karne ke liye keyword ya Day number use karein. Lesson titles approved curriculum se liye gaye hain.
01 Data Analysis Foundations & Analytical ThinkingDays 1–10 · 10 lessons
Data Analyst ka role, business questions, data quality aur metrics ki foundation samjhein.
What Does a Data Analyst Actually Do?
Data vs Information vs Insight
The Data Analysis Lifecycle
Turning Business Problems into Analytical Questions
Types of Data: Structured, Semi-Structured, and Unstructured
Rows, Columns, Records, Variables, and Data Types
Metrics, Dimensions, and KPIs
Descriptive, Diagnostic, Predictive, and Prescriptive Analytics
Data Quality, Bias, and Responsible Analysis
Foundation Checkpoint: Turn a Business Question into an Analysis Plan
02 Spreadsheet Skills: Excel / Google SheetsDays 11–30 · 20 lessons
Data ko clean, calculate, summarize aur report karna seekhein.
Spreadsheet Fundamentals: Workbooks, Sheets, Cells, and Ranges
Build a Clean, Analysis-Ready Data Table
Data Types and Number Formatting
Sort, Filter, and Inspect Records
Clean Spreadsheet Data: Duplicates, Blanks, and Extra Spaces
Text Functions for Data Cleaning
Date Functions and Time-Based Fields
Formulas, Cell References, and Relative vs Absolute References
Core Aggregation Functions: SUM, AVERAGE, COUNT, MIN, MAX
Logical Formulas: IF, AND, OR, and IFS
Conditional Aggregation: SUMIF(S), COUNTIF(S), AVERAGEIF(S)
Lookups: XLOOKUP, VLOOKUP, and INDEX/MATCH Concepts
Data Validation and Controlled Inputs
Conditional Formatting for Review and Exceptions
Spreadsheet Charts: Choose the Right Visual
PivotTables / Pivot Tables for Quick Analysis
Explore Data with Pivot Summaries and Calculated Metrics
Create a Simple Spreadsheet Report
Spreadsheet Quality Checks and Reconciliation
Spreadsheet Mini-Project: Retail Sales Analysis
03 SQL for Data AnalysisDays 31–60 · 30 lessons
Tables query karein, data combine karein, metrics calculate karein aur trends investigate karein.
Databases and Relational Data: Why Analysts Use SQL
Tables, Rows, Columns, Keys, and Data Grain
SQL Environment and First Dataset Inspection
SELECT, FROM, and Column Aliases
WHERE: Filter Records
ORDER BY and LIMIT
DISTINCT, NULL, and IS NULL
Comparison and Logical Operators
BETWEEN, IN, LIKE, and Pattern Matching
CASE WHEN: Build Conditional Categories
Aggregate Functions: COUNT, SUM, AVG, MIN, MAX
GROUP BY: Analyze Categories
HAVING: Filter Aggregated Results
Date Functions and Time-Based SQL Analysis
String Functions for Text Cleaning
Calculated Fields, NULL Handling, and Safe Arithmetic
JOIN Concepts: Keys and Table Relationships
INNER JOIN: Combine Matching Records
LEFT JOIN: Keep All Records from the Left Table
Multiple JOINs and Duplicate-Row Problems
UNION vs UNION ALL
Subqueries for Multi-Step Questions
Common Table Expressions (CTEs)
Conditional Aggregation with CASE WHEN
Window Functions: ROW_NUMBER, RANK, and DENSE_RANK
LAG, LEAD, and Running Totals
SQL for Trend and Period-over-Period Analysis
SQL Data Quality Checks
Readable SQL, Debugging, and Query Efficiency Basics
SQL Mini-Project: End-to-End Business Analysis
04 Statistics and Analytical ThinkingDays 61–75 · 15 lessons
Variation, distributions, comparisons aur uncertainty ko sahi tarah interpret karein.
Population, Sample, and Sampling Frames
Mean, Median, and Mode
Range, Quartiles, Percentiles, and IQR
Variance and Standard Deviation
Distributions, Skewness, and Outliers
Missing Data: Patterns and Practical Treatment
Probability Basics for Analysts
Sampling Bias and Selection Bias
Correlation vs Causation
Compare Groups Fairly
Percentages, Percentage Change, and Growth Rates
Time-Series Basics: Trend, Seasonality, and Noise
Uncertainty, Confidence, and Limits of a Conclusion
A/B Testing Fundamentals
Statistics Checkpoint: Explain What the Data Does and Does Not Show
05 Power BI and Data VisualizationDays 76–100 · 25 lessons
Data transform karein, model banayein, measures likhein aur meaningful dashboards create karein.
What Is Business Intelligence? Reports vs Dashboards
Power BI Setup and Connecting to Data
Power Query: Understand the Transformation Workflow
Inspect Columns and Assign Correct Data Types
Clean Data in Power Query
Split, Merge, Replace, and Standardize Columns
Append vs Merge in Power Query
Data Model Relationships and Cardinality
Star Schema: Fact Tables and Dimension Tables
Date Tables and Reliable Time Analysis
Measures vs Calculated Columns
DAX Basics: SUM, COUNTROWS, and DISTINCTCOUNT
CALCULATE and Filter Context Fundamentals
Ratios, Rates, and Safe Measures with DIVIDE
Time-Based Measures and Period Comparisons
Choose the Right Chart for the Question
Slicers, Filters, and Report Interactions
Drill-Down, Drill-Through, and Detail Exploration
Design a Clear Report Layout
Dashboard Storytelling: From Metric to Insight
Accessible and Responsible Dashboard Design
Refresh, Publish, and Share: Understand the Workflow
Dashboard Quality Checks and Performance Basics
Power BI Project: Build a Business Dashboard
Power BI Checkpoint: Validate and Present the Dashboard
06 Python for Data AnalysisDays 101–130 · 30 lessons
Python aur pandas se data load, clean, transform, analyze aur visualize karein.
Why Analysts Use Python and Setting Up the Environment
Variables, Data Types, Operators, and Basic Expressions
String Operations and Formatting
Lists, Tuples, Dictionaries, and Sets
Conditional Logic: if, elif, and else
Loops and Iteration Fundamentals
Functions: Reuse Analysis Logic
Errors, Exceptions, and Debugging
File Paths and Reading CSV Files
Jupyter Notebooks and Reproducible Analysis
pandas Series and DataFrames
Inspect a DataFrame: head, shape, info, describe
Select Columns, Rows, loc, and iloc
Filter, Sort, and Rank Data in pandas
Missing Values in pandas
Duplicate and Invalid Records
Convert Data Types and Work with Dates
Clean Text Columns with String Methods
Create Calculated Columns and Apply Vectorized Operations
groupby and Aggregation
Merge DataFrames with Keys
Concatenate, Reshape, and Change Data Structure
pivot_table and crosstab
Datetime Analysis with pandas
Numeric Cleaning, Outliers, and Validation
Data Visualization with Matplotlib
Exploratory Data Analysis: A Repeatable Workflow
Validate, Export, and Document Analysis Results
Python Mini-Project: Analyze a Business Dataset
Python Checkpoint: Reproducible Analysis Notebook
07 Portfolio Project Lab: End-to-End AnalysisDays 131–160 · 30 lessons
Teen realistic business problems par end-to-end analysis aur documented recommendations banayein.
Project 1 Brief: Define a Sales Performance Question
Project 1: Understand the Dataset and Its Grain
Project 1: Audit Data Quality and Risks
Project 1: Create a Cleaning and Validation Plan
Project 1: Prepare and Query the Data
Project 1: Define Sales and Performance Metrics
Project 1: Analyze Product, Category, and Customer Segments
Project 1: Analyze Trends and Period Comparisons
Project 1: Visualize Findings and Draft Recommendations
Project 1: Final Sales Analysis Report and Review
Project 2 Brief: Customer Retention and Repeat Purchase
Project 2: Define Customer Grain, Cohorts, and Retention
Project 2: Inspect Customer and Transaction Tables
Project 2: Clean and Validate Customer Data
Project 2: Build SQL Analysis for Repeat Behavior
Project 2: Perform Cohort and Segment Analysis
Project 2: Explore Results with Python or Spreadsheets
Project 2: Visualize Retention Patterns
Project 2: Recommendations, Risks, and Limitations
Project 2: Final Customer Analysis Case Study
Project 3 Brief: Operations or Business KPI Analysis
Project 3: Data Dictionary, Grain, and KPI Definitions
Project 3: Data Quality and Transformation Checks
Project 3: Query Operational Metrics with SQL
Project 3: Design a Dashboard Wireframe
Project 3: Build the Operational Dashboard
Project 3: Identify Bottlenecks and Exceptions
Project 3: Write an Insight and Recommendation Brief
Project 3: Quality Assurance and Reproducibility
Portfolio Project Lab: Package Three Projects
08 Portfolio, Interview, and Job ReadinessDays 161–180 · 20 lessons
Projects present karein, timed skills practice karein, aur relevant opportunities ke liye prepare karein.
Build a Clear Data Analyst Portfolio Structure
Write Strong Analytical Case Studies
Write Project READMEs and Documentation
Data Privacy, Ethics, and Responsible Portfolio Work
Turn Project Work into Resume Evidence
Read Data Analyst Job Descriptions and Map Skill Gaps
Communicate Insights to Non-Technical Stakeholders
Timed SQL Practice: Filters, Aggregations, and Joins
Timed SQL Practice: CTEs and Window Functions
Spreadsheet Skills Assessment
Python and pandas Skills Assessment
Dashboard Review and Visualization Assessment
Statistics and Analytical Reasoning Interview Questions
Solve a Business Case Study Step by Step
Explain Projects in an Interview Using STAR Where Appropriate
Take-Home Assignment: Plan and Validate the Work
Tailor Applications to Relevant Roles
Freelance Readiness: Scope a Small Analytics Task
Final Mock Assessment: End-to-End Data Analysis
Day 180: Portfolio Review and Continued Development Plan
Lesson URLs tabhi add honge jab exact published URLs available honge. Is roadmap mein guessed ya invented lesson links use nahi kiye gaye hain.
Sirf lessons nahi — 3 portfolio projects
Days 131–160 mein pehle seekhe tools ko realistic analytical problems par apply karenge. Data aur metrics ko validate karna project ka part hoga, sirf final dashboard banana nahi.
Retail Sales Performance Analysis
Sales performance par ek clear business question define karein, data quality check karein aur findings ko useful report mein present karein.
- Data cleaning and validation
- Sales metrics and segment analysis
- Trend analysis and report
- Recommendations and limitations
Customer Repeat-Purchase / Retention Analysis
Customer activity aur repeat behaviour ko defined time period ke andar analyze karein. Cohorts aur metrics ko clearly define karna zaroori hoga.
- Customer and transaction tables
- Repeat-purchase / cohort analysis
- Segment comparisons
- Evidence-based recommendations
Operations / Business KPI Analysis
Operational metrics ko investigate karein, exceptions identify karein aur stakeholders ke liye clear recommendations prepare karein.
- KPI definitions and data checks
- SQL-based metric analysis
- Dashboard and bottleneck investigation
- Quality assurance and documentation
Dev Sir ke saath kaise seekhenge?
Har lesson ko ek practical step ki tarah design kiya jayega. Pehle samjhein, phir guided walkthrough follow karein, uske baad khud try karein.
Concept samjhein
Simple explanation, mental model aur relevant business example se foundation build karein.
Step-by-step follow karein
Formula, SQL query, Python code ya dashboard workflow ko expected result ke saath follow karein.
Khud practice karein
Independent task aur homework ko solution dekhe bina attempt karne ki koshish karein.
Check aur improve karein
Hints/solutions se reasoning verify karein, mistakes samjhein aur skill ka evidence save karein.
Kaun-se tools use honge?
Course ek practical analyst stack par focus karta hai. Tools ko random tareeke se nahi, relevant analysis questions solve karne ke liye seekhenge.
Python practice ke liye Google Colab default environment hai; local Jupyter optional hai. Power BI Desktop ke liye compatible computer/system access required ho sakta hai. Exact setup aur requirements corresponding lessons mein explain ki jayengi.
Ye journey kin learners ke liye hai?
Course beginner-friendly progression follow karega, lekin skill develop karne ke liye regular practice aur independent effort zaroori hoga.
Ek lesson se shuru karein. Practice se confidence build karein.
180-day roadmap ko apni pace par follow karein. Concepts ko sirf read na karein—examples run karein, homework attempt karein, results validate karein aur projects ke through apni progress demonstrate karein.
Frequently Asked Questions
Course ke format, tools aur expected outcomes ke baare mein common questions.
Kya main bilkul beginner hote hue start kar sakta/sakti hoon?
Haan. Roadmap foundations se start hota hai aur spreadsheets, SQL, statistics, Power BI, Python/pandas aur projects tak step by step progress karta hai. Regular practice phir bhi zaroori hogi.
Kya 180 days mein job guaranteed hai?
Nahi. Ye 180-day learning roadmap skills aur portfolio evidence build karne ke liye design kiya gaya hai. Job ya income ka result guarantee nahi kiya ja sakta.
Kya advanced mathematics ya prior coding knowledge chahiye?
Curriculum beginner-friendly foundations se start karta hai. Statistics aur Python ki zaroori concepts gradually introduce hongi; pehle se expert hona required nahi hai.
Kya mujhe paid software ya API kharidne honge?
Course free ya accessible tools ko preference deta hai. Kuch tools, operating systems ya service features ki access requirements alag ho sakti hain; relevant lesson mein unhe clarify kiya jayega.
Kya course mein practical projects honge?
Haan. Curriculum mein sales performance, customer repeat-purchase/retention aur operations/KPI analysis ke teen end-to-end portfolio projects planned hain, saath hi earlier stages mein mini-projects aur checkpoints bhi hain.
English version bhi available hoga?
Series ka publishing plan bilingual hai. Pehle Hinglish version publish kiya jayega; English version baad mein separately prepare aur publish hoga. Dono versions ka core curriculum aur learning outcomes consistent rahenge.
Kya har lesson ke saath homework aur solutions honge?
Relevant lessons mein independent practice, homework aur hints/solutions diye jayenge. Solution ko attempt ke baad check karne ke liye use karna recommended hai.